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22 pages, 8844 KiB

Open AccessArticle

Modeling COVID-19 Incidence by the Renewal Equation after Removal of Administrative Bias and Noise

by Luis Alvarez, Jean-David Morel and Jean-Michel Morel

Biology 2022, 11(4), 540; https://doi.org/10.3390/biology11040540 - 31 Mar 2022

Cited by 3 | Viewed by 2550

Abstract

The sanitary crisis of the past two years has focused the public’s attention on quantitative indicators of the spread of the COVID-19 pandemic. The daily reproduction number

R_{t}

, defined by the average number of new infections caused by a single infected [...] Read more.

The sanitary crisis of the past two years has focused the public’s attention on quantitative indicators of the spread of the COVID-19 pandemic. The daily reproduction number

R_{t}

, defined by the average number of new infections caused by a single infected individual at time t, is one of the best metrics for estimating the epidemic trend. In this paper, we provide a complete observation model for sampled epidemiological incidence signals obtained through periodic administrative measurements. The model is governed by the classic renewal equation using an empirical reproduction kernel, and subject to two perturbations: a time-varying gain with a weekly period and a white observation noise. We estimate this noise model and its parameters by extending a variational inversion of the model recovering its main driving variable

R_{t}

. Using

R_{t}

, a restored incidence curve, corrected of the weekly and festive day bias, can be deduced through the renewal equation. We verify experimentally on many countries that, once the weekly and festive days bias have been corrected, the difference between the incidence curve and its expected value is well approximated by an exponential distributed white noise multiplied by a power of the magnitude of the restored incidence curve. Full article

(This article belongs to the Special Issue Theories and Models on COVID-19 Epidemics)

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14 pages, 1831 KiB

Open AccessArticle

Modeling Vaccine Efficacy for COVID-19 Outbreak in New York City

by Jacques Demongeot, Quentin Griette, Pierre Magal and Glenn Webb

Biology 2022, 11(3), 345; https://doi.org/10.3390/biology11030345 - 22 Feb 2022

Cited by 18 | Viewed by 3384

Abstract

In this article we study the efficacy of vaccination in epidemiological reconstructions of COVID-19 epidemics from reported cases data. Given an epidemiological model, we developed in previous studies a method that allowed the computation of an instantaneous transmission rate that produced an exact [...] Read more.

In this article we study the efficacy of vaccination in epidemiological reconstructions of COVID-19 epidemics from reported cases data. Given an epidemiological model, we developed in previous studies a method that allowed the computation of an instantaneous transmission rate that produced an exact fit of reported cases data of the COVID-19 outbreak. In this article, we improve the method by incorporating vaccination data. More precisely, we develop a model in which vaccination is variable in its effectiveness. We develop a new technique to compute the transmission rate in this model, which produces an exact fit to reported cases data, while quantifying the efficacy of the vaccine and the daily number of vaccinated. We apply our method to the reported cases data and vaccination data of New York City. Full article

(This article belongs to the Special Issue Theories and Models on COVID-19 Epidemics)

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22 pages, 5510 KiB

Open AccessArticle

A Continuous Markov-Chain Model for the Simulation of COVID-19 Epidemic Dynamics

by Zhaobin Xu, Hongmei Zhang and Zuyi Huang

Biology 2022, 11(2), 190; https://doi.org/10.3390/biology11020190 - 26 Jan 2022

Cited by 18 | Viewed by 5217

Abstract

To address the urgent need to accurately predict the spreading trend of the COVID-19 epidemic, a continuous Markov-chain model was, for the first time, developed in this work to predict the spread of COVID-19 infection. A probability matrix of infection was first developed [...] Read more.

To address the urgent need to accurately predict the spreading trend of the COVID-19 epidemic, a continuous Markov-chain model was, for the first time, developed in this work to predict the spread of COVID-19 infection. A probability matrix of infection was first developed in this model based upon the contact frequency of individuals within the population, the individual’s characteristics, and other factors that can effectively reflect the epidemic’s temporal and spatial variation characteristics. The Markov-chain model was then extended to incorporate both the mutation effect of COVID-19 and the decaying effect of antibodies. The developed comprehensive Markov-chain model that integrates the aforementioned factors was finally tested by real data to predict the trend of the COVID-19 epidemic. The result shows that our model can effectively avoid the prediction dilemma that may exist with traditional ordinary differential equations model, such as the susceptible–infectious–recovered (SIR) model. Meanwhile, it can forecast the epidemic distribution and predict the epidemic hotspots geographically at different times. It is also demonstrated in our result that the influence of the population’s spatial and geographic distribution in a herd infection event is needed in the model for a better prediction of the epidemic trend. At the same time, our result indicates that no simple derivative relationship exists between the threshold of herd immunity and the virus basic reproduction number R₀. The threshold of herd immunity achieved through natural immunity is significantly higher than 1 − 1/R₀. These not only explain the theoretical misconceptions of herd immunity thresholds in herd immunity theory but also provide a guidance for predicting the optimal vaccination coverage. In addition, our model can predict the temporal and spatial distribution of infections in different epidemic waves. It is implied from our model that it is challenging to eradicate COVID-19 in the short term for a large population size and a wide spatial distribution. It is predicted that COVID-19 is likely to coexist with humans for a long time and that it will exhibit multipoint epidemic effects at a later stage. The statistical evidence is consistent with our prediction and strongly supports our modeling results. Full article

(This article belongs to the Special Issue Theories and Models on COVID-19 Epidemics)

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17 pages, 2246 KiB

Open AccessArticle

A Predictive Model for Severe COVID-19 in the Medicare Population: A Tool for Prioritizing Primary and Booster COVID-19 Vaccination

by Bettina Experton, Hassan A. Tetteh, Nicole Lurie, Peter Walker, Adrien Elena, Christopher S. Hein, Blake Schwendiman, Justin L. Vincent and Christopher R. Burrow

Biology 2021, 10(11), 1185; https://doi.org/10.3390/biology10111185 - 15 Nov 2021

Cited by 8 | Viewed by 6516

Abstract

Recommendations for prioritizing COVID-19 vaccination have focused on the elderly at higher risk for severe disease. Existing models for identifying higher-risk individuals lack the needed integration of socio-demographic and clinical risk factors. Using multivariate logistic regression and random forest modeling, we developed a [...] Read more.

Recommendations for prioritizing COVID-19 vaccination have focused on the elderly at higher risk for severe disease. Existing models for identifying higher-risk individuals lack the needed integration of socio-demographic and clinical risk factors. Using multivariate logistic regression and random forest modeling, we developed a predictive model of severe COVID-19 using clinical data from Medicare claims for 16 million Medicare beneficiaries and socio-economic data from the CDC Social Vulnerability Index. Predicted individual probabilities of COVID-19 hospitalization were then calculated for population risk stratification and vaccine prioritization and mapping. The leading COVID-19 hospitalization risk factors were non-white ethnicity, end-stage renal disease, advanced age, prior hospitalization, leukemia, morbid obesity, chronic kidney disease, lung cancer, chronic liver disease, pulmonary fibrosis or pulmonary hypertension, and chemotherapy. However, previously reported risk factors such as chronic obstructive pulmonary disease and diabetes conferred modest hospitalization risk. Among all social vulnerability factors, residence in a low-income zip code was the only risk factor independently predicting hospitalization. This multifactor risk model and its population risk dashboard can be used to optimize COVID-19 vaccine allocation in the higher-risk Medicare population. Full article

(This article belongs to the Special Issue Theories and Models on COVID-19 Epidemics)

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21 pages, 2884 KiB

Open AccessArticle

Unpredictable, Counter-Intuitive Geoclimatic and Demographic Correlations of COVID-19 Spread Rates

by Hervé Seligmann, Nicolas Vuillerme and Jacques Demongeot

Biology 2021, 10(7), 623; https://doi.org/10.3390/biology10070623 - 5 Jul 2021

Cited by 8 | Viewed by 3422

Abstract

We present spread parameters for first and second waves of the COVID-19 pandemic for USA states, and for consecutive nonoverlapping periods of 20 days for the USA and 51 countries across the globe. We studied spread rates in the USA states and 51 [...] Read more.

We present spread parameters for first and second waves of the COVID-19 pandemic for USA states, and for consecutive nonoverlapping periods of 20 days for the USA and 51 countries across the globe. We studied spread rates in the USA states and 51 countries, and analyzed associations between spread rates at different periods, and with temperature, elevation, population density and age. USA first/second wave spread rates increase/decrease with population density, and are uncorrelated with temperature and median population age. Spread rates are systematically inversely proportional to those estimated 80–100 days later. Ascending/descending phases of the same wave only partially explain this. Directions of correlations with factors such as temperature and median age flip. Changes in environmental trends of the COVID-19 pandemic remain unpredictable; predictions based on classical epidemiological knowledge are highly uncertain. Negative associations between population density and spread rates, observed in independent samples and at different periods, are most surprising. We suggest that systematic negative associations between spread rates 80–100 days apart could result from confinements selecting for greater contagiousness, a potential double-edged sword effect of confinements. Full article

(This article belongs to the Special Issue Theories and Models on COVID-19 Epidemics)

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21 pages, 3466 KiB

Open AccessArticle

Symptom and Age Homophilies in SARS-CoV-2 Transmission Networks during the Early Phase of the Pandemic in Japan

by Ali Andalibi, Naoru Koizumi, Meng-Hao Li and Abu Bakkar Siddique

Biology 2021, 10(6), 499; https://doi.org/10.3390/biology10060499 - 3 Jun 2021

Cited by 4 | Viewed by 3731

Abstract

Kanagawa and Hokkaido were affected by COVID-19 in the early stage of the pandemic. Japan’s initial response included contact tracing and PCR analysis on anyone who was suspected of having been exposed to SARS-CoV-2. In this retrospective study, we analyzed publicly available COVID-19 [...] Read more.

Kanagawa and Hokkaido were affected by COVID-19 in the early stage of the pandemic. Japan’s initial response included contact tracing and PCR analysis on anyone who was suspected of having been exposed to SARS-CoV-2. In this retrospective study, we analyzed publicly available COVID-19 registry data from Kanagawa and Hokkaido (n = 4392). Exponential random graph model (ERGM) network analysis was performed to examine demographic and symptomological homophilies. Age, symptomatic, and asymptomatic status homophilies were seen in both prefectures. Symptom homophilies suggest that nuanced genetic differences in the virus may affect its epithelial cell type range and can result in the diversity of symptoms seen in individuals infected by SARS-CoV-2. Environmental variables such as temperature and humidity may also play a role in the overall pathogenesis of the virus. A higher level of asymptomatic transmission was observed in Kanagawa. Moreover, patients who contracted the virus through secondary or tertiary contacts were shown to be asymptomatic more frequently than those who contracted it from primary cases. Additionally, most of the transmissions stopped at the primary and secondary levels. As expected, significant viral transmission was seen in healthcare settings. Full article

(This article belongs to the Special Issue Theories and Models on COVID-19 Epidemics)

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27 pages, 563 KiB

Open AccessArticle

A Hybrid Modeling Technique of Epidemic Outbreaks with Application to COVID-19 Dynamics in West Africa

by Chénangnon Frédéric Tovissodé, Jonas Têlé Doumatè and Romain Glèlè Kakaï

Biology 2021, 10(5), 365; https://doi.org/10.3390/biology10050365 - 23 Apr 2021

Cited by 6 | Viewed by 3503

Abstract

The widely used logistic model for epidemic case reporting data may be either restrictive or unrealistic in presence of containment measures when implemented after an epidemic outbreak. For flexibility in epidemic case reporting data modeling, we combined an exponential growth curve for the [...] Read more.

The widely used logistic model for epidemic case reporting data may be either restrictive or unrealistic in presence of containment measures when implemented after an epidemic outbreak. For flexibility in epidemic case reporting data modeling, we combined an exponential growth curve for the early epidemic phase with a flexible growth curve to account for the potential change in growth pattern after implementation of containment measures. We also fitted logistic regression models to recoveries and deaths from the confirmed positive cases. In addition, the growth curves were integrated into a SIQR (Susceptible, Infective, Quarantined, Recovered) model framework to provide an overview on the modeled epidemic wave. We focused on the estimation of: (1) the delay between the appearance of the first infectious case in the population and the outbreak (“epidemic latency period”); (2) the duration of the exponential growth phase; (3) the basic and the time-varying reproduction numbers; and (4) the peaks (time and size) in confirmed positive cases, active cases and new infections. The application of this approach to COVID-19 data from West Africa allowed discussion on the effectiveness of some containment measures implemented across the region. Full article

(This article belongs to the Special Issue Theories and Models on COVID-19 Epidemics)

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11 pages, 3042 KiB

Open AccessArticle

A Retrospective Analysis of the COVID-19 Pandemic Evolution in Italy

by Anna Fochesato, Giulia Simoni, Federico Reali, Giulia Giordano, Enrico Domenici and Luca Marchetti

Biology 2021, 10(4), 311; https://doi.org/10.3390/biology10040311 - 8 Apr 2021

Cited by 8 | Viewed by 3729

Abstract

Late 2019 saw the outbreak of COVID-19, a respiratory disease caused by the new coronavirus SARS-CoV-2, which rapidly turned into a pandemic, killing more than 2.77 million people and infecting more than 126 million as of late March 2021. Daily collected data on [...] Read more.

Late 2019 saw the outbreak of COVID-19, a respiratory disease caused by the new coronavirus SARS-CoV-2, which rapidly turned into a pandemic, killing more than 2.77 million people and infecting more than 126 million as of late March 2021. Daily collected data on infection cases and hospitalizations informed decision makers on the ongoing pandemic emergency, enabling the design of diversified countermeasures, from behavioral policies to full lockdowns, to curb the virus spread. In this context, mechanistic models could represent valuable tools to optimize the timing and stringency of interventions, and to reveal non-trivial properties of the pandemic dynamics that could improve the design of suitable guidelines for future epidemics. We performed a retrospective analysis of the Italian epidemic evolution up to mid-December 2020 to gain insight into the main characteristics of the original strain of SARS-CoV-2, prior to the emergence of new mutations and the vaccination campaign. We defined a time-varying optimization procedure to calibrate a refined version of the SIDARTHE (Susceptible, Infected, Diagnosed, Ailing, Recognized, Threatened, Healed, Extinct) model and hence accurately reconstruct the epidemic trajectory. We then derived additional features of the COVID-19 pandemic in Italy not directly retrievable from reported data, such as the estimate of the day zero of infection in late November 2019 and the estimate of the spread of undetected infection. The present analysis contributes to a better understanding of the past pandemic waves, confirming the importance of epidemiological modeling to support an informed policy design against epidemics to come. Full article

(This article belongs to the Special Issue Theories and Models on COVID-19 Epidemics)

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10 pages, 5176 KiB

Open AccessArticle

Are There Any Parameters Missing in the Mathematical Models Applied in the Process of Spreading COVID-19?

by Pietro M. Boselli, Massimo Basagni and Jose M. Soriano

Biology 2021, 10(2), 165; https://doi.org/10.3390/biology10020165 - 19 Feb 2021

Cited by 2 | Viewed by 3224

Abstract

On 11 March 2020, coronavirus disease 2019 (COVID-19) was declared a pandemic by the World Health Organization (WHO). As of 12.44 GMT on 15 January 2021, it has produced 93,640,296 cases and 2,004,984 deaths. The use of mathematical modelling was applied in Italy, [...] Read more.

On 11 March 2020, coronavirus disease 2019 (COVID-19) was declared a pandemic by the World Health Organization (WHO). As of 12.44 GMT on 15 January 2021, it has produced 93,640,296 cases and 2,004,984 deaths. The use of mathematical modelling was applied in Italy, Spain, and UK to help in the prediction of this pandemic. We used equations from general and reduced logistic models to describe the epidemic development phenomenon and the trend over time. We extracted this information from the Italian Ministry of Health, the Spanish Ministry of Health, Consumer Affairs, and Social Welfare, and the UK Statistics Authority from 3 February to 30 April 2020. We estimated that, from the seriousness of the phenomenon, the consequent pathology, and the lethal outcomes, the COVID-19 trend relate to the same classic laws that govern epidemics and their evolution. The curve d(t) helps to obtain information on the duration of the epidemic phenomenon, as its evolution is related to the efficiency and timeliness of the system, control, diagnosis, and treatment. In fact, the analysis of this curve, after acquiring the data of the first three weeks, also favors the advantage to formulate forecast hypotheses on the progress of the epidemic. Full article

(This article belongs to the Special Issue Theories and Models on COVID-19 Epidemics)

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18 pages, 2120 KiB

Open AccessArticle

SARS-CoV-2 and Rohingya Refugee Camp, Bangladesh: Uncertainty and How the Government Took Over the Situation

by Md. Kamrujjaman, Md. Shahriar Mahmud, Shakil Ahmed, Md. Omar Qayum, Mohammad Morshad Alam, Md Nazmul Hassan, Md Rafiul Islam, Kaniz Fatema Nipa and Ummugul Bulut

Biology 2021, 10(2), 124; https://doi.org/10.3390/biology10020124 - 5 Feb 2021

Cited by 16 | Viewed by 5021

Abstract

Background: Bangladesh hosts more than 800,000 Rohingya refugees from Myanmar. The low health immunity, lifestyle, access to good healthcare services, and social-security cause this population to be at risk of far more direct effects of COVID-19 than the host population. Therefore, evidence-based forecasting [...] Read more.

Background: Bangladesh hosts more than 800,000 Rohingya refugees from Myanmar. The low health immunity, lifestyle, access to good healthcare services, and social-security cause this population to be at risk of far more direct effects of COVID-19 than the host population. Therefore, evidence-based forecasting of the COVID-19 burden is vital in this regard. In this study, we aimed to forecast the COVID-19 obligation among the Rohingya refugees of Bangladesh to keep up with the disease outbreak’s pace, health needs, and disaster preparedness. Methodology and Findings: To estimate the possible consequences of COVID-19 in the Rohingya camps of Bangladesh, we used a modified Susceptible-Exposed-Infectious-Recovered (SEIR) transmission model. All of the values of different parameters used in this model were from the Bangladesh Government’s database and the relevant emerging literature. We addressed two different scenarios, i.e., the best-fitting model and the good-fitting model with unique consequences of COVID-19. Our best fitting model suggests that there will be reasonable control over the transmission of the COVID-19 disease. At the end of December 2020, there will be only 169 confirmed COVID-19 cases in the Rohingya refugee camps. The average basic reproduction number (

R_{0}

) has been estimated to be 0.7563. Conclusions: Our analysis suggests that, due to the extensive precautions from the Bangladesh government and other humanitarian organizations, the coronavirus disease will be under control if the maintenance continues like this. However, detailed and pragmatic preparedness should be adopted for the worst scenario. Full article

(This article belongs to the Special Issue Theories and Models on COVID-19 Epidemics)

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18 pages, 2430 KiB

Open AccessArticle

A Modelization of the Propagation of COVID-19 in Regions of Spain and Italy with Evaluation of the Transmission Rates Related to the Intervention Measures

by Raul Nistal, Manuel de la Sen, Jon Gabirondo, Santiago Alonso-Quesada, Aitor J. Garrido and Izaskun Garrido

Biology 2021, 10(2), 121; https://doi.org/10.3390/biology10020121 - 5 Feb 2021

Cited by 6 | Viewed by 2506

Abstract

Two discrete mathematical SIR models (Susceptible-Infectious-Recovered) are proposed for modelling the propagation of the SARS-CoV-2 (COVID-19) through Spain and Italy. One of the proposed models is delay-free while the other one considers a delay in the propagation of the infection. The objective is [...] Read more.

Two discrete mathematical SIR models (Susceptible-Infectious-Recovered) are proposed for modelling the propagation of the SARS-CoV-2 (COVID-19) through Spain and Italy. One of the proposed models is delay-free while the other one considers a delay in the propagation of the infection. The objective is to estimate the transmission, also known as infectivity rate, through time taking into account the infection evolution data supplied by the official health care systems in both countries. Such a parameter is estimated through time at different regional levels and it is seen to be strongly dependent on the intervention measures such as the total (except essential activities) or partial levels of lockdown. Typically, the infectivity rate evolves towards a minimum value under total lockdown and it increases again when the confinement measures are partially or totally removed. Full article

(This article belongs to the Special Issue Theories and Models on COVID-19 Epidemics)

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12 pages, 1958 KiB

Open AccessArticle

Evaluating the Impact of Intervention Strategies on the First Wave and Predicting the Second Wave of COVID-19 in Thailand: A Mathematical Modeling Study

by Wiriya Mahikul, Palang Chotsiri, Kritchavat Ploddi and Wirichada Pan-ngum

Biology 2021, 10(2), 80; https://doi.org/10.3390/biology10020080 - 22 Jan 2021

Cited by 22 | Viewed by 5727

Abstract

Coronavirus disease 2019 (COVID-19) has spread rapidly worldwide. This study aimed to assess and predict the incidence of COVID-19 in Thailand, including the preparation and evaluation of intervention strategies. An SEIR (susceptible, exposed, infected, recovered) model was implemented with model parameters estimated using [...] Read more.

Coronavirus disease 2019 (COVID-19) has spread rapidly worldwide. This study aimed to assess and predict the incidence of COVID-19 in Thailand, including the preparation and evaluation of intervention strategies. An SEIR (susceptible, exposed, infected, recovered) model was implemented with model parameters estimated using the Bayesian approach. The model’s projections showed that the highest daily reported incidence of COVID-19 would be approximately 140 cases (95% credible interval, CrI: 83–170 cases) by the end of March 2020. After Thailand declared an emergency decree, the numbers of new cases and case fatalities decreased, with no new imported cases. According to the model’s predictions, the incidence would be zero at the end of June if non-pharmaceutical interventions (NPIs) were strictly and widely implemented. These stringent NPIs reduced the effective reproductive number (Rt) to 0.73 per day (95% CrI: 0.53–0.93) during April and May. Sensitivity analysis showed that contact rate, hand washing, and face mask wearing effectiveness were the parameters that most influenced the number of reported daily new cases. Our evaluation shows that Thailand’s intervention strategies have been highly effective in mitigating disease propagation. Continuing with these strict disease prevention behaviors could minimize the risk of a new COVID-19 outbreak in Thailand. Full article

(This article belongs to the Special Issue Theories and Models on COVID-19 Epidemics)

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42 pages, 2823 KiB

Open AccessArticle

Epidemiological Forecasting with Model Reduction of Compartmental Models. Application to the COVID-19 Pandemic

by Athmane Bakhta, Thomas Boiveau, Yvon Maday and Olga Mula

Biology 2021, 10(1), 22; https://doi.org/10.3390/biology10010022 - 31 Dec 2020

Cited by 18 | Viewed by 4752

Abstract

We propose a forecasting method for predicting epidemiological health series on a two-week horizon at regional and interregional resolution. The approach is based on the model order reduction of parametric compartmental models and is designed to accommodate small amounts of sanitary data. The [...] Read more.

We propose a forecasting method for predicting epidemiological health series on a two-week horizon at regional and interregional resolution. The approach is based on the model order reduction of parametric compartmental models and is designed to accommodate small amounts of sanitary data. The efficiency of the method is shown in the case of the prediction of the number of infected people and people removed from the collected data, either due to death or recovery, during the two pandemic waves of COVID-19 in France, which took place approximately between February and November 2020. Numerical results illustrate the promising potential of the approach. Full article

(This article belongs to the Special Issue Theories and Models on COVID-19 Epidemics)

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10 pages, 401 KiB

Open AccessArticle

A New Transmission Route for the Propagation of the SARS-CoV-2 Coronavirus

by Antoine Danchin, Tuen Wai Ng and Gabriel Turinici

Biology 2021, 10(1), 10; https://doi.org/10.3390/biology10010010 - 26 Dec 2020

Cited by 19 | Viewed by 3752

Abstract

Background: Starting late 2019, a novel coronavirus spread from the capital of the Hubei province in China to the rest of the country, then to most of the world. To anticipate future trends in the development of the pandemic, we explore here, based [...] Read more.

Background: Starting late 2019, a novel coronavirus spread from the capital of the Hubei province in China to the rest of the country, then to most of the world. To anticipate future trends in the development of the pandemic, we explore here, based on public records of infected persons, how variation in the virus tropism could end up in different patterns, warranting a specific strategy to handle the epidemic. Methods: We use a compartmental model to describe the evolution of an individual through several possible states: susceptible, infected, alternative infection, detected, and removed. We fit the parameters of the model to the existing data, taking into account significant quarantine changes where necessary. Results: The model indicates that Wuhan quarantine measures were effective, but that alternative virus forms and a second propagation route are compatible with available data. For the Hong Kong, Singapore, and Shenzhen regions, the secondary route does not seem to be active. Conclusions: Hypotheses of an alternative infection tropism (the gut tropism) and a secondary propagation route are discussed using a model fitted by the available data. Corresponding prevention measures that take into account both routes should be implemented to the benefit of epidemic control. Full article

(This article belongs to the Special Issue Theories and Models on COVID-19 Epidemics)

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21 pages, 2036 KiB

Open AccessArticle

Epidemic Dynamics via Wavelet Theory and Machine Learning with Applications to Covid-19

by Tô Tat Dat, Protin Frédéric, Nguyen T. T. Hang, Martel Jules, Nguyen Duc Thang, Charles Piffault, Rodríguez Willy, Figueroa Susely, Hông Vân Lê, Wilderich Tuschmann and Nguyen Tien Zung

Biology 2020, 9(12), 477; https://doi.org/10.3390/biology9120477 - 18 Dec 2020

Cited by 11 | Viewed by 4782

Abstract

We introduce the concept of epidemic-fitted wavelets which comprise, in particular, as special cases the number

I (t)

of infectious individuals at time t in classical SIR models and their derivatives. We present a novel method for modelling epidemic dynamics by [...] Read more.

We introduce the concept of epidemic-fitted wavelets which comprise, in particular, as special cases the number

I (t)

of infectious individuals at time t in classical SIR models and their derivatives. We present a novel method for modelling epidemic dynamics by a model selection method using wavelet theory and, for its applications, machine learning-based curve fitting techniques. Our universal models are functions that are finite linear combinations of epidemic-fitted wavelets. We apply our method by modelling and forecasting, based on the Johns Hopkins University dataset, the spread of the current Covid-19 (SARS-CoV-2) epidemic in France, Germany, Italy and the Czech Republic, as well as in the US federal states New York and Florida. Full article

(This article belongs to the Special Issue Theories and Models on COVID-19 Epidemics)

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18 pages, 605 KiB

Open AccessArticle

Mathematical Modeling and Robustness Analysis to Unravel COVID-19 Transmission Dynamics: The Italy Case

by Chiara Antonini, Sara Calandrini, Fabrizio Stracci, Claudio Dario and Fortunato Bianconi

Biology 2020, 9(11), 394; https://doi.org/10.3390/biology9110394 - 11 Nov 2020

Cited by 5 | Viewed by 4177

Abstract

This study started from the request of providing predictions on hospitalization and Intensive Care Unit (ICU) rates that are caused by COVID-19 for the Umbria region in Italy. To this purpose, we propose the application of a computational framework to a SEIR-type (Susceptible, [...] Read more.

This study started from the request of providing predictions on hospitalization and Intensive Care Unit (ICU) rates that are caused by COVID-19 for the Umbria region in Italy. To this purpose, we propose the application of a computational framework to a SEIR-type (Susceptible, Exposed, Infected, Removed) epidemiological model describing the different stages of COVID-19 infection. The model discriminates between asymptomatic and symptomatic cases and it takes into account possible intervention measures in order to reduce the probability of transmission. As case studies, we analyze not only the epidemic situation in Umbria but also in Italy, in order to capture the evolution of the pandemic at a national level. First of all, we estimate model parameters through a Bayesian calibration method, called Conditional Robust Calibration (CRC), while using the official COVID-19 data of the Italian Civil Protection. Subsequently, Conditional Robustness Analysis (CRA) on the calibrated model is carried out in order to quantify the influence of epidemiological and intervention parameters on the hospitalization rates. The proposed pipeline properly describes the COVID-19 spread during the lock-down phase. It also reveals the underestimation of new positive cases and the need of promptly isolating asymptomatic and presymptomatic cases. The results emphasize the importance of the lock-down timeliness and provide accurate predictions on the current evolution of the pandemic. Full article

(This article belongs to the Special Issue Theories and Models on COVID-19 Epidemics)

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18 pages, 1908 KiB

Open AccessArticle

Modeling the Impact of Unreported Cases of the COVID-19 in the North African Countries

by Salih Djilali, Lahbib Benahmadi, Abdessamad Tridane and Khadija Niri

Biology 2020, 9(11), 373; https://doi.org/10.3390/biology9110373 - 3 Nov 2020

Cited by 41 | Viewed by 3194

Abstract

In this paper, we study a mathematical model investigating the impact of unreported cases of the COVID-19 in three North African countries: Algeria, Egypt, and Morocco. To understand how the population respects the restriction of population mobility implemented in each country, we use [...] Read more.

In this paper, we study a mathematical model investigating the impact of unreported cases of the COVID-19 in three North African countries: Algeria, Egypt, and Morocco. To understand how the population respects the restriction of population mobility implemented in each country, we use Google and Apple’s mobility reports. These mobility reports help to quantify the effect of the population movement restrictions on the evolution of the active infection cases. We also approximate the number of the population infected unreported, the proportion of those that need hospitalization, and estimate the end of the epidemic wave. Moreover, we use our model to estimate the second wave of the COVID-19 Algeria and Morocco and to project the end of the second wave. Finally, we suggest some additional measures that can be considered to reduce the burden of the COVID-19 and would lead to a second wave of the spread of the virus in these countries. Full article

(This article belongs to the Special Issue Theories and Models on COVID-19 Epidemics)

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20 pages, 505 KiB

Open AccessArticle

When the Best Pandemic Models are the Simplest

by Sana Jahedi and James A. Yorke

Biology 2020, 9(11), 353; https://doi.org/10.3390/biology9110353 - 23 Oct 2020

Cited by 10 | Viewed by 3460

Abstract

As the coronavirus pandemic spreads across the globe, people are debating policies to mitigate its severity. Many complex, highly detailed models have been developed to help policy setters make better decisions. However, the basis of these models is unlikely to be understood by [...] Read more.

As the coronavirus pandemic spreads across the globe, people are debating policies to mitigate its severity. Many complex, highly detailed models have been developed to help policy setters make better decisions. However, the basis of these models is unlikely to be understood by non-experts. We describe the advantages of simple models for COVID-19. We say a model is “simple” if its only parameter is the rate of contact between people in the population. This contact rate can vary over time, depending on choices by policy setters. Such models can be understood by a broad audience, and thus can be helpful in explaining the policy decisions to the public. They can be used to evaluate the outcomes of different policies. However, simple models have a disadvantage when dealing with inhomogeneous populations. To augment the power of a simple model to evaluate complicated situations, we add what we call “satellite” equations that do not change the original model. For example, with the help of a satellite equation, one could know what his/her chance is of remaining uninfected through the end of an epidemic. Satellite equations can model the effects of the epidemic on high-risk individuals, death rates, and nursing homes and other isolated populations. To compare simple models with complex models, we introduce our “slightly complex” Model J. We find the conclusions of simple and complex models can be quite similar. However, for each added complexity, a modeler may have to choose additional parameter values describing who will infect whom under what conditions, choices for which there is often little rationale but that can have big impacts on predictions. Our simulations suggest that the added complexity offers little predictive advantage. Full article

(This article belongs to the Special Issue Theories and Models on COVID-19 Epidemics)

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19 pages, 2408 KiB

Open AccessArticle

Pandæsim: An Epidemic Spreading Stochastic Simulator

by Patrick Amar

Biology 2020, 9(9), 299; https://doi.org/10.3390/biology9090299 - 18 Sep 2020

Cited by 4 | Viewed by 2949

Abstract

Many methods have been used to model epidemic spreading. They include ordinary differential equation systems for globally homogeneous environments and partial differential equation systems to take into account spatial localisation and inhomogeneity. Stochastic differential equations systems have been used to model the inherent [...] Read more.

Many methods have been used to model epidemic spreading. They include ordinary differential equation systems for globally homogeneous environments and partial differential equation systems to take into account spatial localisation and inhomogeneity. Stochastic differential equations systems have been used to model the inherent stochasticity of epidemic spreading processes. In our case study, we wanted to model the numbers of individuals in different states of the disease, and their locations in the country. Among the many existing methods we used our own variant of the well known Gillespie stochastic algorithm, along with the sub-volumes method to take into account the spatial localisation. Our algorithm allows us to easily switch from stochastic discrete simulation to continuous deterministic resolution using mean values. We applied our approaches on the study of the Covid-19 epidemic in France. The stochastic discrete version of Pandæsim showed very good correlations between the simulation results and the statistics gathered from hospitals, both on day by day and on global numbers, including the effects of the lockdown. Moreover, we have highlighted interesting differences in behaviour between the continuous and discrete methods that may arise in some particular conditions. Full article

(This article belongs to the Special Issue Theories and Models on COVID-19 Epidemics)

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28 pages, 2398 KiB

Open AccessArticle

A Putative Prophylactic Solution for COVID-19: Development of Novel Multiepitope Vaccine Candidate against SARS-COV-2 by Comprehensive Immunoinformatic and Molecular Modelling Approach

by Hafiz Muzzammel Rehman, Muhammad Usman Mirza, Mian Azhar Ahmad, Mahjabeen Saleem, Matheus Froeyen, Sarfraz Ahmad, Roquyya Gul, Huda Ahmed Alghamdi, Muhammad Shahbaz Aslam, Muhammad Sajjad and Munir Ahmad Bhinder

Biology 2020, 9(9), 296; https://doi.org/10.3390/biology9090296 - 18 Sep 2020

Cited by 18 | Viewed by 5482

Abstract

The outbreak of 2019-novel coronavirus (SARS-CoV-2) that causes severe respiratory infection (COVID-19) has spread in China, and the World Health Organization has declared it a pandemic. However, no approved drug or vaccines are available, and treatment is mainly supportive and through a few [...] Read more.

The outbreak of 2019-novel coronavirus (SARS-CoV-2) that causes severe respiratory infection (COVID-19) has spread in China, and the World Health Organization has declared it a pandemic. However, no approved drug or vaccines are available, and treatment is mainly supportive and through a few repurposed drugs. The urgency of the situation requires the development of SARS-CoV-2-based vaccines. Immunoinformatic and molecular modelling are time-efficient methods that are generally used to accelerate the discovery and design of the candidate peptides for vaccine development. In recent years, the use of multiepitope vaccines has proved to be a promising immunization strategy against viruses and pathogens, thus inducing more comprehensive protective immunity. The current study demonstrated a comprehensive in silico strategy to design stable multiepitope vaccine construct (MVC) from B-cell and T-cell epitopes of essential SARS-CoV-2 proteins with the help of adjuvants and linkers. The integrated molecular dynamics simulations analysis revealed the stability of MVC and its interaction with human Toll-like receptors (TLRs), which trigger an innate and adaptive immune response. Later, the in silico cloning in a known pET28a vector system also estimated the possibility of MVC expression in Escherichia coli. Despite that this study lacks validation of this vaccine construct in terms of its efficacy, the current integrated strategy encompasses the initial multiple epitope vaccine design concepts. After validation, this MVC can be present as a better prophylactic solution against COVID-19. Full article

(This article belongs to the Special Issue Theories and Models on COVID-19 Epidemics)

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19 pages, 9384 KiB

Open AccessArticle

Computational and Transcriptome Analyses Revealed Preferential Induction of Chemotaxis and Lipid Synthesis by SARS-CoV-2

by Hibah Shaath and Nehad M. Alajez

Biology 2020, 9(9), 260; https://doi.org/10.3390/biology9090260 - 1 Sep 2020

Cited by 10 | Viewed by 3724

Abstract

The continuous and rapid emergence of new viral strains calls for a better understanding of the fundamental changes occurring within the host cell upon viral infection. In this study, we analyzed RNA-seq transcriptome data from Calu-3 human lung epithelial cells infected with severe [...] Read more.

The continuous and rapid emergence of new viral strains calls for a better understanding of the fundamental changes occurring within the host cell upon viral infection. In this study, we analyzed RNA-seq transcriptome data from Calu-3 human lung epithelial cells infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) compared to five other viruses namely, severe acute respiratory syndrome coronavirus (SARS-CoV), Middle East Respiratory Syndrome (SARS-MERS), influenzavirus A (FLUA), influenzavirus B (FLUB), and rhinovirus (RHINO) compared to mock-infected cells and characterized their coding and noncoding RNA transcriptional portraits. The induction of interferon, inflammatory, and immune response was a hallmark of SARS-CoV-2 infection. Comprehensive bioinformatics revealed the activation of immune response and defense response to the virus as a common feature of viral infection. Interestingly however, the degree of functional categories and signaling pathways activation varied among different viruses. Ingenuity pathways analysis highlighted altered conical and casual pathways related to TNF, IL1A, and TLR7, which are seen more predominantly during SARS-CoV-2 infection. Nonetheless, the activation of chemotaxis and lipid synthesis was prominent in SARS-CoV-2-infected cells. Despite the commonality among all viruses, our data revealed the hyperactivation of chemotaxis and immune cell trafficking as well as the enhanced fatty acid synthesis as plausible mechanisms that could explain the inflammatory cytokine storms associated with severe cases of COVID-19 and the rapid spread of the virus, respectively. Full article

(This article belongs to the Special Issue Theories and Models on COVID-19 Epidemics)

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16 pages, 1427 KiB

Open AccessCommunication

Inverted Covariate Effects for First versus Mutated Second Wave Covid-19: High Temperature Spread Biased for Young

by Hervé Seligmann, Siham Iggui, Mustapha Rachdi, Nicolas Vuillerme and Jacques Demongeot

Biology 2020, 9(8), 226; https://doi.org/10.3390/biology9080226 - 14 Aug 2020

Cited by 26 | Viewed by 4559

Abstract

(1) Background: Here, we characterize COVID-19’s waves, following a study presenting negative associations between first wave COVID-19 spread parameters and temperature. (2) Methods: Visual examinations of daily increases in confirmed COVID-19 cases in 124 countries, determined first and second waves in 28 countries. [...] Read more.

(1) Background: Here, we characterize COVID-19’s waves, following a study presenting negative associations between first wave COVID-19 spread parameters and temperature. (2) Methods: Visual examinations of daily increases in confirmed COVID-19 cases in 124 countries, determined first and second waves in 28 countries. (3) Results: The first wave spread rate increases with country mean elevation, median population age, time since wave onset, and decreases with temperature. Spread rates decrease above 1000 m, indicating high ultraviolet lights (UVs) decrease the spread rate. The second wave associations are the opposite, i.e., spread increases with temperature and young age, and decreases with time since wave onset. The earliest second waves started 5–7 April at mutagenic high elevations (Armenia, Algeria). The second waves also occurred at the warm-to-cold season transition (Argentina, Chile). Second vs. first wave spread decreases in most (77%) countries. In countries with late first wave onset, spread rates better fit second than first wave-temperature patterns. In countries with ageing populations (for example, Japan, Sweden, and Ukraine), second waves only adapted to spread at higher temperatures, not to infect the young. (4) Conclusions: First wave viruses evolved towards lower spread. Second wave mutant COVID-19 strain(s) adapted to higher temperature, infecting younger ages and replacing (also in cold conditions) first wave COVID-19 strains. Counterintuitively, low spread strains replace high spread strains, rendering prognostics and extrapolations uncertain. Full article

(This article belongs to the Special Issue Theories and Models on COVID-19 Epidemics)

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26 pages, 3332 KiB

Open AccessArticle

Mathematical Parameters of the COVID-19 Epidemic in Brazil and Evaluation of the Impact of Different Public Health Measures

by Renato M. Cotta, Carolina P. Naveira-Cotta and Pierre Magal

Biology 2020, 9(8), 220; https://doi.org/10.3390/biology9080220 - 12 Aug 2020

Cited by 23 | Viewed by 4228

Abstract

A SIRU-type epidemic model is employed for the prediction of the COVID-19 epidemy evolution in Brazil, and analyze the influence of public health measures on simulating the control of this infectious disease. The proposed model allows for a time variable functional form of [...] Read more.

A SIRU-type epidemic model is employed for the prediction of the COVID-19 epidemy evolution in Brazil, and analyze the influence of public health measures on simulating the control of this infectious disease. The proposed model allows for a time variable functional form of both the transmission rate and the fraction of asymptomatic infectious individuals that become reported symptomatic individuals, to reflect public health interventions, towards the epidemy control. An exponential analytical behavior for the accumulated reported cases evolution is assumed at the onset of the epidemy, for explicitly estimating initial conditions, while a Bayesian inference approach is adopted for the estimation of parameters by employing the direct problem model with the data from the first phase of the epidemy evolution, represented by the time series for the reported cases of infected individuals. The evolution of the COVID-19 epidemy in China is considered for validation purposes, by taking the first part of the dataset of accumulated reported infectious individuals to estimate the related parameters, and retaining the rest of the evolution data for direct comparison with the predicted results. Then, the available data on reported cases in Brazil from 15 February until 29 March, is used for estimating parameters and then predicting the first phase of the epidemy evolution from these initial conditions. The data for the reported cases in Brazil from 30 March until 23 April are reserved for validation of the model. Then, public health interventions are simulated, aimed at evaluating the effects on the disease spreading, by acting on both the transmission rate and the fraction of the total number of the symptomatic infectious individuals, considering time variable exponential behaviors for these two parameters. This first constructed model provides fairly accurate predictions up to day 65 below 5% relative deviation, when the data starts detaching from the theoretical curve. From the simulated public health intervention measures through five different scenarios, it was observed that a combination of careful control of the social distancing relaxation and improved sanitary habits, together with more intensive testing for isolation of symptomatic cases, is essential to achieve the overall control of the disease and avoid a second more strict social distancing intervention. Finally, the full dataset available by the completion of the present work is employed in redefining the model to yield updated epidemy evolution estimates. Full article

(This article belongs to the Special Issue Theories and Models on COVID-19 Epidemics)

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13 pages, 453 KiB

Open AccessArticle

SARS-CoV-2 Positive Hospitalized Cancer Patients during the Italian Outbreak: The Cohort Study in Reggio Emilia

by Carmine Pinto, Annalisa Berselli, Lucia Mangone, Angela Damato, Francesco Iachetta, Marco Foracchia, Francesca Zanelli, Erika Gervasi, Alessandra Romagnani, Giuseppe Prati, Stefania Lui, Francesco Venturelli, Massimo Vicentini, Giulia Besutti, Rossana De Palma and Paolo Giorgi Rossi

Biology 2020, 9(8), 181; https://doi.org/10.3390/biology9080181 - 22 Jul 2020

Cited by 12 | Viewed by 3855

Abstract

In the coronavirus disease (COVID-19) pandemic, cancer patients could be a high-risk group due to their immunosuppressed status; therefore, data on cancer patients must be available in order to consider the most adequate strategy of care. We carried out a cohort study on [...] Read more.

In the coronavirus disease (COVID-19) pandemic, cancer patients could be a high-risk group due to their immunosuppressed status; therefore, data on cancer patients must be available in order to consider the most adequate strategy of care. We carried out a cohort study on the risk of hospitalization for COVID-19, oncological history, and outcomes on COVID-19 infected cancer patients admitted to the Hospital of Reggio Emilia. Between 1 February and 3 April 2020, a total of 1226 COVID-19 infected patients were hospitalized. The number of cancer patients hospitalized with COVID-19 infection was 138 (11.3%). The median age was slightly higher in patients with cancers than in those without (76.5 vs. 73.0). The risk of intensive care unit (ICU) admission (10.1% vs. 6.7%; RR 1.23, 95% Confidence Interval (CI) 0.63–2.41) and risk of death (34.1% vs. 26.0%; RR 1.07, 95% CI 0.61–1.71) were similar in cancer and non-cancer patients. In the cancer patients group, 89/138 (64.5%) patients had a time interval >5 years between the diagnosis of the tumor and hospitalization. Male gender, age > 74 years, metastatic disease, bladder cancer, and cardiovascular disease were associated with mortality risk in cancer patients. In the Reggio Emilia Study, the incidence of hospitalization for COVID-19 in people with previous diagnosis of cancer is similar to that in the general population (standardized incidence ratio 98; 95% CI 73–131), and it does not appear to have a more severe course or a higher mortality rate than patients without cancer. The phase II of the COVID-19 epidemic in cancer patients needs a strategy to reduce the likelihood of infection and identify the vulnerable population, both in patients with active antineoplastic treatment and in survivors with frequently different coexisting medical conditions. Full article

(This article belongs to the Special Issue Theories and Models on COVID-19 Epidemics)

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11 pages, 614 KiB

Open AccessArticle

On a Coupled Time-Dependent SIR Models Fitting with New York and New-Jersey States COVID-19 Data

by Benjamin Ambrosio and M. A. Aziz-Alaoui

Biology 2020, 9(6), 135; https://doi.org/10.3390/biology9060135 - 24 Jun 2020

Cited by 17 | Viewed by 4204

Abstract

This article describes a simple Susceptible Infected Recovered (SIR) model fitting with COVID-19 data for the month of March 2020 in New York (NY) state. The model is a classical SIR, but is non-autonomous; the rate of susceptible people becoming infected is adjusted [...] Read more.

This article describes a simple Susceptible Infected Recovered (SIR) model fitting with COVID-19 data for the month of March 2020 in New York (NY) state. The model is a classical SIR, but is non-autonomous; the rate of susceptible people becoming infected is adjusted over time in order to fit the available data. The death rate is also secondarily adjusted. Our fitting is made under the assumption that due to limiting number of tests, a large part of the infected population has not been tested positive. In the last part, we extend the model to take into account the daily fluxes between New Jersey (NJ) and NY states and fit the data for both states. Our simple model fits the available data, and illustrates typical dynamics of the disease: exponential increase, apex and decrease. The model highlights a decrease in the transmission rate over the period which gives a quantitative illustration about how lockdown policies reduce the spread of the pandemic. The coupled model with NY and NJ states shows a wave in NJ following the NY wave, illustrating the mechanism of spread from one attractive hot spot to its neighbor. Full article

(This article belongs to the Special Issue Theories and Models on COVID-19 Epidemics)

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10 pages, 1326 KiB

Open AccessArticle

A Brief Theory of Epidemic Kinetics

by François Louchet

Biology 2020, 9(6), 134; https://doi.org/10.3390/biology9060134 - 22 Jun 2020

Cited by 3 | Viewed by 3062

Abstract

In the context of the COVID-19 epidemic, and on the basis of the Theory of Dynamical Systems, we propose a simple theoretical approach for the expansion of contagious diseases, with a particular focus on viral respiratory tracts. The infection develops through contacts between [...] Read more.

In the context of the COVID-19 epidemic, and on the basis of the Theory of Dynamical Systems, we propose a simple theoretical approach for the expansion of contagious diseases, with a particular focus on viral respiratory tracts. The infection develops through contacts between contagious and exposed people, with a rate proportional to the number of contagious and of non-immune individuals, to contact duration and turnover, inversely proportional to the efficiency of protection measures, and balanced by the average individual recovery response. The obvious initial exponential increase is readily hindered by the growing recovery rate, and also by the size reduction of the exposed population. The system converges towards a stable attractor whose value is expressed in terms of the “reproductive rate” R₀, depending on contamination and recovery factors. Various properties of the attractor are examined, and particularly its relations with R₀. Decreasing this ratio below a critical value leads to a tipping threshold beyond which the epidemic is over. By contrast, significant values of the above ratio may bring the system through a bifurcating hierarchy of stable cycles up to a chaotic behaviour. Full article

(This article belongs to the Special Issue Theories and Models on COVID-19 Epidemics)

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21 pages, 3438 KiB

Open AccessArticle

Unreported Cases for Age Dependent COVID-19 Outbreak in Japan

by Quentin Griette, Pierre Magal and Ousmane Seydi

Biology 2020, 9(6), 132; https://doi.org/10.3390/biology9060132 - 17 Jun 2020

Cited by 16 | Viewed by 4265

Abstract

We investigate the age structured data for the COVID-19 outbreak in Japan. We consider a mathematical model for the epidemic with unreported infectious patient with and without age structure. In particular, we build a new mathematical model and a new computational method to [...] Read more.

We investigate the age structured data for the COVID-19 outbreak in Japan. We consider a mathematical model for the epidemic with unreported infectious patient with and without age structure. In particular, we build a new mathematical model and a new computational method to fit the data by using age classes dependent exponential growth at the early stage of the epidemic. This allows to take into account differences in the response of patients to the disease according to their age. This model also allows for a heterogeneous response of the population to the social distancing measures taken by the local government. We fit this model to the observed data and obtain a snapshot of the effective transmissions occurring inside the population at different times, which indicates where and among whom the disease propagates after the start of public mitigation measures. Full article

(This article belongs to the Special Issue Theories and Models on COVID-19 Epidemics)

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14 pages, 3967 KiB

Open AccessArticle

De-Escalation by Reversing the Escalation with a Stronger Synergistic Package of Contact Tracing, Quarantine, Isolation and Personal Protection: Feasibility of Preventing a COVID-19 Rebound in Ontario, Canada, as a Case Study

by Biao Tang, Francesca Scarabel, Nicola Luigi Bragazzi, Zachary McCarthy, Michael Glazer, Yanyu Xiao, Jane M. Heffernan, Ali Asgary, Nicholas Hume Ogden and Jianhong Wu

Biology 2020, 9(5), 100; https://doi.org/10.3390/biology9050100 - 16 May 2020

Cited by 33 | Viewed by 12015

Abstract

Since the beginning of the COVID-19 pandemic, most Canadian provinces have gone through four distinct phases of social distancing and enhanced testing. A transmission dynamics model fitted to the cumulative case time series data permits us to estimate the effectiveness of interventions implemented [...] Read more.

Since the beginning of the COVID-19 pandemic, most Canadian provinces have gone through four distinct phases of social distancing and enhanced testing. A transmission dynamics model fitted to the cumulative case time series data permits us to estimate the effectiveness of interventions implemented in terms of the contact rate, probability of transmission per contact, proportion of isolated contacts, and detection rate. This allows us to calculate the control reproduction number during different phases (which gradually decreased to less than one). From this, we derive the necessary conditions in terms of enhanced social distancing, personal protection, contact tracing, quarantine/isolation strength at each escalation phase for the disease control to avoid a rebound. From this, we quantify the conditions needed to prevent epidemic rebound during de-escalation by simply reversing the escalation process. Full article

(This article belongs to the Special Issue Theories and Models on COVID-19 Epidemics)

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10 pages, 783 KiB

Open AccessArticle

Using Early Data to Estimate the Actual Infection Fatality Ratio from COVID-19 in France

by Lionel Roques, Etienne K Klein, Julien Papaïx, Antoine Sar and Samuel Soubeyrand

Biology 2020, 9(5), 97; https://doi.org/10.3390/biology9050097 - 8 May 2020

Cited by 60 | Viewed by 17833

Abstract

The number of screening tests carried out in France and the methodology used to target the patients tested do not allow for a direct computation of the actual number of cases and the infection fatality ratio (IFR). The main objective of this work [...] Read more.

The number of screening tests carried out in France and the methodology used to target the patients tested do not allow for a direct computation of the actual number of cases and the infection fatality ratio (IFR). The main objective of this work is to estimate the actual number of people infected with COVID-19 and to deduce the IFR during the observation window in France. We develop a ‘mechanistic-statistical’ approach coupling a SIR epidemiological model describing the unobserved epidemiological dynamics, a probabilistic model describing the data acquisition process and a statistical inference method. The actual number of infected cases in France is probably higher than the observations: we find here a factor ×8 (95%-CI: 5–12) which leads to an IFR in France of 0.5% (95%-CI: 0.3–0.8) based on hospital death counting data. Adjusting for the number of deaths in nursing homes, we obtain an IFR of 0.8% (95%-CI: 0.45–1.25). This IFR is consistent with previous findings in China (0.66%) and in the UK (0.9%) and lower than the value previously computed on the Diamond Princess cruse ship data (1.3%). Full article

(This article belongs to the Special Issue Theories and Models on COVID-19 Epidemics)

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10 pages, 1545 KiB

Open AccessArticle

Temperature Decreases Spread Parameters of the New Covid-19 Case Dynamics

by Jacques Demongeot, Yannis Flet-Berliac and Hervé Seligmann

Biology 2020, 9(5), 94; https://doi.org/10.3390/biology9050094 - 3 May 2020

Cited by 104 | Viewed by 10993

Abstract

(1) Background: The virulence of coronavirus diseases due to viruses like SARS-CoV or MERS-CoV decreases in humid and hot weather. The putative temperature dependence of infectivity by the new coronavirus SARS-CoV-2 or covid-19 has a high predictive medical interest. (2) Methods: External temperature [...] Read more.

(1) Background: The virulence of coronavirus diseases due to viruses like SARS-CoV or MERS-CoV decreases in humid and hot weather. The putative temperature dependence of infectivity by the new coronavirus SARS-CoV-2 or covid-19 has a high predictive medical interest. (2) Methods: External temperature and new covid-19 cases in 21 countries and in the French administrative regions were collected from public data. Associations between epidemiological parameters of the new case dynamics and temperature were examined using an ARIMA model. (3) Results: We show that, in the first stages of the epidemic, the velocity of contagion decreases with country- or region-wise temperature. (4) Conclusions: Results indicate that high temperatures diminish initial contagion rates, but seasonal temperature effects at later stages of the epidemy remain questionable. Confinement policies and other eviction rules should account for climatological heterogeneities, in order to adapt the public health decisions to possible geographic or seasonal gradients. Full article

(This article belongs to the Special Issue Theories and Models on COVID-19 Epidemics)

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Review

Jump to: Research, Other

13 pages, 734 KiB

Open AccessReview

COVID-19 in Light of Seasonal Respiratory Infections

by Irina Kiseleva, Elena Grigorieva, Natalie Larionova, Mohammad Al Farroukh and Larisa Rudenko

Biology 2020, 9(9), 240; https://doi.org/10.3390/biology9090240 - 20 Aug 2020

Cited by 10 | Viewed by 4811

Abstract

A wide diversity of zoonotic viruses that are capable of overcoming host range barriers facilitate the emergence of new potentially pandemic viruses in the human population. When faced with a new virus that is rapidly emerging in the human population, we have a [...] Read more.

A wide diversity of zoonotic viruses that are capable of overcoming host range barriers facilitate the emergence of new potentially pandemic viruses in the human population. When faced with a new virus that is rapidly emerging in the human population, we have a limited knowledge base to work with. The pandemic invasion of the new SARS-CoV-2 virus in 2019 provided a unique possibility to quickly learn more about the pathogenesis of respiratory viruses. In this review, the impact of pandemics on the circulation of seasonal respiratory viruses is considered. The emergence of novel respiratory viruses has often been accompanied by the disappearance of existing circulating strains. Some issues arising from the spread of pandemic viruses and underlying the choices of a strategy to fight the coronavirus infection are discussed. Full article

(This article belongs to the Special Issue Theories and Models on COVID-19 Epidemics)

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8 pages, 1591 KiB

Open AccessConcept Paper

Immune Responses to SARS-CoV2 Mirror Societal Responses to COVID-19: Identifying Factors Underlying a Successful Viral Response

by Shahar Lev-Ari, Benjamin Rolnik and Ilan Volovitz

Biology 2021, 10(6), 485; https://doi.org/10.3390/biology10060485 - 29 May 2021

Viewed by 3046

Abstract

The adaptive immune system was sculpted to protect individuals, societies, and species since its inception, developing effective strategies to cope with emerging pathogens. Here, we show that similar successful or failed dynamics govern personal and societal responses to a pathogen as SARS-CoV2. Understanding [...] Read more.

The adaptive immune system was sculpted to protect individuals, societies, and species since its inception, developing effective strategies to cope with emerging pathogens. Here, we show that similar successful or failed dynamics govern personal and societal responses to a pathogen as SARS-CoV2. Understanding the self-similarity between the health-protective measures taken to protect the individual or the society, help identify critical factors underlying the effectiveness of societal response to a pathogenic challenge. These include (1) the quick employment of adaptive-like, pathogen-specific strategies to cope with the threat including the development of “memory-like responses”; (2) enabling productive coaction and interaction within the society by employing effective decision-making processes; and (3) the quick inhibition of positive feedback loops generated by hazardous or false information. Learning from adaptive anti-pathogen immune responses, policymakers and scientists could reduce the direct damages associated with COVID-19 and avert an avoidable “social cytokine storm” with its ensuing socioeconomic damage. Full article

(This article belongs to the Special Issue Theories and Models on COVID-19 Epidemics)

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