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Article

Marburg Virus Outbreak and a New Conspiracy Theory: Findings from a Comprehensive Analysis and Forecasting of Web Behavior

1
Department of Computer Science, Emory University, Atlanta, GA 30322, USA
2
Department of Mathematics, Emory University, Atlanta, GA 30322, USA
3
Program in Linguistics, Emory University, Atlanta, GA 30322, USA
*
Author to whom correspondence should be addressed.
Computation 2023, 11(11), 234; https://doi.org/10.3390/computation11110234
Submission received: 30 October 2023 / Revised: 13 November 2023 / Accepted: 14 November 2023 / Published: 17 November 2023
(This article belongs to the Special Issue Artificial Intelligence Applications in Public Health)

Abstract

:
During virus outbreaks in the recent past, web behavior mining, modeling, and analysis have served as means to examine, explore, interpret, assess, and forecast the worldwide perception, readiness, reactions, and response linked to these virus outbreaks. The recent outbreak of the Marburg Virus disease (MVD), the high fatality rate of MVD, and the conspiracy theory linking the FEMA alert signal in the United States on 4 October 2023 with MVD and a zombie outbreak, resulted in a diverse range of reactions in the general public which has transpired in a surge in web behavior in this context. This resulted in “Marburg Virus” featuring in the list of the top trending topics on Twitter on 3 October 2023, and “Emergency Alert System” and “Zombie” featuring in the list of top trending topics on Twitter on 4 October 2023. No prior work in this field has mined and analyzed the emerging trends in web behavior in this context. The work presented in this paper aims to address this research gap and makes multiple scientific contributions to this field. First, it presents the results of performing time-series forecasting of the search interests related to MVD emerging from 216 different regions on a global scale using ARIMA, LSTM, and Autocorrelation. The results of this analysis present the optimal model for forecasting web behavior related to MVD in each of these regions. Second, the correlation between search interests related to MVD and search interests related to zombies was investigated. The findings show that there were several regions where there was a statistically significant correlation between MVD-related searches and zombie-related searches on Google on 4 October 2023. Finally, the correlation between zombie-related searches in the United States and other regions was investigated. This analysis helped to identify those regions where this correlation was statistically significant.

1. Introduction

The 2023 outbreak of the Marburg Virus Disease (MVD) was officially declared by the Ministry of Health and Social Welfare of Equatorial Guinea on 13 February 2023. This declaration followed the reporting of suspected fatalities caused by viral hemorrhagic fever from 7 January 2023 to 7 February 2023, and a positive RT-PCR case for Marburg virus on 12 February 2023, at the Institut Pasteur de Dakar in Senegal [1]. Between 13 February 2023, and 7 June 2023, 17 confirmed cases and 23 suspected cases were documented in the continental area of Equatorial Guinea. A total of 12 individuals among the confirmed cases succumbed to the illness, while all of the likely cases were reported as fatalities. It is worth noting that the case–fatality ratio among the confirmed cases of this MVD outbreak was 75% (omitting one confirmed case for which the outcome was not known). The most recently confirmed patient was released from a Marburg treatment center in the Bata area of Litoral province on 26 April 2023, after the administration of two successive negative PCR tests for MVD. The Ministry of Health of Equatorial Guinea officially declared the conclusion of the outbreak on June 8, 2023, after a period of 42 days including two successive incubation periods during which no new confirmed cases were recorded [2].
As a result of this outbreak and the high fatality rate of MVD [3], in the last few months people from all over the world have been spending a lot more time than ever before on social media platforms and the internet in general to seek, share, access, and disseminate information about MVD [4,5]. During virus outbreaks of the past such as COVID-19 [6,7,8], MPox [9,10,11], Ebola [12,13,14], H1N1 [15,16,17], and MERS [18,19,20], researchers from different disciplines such as Healthcare, Epidemiology, Big Data, Data Analysis, Data Science, and Computer Science have studied and analyzed the underlining web behavior, as web behavior provides insights into the public health needs, interests, motives, concerns, perspectives, and opinions related to virus outbreaks. Furthermore, web behavior analysis related to a virus outbreak has also had several applications related to the real-time surveillance of outbreaks [21], prediction of cases [22], forecasting the behavior of different strains of a virus [23], timely preparation of public health policies [24], better preparedness of healthcare systems [25], identification of the themes of conversations of the general public [26], and timely implementation of public health policies and guidelines [27]. In addition to this, during virus outbreaks of the recent past, for example, COVID-19, such paradigms of information-seeking and sharing behavior on the internet [28,29,30,31,32] led to the development and dissemination of different conspiracy theories which led to a range of reactions, both positive and negative, in the general public [33,34,35]. An example of a conspiracy theory in the context of COVID-19 was related to the role of 5G towers in spreading COVID-19 [36]. In January 2020, this conspiracy theory started on social media, and it soon gained unprecedented attention, leading to a surge in Google Searches related to 5G and COVID-19 around that time. Furthermore, the rapid dissemination of this conspiracy theory led to people burning 5G towers in different regions in the United Kingdom [37]. Researchers from different disciplines have also investigated such patterns of web behavior related to the conspiracy theories associated with virus outbreaks of the past [38,39,40]. The recent outbreak of MVD followed by a warning signal (for testing purposes) sent by the United States Federal Emergency Management Agency (FEMA) to every TV, radio, and cellphone in the U.S. on 4 October 2023, led to an unusual conspiracy theory involving the MVD and zombies.
As per online reports, this conspiracy theory seems to have been initiated by a QAnon influencer behind a Telegram channel called “The Patriot Voice”, which is followed by more than 50,000 people, in a post shared at the end of September. That post from this influencer cited a supposed military expert’s claim that COVID-19 vaccines contain sealed pathogens including the Marburg Virus which can be released by an 18 Gigahertz 5G frequency [41,42]. As per a study conducted by the Pew Research Center, the awareness about QAnon in Americans has increased by more than double in the recent past [43]. Therefore, this conspiracy theory spread like wildfire on different social media networks and the internet in general. One post about this conspiracy theory on Twitter [44], viewed close to 11 million times at the time of writing of this paper, states—“Turn off your cell phones on October 4th. The EBS is going to “test” the system using 5G. This will activate the Marburg virus in people who have been vaccinated. And sadly turn some of them into zombies”. In the past, there have been examples where just one Tweet started a conspiracy theory [45]. Since the publication of this Tweet, there have been several other posts on Twitter associated with this conspiracy theory which reveal the views, opinions, reactions, responses, and concerns of the general public in this regard. This conspiracy theory created a buzz in the global population to the extent that “Marburg Virus” featured in the list of the top trending topics on Twitter on 3 October 2023 [46]. To add to this, “Emergency Alert System” and “Zombie” featured in the list of top trending topics on Twitter on 4 October 2023 [47]. Furthermore, this conspiracy theory was covered by several popular and widely viewed news outlets such as BBC News [48], The Standard [49], Yahoo News [50], AP News [51], The Mirror [52], New York Magazine [53], The Messenger [54], Daily Mail [55], Sportskeeda [56], Daily Dot [57], and USA Today [58]. As a result of the widespread nature of this conspiracy theory and the associated concern and public reactions, Jeremy Edwards (press secretary and deputy director of public affairs at FEMA) stated publicly—“I received it on my phone and saw it on the TV. And I can confirm to you that I am not a zombie”, soon after the broadcast of the FEMA emergency alert signal [59]. In view of this recent outbreak of MVD and the associated conspiracy theory that created a significant buzz on the internet, which included this conspiracy theory being amongst the trending topics on Twitter for two days—3 October 2023 and 4 October 2023, modeling and analyzing the underlining patterns of web behavior of the general public in this context becomes highly crucial to investigate. This serves as the main motivation for this research work.

1.1. Marburg Virus: A Brief Overview

In August of 1967, thirty people in Marburg and Frankfurt, Germany, became mysteriously and dangerously ill. This was the first known outbreak of MVD. The virus was traced to African green monkeys that had previously been imported from Uganda. Infection occurred when autopsies were performed on the monkeys for the purpose of collecting kidney cell samples [60]. When the Ebola virus (EBOV) emerged in Africa nearly ten years later, in 1976, the two viruses were classified together as Filoviridae [60,61]. MVD appeared sporadically between the years 1975 and 1985, but it did not result in deaths the way that EBOV did, leading people to believe that MVD was not as deadly [62]. Though MVD is not a prevailing threat in endemic locations, it poses a threat to tourists or travelers, especially as they might bring the virus to other countries; the risk of infection also exists in laboratory workers [63]. Because of its danger, transmissibility, and lack of vaccine, the World Health Organization (WHO) categorizes MVD as Risk 4 Group (RG4). RG4 is the highest risk group and defines pathogens as a serious risk to individuals and communities [64]. The MVD infection is a zoonotic disease, but the original or natural host of the virus is yet to be identified [65,66,67]. However, researchers speculate that bats could be vital to the transmission of the disease, or they may also be the original carriers of MVD [68]. In fact, MVD was isolated from Egyptian fruit bats after the initial outbreak [69,70,71,72,73]. Research works involving the Gabonese bat populations suggest that MVD is enzootic, and its transmissibility poses a risk of appearing in other countries [74,75]. Transmission between humans usually occurs through bodily fluids such as blood, saliva, and urine. Such interactions tend to happen when caring for a sick patient but can include the handling of an infected corpse [76].
The disease is observed over three phases: generalization, early organ, and late organ or convalescence [77,78,79]. During the generalization phase, the patient usually displays symptoms similar to the flu. During the second phase, which occurs between days five to thirteen of the illness, patients may display psychological symptoms. This may manifest as general confusion and irritability but could also include swelling of the brain and delirium. During this stage, patients might face difficulty in performing Activities of Daily Living (ADLs) [80,81,82]. The last stage of the disease is either late organ or convalescence, depending on if the patient is able to recover. After a patient enters the late organ stage, they may experience dementia or a coma. Death usually comes about by shock from multiorgan failure. The convalescence phase is marked by a slow recovery with symptoms like muscle pain, exhaustion, and peeling of the skin where the rash appeared [77,78,79]. Nearly 600 MVD cases have been reported since the first outbreak. These recent cases of MVD have catalyzed the creation of MARVAC, a WHO-coordinated cooperative aimed at tackling the Marburg vaccine [83,84]. The vaccine has since been under development through the use of the MVD glycoprotein and animal testing. Of approved vaccines, Ad26-MARV, developed using the Ad26 vector encoding of MVD, is set to be moved forward in development. It is currently available for emergency use alongside another Ad-based vaccine, ChAd3-MARV, which takes the Ad vector from a chimpanzee. Several vesicular stomatitis virus-based vaccines are scheduled to advance to clinical testing after manufacturing, namely VSV-N4CTI-MARV, VSV-MARV Musoke vector, and VSV-MARV Angola vector [85].

1.2. Concept of Conspiracy Theories

A conspiracy theory is an explanation for an event or occurrence that typically cites outgroups or authority powers as the perpetrators. Douglas et al. [86] proposed that people believe in conspiracy theories due to three key psychological motives: knowledge, existential, and social. Knowledge refers to certainty and the desire to create patterns or fill gaps in understanding. Existential motives include exerting control or safety in one’s own situation, and knowledge allows people to have the certainty to feel safe. Lastly, social motives may be a person’s desire to fit into a group, and following conspiracy theories may provide them with the agency to look good or feel desirable in social settings.
In addition to the core psychological motives, conspiracy theories can appeal to certain demographics [87,88]. People who are more likely to believe in conspiracy theories tend to include those with lower levels of education, lower levels of income, weak social networks, and low media literacy [89,90,91,92]. Males, unmarried people, and unemployed people are also seen to have a higher belief in conspiracy theories [92]. A final reason why people might believe in conspiracy theories could be attributed to politics. Politically motivated conspiracy theories give people of a particular party the reasoning needed to further a point, argument, or campaign, regardless of whether the content is true or not [87]. Conspiracy theories tend to have largely negative social and/or psychological impacts [93]. Research indicates that people who participate in conspiracy theory dialogue are less likely to vote or participate in politics in general, due to a lack of trust in the political system [94,95,96]. Conspiracy theories can also be associated with prejudiced views of certain groups of people. Research into conspiracy theories suggests that said conspiracy theories can portend anti-Semitic beliefs, discrimination against Jewish people, and sometimes even racism towards groups who are not a part of the conspiracy theory at all. Such sentiments contribute to and exacerbate division between groups of people [97,98,99].
One of the more significant impacts of conspiracy theories may be scientific skepticism. Climate change, for example, is commonly the target of many conspiracy theories, driving people away from caring about the core issue [100]. Those who might believe in climate change conspiracy theories may also believe in theories that surround scientific evidence, like GMOs or the forensics of the 9/11 attacks [101,102]. Scientific skepticism of this nature can extend to issues of human health as well. Belief in anti-science theories correlates with unsafe health choices, like being anti-vaccines (especially the COVID-19 vaccine), not using contraceptives, alternative medicinal practices, or refusing professional help for physical or mental illnesses [103,104,105,106,107,108,109]. Conspiracy theories surrounding COVID-19 specifically contributed to an unwillingness to comply with COVID-19 regulations [110,111]. The insights into why people believe in conspiracy theories may play a role in how they are transmitted as well. People generally only believe in conspiracy theories after learning about them, and they may come across them in certain political spheres. Prior works in this field have found that political agendas could be furthered by conspiracy theories, making people who fall into particular political categories more inclined to share conspiracy theories [112,113,114,115]. Conspiracy theories may also be used to generate doubt in mainstream politics and media [116]. Research work in this field has shown that people commonly avoid sharing conspiracy theories out of fear of ostracization. However, the involvement in politics and feelings generated by it may be so strong that it negates this fear anyway. This may further indicate how conspiracy theorists find community among each other [117].
The remainder of the paper is presented as follows. A review of recent works in this field is presented in Section 2. Section 3 presents the detailed methodology that was followed for the investigation, interpretation, and analysis of the underlying web behavior. The results are presented and discussed in Section 4, which is followed by the conclusion.

2. Literature Review

A review of recent works related to web behavior investigation, interpretation, and analysis during recent virus outbreaks is presented in this section. This section is divided into three parts. Section 2.1 presents a review of works related to time-series forecasting in the context of recent virus outbreaks such as COVID-19 and MPox as time-series forecasting approaches have been popular in the last few years for modeling web behavior. Section 2.2 presents a review of various conspiracy theories that were associated with virus outbreaks in the recent past. Section 2.3 presents an overview of healthcare research based on web behavior analysis from Google Trends, as Google Trends is the most popular platform for web behavior analysis [118] and it was used for data collection in this research project.

2.1. Review of Works Related to Time-Series Forecasting in the Context of Recent Virus Outbreaks Such as COVID-19 and MPox

To predict the spread of COVID-19, Shahid et al. [119] used Auto Regressive Integrated Moving Average model (ARIMA), Support Vector Regression (SVR), Long Short-Term Memory (LSTM), and Bidirectional Long-Short Term Memory (Bi-LSTM). They found that Bi-LSTM outperformed the rest when trying to predict cases of COVID-19. In a similar study, Chandra et al. [120] found that different types of LSTM models could be used to predict COVID-19 with high levels of accuracy. They used LSTM, Bi-LSTM, and encoder-decoder LSTM (ED-LSTM) to predict cases. While ED-LSTM tended to underperform compared to LSTM and Bi-LSTM models, it performed at the highest accuracy with static-split training. Alabduldrazzaq et al. [121] also used ARIMA in their study. Their work used cases in Kuwait and resulted in a correlation coefficient of 0.996, indicating that ARIMA was a strong contender for the best prediction model. In a similar study, the authors used ARMIA to predict where COVID-19 infections might occur [122]. Using data from Johns Hopkins University, they were able to accurately predict COVID-19 cases. Katoch et al. [123] used ARIMA modeling to devise numbers for the COVID-19 outbreak during the time of 30 January 2020 to 16 September 2020, in India. Ospina et al. [124] found that ARIMA models successfully predicted cases in Recife, contributing to the prevention effort. In the work carried out by Vilinová et al., a spatiotemporal analysis was used to analyze the spread of COVID-19 [125]. More specifically, spatial autocorrelation was used by the authors to analyze cases across Slovakian districts, and data was synthesized with Moran’s global autocorrelation index and local index. A similar study was carried out by El Deeb et al. [126]. In this study, spatial autocorrelation was used with certain parameters to analyze COVID-19 cases across Lebanese districts, and the authors found that geographic bordering, resident population, density, distance between district centers, and poverty density correlated with disease clustering and spread.
The work of Iftikhar et al. [127] focused on forecasting new cases and death counts related to the MPox virus using a hybrid forecasting system that combined time-series and stochastic models. Long et al. [128] worked on addressing the global health concern during the MPox outbreak, particularly in the United States, using short-term forecasting, and, somewhat similar to the comparative studies discussed in [129,130], the authors compared the working and performance of multiple machine learning models. Among the models tested, NeuralProphet emerged as the most efficient, achieving a low RMSE and high accuracy in predicting future cases. The work of Wei et al. [131] addressed the increasing prevalence of MPox cases in non-endemic countries, particularly in North America and Europe since May 2022. The researchers employed various forecasting models, such as ARIMA, exponential smoothing, LSTM, and GM(1,1), to predict daily cumulative confirmed MPox cases in different regions. Similar to the comparative study of machine learning models presented in [132], Priyadarshini et al. [133] used different machine learning models—linear regression, decision trees, random forests, elastic net regression, ANN, and CNN to assess the spread of the MPox virus across different countries. The results indicated that CNNs performed the best in modeling the virus’s spread, while time-series analysis using ARIMA and SARIMA models provided valuable insights for risk assessment and preventive measures. Pathan et al. [134] used a deep learning-based LSTM model to analyze the gene mutation rate of the MPox virus. The work of Eid et al. [135] introduced a novel approach called BER-LSTM, which optimized LSTM deep networks using the Al-Biruni Earth Radius (BER) algorithm to predict MPox cases accurately. Patwary et al. [136] examined the global spread of MPox using concepts of GIS technology and spatial data analysis. Du et al. [137] examined online search activity related to the MPox outbreak in China. The findings showed that regions with higher economic levels, particularly Beijing and Shanghai, exhibited more interest in MPox.
To summarize, these works have used time-series forecasting models such as ARIMA, Autocorrelation, and LSTM, to analyze web behavior, internet activity, and related information during virus outbreaks in the recent past. However, none of these works have focused on applying any such models to the recent surge in web behavior related to the 2023 MVD outbreak.

2.2. Review of Various Conspiracy Theories That Were Associated with Virus Outbreaks in the Recent Past

The COVID-19 pandemic was plagued by the proliferation of conspiracy theories and false information. These encompassed claims suggesting that COVID-19 was a fabrication, insinuations that the virus was artificially engineered and released as a bioweapon, and accusations of governments capitalizing on the crisis for anti-democratic purposes [138]. In the early stages of the pandemic, social media stories even propagated the idea that 5G technology was responsible for the spread of the virus [139]. Some conspiracy theories contended that the pandemic served as a guise for the clandestine injection of microchip quantum-dot spy software into individuals for surveillance purposes, gaining substantial traction on social media platforms [140]. Furthermore, there were assertions that COVID-19 testing, especially the use of nasopharyngeal swabs, could harm the blood–brain barrier or even infect individuals with the virus [141]. The conspiracy theories related to face masks included claims that masks could facilitate viral transmission or lead to oxygen deprivation and carbon dioxide poisoning [142]. Furthermore, misinformation extended to unverified therapies and remedies, encompassing homeopathic arsenic-based products, colloidal silver solutions, the use of high-dose vitamins as preventive measures, and various herbal remedies [143,144].
In general, conspiracy theories have the potential to have a significant negative impact. For example, false claims connecting 5G technology to the pandemic triggered attacks on telecommunication masts and subjected engineers to verbal and physical abuse in multiple countries, including the UK [145]. The repercussions of misinformation during infectious disease crises draw historical parallels, such as the HIV/AIDS pandemic, where denial of the virus’s existence and the promotion of untested alternative solutions led to substantial public health concerns and loss of lives [146,147,148]. The findings from recent works indicate that belief in COVID-19 conspiracy theories was inversely related to adherence to health-protective behaviors and trust in guidance from public health experts [149,150]. In a comprehensive study of 82 hoaxes related to the 2023 MPox outbreak and their spread on social media, researchers found that the sources behind these hoaxes were mostly unknown (73.17%), making it challenging to identify the primary disinformants. In the remaining instances (26.83%), sources included figures with public notoriety (18.29%), fictitious sources (6.1%), and impersonated identities (2.44%). The predominant format of these hoaxes was a combination of image and text (39%), followed by primarily text-based hoaxes (36.6%) [151]. In a separate study analyzing conspiracy theories related to the MPox outbreak on TikTok, 153 videos were identified and analyzed. The most prevalent theme (46.4% of videos) asserted that MPox was a deliberately orchestrated pandemic introduced for power, control, or financial gain. A second category (33.3% of videos) revolved around vaccines, with content alleging that MPox was an excuse to mandate vaccines worldwide. To add to this, approximately 17.6% of videos claimed that the WHO was involved in the MPox outbreak to gain more power and potentially override national laws [152].
To summarize, these works show that virus outbreaks in the recent past have been associated with several conspiracy theories which have been investigated and analyzed by researchers from different disciplines. However, none of those works studied the emergence of the new conspiracy theory involving the MVD and the emergency alert signal sent by FEMA in the United States on 4 October 2023.

2.3. Review of Applications of Google Trends in Healthcare

Google Trends data has been of interest to researchers for the mining and analysis of the underlying web behavior related to various emerging technologies [153,154], global affairs [155,156], humanitarian issues [157,158], societal problems [159,160], and needs of different diversity groups [161,162]. In the last decade and a half, the utilization, applications, and use cases of Google Trends to mine, monitor, interpret, and analyze web behavior during epidemics, pandemics, and virus outbreaks have attracted a significant amount of attention from researchers from different disciplines [163,164,165,166,167,168]. Ginsberg et al. [169] used Google Trends to track influenza-like illness (ILI) for early detection and rapid response. By analyzing the relative frequency of specific queries, the authors accurately estimated ILI activity in various regions of the United States. Kapitány-Fövény et al. [170] utilized Google Trends to forecast the incidence of Lyme disease in Germany. The study spanned from 2013 to 2018, with data on Lyme disease incidence obtained from the Robert Koch Institute and Google search volumes for “Borreliose” in Germany. The authors applied a SARIMA model to the Lyme-disease-incidence time series and incorporated Google Trends data as an external regressor. The results showed that Google Trends data correlated well with reported Lyme disease incidence. Verma et al. [171] used Google Trends to predict disease outbreaks in India. The research explored the correlation between Google Trends data for diseases like malaria, dengue fever, chikungunya, and enteric fever in 2016 in Haryana and Chandigarh and IDSP data. The results show a strong temporal correlation between Google Trends data and the IDSP data, suggesting that Google Trends could be used as an early warning tool for disease outbreaks. The work of Young et al. [172] involved using Google Trends to predict weekly state-level cases of syphilis in the United States. By analyzing web behavior related to keywords associated with syphilis, the study aimed to determine whether such data could serve as a supplementary tool for monitoring and predicting syphilis outbreaks. Another work by Young et al. [173] involved using Google Trends to forecast new HIV diagnosis cases in the United States. The study collected Google Trends search-volume data for HIV-related keywords and combined it with state-level HIV case reports from the CDC. They developed a predictive model using a negative binomial approach and the Least Absolute Shrinkage and Selection Operator (LASSO) method. Morsy et al. [174] used Google Trends to predict the Zika virus in Brazil and Columbia. They aimed to determine whether the search volume for the term ‘Zika’ on Google Trends could serve as an early surveillance system for anticipating Zika outbreaks. The researchers used time-series forecasting models to establish a relationship between the weekly Zika cases and the corresponding Google search-query data. As can be seen from this review, in these works Google Trends was used for the mining and analysis of relevant web behavior during virus outbreaks of the past such as Lyme disease, malaria, syphilis, HIV, ILI, and Zika virus. However, none of these works focused on the analysis of web behavior in the context of the 2023 MVD outbreak.
To summarize, time-series forecasting, investigation of conspiracy theories, and web behavior mining and analysis using Google Trends during virus outbreaks have attracted the attention of researchers from different disciplines such as Healthcare, Epidemiology, Big Data, Data Analysis, Data Science, and Computer Science in the last few years. However, prior works in this field have multiple limitations, as follows:
  • The works that applied time-series forecasting models on relevant web behavior did not investigate the web behavior data related to the 2023 MVD outbreak.
  • Some of the works related to the applications of time-series forecasting models to model web behavior during virus outbreaks did not focus on:
    studying the web behavior from different geographic regions
    comparing the performance of different time-series forecasting models to determine the optimal model for studying web behavior in different regions
  • Even though several works in this field have studied the development and dissemination of conspiracy theories related to virus outbreaks in the recent past such as COVID-19 and MPox, none of those works studied the relevant web behavior data in the context of the new conspiracy theory involving the MVD outbreak and the FEMA emergency alert signal.
  • Relevant web behavior data from Google Trends has been mined and analyzed in several prior works in this field to understand and interpret multimodal components of web behavior during virus outbreaks. However, such works did not focus on mining, analyzing, or interpreting the web behavior related to new conspiracy theories involving the MVD outbreak and the FEMA emergency alert signal.
The work presented in this paper aims to address these research gaps. The step-by-step methodology that was followed is outlined in Section 3 and the results are presented and discussed in Section 4.

3. Methodology

This section is divided into three parts. In Section 3.1 an overview of the working of Google Trends and the procedure that was followed for data collection using Google Trends is presented. Section 3.2 presents the methodology that was followed for the development of the time-series forecasting models which were applied to the data collected from Google Trends. In Section 3.3, the approach that was followed for correlation analysis in this context is discussed.

3.1. Overview of the Data Collection Architecture and Description of Data Collection

The data analyzed in this research work was collected from Google Trends [175]. Google Trends is a web-based tool provided by Google that allows users to delve into and assess the search interest and prevalence of topics, keywords, or search queries over time. It equips individuals with the means to gauge how frequently specific terms are queried on Google from different geographic regions, offering valuable insights into the dynamic trends and curiosities of online users [176,177]. Furthermore, Google Trends provides geographic data, facilitating the identification of regions where a topic garners the greatest attention. This tool also provides information regarding related queries, spotlighting frequently associated search terms with the chosen topic, and facilitating the exploration of interconnected trends and inquiries of interest to users. Google Trends also supports comparative analysis, allowing users to gauge the relative popularity of multiple search terms [178].
Google Trends offers three key benefits when compared to traditional surveys. First, it eliminates the cost associated with data collection and analysis, in contrast to conventional surveys, which often come with financial implications. Second, conducting routine surveys across a diverse global user base can be a formidable challenge, whereas Google Trends effortlessly taps into the worldwide search data generated daily on Google, simplifying the process of data collection and analysis. Third, Google Trends provides data that can be easily mined and analyzed, avoiding delays inherent in traditional surveys, which may be subject to time constraints related to participant recruitment and inclusion criteria [179,180]. There are two mathematical equations that underline the functioning of Google Trends, which are shown as Equations (1) and (2). In these equations, “q” represents the number of searches for the query in the location “l” during the period “t”. Here, Q(l,t) is the set of all the queries made from “l” during t, and π ( n(q,l,t) > τ) is a dummy variable. The dummy variable serves as an indicator, taking the value 1 when the query meets the popularity threshold n(q,l,t) > τ and 0 otherwise. To add to this, Equation (1) yields Relative Popularity (RP) values that are subsequently scaled to fit within a range of 0 to 100, and Equation (2) provides the numerical value of the Google Trends Index (GTI) [178,181].
R P q ,   l   ,   t = n ( q ,   l ,   t ) q Q ( l ,   t ) n ( q ,   l ,   t ) × π ( n ( q , l , t ) > τ )
G T I q , l , t = R P ( q , l , t ) m a x R P ( q , l , t ) t 1 , 2 , , T   × 100
Google Trends offers a range of features that provide valuable insights related to web behavior on Google. The “Explore” feature allows users to dig deeper into online interests, enabling the exploration of keyword popularity over chosen time periods and regions. Google Trends also provides “Trending Searches”, offering both daily search trends and real-time search trends for a selected region. For those interested in historical trends, the “Year in Searches” feature lets users explore what was trending in a specific region during a particular year. Additionally, Google Trends offers “Subscriptions”, allowing users to receive updates on specific topics or trending searches via email. These features collectively make Google Trends a powerful tool for the mining and analysis of web behavior on different topics, with a specific focus on virus outbreaks [182,183,184,185].
For the work presented in this paper, Google Trends was used for collecting data regarding the 2023 MVD outbreak and the conspiracy theory linking the MVD outbreak, a zombie outbreak, and the FEMA emergency alert signal. The workflow diagram in Figure 1 shows the step-by-step procedure that was followed for data collection using Google Trends. At first, the search queries were set to compile MVD-related and zombie-related search interests, and the geolocation was set to worldwide. Thereafter, in the “Search Category” option on Google Trends, the “All categories” option was selected and for the type of search data to be mined, “Web Search” was selected as the relevant web behavior data was being mined. After setting these specifications for the data mining process, an API call to Google Trends was performed for the weekly data between 2 October 2023 and October 9, 2023. There were primarily two reasons why the data mining was performed for this time range. First, the date when the FEMA emergency alert signal was broadcasted was 4 October 2023, and the search-interest data on that day as well as around that day is relevant to investigate. Second, Google Trends provides several options for data mining. Although custom timelines can be provided to the Google Trends API. However, selecting the timeline as “Past 7 days” provides the hourly search-interest data for each day in the 7-day period. In this work, the investigation also included the analysis of search interests related to this conspiracy theory right after the broadcasting of the FEMA emergency alert signal. So, obtaining the hourly search-interest data was necessary. After this data collection was completed, the master dataset comprised the hourly search interests related to MVD and search interests related to zombies between 2 October 2023 and 9 October 2023, for 216 regions. As this data was collected using Google Trends, the highest value of the search interest was 100 and the lowest value was 0. The names of these 216 regions are shown in Table 1. These regions recorded significant search interests related to MVD and this conspiracy theory, so the data of search interests from these regions was included in the development of the master dataset.
This dataset is available at https://dx.doi.org/10.21227/jm5y-e993. This dataset contains 216 data files where the search interests related to MVD and search interests related to zombies between 2 October 2023 and 9 October 2023, are presented for the 216 regions. For each region, this dataset presents the search-interest data as a separate data file. Each data file contains three attributes that represent the time (in an hourly format), the search interests related to MVD, and the search interests related to zombies. These second and third attributes are named as per the data file. For instance, in the data file named “United States”, the data originating from the United States is available. The first attribute in this data file is “Time”, which represents the hourly information. The second attribute in this data file is “zombie: (United States)”, which represents the search interests related to zombies originating from the United States. The third attribute in this data file is “marburg virus: (United States)”, which represents the search interests related to MVD originating from the United States. In a similar manner, in the data file named “Canada”, the data originating from Canada is available. The first attribute in this data file is “Time”, which represents the hourly information. The second attribute in this data file is “zombie: (Canada)”, which represents the search interests related to zombies originating from Canada. The third attribute in this data file is “marburg virus: (Canada)”, which represents the search interests related to MVD originating from Canada. The compliance of this dataset with the FAIR principles of Scientific Data Management [186] is explained next. Several prior works in the field of dataset development have discussed how the developed datasets such as the human metabolome database for 2022 [187], WikiPathways dataset [188], datasets of Tweets about COVID-19 [189,190], a dataset of Tweets about MPox [191], computational 2D materials database (C2DB) [192], the open reaction database [193], RCSB Protein Data Bank [194], and the PHI-base: pathogen–host interactions database [195], to name a few, complied with the FAIR principles of scientific data management. The FAIR principles include four key aspects of scientific data management, namely Findability, Accessibility, Interoperability, and Reusability. This dataset, which can be accessed at https://dx.doi.org/10.21227/jm5y-e993, has the characteristic of findability, as it is assigned a distinct Digital Object Identifier (DOI) by IEEE Dataport. This dataset is accessible, as researchers from any discipline may use this DOI to access the dataset online as long as they have a working internet connection and a system that can connect to the internet and download data files. The dataset exhibits interoperability, as it contains data that is presented in a standardized format (.CSV files), enabling its comprehension and analysis across different platforms, devices, and operating systems. This dataset fulfills the reusability criterion, as the data files may be re-used several times to examine and explore various research problems related to the MVD outbreak and this conspiracy theory.

3.2. Methodology for Performing Time-Series Forecasting

The data collected using Google Trends (discussed in Section 3.1) comprised the search interests related to MVD recorded from relevant Google Searches from the 216 regions. As Google Searches serve as an indicator of the needs, interests, motives, concerns, perspectives, and opinions of the global population during a virus outbreak, several prior works in this field have developed time-series forecasting models to accurately predict web behavior during virus outbreaks (reviewed in Section 2.1). As discussed in Section 2.1, such works did not focus on predicting web behavior related to the recent outbreak of MVD. To add to this, several works related to time-series forecasting used only one specific model for time-series forecasting out of some of the most popular models such as ARIMA, Autocorrelation, or Long Short-Term Memory network (LSTM). The work presented in this paper addresses both limitations. More specifically, programs were written in Python 3.11.5 to develop and apply all these models—ARIMA, Autocorrelation, and LSTM on the web searches related to MVD emerging from 216 regions (Table 1) and the performance characteristics of these models per region for all the 216 regions was computed. The pseudocodes of these programs are shown in Algorithms 1, 2 and 3, respectively.
Algorithm 1: ARIMA for Time-Series Forecasting of Web Behavior related to MVD
Input: Master Dataset for Analysis
Output: ARIMA Forecast for the Data, Performance Metrics (RMSE, MSE, AE)
File Path
dataframe = load the data files
regions[] = region names
for each region in regions do:
  dataset = get values from dataframe: marburg virus: <region>
  dataset = convert dataset to float32
  x = dataset
  size = calculate size as 75% of all x
  split x into:
    train: from start to size
    test: from size to end x
  history = train value predictions_test = empty list
  for data in test do:
    model = history, order = (0,1,0)
    model_fit_test = fit model
    output_test = forecast by fitted model
    yhat_test = output[0]
    predictions_test ⟵ append(yhat)
    obs_test = test[data]
    history ⟵ append(obs)
  end for
  predictions_train = empty list
  for data in train do:
    model_train = ARIMA history, order(0,1,0)
    model_fit_train ⟵ fit model
    output_train ⟵ get forecast
    yhat_train ⟵ output_train[0]
    predictions_train ⟵ append(yhat_train)
    obs_train ⟵ train[data]
    history ⟵ append(obs_train)
  RMSE = calculate RMSE (test, prediction_test), calculate RMSE (train, prediction_train)
  MSE = calculate MSE (test, prediction_test), calculate MSE (train, prediction_train)
  AE = calculate AE (test, prediction_test), calculate AE (train, prediction_train)
  predictionsplot = empty list
  end for
  for data from 0 to dataset length do:
    if data ≤ predicitons length do:
      predictionsplot ⟵ append(np.nan)
    else:
      index = length of dataset − data
      predictionsplot ⟵ append_prediction(index)
  plot (dataset label = ground truth, predictions_train, predictions_test)
  show and save the plot
  end for
end for
Algorithm 2: Autocorrelation for Time-Series Forecasting of Web Behavior related to MVD
Input: Master Dataset for Analysis
Output: Autocorrelation Forecast for the Data, Performance Metrics (RMSE, MSE, AE)
dataframe = load the data files
for each region in regions do:
  dataset = get values from dataframe: marburg virus: <region>
  dataset = convert dataset to float32
  x = dataset
  size = calculate size as 75% of all x
  split x into:
    train: from start to size
    test: from size to end x
  windows = 24
  model = Autoreg(train, lags = 24)
  model_fit = fit the model(training data)
  coef = coefficients from the model fit
  lag = last 24 values of the dataset
  prediction_test = empty list
  for each data in test do:
    length = history length
    lag = last window value in history
    yhat = coef[0]
    for each d in 0 to windows − 1 do:
      yhat_test+ = coef[d + 1] * lag[windows − d − 1]
    obs_test = test [data]
    prediction_test ⟵ append(yhat_test)
    history ⟵ append(obs_test)
  end for
  prediction_train = empty list
  for data in train do:
    length = length of history
    lag = last window values from history
    yhat_train = coef[0]
  end for
  for each data in history do:
      yhat_train += coef[d + 1] * lag[window − d − 1]
  obs_train ⟵ train[data]
  prediction_train ⟵ append(yhat_train)
  history ⟵ append(obs_train)
  end for
  RMSE = calculate RMSE (test, prediction_test), calculate RMSE (train, prediction_train)
  MSE = calculate MSE (test, prediction_test), calculate MSE (train, prediction_train)
  AE = calculate AE (test, prediction_test), calculate AE (train, prediction_train)
  for each t3 from 0 to the length of the series do:
    if t3 ≤ length of predictions2 then:
      predicionsplot ⟵ append(np.nan)
    else:
      index2 ⟵ length of dataset − data
      predictionsplot ⟵ append_prediction(index)
  plot (dataset label = ground truth, predictions_train, predictions_test)
  show and save the plot
  end for
end for
Algorithm 3: LSTM for Time-Series Forecasting of Web Behavior related to MVD
Input: Master Dataset for Analysis
Output: Autocorrelation Forecast for the Data, Performance Metrics (RMSE, MSE, AE)
dataframe = load the data files
for each region in regions do:
  tf.keras.utils.set_random_seed(1)
  tf.config.experimental.enable_op_determinism()
  Function create_dataset(dataset, look_back = 1):
      dataX = empty list
      dataY = empty list
      for i from 0 to (len(dataset)-look_back-1 do:
        a = dataset segment from i and size look_back
        dataX ⟵ append(a)
        dataY ⟵ append(dataset[i + look_back, 0]
        np.array (dataX)
        np.array (dataY)
        return (data)
      end for
  end of Function
  dataset = get values from dataframe: marburg virus: <region>
  dataset = dataframe.values
  dataset = convert dataset (float32)
  scaler = MiniMaxScaler(feature_range = (0, 1))
  dataset = fit, transform dataset
  train_size = 75% of all dataset
  test_size = len(dataset) − train_size
  look_back = 1
    trainX, trainY = create_dataset(train, look_back)
    testX, testY = create_dataset(test, look_back)
    trainX = reshape trainX with dimension
    testX = reshape testX with dimension
  model = Sequential()
  model.add(LSTM(100, input_shape = (1, look_back)))
  model.add(Dense(1))
  model.compile(loss = ‘mean_squared_error’, optimizer = ‘adam’)
  model.fit(trainX, trainY, epochs = 100, batch_size = 1, verbose = 2)
  trainPredict = inverse transform by scaler
  trainY = inverse transform by scaler
  testPredict = inverse transform by scaler
  testY = inverse transform by scaler
  trainPredict = model.predict(trainX)
  testPredict = model.predict(testX)
    RMSE = calculate RMSE (test, testPredict), calculate RMSE (train, trainPredict)
    MSE = calculate MSE (test, testPredict), calculate MSE (train, trainPredict)
    AE = calculate AE (test, testPredict), calculate AE (train, trainPredict)
  testPredictPlot = np.empty_like(dataset)
  testPredictPlot[len(trainPredict) + (look_back * 2) + 1:len(dataset)-1, :] = testPredict
  trainPredictPlot = np.empty_like(dataset)
  trainPredictPlot[look_back:len(trainPredict) + look_back, :] = trainPredict
  plot (dataset label = ground truth, trainPredictPlot, testPredictPlot)
  show and save the plot
end for
Figure 2 shows a flowchart that outlines the working of these models and how the same were applied to the master dataset. As can be seen from Figure 2 and Algorithms 1–3, the performance of these models for time series forecasting was evaluated by computing the Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE) for both the train set and the test set. The results of the same are presented in Section 4.

3.3. Methodology for Correlation Analysis

The section presents the specifics of the correlation analysis that was performed on the master dataset. The dataset contained search interests from relevant Google Searches related to MVD and the conspiracy theory for each region in the list of 216 regions. For each region, the correlations between these two types of search interests were investigated using Pearson’s correlation. Thereafter, the nature of the correlation i.e., statistically significant, or not statistically significant was determined based on the p-value of the correlation. To add to this, the correlation between the search interest data related to this conspiracy theory in the United States and the remaining countries was also evaluated using Pearson’s correlation to determine the nature of the correlation, i.e., statistically significant or not statistically significant. Figure 3 represents a flowchart that shows the step-by-step process that was performed in this regard to develop and apply the models for correlation analysis. Algorithm 4 represents the pseudocode of the program that was written in Python 3.11.5 to check for correlations between web behavior related to MVD and this conspiracy theory and to determine the nature of the same. Another program was also written to check for correlations between the web behavior related to this conspiracy theory in the United States and other countries. To avoid possible redundancy, the pseudocode of that program is not presented in this paper.
Algorithm 4: Correlation between MVD and Conspiracy Theory-related Web Behavior
Input: Master Dataset for Analysis
Output: Pearson’s r-value and p-value for each region
dataframe = load the data files
files = get the list of all CSV files in the master dataset using a recursive search
country = empty list
Name = empty list
for each file_name in files do:
  i ⟵ 0, col1 ⟵ empty list, col2 ⟵ empty list
  for each date in the first column of f do:
    if specific date exists then:
      if second column of f at the ith row is an integer or is digit then:
        col1 ⟵ append the integer value
      else
        col1 ⟵ append 0
      if third column of f at the ith row is an integer or is digit then:
        col2 ⟵ append the integer value
      else
        col2 ⟵ append 0
    end if
  increment i
end for
country ⟵ append col, col2
r_value = empty list, p_value = empty list, significance = empty list
for each entry c in country do:
  stat_1 = calculate pearson correlation between c[0] and c[1]
  p_1 ⟵ extract second value from stat_1
  p_0 ⟵ extract first value from stat_1
  r_value ⟵ p_0, p_value ⟵ p_1
  if p_1 is less than 0.05 then:
    significance ⟵ statically significant
  else:
    significance ⟵ not significant
end for
open file in writing mode as CSV output:
    writer = CSV writer for CSV output
    write the header row with columns
    for i from 0 to length of country do:
      row ⟵ empty list
      row [i] ⟵ append(name[i], r_value[i], p_value[i], significance[i])
      write row to the CSV
    end for

4. Results and Discussion

This section presents the results and highlights the novel findings of this work. As discussed in Section 3, Algorithms 1–3 were applied to the web behavior data related to MVD present in the dataset, and the results of forecasting for each region were plotted and computed using RMSE, MSE, and MAE. As a result of the same, a graph was plotted per model per region resulting in 648 graphs (three plots per region × 216 regions). To avoid possible redundancy, the graphs of nine regions (selected at random) are shown in Figure 4, Figure 5, Figure 6, Figure 7, Figure 8, Figure 9, Figure 10, Figure 11 and Figure 12, respectively.
The complete results (RMSE, MSE, and MAE on Train and Test sets) of running Algorithms 1, 2 and 3 (ARIMA, Autocorrelation, and LSTM) on the data of all 216 regions are presented in Table 2, Table 3 and Table 4, respectively.
It is worth mentioning here that for multiple regions the search interests related to MVD were constant during this 7-day period. So, for those regions, the RMSE, MSE, and MAE are reported to be 0 in Table 2, Table 3 and Table 4. The performance metrics reported in Table 2, Table 3 and Table 4, allow comparisons of the performance of the time-series forecasting models (ARIMA, Autocorrelation, and LSTM) which were developed and implemented on the dataset using Algorithms 1, 2, and 3, respectively. These performance metrics reveal that there was not any particular time-series forecasting model that always outperformed the other two models for every region. However, the results presented in Table 2, Table 3 and Table 4 serve as a framework for the identification of the optimal time-series forecasting model for predicting MVD virus-related web behavior in each region out of this collection of 216 regions (Table 1). For instance, for the United States, the RMSE values generated by ARIMA, Autocorrelation, and LSTM for the test set are 0.46291, 0.805232, and 0.7681, respectively. So, based on the same, it can be concluded that the ARIMA model (Algorithm 1) is best suited to forecast web behavior related to MVD emerging from the United States. Similarly, for Canada, the RMSE values generated by ARIMA, Autocorrelation, and LSTM for the test set are 0.845154, 0.932133, and 1.1596. So, based on the same, it can once again be concluded that the ARIMA model (Algorithm 1) is best suited to forecast web behavior related to MVD emerging from Canada. However, for China, the RMSE values generated by ARIMA, Autocorrelation, and LSTM for the test set are 10.89779, 11.35232, and 8.1723. So, based on the same, it can be concluded that the LSTM model (Algorithm 3) is best suited to forecast web behavior related to MVD emerging from China. In a similar manner, an optimal model for performing forecasting of MVD-related web behavior can be deduced for each region out of all the 216 regions (Table 1), based on a comparison of the results and findings presented in Table 2, Table 3 and Table 4.
Thereafter, the results of correlation analysis are presented. As shown in Figure 3, two types of correlations were investigated. First, the correlation between search interests related to MVD and search interests related to zombies stated as Model 1 in Figure 3, was investigated. Second, the correlation between the zombie-related search interests in the United States and other regions, stated as Model 2 in Figure 3, was investigated. The results of applying Model 1 to the master dataset are shown in Table 5.
As can be seen from Table 5, the list of regions where there was a statistically significant correlation between MVD-related searches and zombie-related searches on Google on 4 October 2023, were Argentina, Bhutan, Burundi, France, Ghana, Lebanon, Madagascar, Myanmar (Burma), Peru, Romania, South Africa, South Korea, United States, and Uruguay. This is an interesting finding, as historically zombie-related web searches on Google had no correlation with web searches on Google related to MVD. In this context, 4 October 2023 was selected as the date for investigation because the FEMA emergency alert signal was broadcast on that day and the conspiracy theory was that this signal would activate the Marburg virus in people who have been vaccinated and turn some of them into zombies. Thereafter, the second correlation model (Model 2 in Figure 3) was run on the master dataset to check for correlations between zombie-related web searches on Google in the United States and zombie-related web searches from the list of 215 remaining regions. The results of the same are shown in Table 6.
As can be seen from Table 6, the list of regions where this correlation was statistically significant were Canada, Hong Kong, Mauritania, Mongolia, Northern Mariana Islands, Taiwan, Timor-Leste, and Uzbekistan. This is also an interesting finding, as the FEMA emergency alert signal was broadcast only in the United States. However, the results show that the zombie-related searches from the United States had a statistically significant correlation with zombie-related searches emerging from multiple other regions, even though no emergency signal or similar was broadcast in those regions. Thereafter, an analysis was also performed to determine the list of regions out of these 216 regions where there was a positive increase in zombie-related searches between 2 PM and 3 PM (EST) on 4 October 2023. This time range was specifically chosen for this analysis as the FEMA emergency alert signal was broadcast at 2.20 PM (EST) on 4 October 2023. The results are shown in Table 7.
Thereafter, further analysis of the trends of search interests in regions where there was a statistically significant correlation between MVD-related web searches and zombie-related web searches was performed. In this analysis, the trends of zombie-related web searches during the entire day on 4 October 2023 were analyzed.
It is worth noting that in Figure 13 and Figure 14, the Y-axis represents the value of search interests as obtained from Google Trends and the X-axis represents the hour, where 12.01 to 1.00 is considered hour 1, 1.01 to 2.00 is considered hour 2, and so on. From Figure 13 and Figure 14, the trends and variations of searches in these regions can be observed. For instance, there was a peak in search interests in multiple regions between 2 PM and 3 PM. At the same time, it is interesting to note that there was a peak in search interests in Bhutan between 5 PM and 8 PM. A different pattern can be seen in Argentina, where the peak in search interests occurred between 2 AM and 5 AM. In a similar manner, these Figures can be analyzed to interpret the similarities and variations in terms of the trends in zombie-related web searches on 4 October 2023, in different geographic regions where there was a statistically significant correlation between MVD-related web searches on Google and zombie-related web searches on Google.
The research work presented and discussed in this paper has a few limitations. First, the data obtained by Google Trends is the data generated by only a certain percentage of the worldwide population who have access to the internet and opt to use Google as their primary search engine. Second, it is important to note that the data collected from Google Trends and analyzed in this work represent the relative search volumes rather than absolute values of the total amount of Google Searches emerging from different geographic regions. Third, there is a notable inadequacy related to the disclosure of the methodology and underlying algorithms used by Google in producing search-interest data.
As per Seltzer [196], “From the perspective of statistical practice, data mining raises three quite different sorts of ethical issues. These are (a) the suitability and validity of the methods employed in any given data mining application, (b) the degree to which confidentiality and privacy obligations are respected, and (c) the overall aims of a given data mining application”. Each of these issues highlighted by Seltzer [196] are discussed in detail in the American Statistical Association’s Ethical Guideline for Statistical Practice [197]. The suitability and validity of the models used in this work (Algorithms 1–4) have already been discussed in Section 3 and Section 4, respectively. The purpose of performing data mining was also discussed in Section 3. The collected data has been uploaded as a dataset on IEEE Dataport, available at https://dx.doi.org/10.21227/jm5y-e993, as per the CC BY 4.0 License, so that the results presented in this paper may be replicated and/or any similar research questions may be investigated using this dataset. As stated in the support section of Google Trends [198], the data provided by Google Trends is “anonymized (no one is personally identified)”. Finally, the Privacy Policies of Google state [199] “We restrict access to personal information to Google employees, contractors, and agents who need that information in order to process it. Anyone with this access is subject to strict contractual confidentiality obligations and may be disciplined or terminated if they fail to meet these obligations”. None of the authors of this paper were Google employees, contractors, or agents at the time of writing this paper or prior to the same. To summarize, as per the best knowledge of the authors, the work of this paper met the standards of ethical research in this field [200].

5. Conclusions

As a result of the outbreak of the MVD in February 2023 and the high fatality rate of the same on a global scale, people have been devoting a substantial amount of time to social media platforms and the internet in general over the last few months to acquire and disseminate information pertaining to MVD. During virus outbreaks in the recent past, such as COVID-19, Influenza, Lyme Disease, and Zika virus, researchers from different fields such as Healthcare, Epidemiology, Big Data, Data Analysis, Data Science, and Computer Science utilized Google Trends to extract and analyze multimodal components of web behavior of the general public in order to examine, explore, interpret, assess, and forecast the worldwide perception, readiness, reactions, and response linked to these virus outbreaks. During such virus outbreaks of the past, the application of time-series forecasting models such as ARIMA, LSTM, and Autocorrelation to web searches to model, predict, and forecast the web behavior of the general public in the context of the outbreaks also attracted the attention of researchers from different disciplines. Furthermore, the paradigms of web behavior on the internet during virus outbreaks of the past also led to the development and dissemination of conspiracy theories that led to a range of reactions in the general public. For example, during the outbreak of COVID-19, a popular conspiracy theory was that 5G towers had a role in the transmission of the virus. The analysis of such conspiracy theories during virus outbreaks of the past has also been relevant to understanding the underlying patterns of information seeking and sharing on the internet. The outbreak of MVD and an electronic alert (for testing purposes) sent by the Federal Emergency Management Agency (FEMA) to all television, radio, and mobile devices throughout the United States on 4 October 2023 has given rise to an unconventional conspiracy theory that associates the Marburg Virus with a zombie outbreak. Specifically, the conspiracy theory was centered around the concept that the FEMA alert would activate the Marburg virus in people who have been vaccinated and turn some of them into zombies. This conspiracy theory spread like wildfire on the internet to the extent that soon after the FEMA alert signal was broadcast, Jeremy Edwards (press secretary and deputy director of public affairs at FEMA) provided a statement to the public to clarify that he was not a zombie. Due to this recent outbreak of MVD and the conspiracy theory involving the same, it is imperative to conduct an investigation into the underlying patterns of web behavior in order to obtain a comprehensive understanding of the paradigms of information seeking and sharing used by the general public in this particular context. No prior work in this field thus far has focused on the same. Therefore, the work presented in this paper aims to address this research gap and makes multiple scientific contributions to this field. It presents the results of performing time-series forecasting of the search interests related to MVD emerging from 216 different regions on a global scale using three models—ARIMA, LSTM, and Autocorrelation. The results of this analysis in terms of RMSE, MSE, and MAE are presented and discussed. The results of this analysis present the optimal model for forecasting web behavior related to MVD in each of these regions. For instance, for the United States, the RMSE values generated by ARIMA, Autocorrelation, and LSTM for the test set are 0.46291, 0.805232, and 0.7681, respectively. So, based on the same, it can be concluded that the ARIMA model is best suited to forecast web behavior related to MVD emerging from the United States. Similarly, for Canada, the RMSE values generated by ARIMA, Autocorrelation, and LSTM for the test set are 0.845154, 0.932133, and 1.1596. So, based on the same, it can once again be concluded that the ARIMA model is best suited to forecast web behavior related to MVD emerging from Canada. However, for China, the RMSE values generated by ARIMA, Autocorrelation, and LSTM for the test set are 10.89779, 11.35232, and 8.1723. So, based on the same, it can be concluded that the LSTM model is best suited to forecast web behavior related to MVD emerging from China. The paper also presents the findings from investigating two types of web behavior for correlations. First, the correlation between search interests related to MVD and search interests related to zombies was investigated. Second, the correlation between zombie-related search interests in the United States and other regions was investigated. The findings from the first analysis show that the list of regions where there was a statistically significant correlation between MVD-related searches and zombie-related searches on Google on 4 October 2023 were Argentina, Bhutan, Burundi, France, Ghana, Lebanon, Madagascar, Myanmar (Burma), Peru, Romania, South Africa, South Korea, United States, and Uruguay. This is an interesting finding, as historically zombie-related web searches on Google had no correlation with web searches on Google related to MVD. The findings from the second analysis show that the list of regions where this correlation was statistically significant were Canada, Hong Kong, Mauritania, Mongolia, Northern Mariana Islands, Taiwan, Timor-Leste, and Uzbekistan. This is also an interesting finding, as the FEMA emergency alert signal was broadcast only in the United States. Finally, the paper also presents an analysis of variation and degree of increase of search interests in the context of this conspiracy theory emerging from different geographic regions. As per the best knowledge of the authors, no similar work has been carried out in this field thus far. Future work would involve detecting and analyzing the popular topics represented in Google Searches in relation to this conspiracy theory to interpret the specific themes of information seeking and sharing on Google in the context of this conspiracy theory.

Author Contributions

Conceptualization, N.T.; methodology, N.T., S.C., K.A.P., N.A., A.P. and R.S.; software, N.T., S.C., K.A.P., A.P. and R.S.; validation, N.T.; formal analysis, N.T., S.C., K.A.P., N.A., A.P. and R.S.; investigation, N.T., S.C., K.A.P., A.P. and R.S.; resources, N.T.; data curation, N.T.; writing—original draft preparation, N.T., C.H. and V.K.; writing—review and editing, N.T.; visualization, N.T., S.C., K.A.P., A.P. and R.S.; supervision, N.T.; project administration, N.T. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

This work resulted in the creation of a dataset that is available at https://dx.doi.org/10.21227/jm5y-e993, as per the CC BY 4.0 License.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. A workflow diagram to represent the data collection and the development of the master dataset using Google Trends.
Figure 1. A workflow diagram to represent the data collection and the development of the master dataset using Google Trends.
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Figure 2. A flowchart to represent the application of Algorithm 1 (Model 1), Algorithm 2 (Model 2), and Algorithm 3 (Model 3) to the master dataset.
Figure 2. A flowchart to represent the application of Algorithm 1 (Model 1), Algorithm 2 (Model 2), and Algorithm 3 (Model 3) to the master dataset.
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Figure 3. A flowchart that represents different forms of correlation analysis that was performed on the dataset.
Figure 3. A flowchart that represents different forms of correlation analysis that was performed on the dataset.
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Figure 4. Representation of the results of Time-Series Forecasting of the Search Interests related to MVD in Australia using Autocorrelation, ARIMA, and LSTM.
Figure 4. Representation of the results of Time-Series Forecasting of the Search Interests related to MVD in Australia using Autocorrelation, ARIMA, and LSTM.
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Figure 5. Representation of the results of Time-Series Forecasting of the Search Interests related to MVD in Canada using Autocorrelation, ARIMA, and LSTM.
Figure 5. Representation of the results of Time-Series Forecasting of the Search Interests related to MVD in Canada using Autocorrelation, ARIMA, and LSTM.
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Figure 6. Representation of the results of Time-Series Forecasting of the Search Interests related to MVD in Morocco using Autocorrelation, ARIMA, and LSTM.
Figure 6. Representation of the results of Time-Series Forecasting of the Search Interests related to MVD in Morocco using Autocorrelation, ARIMA, and LSTM.
Computation 11 00234 g006
Figure 7. Representation of the results of Time-Series Forecasting of the Search Interests related to MVD in Ukraine using Autocorrelation, ARIMA, and LSTM.
Figure 7. Representation of the results of Time-Series Forecasting of the Search Interests related to MVD in Ukraine using Autocorrelation, ARIMA, and LSTM.
Computation 11 00234 g007
Figure 8. Representation of the results of Time-Series Forecasting of the Search Interests related to MVD in the USA using Autocorrelation, ARIMA, and LSTM.
Figure 8. Representation of the results of Time-Series Forecasting of the Search Interests related to MVD in the USA using Autocorrelation, ARIMA, and LSTM.
Computation 11 00234 g008
Figure 9. Representation of the results of Time-Series Forecasting of the Search Interests related to MVD in Uruguay using Autocorrelation, ARIMA, and LSTM.
Figure 9. Representation of the results of Time-Series Forecasting of the Search Interests related to MVD in Uruguay using Autocorrelation, ARIMA, and LSTM.
Computation 11 00234 g009
Figure 10. Representation of the results of Time-Series Forecasting of the Search Interests related to MVD in Ireland using Autocorrelation, ARIMA, and LSTM.
Figure 10. Representation of the results of Time-Series Forecasting of the Search Interests related to MVD in Ireland using Autocorrelation, ARIMA, and LSTM.
Computation 11 00234 g010
Figure 11. Representation of the results of Time-Series Forecasting of the Search Interests related to MVD in France using Autocorrelation, ARIMA, and LSTM.
Figure 11. Representation of the results of Time-Series Forecasting of the Search Interests related to MVD in France using Autocorrelation, ARIMA, and LSTM.
Computation 11 00234 g011
Figure 12. Representation of the results of Time-Series Forecasting of the Search Interests related to MVD in Denmark using Autocorrelation, ARIMA, and LSTM.
Figure 12. Representation of the results of Time-Series Forecasting of the Search Interests related to MVD in Denmark using Autocorrelation, ARIMA, and LSTM.
Computation 11 00234 g012
Figure 13. Trends in zombie-related web searches on 4 October 2023 in Argentina, Bhutan, Burundi, France, Ghana, Lebanon, and Madagascar.
Figure 13. Trends in zombie-related web searches on 4 October 2023 in Argentina, Bhutan, Burundi, France, Ghana, Lebanon, and Madagascar.
Computation 11 00234 g013
Figure 14. Trends in zombie-related web searches on 4 October 2023 in Myanmar (Burma), Peru, Romania, South Africa, South Korea, the United States, and Uruguay.
Figure 14. Trends in zombie-related web searches on 4 October 2023 in Myanmar (Burma), Peru, Romania, South Africa, South Korea, the United States, and Uruguay.
Computation 11 00234 g014
Table 1. List of 216 regions for which data was collected using Google Trends.
Table 1. List of 216 regions for which data was collected using Google Trends.
List of Regions
Afghanistan, Åland Islands, Albania, Algeria, American Samoa, Andorra, Angola, Antigua and Barbuda, Argentina, Armenia, Aruba, Australia, Austria, Azerbaijan, Bahamas, Bahrain, Bangladesh, Barbados, Belarus, Belgium, Belize, Benin, Bermuda, Bhutan, Bolivia, Bosnia and Herzegovina, Botswana, Brazil, British Virgin Islands, Brunei, Bulgaria, Burkina, Faso, Burundi, Cambodia, Cameroon, Canada, Cape Verde, Cayman Islands, Chad, Chile, China, Colombia, Comoros, Congo—Brazzaville, Congo—Kinshasa, Costa Rica, Côte d’Ivoire, Croatia, Cuba, Curaçao, Cyprus, Czechia, Denmark, Djibouti, Dominica, Dominican Republic, Ecuador, Egypt, El Salvador, Equatorial Guinea, Estonia, Eswatini, Ethiopia, Faroe Islands, Fiji, Finland, France, French, Guiana, French, Polynesia, Gabon, Gambia, Georgia, Germany, Ghana, Gibraltar, Greece, Greenland, Grenada, Guadeloupe, Guam, Guatemala, Guernsey, Guinea, Guinea-Bissau, Guyana, Haiti, Honduras, Hong Kong, Hungary, Iceland, India, Indonesia, Iran, Iraq, Ireland, Isle of Man, Israel, Italy, Jamaica, Japan, Jersey, Jordan, Kazakhstan, Kenya, Kosovo, Kuwait, Kyrgyzstan, Laos, Latvia, Lebanon, Lesotho, Liberia, Libya, Liechtenstein, Lithuania, Luxembourg, Macao, Madagascar, Malawi, Malaysia, Maldives, Mali, Malta, Martinique, Mauritania, Mauritius, Mexico, Moldova, Mongolia, Montenegro, Morocco, Mozambique, Myanmar (Burma), Namibia, Nepal, Netherlands, New Caledonia, New Zealand, Nicaragua, Niger, Nigeria, North Macedonia, Northern Mariana Islands, Norway, Oman, Pakistan, Palestine, Panama, Papua New Guinea, Paraguay, Peru, Philippines, Poland, Portugal, Puerto Rico, Qatar, Réunion, Romania, Russia, Rwanda, Samoa, San Marino, Saudi Arabia, Senegal, Serbia, Seychelles, Sierra Leone, Singapore, Sint Maarten, Slovakia, Slovenia, Solomon Islands, Somalia, South Africa, South Korea, South Sudan, Spain, Sri Lanka, St. Barthélemy, St. Helena, St. Kitts and Nevis, St. Lucia, St. Martin, St. Pierre and Miquelon, St. Vincent and Grenadines, Sudan, Suriname, Sweden, Switzerland, Syria, Taiwan, Tajikistan, Tanzania, Thailand, Timor-Leste, Togo, Trinidad and Tobago, Tunisia, Türkiye, Turkmenistan, Turks and Caicos Islands, U.S. Virgin Islands, Uganda, Ukraine, United Arab Emirates, United Kingdom, Uruguay, USA, Uzbekistan, Vanuatu, Venezuela, Vietnam, Western Sahara, Yemen, Zambia, Zimbabwe
Table 2. Results (RMSE, MSE, and MAE on Train and Test sets) of running Algorithm 1 on the master dataset.
Table 2. Results (RMSE, MSE, and MAE on Train and Test sets) of running Algorithm 1 on the master dataset.
Country NameRMSE for ARIMA (Train Set)MSE for ARIMA (Train Set)MAE for ARIMA (Train Set)RMSE for ARIMA (Test Set)MSE for ARIMA (Test Set)MAE for ARIMA (Test Set)
Afghanistan000000
Åland Islands000000
Albania13.78808190.11113.80952420.00595400.23816.095238
Algeria9.5924992.015873.6825422.13272489.85716.571429
American Samoa000000
Andorra000000
Angola9.02993381.539683.3968252.4880676.1904760.761905
Antigua and Barbuda16.74837280.50797.2539685.44233829.619052.380952
Argentina8.27695268.507942.5714291.0910891.1904760.47619
Armenia000000
Aruba000000
Australia5.37040728.841272.2222227.30948553.428572.952381
Austria16.61277275.98415.1269845.01901125.190481.809524
Azerbaijan16.67762278.14295.476194.39696919.333332.190476
Bahamas13.89616193.10325.2619057.85735961.73813.404762
Bahrain12.02181144.52385.87301616.74885280.52388.095238
Bangladesh7.38026154.468253.8015875.56134630.928572.166667
Barbados10.33257106.76193.8730169.20920984.809523.904762
Belarus000000
Belgium14.64772214.55564.2857143.0705989.4285710.952381
Belize9.09648582.746033.682546.48808442.095242.904762
Benin7.52983556.698413.65079413.73386188.6195.095238
Bermuda000000
Bhutan12.06892145.65874.8333338.06373465.023812.880952
Bolivia11.99669143.92064.0476198.52447572.666673.809524
Bosnia and Herzegovina7.26810852.82542.0793657.05758649.809522
Botswana18.51833342.92865.8650797.85129761.642863.833333
Brazil1.3392721.7936510.6190482.6547357.0476191.238095
British Virgin Islands000000
Brunei16.08213258.63495.73015914.56512212.14295.095238
Bulgaria17.32463300.14296.46031713.12758172.33335.47619
Burkina Faso21.58924466.09527.8253976.63683844.047622.904762
Burundi9.04047381.730162.9841279.82949996.619054
Cambodia16.83628283.46037.92063515.45654238.90488.809524
Cameroon6.50884642.365082.73015924.94756622.38110.57143
Canada2.8757338.2698411.7460320.8451540.7142860.47619
Cape Verde18.35886337.04767.76190512.40584153.90486.095238
Cayman Islands6.67023744.492062.1428575.30049428.095242.380952
Chad8.38744370.349213.0476193.69039913.619051.666667
Chile9.13913683.523811.44444415.43651238.28572.571429
China20.72534429.53979.04761910.89779118.76194.285714
Côte d’Ivoire7.04182549.58732.41269813.30592177.04765.047619
Colombia5.54061530.698411.6031750.8728720.7619050.380952
Comoros5.88918834.682542.1269846.11399637.380952.714286
Congo—Brazzaville11.52774132.88893.8095248.58292973.666673.47619
Congo—Kinshasa13.79383190.26985.1904764.91838124.190482.142857
Costa Rica11.30599127.82544.61904827.79431772.523811.28571
Croatia15.55431241.93655.25396816.54719273.80955.238095
Cuba14.12754199.58735.65079415.76615248.57143.714286
Curaçao000000
Cyprus14.44969208.79375.1428579.53439990.904764.619048
Czechia9.92231798.452383.2460327.45302855.547623.5
Denmark13.02013169.52384.3968259.32993187.047623.571429
Djibouti000000
Dominica000000
Dominican Republic18.38305337.93656.5714296.87992247.333332.47619
Ecuador8.28605668.658732.7222224.4960320.214291.928571
Egypt9.95186899.039685.2142867.12306850.73813.404762
El Salvador14.10449198.93653.2222222.439755.9523811
Equatorial Guinea14.94275223.28575.60317518.76547352.14297.952381
Estonia13.93238194.11114.42857122.47856505.28579.333333
Eswatini17.02706289.92066.96825421.07809444.28579.333333
Ethiopia18.62879347.03177.71428623.99603575.809510.47619
Faroe Islands000000
Fiji20.84523434.52388.63492113.99149195.76196.428571
Finland7.61889958.047622.4126983.45032811.904761.52381
France1.4800262.1904760.6031751.6475092.7142860.761905
French Guiana000000
French Polynesia000000
Gabon8.5467973.047623.14285714.2361202.66676.095238
Gambia14.50999210.53975.96825411.532561333.952381
Georgia15.58082242.76195.5873025.55063330.809522.190476
Germany2.1730674.7222221.182542.1984844.8333331.214286
Ghana13.6376185.98417.09523825.91837671.761911.66667
Gibraltar000000
Greece7.66252558.714292.85714319.79177391.71437.238095
Greenland000000
Grenada14.38363206.88894.4761912.02775144.66674.619048
Guadeloupe5.74732533.031752.1269842.1157014.476190.666667
Guam000000
Guatemala14.58799212.80955.3650795.8999634.809522.380952
Guernsey000000
Guinea12.59567158.65085.28571411.49534132.14294.952381
Guinea-Bissau17.11956293.07944.95238112.12828147.09524.333333
Guyana9.76306695.317461.85714314.83561220.09522.619048
Haiti10.07433101.49213.6507941.6903092.8571430.761905
Honduras11.29827127.65084.317469.89227797.857143.952381
Hong Kong10.86497118.04764.4603179.17034684.095243.52381
Hungary7.07779950.095242.93650815.231552326.571429
Iceland8.99117780.841273.61904828.24721797.90489.142857
India1.9395633.7619050.8888890.97590.9523810.380952
Indonesia1.4253932.0317460.8095241.1952291.4285710.714286
Iran8.36944670.047623.317466.3320840.095243.238095
Iraq17.57027308.71438.12698412.94126167.47626.380952
Ireland1.2680691.6080.4881.6256872.6428570.642857
Isle of Man000000
Israel11.54494133.28575.03174619.18705368.142910.2381
Italy4.05908716.476191.8888892.777467.7142861.142857
Jamaica6.16312637.984132.87301610.28175105.71433.857143
Japan11.6585135.92064.49206313.88216192.71435.095238
Jersey000000
Jordan5.6160231.539682.2063499.33758487.190483.571429
Kazakhstan000000
Kenya9.20489984.730163.96825416.88617285.14298
Kosovo8.07307965.17462.158739.62387992.619053.52381
Kuwait15.4509238.7302724.9819624.095210.85714
Kyrgyzstan000000
Laos000000
Latvia12.78454163.44444.49206321.67839469.95246.619048
Lebanon16.85701284.15876.2857147.41298754.952382.380952
Lesotho11.6986136.85715.36507926.57245706.095212.90476
Liberia11.81303139.54765.03968316.28248265.1197.833333
Libya10.5492111.28575.19047611.81605139.6194.809524
Liechtenstein000000
Lithuania11.90038141.6193.8095246.4586641.714292.380952
Luxembourg6.73771745.39683224.15919583.666710.2381
Macao14.08985198.52384.2857146.56106843.047622.190476
Madagascar11.76894138.50793.5238125.11213630.6199.190476
Malawi13.69973187.68254.60317517.36718301.6196.095238
Malaysia4.10187716.82542.2222224.64962921.619052
Maldives12.7895163.57144.30158721.52629463.3819.333333
Mali10.19103103.85713.87301619.22548369.61910.90476
Malta13.12093172.15874.8571435.19156826.952382.285714
Martinique12.62336159.34924.28571429.56188873.904813.38095
Mauritania20.30404412.2548.76190512.12043146.90484.857143
Mauritius12.63593159.66674.77777824.51336600.904811.04762
Mexico4.03752216.301591.3809521.7593293.0952381
Moldova000000
Mongolia8.12550466.023812.6269844.75344522.595242.214286
Montenegro13.7708189.63493.4444448.79935177.428573.142857
Morocco17.04033290.3737.30952417.09985292.40487.119048
Mozambique11.9227142.15083.0079378.61753574.26193.595238
Myanmar (Burma)9.76062795.269842.34920615.51497240.71433.047619
Namibia12.94524167.57944.94444422.81969520.73817.166667
Nepal16.66381277.68256.523814.35343318.952381.428571
Netherlands15.66363245.34924.5238110.36937107.52384.333333
New Caledonia000000
New Zealand6.94879248.285713.04761910.13246102.66673.904762
Nicaragua5.08733325.880951.5158731.8708293.50.880952
Niger4.73923222.460321.80952422.90872524.80957.52381
Nigeria9.81495596.333333.8888899.76387995.333333.52381
North Macedonia14.08928198.50794.0634924.20317317.666671.857143
Northern Mariana Islands000000
Norway8.16156366.611113.023814.89654923.976192.309524
Oman12.69921161.26985.66666721.22218450.3818.52381
Pakistan9.56846791.555563.3492066.0631636.76192.190476
Palestine000000
Panama14.65097214.65085.444444183245.571429
Papua New Guinea000000
Paraguay15.67882245.82544.98412718.52926343.33337.190476
Peru10.9982120.96033.7380951.7795133.1666670.833333
Philippines1.430952.0476190.7142862.0354014.1428570.809524
Poland4.71236122.206351.9206356.61167843.714292.952381
Portugal15.74348247.85715.73015913.20714174.42864.428571
Puerto Rico15.98064255.3815.8095242.214674.9047620.904762
Qatar15.13694229.1276.04761930.71451943.38112.71429
Réunion13.52159182.83334.27777812.2756150.69055.880952
Romania5.27497827.82542.4126989.16255383.952383.190476
Russia3.82556114.634921.3333331.8898223.5714290.904762
Rwanda19.74721389.95246.55555619.97141398.85719.571429
Samoa000000
San Marino000000
Saudi Arabia13.8587192.06356.66666711.56966133.85715.952381
Senegal17.12605293.30166.5079375.92814135.142862.809524
Serbia10.56123111.53973.4761923.95929574.04768.666667
Seychelles13.68118187.17465.93650816.06831258.19057.047619
Sierra Leone16.17881261.7544.9761917.98611323.58.02381
Singapore4.70814922.166671.6428576.55925443.023812.738095
Sint Maarten000000
Slovakia18.30973335.2467.35714312.3645152.8814.738095
Slovenia16.02379256.76195.57142916.71754279.47626.285714
Solomon Islands000000
Somalia7.0643349.904763.28571421.977264839.571429
South Africa4.97453824.746032.7619058.74506776.476193.809524
South Korea10.1848103.73024.6984138.80746477.571434.952381
South Sudan15.41799237.71435.07936514.0153196.42867.095238
Spain2.8171817.9365081.158731.8126543.2857140.809524
Sri Lanka19.99127399.65087.88888912.70171161.33334.952381
St. Barthelemy000000
St. Helena20.96747439.634911.0952420.79034432.23819.857143
St. Kitts and Nevis000000
St. Lucia6.98865348.841272.61904822.619511.6196.952381
St. Martin000000
St. Pierre and Miquelon000000
St. Vincent and Grenadines11.6046134.66672.8412715.86551251.71434.190476
Sudan20.89182436.46839.45238114.86046220.83338.261905
Suriname000000
Sweden13.94661194.50794.6507949.79552995.952382.761905
Switzerland14.39246207.14294.11111114.48973209.95243.857143
Syria000000
Taiwan12.05543145.33335.317462.5819896.6666671.238095
Tajikistan000000
Tanzania20.469494196.96825426.45301699.761913.19048
Thailand2.6034176.7777781.0158732.786027.7619051.190476
Timor-Leste000000
Togo15.50627240.44445.71428626.70741713.285711.7619
Trinidad and Tobago11.38294129.57144.42857133.986691155.09516.71429
Türkiye2.1343754.5555560.9682542.8452138.0952381.190476
Tunisia17.81341317.31756.4761922.72192516.28577.333333
Turkmenistan000000
Turks and Caicos Islands000000
U.S. Virgin Islands000000
Uganda16.43216270.01596.92063529.342886112.52381
Ukraine7.14864851.103172.8492066.26973139.309522.642857
United Arab Emirates13.60964185.22226.98412710.91962119.23814.380952
United Kingdom0.6299410.3968250.2857140.8997350.8095240.238095
United States2.338435.4682540.9285710.462910.2142860.214286
Uruguay14.211201.95245.28571410.68154114.09524.904762
Uzbekistan14.3737206.60323.8730168.4120170.76193.47619
Vanuatu000000
Venezuela9.13653183.476192.3015875.830952341.809524
Vietnam2.0816664.3333330.8571432.1712414.7142861
Western Sahara000000
Yemen000000
Zambia13.4772181.63495.69841314.46342209.19056.285714
Zimbabwe12.54832157.46035.19047614.32613205.23816.380952
Table 3. Results (RMSE, MSE, and MAE on Train and Test sets) of running Algorithm 2 on the master dataset.
Table 3. Results (RMSE, MSE, and MAE on Train and Test sets) of running Algorithm 2 on the master dataset.
Country NameRMSE of Autocorrelation (Train Set)MSE of Autocorrelation (Train Set)MAE of Autocorrelation
(Train Set)
RMSE of Autocorrelation
(Test Set)
MSE of Autocorrelation (Test Set)MAE of Autocorrelation
(Test Set)
Afghanistan000000
Åland Islands000000
Albania9.547236266.67623.44984216.33022266.67626.070559
Algeria5.983334309.87423.02679917.60324309.87428.755387
American Samoa000000
Andorra000000
Angola6.0666131.053373.1820885.57255531.053373.188894
Antigua and Barbuda10.5313929.90325.7865295.46838229.90324.685652
Argentina5.1604073.1248341.9152641.767723.1248340.977066
Armenia000000
Aruba000000
Australia4.09686433.997332.0789195.83072333.997332.770755
Austria10.8343322.503784.5182174.74381522.503783.749277
Azerbaijan11.0155713.877583.860893.72526213.877583.252013
Bahamas9.78905248.233015.3907876.94499948.233014.775422
Bahrain8.451745203.85616.22055914.27782203.85619.989248
Bangladesh4.52482724.085742.8648644.90772224.085743.710554
Barbados8.05177944.855212.9380336.69740344.855212.92712
Belarus000000
Belgium8.852798.4227252.90682.9021938.4227252.344049
Belize6.02770622.242023.1901054.71614522.242022.821407
Benin4.729482112.3212.86987610.59816112.3214.86543
Bermuda000000
Bhutan7.64140861.584254.0801887.84756361.584254.348069
Bolivia8.97899341.017374.2086746.40448141.017373.756414
Bosnia and Herzegovina4.77915128.626551.9926475.35037928.626552.262842
Botswana12.6714978.448286.2461218.85710378.448286.110068
Brazil0.8823563.9356640.5164441.9838513.9356640.931681
British Virgin Islands000000
Brunei9.480926308.67514.70031317.56915308.67518.426272
Bulgaria11.9563583.693366.3158049.14840883.693366.237365
Burkina Faso14.3146741.758487.1483786.4620841.758485.400082
Burundi7.37345165.444323.4228078.08976765.444325.022543
Cambodia11.88999345.24648.28258118.58081345.246412.45667
Cameroon4.86618348.11622.91528818.65787348.11629.084295
Canada2.2640040.8688711.6002110.9321330.8688710.783274
Cape Verde11.46525110.74885.87399510.52372110.74886.187318
Cayman Islands5.53461922.403852.64014.7332722.403853.189553
Chad5.27481810.572322.4188893.2515110.572321.981079
Chile1.705141237.03630.79775815.39598237.03633.170539
China14.75331128.87518.07744411.35232128.87517.216554
Côte d’Ivoire9.282463432.4343.35010720.79505432.43411.19471
Colombia3.8247961.9919841.4838241.4113771.9919841.206169
Comoros4.5712852.324652.7823057.23357852.324654.655305
Congo—Brazzaville7.25842654.23493.1046427.36443554.23493.767057
Congo—Kinshasa9.46679317.270124.4889464.15573417.270123.452318
Costa Rica6.153947677.87513.52139226.03604677.875114.58405
Croatia9.937297322.43614.84989517.95651322.43618.571793
Cuba9.673677115.82694.78852610.76229115.82693.96974
Curaçao000000
Cyprus10.9392105.71625.73428410.28184105.71627.269532
Czechia6.52532838.413612.8272526.19787238.413613.605674
Denmark9.85013956.468454.8418257.51454956.468454.829783
Djibouti000000
Dominica000000
Dominican Republic12.2100351.484435.8754817.17526551.484435.859984
Ecuador5.59139215.128052.3552743.88947915.128052.040708
Egypt6.14914645.769674.3043016.76532945.769674.941281
El Salvador47.7954031.20217.6165963.491754031.20228.50461
Equatorial Guinea10.05492185.16634.27323613.60758185.16636.827011
Estonia9.493435320.63424.46136117.90626320.634210.35126
Eswatini10.11582292.29515.5025917.09664292.29518.906344
Ethiopia11.90093255.03957.50692115.96996255.03958.341224
Faroe Islands000000
Fiji14.37325132.60017.86122711.51521132.60017.789546
Finland5.2334776.1948742.3255772.4889516.1948741.932455
France1.1204852.0038280.6076291.4155662.0038280.863699
French Guiana000000
French Polynesia000000
Gabon6.045124165.84783.72749712.87819165.84787.123294
Gambia9.5216971.514025.3966298.45659671.514025.872677
Georgia12.25343100.24557.12127110.01227100.24557.364313
Germany1.5756772.5209111.0364211.5877382.5209111.119649
Ghana8.127446471.28255.61431721.70904471.282513.16765
Gibraltar000000
Greece5.555145244.89152.74784515.64901244.89157.481647
Greenland000000
Grenada12.86441151.63876.15925212.31417151.63878.262845
Guadeloupe4.1340323.2221241.9499291.7950283.2221241.376126
Guam000000
Guatemala9.34464629.695015.263855.44931329.695014.369718
Guernsey000000
Guinea8.02051995.657074.6615259.78044395.657075.539834
Guinea-Bissau11.1623485.92414.9714469.26952685.92414.876213
Guyana2.517421235.72080.90220415.3532235.72083.061355
Haiti6.5858086.7926833.1448372.6062786.7926832.40467
Honduras9.29695257.773984.4007557.6009257.773984.649517
Hong Kong7.35397741.404773.823636.43465441.404773.466788
Hungary5.225407122.70433.14039111.0772122.70435.309302
Iceland5.922715419.91423.11392220.49181419.91428.49435
India1.2898050.9004970.7497460.9489450.9004970.731705
Indonesia1.1031120.7827830.6867360.884750.7827830.668795
Iran5.51522531.681462.9941915.62862931.681463.877756
Iraq12.5577587.606577.0578219.35983887.606576.396014
Ireland0.9744911.7291150.5475561.3149581.7291150.70643
Isle of Man000000
Israel7.903272196.61484.96050914.02194196.61488.713525
Italy3.1339174.3164911.6120172.0776174.3164911.404546
Jamaica4.29045971.546442.751348.45851371.546445.632011
Japan7.735241106.00274.23880410.29576106.00274.884764
Jersey000000
Jordan3.70812986.178361.79199.2832386.178364.655009
Kazakhstan000000
Kenya5.960261137.87033.87577811.74182137.87037.767086
Kosovo6.35945949.820431.6863567.05835949.820433.073302
Kuwait10.19731714.71346.00961826.73413714.713415.32199
Kyrgyzstan000000
Laos000000
Latvia8.898493233.33654.32605415.27536233.33656.396053
Lebanon11.1528753.262245.9875077.29809853.262245.779635
Lesotho7.575091401.57494.78126420.03934401.574910.72093
Liberia7.775482175.94864.4422213.26456175.94868.237929
Libya7.048344111.37444.54262610.55341111.37445.525318
Liechtenstein000000
Lithuania9.03148124.02813.6965224.90184624.02812.652109
Luxembourg5.203751584.43372.5530924.17506584.433711.75841
Macao16.37218626.37957.21457325.02758626.379512.00941
Madagascar8.056012373.38133.16660819.32308373.38139.879311
Malawi9.456212214.17514.59058414.63472214.17516.548388
Malaysia3.02736214.559671.9854573.81571314.559672.213564
Maldives11.05308538.44765.32381923.20447538.447613.88415
Mali7.121835306.37474.06498617.50356306.374711.27971
Malta9.05662131.832834.6574675.64205931.832834.712883
Martinique9.748098689.40855.24554626.25659689.408515.4876
Mauritania13.1317188.152717.2006279.38896888.152717.662908
Mauritius8.761748453.35723.83610421.29219453.357211.63681
Mexico2.6121472.9225351.1261631.7095422.9225351.043981
Moldova000000
Mongolia5.16910411.074992.216083.3279111.074992.116271
Montenegro10.46805102.25674.44056110.11221102.25676.26818
Morocco12.27848241.12296.93631615.52813241.12298.58022
Mozambique8.96140735.636362.4134285.96961935.636362.639244
Myanmar (Burma)2.407638235.9121.35241415.35943235.9123.643141
Namibia7.874785239.08424.4906915.46235239.08426.310737
Nepal12.9656729.017895.9683875.38682629.017893.733779
Netherlands10.647361.499484.3205687.84216161.499484.930311
New Caledonia000000
New Zealand5.09261777.351232.8856398.79495577.351235.253943
Nicaragua3.5524051.6620061.3951571.2891881.6620060.812994
Niger3.915178266.44391.52921216.32311266.44394.886317
Nigeria6.69456552.004494.0231987.21141452.004494.40873
North Macedonia10.7591713.581043.5455213.68524613.581042.548404
Northern Mariana Islands000000
Norway5.83232914.176392.8664713.76515514.176392.452245
Oman7.612618479.13394.95587621.88913479.133910.12697
Pakistan6.30530646.192943.0457656.79653946.192944.200318
Palestine000000
Panama7.976283265.36285.03387416.28996265.36288.038425
Papua New Guinea000000
Paraguay9.36601264.24944.17275716.25575264.24946.955869
Peru7.6242325.5344753.7029792.3525475.5344752.082666
Philippines0.8855053.7142830.549721.9272473.7142831.078276
Poland3.66054429.9042.0814325.46845429.9042.813396
Portugal10.46164101.09835.01702810.05477101.09835.165927
Puerto Rico10.5689416.339754.9288824.04224616.339753.629358
Qatar11.173452.48336.3794321.27166452.483310.9481
Réunion8.80980173.179473.4992188.55450173.179474.389758
Romania3.33559744.261072.1331186.65289944.261073.023861
Russia2.2542373.0295261.03171.7405533.0295261.230431
Rwanda11.03336237.30874.61932515.40483237.30877.975922
Samoa000000
San Marino000000
Saudi Arabia9.00273577.403425.9405568.79792177.403426.727711
Senegal11.003335.375835.9094755.94775835.375834.661211
Serbia7.371186269.54613.11569616.41786269.54616.858306
Seychelles8.71973166.63665.42866412.90878166.63668.045354
Sierra Leone10.5304186.56614.74402513.65892186.56617.072295
Singapore3.03764321.661931.648684.65423821.661932.354216
Sint Maarten000000
Slovakia13.65778174.28237.2411813.2016174.28236.851287
Slovenia10.04812190.06844.76265713.78653190.06847.438249
Solomon Islands000000
Somalia5.218272314.78823.1468617.74227314.78828.515299
South Africa3.0329581.689392.2249099.03821981.689395.821945
South Korea6.54442542.947354.2203226.55342342.947354.802469
South Sudan10.60482110.52085.3090410.51289110.52086.626357
Spain1.7719533.1350970.9686081.7706213.1350971.226833
Sri Lanka13.79162122.24388.49234711.05639122.24389.245106
St. Barthélemy000000
St. Helena15.04679340.137411.1695818.44281340.137413.80565
St. Kitts and Nevis000000
St. Lucia4.452625297.26862.35063817.24148297.26867.538465
St. Martin000000
St. Pierre and Miquelon000000
St. Vincent and Grenadines6.327033278.51682.98038416.68882278.51686.829061
Sudan13.20677120.06137.87498710.95725120.06137.766244
Suriname000000
Sweden11.1202858.185565.4514027.62794558.185564.439981
Switzerland9.620514106.23194.17979810.30689106.23194.711903
Syria000000
Taiwan8.54250515.320914.8342993.91419315.320913.708639
Tajikistan000000
Tanzania13.1928315.08445.84314817.75062315.08448.659294
Thailand1.8515184.0610040.9647242.0151934.0610041.023623
Timor-Leste000000
Togo12.46273797.64426.57353928.2426797.644216.86038
Trinidad and Tobago7.231444603.91443.43054424.57467603.914410.77016
Türkiye1.4041363.4805670.8636141.8656283.4805670.956541
Tunisia12.71008298.44447.34420117.27554298.444411.5082
Turkmenistan000000
Turks and Caicos Islands000000
U.S. Virgin Islands000000
Uganda11.36024450.53616.5396221.22584450.53618.999745
Ukraine5.23692222.310772.8803074.72342822.310773.211289
United Arab Emirates9.8129683.89146.3645359.15922583.89146.592083
United Kingdom0.5023790.5186180.3575710.7201520.5186180.395022
United States2.0398790.6483991.1192340.8052320.6483990.716547
Uruguay11.5178367.038624.8612158.18771267.038624.772031
Uzbekistan11.1542432.512573.7559855.7019832.512573.538633
Vanuatu000000
Venezuela6.57366518.054862.5750414.24910118.054862.422974
Vietnam1.3821883.8610880.8887411.9649653.8610881.055109
Western Sahara000000
Yemen000000
Zambia9.028182153.74035.6078212.3992153.74037.333309
Zimbabwe7.700711137.95024.80697911.74522137.95027.465501
Table 4. Results (RMSE, MSE, and MAE on Train and Test sets) of running Algorithm 3 on the master dataset.
Table 4. Results (RMSE, MSE, and MAE on Train and Test sets) of running Algorithm 3 on the master dataset.
Country NameRSME for LSTM
(Train Set)
MSE for
LSTM
(Train Set)
MAE for
LSTM
(Train Set)
RSME for LSTM
(Test Set)
MSE for LSTM
(Test Set)
MAE for LSTM
(Test Set)
Afghanistan000000
Åland Islands000000
Albania9.733694.74363.553916.6119275.95685.6548
Algeria6.630643.96473.546615.7074246.72135.0684
American Samoa000000
Andorra000000
Angola6.445941.54993.34752.44055.9562.2836
Antigua and Barbuda11.0468122.0316.01274.842523.44964.6417
Argentina5.768533.27552.36291.58112.49991.5477
Armenia000000
Aruba000000
Australia4.182517.4932.27485.577931.1132.8582
Austria11.5956134.4584.62214.048316.38843.0249
Azerbaijan11.5166132.63234.55563.438311.82182.9832
Bahamas9.930898.62125.0956.109237.32244.3574
Bahrain8.970680.47246.103611.9741143.387.3561
Bangladesh4.802623.06542.7963.896215.18062.4461
Barbados7.238852.40072.90726.710245.02733.4134
Belarus 000000
Belgium10.2021104.08313.72893.1599.97912.926
Belize5.582431.16362.96764.461319.90312.6012
Benin5.185226.88633.28119.970699.41214.3851
Bermuda000000
Bhutan7.944663.11664.11972.93898.63712.5615
Bolivia9.295186.39954.47676.498842.23464.119
Bosnia and Herzegovina5.098225.99191.98035.009925.09871.9786
Botswana13.3231177.5045.59765.733432.87164.4417
Brazil0.91280.83310.5271.84643.40910.7874
British Virgin Islands000000
Brunei9.733694.74374.10917.554657.07133.8908
Bulgaria12.4939156.09736.13039.121183.19445.3772
Burkina Faso15.8841252.30467.20735.320928.31224.2953
Burundi6.330940.082.55136.823646.56123.018
Cambodia11.8045139.34596.88329.14383.60817.0224
Cameroon5.121626.23122.966618.5825345.31067.9996
Canada2.62116.871.88061.15961.34471.0067
Cape Verde12.4699155.49816.6910.1468102.95736.2734
Cayman Islands4.622621.36841.93593.671913.48271.9941
Chad5.891234.70662.70763.12589.77072.112
Chile1.76693.1220.56520.34840.12140.2118
China15.108228.25218.40728.172366.78646.0264
Colombia3.917415.34561.54930.90760.82360.8517
Comoros4.529220.51362.15524.791522.95882.7124
Congo—Brazzaville7.834961.38543.34394.157117.28152.6733
Congo—Kinshasa9.66693.43135.08034.211117.73353.7272
Costa Rica6.25839.16313.459118.6752348.76496.6876
Côte d’Ivoire4.846123.48451.96039.351987.45753.6104
Croatia11.0201121.44194.75816.1631261.24456.0549
Cuba8.703975.75814.201511.2715127.04624.359
Curaçao000000
Cyprus10.738115.30484.21226.80546.30873.9958
Czechia6.851246.93892.73235.138526.40472.9119
Denmark10.105102.1114.43156.824346.57133.9834
Djibouti000 00
Dominica000000
Dominican Republic12.6183159.22065.36875.381228.95694.1157
Ecuador5.722932.75132.47583.982215.85822.3963
Egypt6.869847.19424.79075.117426.1884.015
El Salvador10.4495109.19143.6532.3585.56032.2743
Equatorial Guinea10.323106.56344.706912.9413167.47775.6865
Estonia9.645893.04243.761415.7026246.57235.7316
Eswatini11.5948134.43866.285814.3755206.65376.7783
Ethiopia13.1243172.24697.204916.5128272.67298.1253
Faroe Islands000000
Fiji14.9577223.73347.94689.79295.88286.61
Finland5.282527.90462.11862.40995.80761.7092
France1.17111.37140.63541.44942.10070.8726
French Guiana000000
French Polynesia000000
Gabon6.250739.07073.263411.2189125.86364.6094
Gambia9.969699.39365.69358.245167.98174.8419
Georgia12.4274154.43965.53884.353118.94943.3823
Germany1.61342.60311.04121.52032.31132.3113
Ghana8.860178.50155.829317.6815312.63478.4393
Gibraltar000000
Greece5.533930.62362.459614.0572197.60364.6971
Greenland000000
Grenada5.943335.32333.66728.691875.54754.3353
Guadeloupe4.509920.33892.08121.73213.00031.3416
Guam000000
Guatemala10.0255100.51124.98734.518720.41873.2909
Guernsey000000
Guinea8.729276.19944.96578.950680.11325.006
Guinea-Bissau11.9285142.28894.43228.48872.05124.0859
Guyana2.84628.10071.31921.10681.22511.0212
Haiti7.16951.39493.45212.20584.86552.136
Honduras9.549791.19695.17037.887762.21585.1749
Hong Kong7.569757.30063.90356.416541.17153.5521
Hungary5.361528.74552.951610.839117.48374.988
Iceland6.177838.16553.044320.0922403.69466.2659
India1.36471.86250.75250.73270.53680.5564
Indonesia1.04161.0850.75640.880.77430.7189
Iran5.807433.72572.82564.248918.05282.6769
Iraq13.019169.49417.79349.119983.17226.6574
Ireland1.0221.04440.43021.3191.73980.5476
Isle of Man000000
Israel8.131666.12375.136912.9617168.00647.0347
Italy3.179110.10641.65582.06214.25231.433
Jamaica4.306718.54792.54037.170551.41663.1201
Japan8.661175.01514.09859.780695.66084.3546
Jersey000000
Jordan3.833614.69671.92768.922879.6163.7557
Kazakhstan000000
Kenya6.211638.58443.323211.6125134.84945.2644
Kosovo5.857134.30562.36656.964848.50853.1255
Kuwait10.7792116.19026.171327.6976767.159613.8547
Kyrgyzstan000000
Laos000000
Latvia9.032981.59354.057715.363236.02224.8141
Lebanon11.8732140.97225.51455.711432.62023.8723
Lesotho7.835161.38875.012119.4631378.81219.5002
Liberia8.860178.5024.763613.7897190.15697.7805
Libya7.275652.93454.807810.27105.47365.4583
Liechtenstein000000
Lithuania9.468789.65543.63074.703922.12692.4119
Luxembourg5.432829.51532.035219.7734390.98858.0012
Macao10.3247106.59864.45865.431729.50373.4083
Madagascar8.185967.00953.083717.8244317.70985.9622
Malawi9.47589.77653.989612.203148.91364.5393
Malaysia3.245710.53442.14383.470312.04282.018
Maldives9.019281.34563.767519.1721367.56947.3871
Mali6.909247.73763.44113.9836195.54147.0168
Malta9.454189.38064.51213.969215.75463.1479
Martinique8.907379.33934.286621.8366476.837710.2019
Mauritania13.8648192.23167.33018.639174.63366.0727
Mauritius8.730176.21393.997119.6992388.05979.2592
Mexico2.77217.68481.11331.14941.3210.7958
Moldova000000
Mongolia5.657732.00972.18583.210.24021.7923
Montenegro9.656893.25312.81876.14537.76092.7985
Morocco13.3511178.25157.062411.0452121.99715.9386
Mozambique8.345969.65482.38255.915334.99142.6254
Myanmar (Burma)2.78627.76321.68961.66212.76261.3676
Namibia8.870378.68224.166616.1122259.60455.3507
Nepal12.7623162.87646.20043.95615.65023.4753
Netherlands10.9354119.5824.18197.246652.5134.3334
New Caledonia000000
New Zealand5.301728.10822.65476.086437.04432.6479
Nicaragua2.02374.09520.9761.27771.63250.8717
Niger3.951715.61592.376516.9702287.98915.9528
Nigeria7.373754.3713.90926.829446.64073.2942
North Macedonia10.1685103.39873.48653.07119.43142.3936
Northern Mariana Islands000000
Norway6.230738.82113.10653.466412.01582.4974
Oman8.570473.4515.291620.3843415.51938.2069
Pakistan6.596443.51233.13474.314718.61692.693
Palestine000000
Panama7.975163.60144.0626.306539.77183.3684
Papua New Guinea000000
Paraguay10.0941101.89013.81057.753160.11034.1264
Peru8.307369.01053.75632.28295.21172.1975
Philippines0.93950.88270.55831.71972.95730.716
Poland3.852514.84162.16984.875923.7742.8208
Portugal11.0247121.54354.75449.336987.1774.4406
Puerto Rico10.9783120.52364.87513.648313.30993.5062
Qatar11.8303139.95546.186722.1425490.29059.8335
Réunion9.409688.54143.80128.31669.15524.3294
Romania3.6413.24982.12456.422841.25242.6677
Russia2.68397.20321.33791.34861.81871.0539
Rwanda13.5939184.79515.491214.0024196.0667.3586
Samoa00 000
San Marino000000
Saudi Arabia9.789495.83276.19337.852661.66325.6638
Senegal11.5231132.78125.64354.832123.34884.1911
Serbia7.61157.9272.991917.0023289.07915.6237
Seychelles9.791895.88015.378311.0476122.05046.0669
Sierra Leone11.3299128.36734.438312.7055161.43055.8939
Singapore3.317811.00791.60584.632721.46232.1795
Sint Maarten000000
Slovakia13.6908187.43726.925110.8927118.65186.2567
Slovenia11.0111121.2454.609816.2637264.50947.3231
Solomon Islands000000
Somalia5.560430.91793.066818.3508336.75357.4377
South Africa3.334611.11962.39297.6358.21633.674
South Korea6.924847.95344.10896.365240.51614.3734
South Sudan12.0022144.05265.496610.1429102.87746.1574
Spain1.91023.64890.97061.52472.32470.991
Sri Lanka14.6585214.8727.50769.058282.05015.1946
St. Barthélemy000000
St. Helena15.9633254.825511.578514.6825215.575110.5333
St. Kitts and Nevis000000
St. Lucia4.785622.90152.155216.1445260.64494.5544
St. Martin000000
St. Pierre and Miquelon000000
St. Vincent and Grenadines5.222227.27191.81752.39395.73061.4965
Sudan14.5817212.62518.562710.8392117.48788.11
Suriname000000
Sweden11.6771136.3554.88317.542856.89323.8568
Switzerland10.4482109.16494.19110.3482107.08473.9763
Syria000000
Taiwan8.864478.57765.01193.203110.26013.1236
Tajikistan000000
Tanzania 14.1549200.36185.93519.8001392.04559.7499
Thailand1.90543.63051.00641.93813.75611.0714
Timor-Leste000000
Togo10.8943118.68655.519818.8062353.6758.7223
Trinidad and Tobago7.753960.12283.799123.8618569.384110.0506
Tunisia13.1773173.64036.524916.0612257.96236.6638
Türkiye1.45362.11310.84581.96693.86860.9816
Turkmenistan000000
Turks and Caicos Islands000000
U.S. Virgin Islands000000
Uganda11.888141.32376.572320.396415.99669.0454
Ukraine5.683332.30012.91194.561420.8062.7815
United Arab Emirates10.7911116.44716.58118.141366.28055.1778
United Kingdom 0.53460.28580.39840.71380.50950.379
Uruguay11.8371140.11644.90077.788360.65794.3273
USA2.0824.33471.09670.76810.590.6998
Uzbekistan10.5539111.3843.66936.598943.54553.4136
Vanuatu000000
Venezuela6.753945.61462.36774.202517.66092.055
Vietnam1.47092.16370.84341.14111.30210.7516
Western Sahara000000
Yemen000000
Zambia9.731494.69925.52589.989199.7825.2326
Zimbabwe8.703475.74994.69129.799696.03185.0848
Table 5. Results of correlation analysis between search interests related to MVD and search interests related to zombies in 216 regions.
Table 5. Results of correlation analysis between search interests related to MVD and search interests related to zombies in 216 regions.
Region NamePearsons r-ValuePearsons p-ValueNature of Correlation
Afghanistanno correlationno correlationnot significant
Åland Islandsno correlationno correlationnot significant
Albania−0.0907023350.673391not significant
Algeria0.0638225650.767019not significant
American Samoano correlationno correlationnot significant
Andorrano correlationno correlationnot significant
Angola−0.1003064460.640968not significant
Antigua and Barbuda−0.1491160750.486791not significant
Argentina0.6005194820.001917statistically significant
Armeniano correlationno correlationnot significant
Arubano correlationno correlationnot significant
Australia0.2925447060.165371not significant
Austria0.1206439130.574431not significant
Azerbaijan−0.1957447030.359316not significant
Bahamas0.1252736820.559721not significant
Bahrain−0.0127871810.952711not significant
Bangladesh−0.357853920.085994not significant
Barbados0.005009110.981467not significant
Belarus no correlationno correlationnot significant
Belgium0.3988590480.053522not significant
Belize−0.0569375920.79158not significant
Benin−0.1047111240.626304not significant
Bermudano correlationno correlationnot significant
Bhutan0.9264319138.35 × 10−11statistically significant
Bolivia0.0524675530.807631not significant
Bosnia and Herzegovina−0.1293389460.546946not significant
Botswana−0.0885227360.680831not significant
Brazil−0.0945902230.660194not significant
British Virgin Islandsno correlationno correlationnot significant
Brunei0.1005333520.640209not significant
Bulgaria−0.1829498390.392176not significant
Burkina Faso−0.0327927530.879096not significant
Burundi0.77068991.05 × 10−5statistically significant
Cambodia0.1799989840.399988not significant
Cameroon−0.1593820010.456936not significant
Canada−0.0826720280.700944not significant
Cape Verde−0.1088913120.612513not significant
Cayman Islands−0.1272801780.553399not significant
Chad−0.1126599740.600189not significant
Chile−0.167144960.435013not significant
China−0.098082070.648424not significant
Colombia−0.0876600280.683784not significant
Comoros0.1386150380.518311not significant
Congo—Brazzaville0.0431582140.841295not significant
Congo—Kinshasa−0.1080706290.615211not significant
Costa Rica0.1591055560.457727not significant
Côte d’Ivoire0.0087149640.967762not significant
Croatia−0.232803040.27363not significant
Cuba−0.2051047290.336332not significant
Curaçaono correlationno correlationnot significant
Cyprus−0.0967852090.652786not significant
Czechia0.1490964140.486849not significant
Denmark0.0758057610.724799not significant
Djiboutino correlationno correlationnot significant
Dominicano correlationno correlationnot significant
Dominican Republic−0.2453343910.247889not significant
Ecuador−0.1090502240.611992not significant
Egypt−0.2133376260.316862not significant
El Salvador−0.0423491420.844235not significant
Equatorial Guinea−0.2181427850.305823not significant
Estonia−0.0754142910.726166not significant
Eswatini0.2793298390.18621not significant
Ethiopia−0.0317970570.882742not significant
Faroe Islandsno correlationno correlationnot significant
Fiji−0.1217509980.570898not significant
Finland0.2090538890.326905not significant
France0.6680537410.00036statistically significant
French Guianano correlationno correlationnot significant
French Polynesiano correlationno correlationnot significant
Gabon0.0954268780.657366not significant
Gambia−0.1713809520.423293not significant
Georgia0.3624782830.08173not significant
Germany−0.0103450170.961736not significant
Ghana0.4143143950.044129statistically significant
Gibraltarno correlationno correlationnot significant
Greece−0.1564442860.46538not significant
Greenlandno correlationno correlationnot significant
Grenada−0.1276547460.552222not significant
Guadeloupe−0.1113155250.604574not significant
Guamno correlationno correlationnot significant
Guatemala−0.1535407230.473804not significant
Guernseyno correlationno correlationnot significant
Guinea−0.0885770530.680645not significant
Guinea-Bissauno correlationno correlationnot significant
Guyana−0.0758721220.724567not significant
Haiti−0.0366628440.864948not significant
Honduras−0.103678760.629729not significant
Hong Kong−0.2926280680.165245not significant
Hungary0.0665028210.757515not significant
Iceland−0.1348591250.529818not significant
India0.1129101950.599374not significant
Indonesia−0.1326319080.536698not significant
Iran0.2555400550.228129not significant
Iraq−0.3178662720.130111not significant
Ireland3.47 × 10−181not significant
Isle of Manno correlationno correlationnot significant
Israel0.0943363620.661052not significant
Italy0.200220650.348213not significant
Jamaica0.2579528730.223615not significant
Japan−0.0298590440.889845not significant
Jerseyno correlationno correlationnot significant
Jordan−0.1037465340.629504not significant
Kazakhstanno correlationno correlationnot significant
Kenya0.0042815250.984159not significant
Kosovo−0.0909090910.672687not significant
Kuwait−0.0986242920.646603not significant
Kyrgyzstanno correlationno correlationnot significant
Laosno correlationno correlationnot significant
Latvia−0.0826790450.70092not significant
Lebanon0.8503990111.42 × 10−7statistically significant
Lesotho0.0150131350.944491not significant
Liberia−0.1399234930.514331not significant
Libya−0.056066390.794702not significant
Liechtensteinno correlationno correlationnot significant
Lithuania−0.1572911590.462937not significant
Luxembourg−0.0752649170.726688not significant
Macao0.0131770240.951271not significant
Madagascar0.8016245292.49 × 10−6statistically significant
Malawi−0.143785950.502668not significant
Malaysia−0.0668969980.75612not significant
Maldives−0.0279058730.897012not significant
Mali−0.1164495570.587902not significant
Malta−0.1116908710.603348not significant
Martinique−0.1167759180.586849not significant
Mauritania−0.0930229480.665502not significant
Mauritius−0.039328810.855225not significant
Mexico−0.0837237370.697314not significant
Moldovano correlationno correlationnot significant
Mongolia−0.1471661740.49257not significant
Montenegro−0.0721677140.737541not significant
Morocco−0.0464903810.829211not significant
Mozambique−0.0205882790.923928not significant
Myanmar (Burma)0.8707712953.14 × 10−8statistically significant
Namibia−0.1193436750.578592not significant
Nepal−0.1991582410.35083not significant
Netherlands0.0776858910.718241not significant
New Caledoniano correlationno correlationnot significant
New Zealand0.0344307840.873103not significant
Nicaragua−0.1471460080.49263not significant
Niger−0.1045900840.626705not significant
Nigeria0.3704030390.074795not significant
North Macedonia−0.1660452520.438084not significant
Northern Mariana Islandsno correlationno correlationnot significant
Norway−0.3164631180.13191not significant
Oman−0.0885562610.680716not significant
Pakistan−0.0550133070.79848not significant
Palestineno correlationno correlationnot significant
Panama−0.0939189250.662465not significant
Papua New Guineano correlationno correlationnot significant
Paraguay−0.3138932170.13525not significant
Peru0.4154752690.04348statistically significant
Philippines0.2159995990.310717not significant
Poland0.1455495990.497387not significant
Portugal0.1780162660.405285not significant
Puerto Rico0.1704195430.425938not significant
Qatar−0.0851692680.692335not significant
Réunion−0.1615772470.450679not significant
Romania0.4362930890.033055statistically significant
Russia−0.2871457680.173678not significant
Rwanda−0.086906830.686366not significant
Samoano correlationno correlationnot significant
San Marinono correlationno correlationnot significant
Saudi Arabia0.0954067040.657434not significant
Senegal0.0734991920.732869not significant
Serbia−0.2672676540.206747not significant
Seychelles0.0707744840.742439not significant
Sierra Leone−0.1466479070.494111not significant
Singapore0.040747780.850058not significant
Sint Maartenno correlationno correlationnot significant
Slovakia−0.1925227890.367435not significant
Slovenia−0.0525800120.807226not significant
Solomon Islandsno correlationno correlationnot significant
Somalia−0.0986820140.64641not significant
South Africa−0.5152883090.009968statistically significant
South Korea0.5056297070.011716statistically significant
South Sudan−0.1032858490.631034not significant
Spain−0.0103953980.961549not significant
Sri Lanka−0.3160117880.132492not significant
St. Barthélemyno correlationno correlationnot significant
St. Helena0.0468225470.828009not significant
St. Kitts and Nevisno correlationno correlationnot significant
St. Lucia−0.0598974910.780996not significant
St. Martinno correlationno correlationnot significant
St. Pierre and Miquelonno correlationno correlationnot significant
St. Vincent and Grenadines−0.1995629520.349832not significant
Sudan−0.0908072310.673034not significant
Surinameno correlationno correlationnot significant
Sweden−0.218574120.304843not significant
Switzerland−0.2454015110.247755not significant
Syriano correlationno correlationnot significant
Taiwan−0.1470826490.492818not significant
Tajikistanno correlationno correlationnot significant
Tanzania −0.1104276080.607477not significant
Thailand0.0695035690.746915not significant
Timor-Lesteno correlationno correlationnot significant
Togo−0.1093247890.611091not significant
Trinidad and Tobago−0.1550649520.469372not significant
Tunisia−0.3289071620.116573not significant
Türkiye−0.1316944080.539607not significant
Turkmenistanno correlationno correlationnot significant
Turks and Caicos Islandsno correlationno correlationnot significant
U.S. Virgin Islandsno correlationno correlationnot significant
Uganda−0.1821968640.394161not significant
Ukraine−0.3385202860.10565not significant
United Arab Emirates−0.038059350.859852not significant
United Kingdom 0.1107228880.606511not significant
USA0.6322441760.000918statistically significant
Uruguay0.7806390336.78 × 10−6statistically significant
Uzbekistan0.1641191110.44349not significant
Vanuatuno correlationno correlationnot significant
Venezuela−0.154838440.47003not significant
Vietnam−0.1926024260.367233not significant
Western Saharano correlationno correlationnot significant
Yemenno correlationno correlationnot significant
Zambia0.0333333740.877117not significant
Zimbabwe−0.1357482660.527083not significant
Table 6. Results of correlation analysis between search interests related to zombies in the United States and the remaining 215 regions.
Table 6. Results of correlation analysis between search interests related to zombies in the United States and the remaining 215 regions.
Region NamePearsons r-ValuePearsons p-ValueNature of Correlation
Afghanistan−0.095950.6556not significant
Åland Islands0.0786970.714724not significant
Albania0.0119930.955647not significant
Algeria−0.11650.587731not significant
American Samoa−0.12630.556475not significant
Andorra0.3436710.100117not significant
Angola−0.040350.851492not significant
Antigua and Barbuda0.0889040.679529not significant
Argentina0.3820870.065397not significant
Armenia−0.161780.450095not significant
Aruba0.0143820.946823not significant
Australia−0.34290.10093not significant
Austria0.0464330.82942not significant
Azerbaijan−0.068980.748768not significant
Bahamas0.1248110.561182not significant
Bahrain−0.016220.940023not significant
Bangladesh−0.031060.885457not significant
Barbados−0.080250.709316not significant
Belarus0.0736150.732464not significant
Belgium0.0341530.874118not significant
Belize−0.202140.343505not significant
Benin0.0796310.711478not significant
Bermuda0.1194920.578115not significant
Bhutan−0.029860.889843not significant
Bolivia0.2556970.227834not significant
Bosnia and Herzegovina−0.026180.903345not significant
Botswana0.0058750.978264not significant
Brazil0.3676150.077181not significant
British Virgin Islands0.0421560.844936not significant
Brunei−0.195610.359643not significant
Bulgaria−0.122170.569568not significant
Burkina Faso0.0974610.650512not significant
Burundi0.0059580.977956not significant
Cambodia0.3429580.10087not significant
Cameroon0.037250.862805not significant
Canada0.4664690.021577statistically significant
Cape Verde0.0966540.653228not significant
Cayman Islands−0.137110.522906not significant
Chad−0.266420.20824not significant
Chile0.2037860.339516not significant
China−0.22260.295803not significant
Colombia−0.041680.846681not significant
Comoros0.165890.438519not significant
Congo—Brazzaville0.0392590.855478not significant
Congo—Kinshasa−0.018330.932257not significant
Costa Rica−0.049590.817994not significant
Côte d’Ivoire0.0882480.681771not significant
Croatia0.0901660.67522not significant
Cuba0.0245160.909469not significant
Curaçao0.1172770.585235not significant
Cyprus−0.036720.864739not significant
Czechia−0.186930.381775not significant
Denmark0.2079120.329615not significant
Djibouti−0.036590.865206not significant
Dominica0.0149710.944645not significant
Dominican Republic−0.069740.74608not significant
Ecuador0.3074630.143872not significant
Egypt0.0665070.757499not significant
El Salvador−0.064320.765254not significant
Equatorial Guinea−0.186340.383297not significant
Estonia−0.006440.976169not significant
Eswatini−0.024060.911145not significant
Ethiopia0.0993360.644216not significant
Faroe Islands−0.000350.998714not significant
Fiji−0.172330.42068not significant
Finland0.0739060.731445not significant
France0.0983150.647641not significant
French Guiana−0.054220.801314not significant
French Polynesia−0.163820.444329not significant
Gabon0.1286040.549245not significant
Gambia−0.098290.647714not significant
Georgia0.0621360.773018not significant
Germany0.1200460.576343not significant
Ghana0.0356620.868604not significant
Gibraltar0.0620930.773169not significant
Greece0.0916590.670135not significant
Greenland0.1517380.479073not significant
Grenada−0.165690.439078not significant
Guadeloupe−0.176540.409241not significant
Guam−0.197680.354478not significant
Guatemala−0.084680.694017not significant
Guernsey0.0164170.939308not significant
Guinea0.0930870.665285not significant
Guinea-Bissauno correlationno correlationnot significant
Guyana−0.053550.803751not significant
Haiti0.0978930.649058not significant
Honduras−0.09470.659824not significant
Hong Kong−0.424240.038813statistically significant
Hungary0.1073590.617554not significant
Iceland0.03980.853508not significant
India0.0399490.852966not significant
Indonesia−0.060990.777109not significant
Iran−0.195580.359735not significant
Iraq0.1357390.527111not significant
Ireland0.0510860.812607not significant
Isle of Man0.0731640.734045not significant
Israel−0.092670.666686not significant
Italy0.072340.736935not significant
Jamaica0.2132370.317096not significant
Japan−0.353390.090271not significant
Jersey0.160450.453885not significant
Jordan0.0889050.679525not significant
Kazakhstan−0.207140.331451not significant
Kenya0.0184380.931856not significant
Kosovo−0.145570.497324not significant
Kuwait0.197820.354143not significant
Kyrgyzstan−0.050250.815624not significant
Laos−0.162470.44816not significant
Latvia0.2874470.173207not significant
Lebanon0.1106450.606766not significant
Lesotho0.065150.762308not significant
Liberia−0.212220.319458not significant
Libya0.088890.679576not significant
Liechtenstein−0.075010.727568not significant
Lithuania−0.115680.590391not significant
Luxembourg0.0949740.658897not significant
Macao−0.056980.791434not significant
Madagascar−0.091650.670158not significant
Malawi−0.044780.835413not significant
Malaysia−0.32190.125036not significant
Maldives0.0767950.721346not significant
Mali−0.085220.692171not significant
Malta−0.045980.831055not significant
Martinique−0.279540.185867not significant
Mauritania0.8197549.51 × 10−7statistically significant
Mauritius−0.036360.86604not significant
Mexico0.3934240.057171not significant
Moldova−0.030680.886823not significant
Mongolia0.4121040.045387statistically significant
Montenegro−0.091370.671121not significant
Morocco0.1604070.454008not significant
Mozambique−0.174880.413754not significant
Myanmar (Burma)0.0344170.873154not significant
Namibia0.1215310.571599not significant
Nepal−0.117230.585373not significant
Netherlands0.142850.505484not significant
New Caledonia−0.26610.208817not significant