A Review of Environmental Factors for an Ontology-Based Risk Analysis for Pandemic Spread
Abstract
:- The Susceptible-Exposed-Infected-Recovered (SEIR) models are widely used to predict possible contagion scenarios. It uses individuals’ contagion statuses, such as not yet infected, incubation period, confirmed cases, and recovered or dead cases to build the pandemic spread model.
- Pandemic spreading, however, depends on how the environmental factors influencing human behaviors of pandemic prevention. It is not a linear problem but is a multi-dimensional and non-linear problem.
- This research, therefore, identified the major environmental factors from literatures, including fear of the spread of the pandemic, attitudes toward hygiene practices, community culture, government policies on pandemic prevention, economic activity restrictions, pandemic education, multimedia, and technologies uses for information dissemination and disclosure, resulting in an increase in the spread of the pandemic.
- The design of ontology-based big data architecture uses ontologies, sentiment analyses, a clustered 3D CNN model, and a clustered GCN model to model the environmental factors into different dimensions and uses the 3D-CNN/GCN architecture to model the contagion scenarios for spread prediction.
- The conceptual design of the big data information architecture allows researchers to continue our work to conduct the sentiment analyses of the government policies and use the 3D-CNN/GCN architecture to model the complex contagion scenarios for predicting individual or community’s pandemic spreading risk that no researchers have done before.
1. Introduction
2. Literature Review
3. Research Methods
4. Results
5. Discussion
5.1. Fear of the Pandemic Spreading
5.2. Hygiene Practices and Pandemic Transmission
5.3. Cultural Behavior of Pandemic Prevention
5.4. Attitude of Government Policies on Pandemic Prevention
5.5. Economic and Business Restrictions
5.6. Pandemic Prevention Education Programs
5.7. Technology Infrastructure and Multimedia for Information Disclosure
6. Recommendations
- First, contact-tracing data, social media data, pandemic prevention methods, government policies for pandemic prevention, educational programs, business activity restrictions, multimedia, and technology infrastructure were collected. The contact-tracing data were modeled using an ontology. The ontology defines the attributes and behaviors of entries [111,112].
- Second, the discussion topics on social media were classified into pandemic fear, hygiene measures, cultural practices, prevention policy, education programs, business activity restriction, multimedia disclosure, and technology infrastructure for sentiment analysis. Commonly used topic classification methods include Naïve Bayes, support vector machine model, and linear discriminant analysis.
- The keywords of the posts for each topic were extracted to measure the pandemic indices of fear, hygiene, culture, and policy in the next step [79,113]. Natural language processing, word frequency count, term frequency-inverse document frequency [114], and n-gram [115] are commonly used text analysis methods for keyword extraction.
- Categorical and dimensional methods are the two major sentiment-analysis methods [116,117]. The categorical method classifies sentiments into different fear descriptors, such as sadness, nervousness, and worry [116,117]. The dimensional method classifies sentiments into positive and negative affectivity [117,118,119]. The extracted keywords and phrases were mapped to the vocabularies of categorical and dimensional databases. The classified sentiments were counted and used to calculate fear, hygiene, culture, and policy indices. Some artificial intelligence methods such as the support vector machine model, word2vec, TextCNN, and 2D CNN methods can be used together with sentiment counts to predict sentiments [120,121,122,123,124].
- After the sentiments were analyzed, a clustered ontology model was constructed to capture the four indices’ values per demographic group to predict the individuals’ pandemic spreading risk based on their demographic information. The contact-tracing information of contact activity, time, location, duration, and contact person can be used to predict the network-driven individual pandemic spread risk based on the connected nodes in the network. The community outbreak risk in the contact network was calculated. The 3D CNN network analysis model and graph convolutional network [125] can be used to train and predict individual risks and network-driven individual risks using the COVID-19 test history and the COVID-19 test result, respectively. Policymakers can use this information to plan pandemic prevention programs.
7. Conclusions and Future Work
Author Contributions
Funding
Institutional Review Board Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Keywords Search with COVID-19 | Relevant/Returned Studies | Environmental Factors | References |
---|---|---|---|
Fear, anxiety, worry | 14/127 | Fear of the spread of pandemic | [9,15,19,20,21,22,23,24,25,26,27,28,29,30] |
Hygiene practice | 5/7 | Intentional behaviors of hygiene practices | [31,32,33,34,35] |
Pandemic prevention | 12/96 | Cultural behaviors of pandemic prevention | [14,36,37,38,39,40,41,42,43,44,45] |
Government policy, pandemic policy | 25/220 | Government policies on pandemic prevention | [11,12,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68] |
Pandemic education program | 2/31 | Attitudes of pandemic education program | [69,70] |
Pandemic economic, business restriction | 2/16 | Attitudes of economic and business restrictions | [71,72] |
Contact-tracking technology, Multimedia, channel, information dissemination | 24/91 | Attitudes of technology infrastructure and multimedia for information dissemination and disclosure | [2,18,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94] |
Environmental Factors | Measurement Metrics or Experiments for Future Work |
---|---|
Fear of the pandemic spreading | Fear index is a measurement metric to measure the risk perception on pandemic spread of individuals. The public sentiments on the fear of the pandemic spread (e.g., stress, anxiety, etc.) from social media are calculated. |
Intentional behaviors of hygiene practices | Hygiene index is a measurement metric to measure the attitudes of hygiene practices of individuals (e.g., handwash, wearing mask, social distancing, contact tracking, decontamination, etc.) for pandemic prevention. The public sentiment scores on hygiene practices are calculated from social media. |
Cultural behaviors of pandemic prevention | Culture index is a measurement metric to measure the cultural attitudes on pandemic prevention (e.g., handwash, wearing masks, social distancing, contact tracking, decontamination, etc.). The public sentiment score of pandemic prevention of different races or religions from social media are calculated. |
Attitudes of government policies on pandemic prevention | Policy index is a measurement metric to measure the attitudes of government policies on pandemic prevention (including pandemic prevention, education programs, economic and business activity restrictions, technology and multimedia infrastructure). The public sentiment score of government policies on pandemic prevention are calculated from social media. |
Attitudes of pandemic education program | It is a sub-score of policy index. This measures the attitudes of the governmental pandemic education programs (e.g., procedures of wearing masks, washing hands, and COVID-19 testing). The public sentiment score on governmental pandemic education programs from social media are calculated. |
Attitudes of economic and business restrictions | It is a sub-score of policy index. This measures the attitudes of the economic and business restriction policies (e.g., lockdown cities, travel ban and quarantine, and COVID-19 vaccination requirement of visitors). The public sentiment score on economic and business restriction policies from social media are calculated. |
Attitudes of technology infrastructure and multimedia for information dissemination and disclosure | It is a sub-score of policy index. This measures the attitudes of the technology infrastructure for information dissemination (e.g., social monitoring app, COVID-19 reporting system, and temperature measurement equipment in public places) and the multimedia for information disclosure (e.g., news, social media, and government web pages). The public sentiment score on technology infrastructure and multimedia for pandemic information dissemination from social media are calculated. |
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Cheung, L.; Lau, A.S.M.; Lam, K.F.; Ng, P.Y. A Review of Environmental Factors for an Ontology-Based Risk Analysis for Pandemic Spread. COVID 2024, 4, 466-480. https://doi.org/10.3390/covid4040031
Cheung L, Lau ASM, Lam KF, Ng PY. A Review of Environmental Factors for an Ontology-Based Risk Analysis for Pandemic Spread. COVID. 2024; 4(4):466-480. https://doi.org/10.3390/covid4040031
Chicago/Turabian StyleCheung, Liege, Adela S. M. Lau, Kwok Fai Lam, and Pauline Yeung Ng. 2024. "A Review of Environmental Factors for an Ontology-Based Risk Analysis for Pandemic Spread" COVID 4, no. 4: 466-480. https://doi.org/10.3390/covid4040031