Modeling as an approach to pandemic uncertainty management: Mortality assessment of the COVID-19 pandemic (original) (raw)
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Scientific Reports, 2021
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Karaelmas Fen ve Mühendislik Dergisi, 2021
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Relevence and Effectiveness of Mathematical Models Dealing with Covid-19 Pandemic
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The COVID-19 pandemic: model-based evaluation of non-pharmaceutical interventions and prognoses
Nonlinear Dynamics
An epidemiological model for COVID-19 was developed and implemented in MATLAB/GNU Octave for use by public health practitioners, policy makers, and the general public. The model distinguishes four stages in the disease: infected, sick, seriously sick, and better. The model was preliminarily parameterized based on observations of the spread of the disease. The model assumes a case mortality rate of 1.5%. Preliminary simulations with the model indicate that concepts such as ''herd immunity'' and containment (''flattening the curve'') are highly misleading in the context of this virus. Public policies based on these concepts are inadequate to protect the population. Only reducing the R 0 of the virus below 1 is an effective strategy for maintaining the death burden of COVID-19 within the normal range of seasonal flu. The model is illustrated with the cases of Italy, France, and Iran and is able to describe the number of deaths as a function of time in all these cases although future projections tend to slightly overestimate the number of deaths when the analysis is made early on. The model can also be used to describe reopenings of the economy after a lockdown. The case mortality rate is still prone to large uncertainty, but modeling combined with an investigation of blood donations in The Netherlands imposes a lower limit of 1%. Keywords SARS-CoV-2 Á Herd immunity Á Social distancing Á R 0 Á Doubling time Á Case mortality rate 1 Introduction The coronavirus disease 2019 (COVID-19) is a respiratory disease caused by SARS-CoV-2 (Severe Acute Respiratory Syndrome coronavirus 2). Typical symptoms are fever, cough, chills, difficulty breathing, and fatigue [1]. The pathology of COVID-19 is similar to that of SARS and Middle Eastern Respiratory Syndrome (MERS). Pulmonary edema and pneumonia, and cytokine storm are common complications [1, 2]. The disease also causes chronic cardiovascular damage [3]. The main comorbidities in hospitalized COVID-19 patients are hypertension (30%), diabetes (19%), and coronary heart disease (8%) [4]. The mortality of COVID-19 has been estimated at 2% [1], 2.2% [5], and 3.7% [2]. The case mortality rate is strongly age-dependent and ranges from 0.2% up to 39 years of age, to nearly 15% at age 80 years and Electronic supplementary material The online version of this article (
2020
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) virus has rapidly spread worldwide since December 2019, and early modelling work of this pandemic has assisted in identifying effective government interventions. The UK government relied in part on the CovidSim model developed by the MRC Centre for Global Infectious Disease Analysis at Imperial College London, to model various non-pharmaceutical intervention strategies, and guide its government policy in seeking to deal with the rapid spread of the COVID-19 pandemic during March and April 2020. CovidSim is subject to different sources of uncertainty, namely parametric uncertainty in the inputs, model structure uncertainty (i.e. missing epidemiological processes) and scenario uncertainty, which relates to uncertainty in the set of conditions under which the model is applied. We have undertaken an extensive parametric sensitivity analysis and uncertainty quantification of the current CovidSim code. From the over 900 param...
Uncertainty Quantification in Epidemiological Models for COVID-19 Pandemic
2020
The main goal of this paper is to develop the forward and inverse modeling of the Coronavirus (COVID-19) pandemic using novel computational methodologies in order to accurately estimate and predict the pandemic. This leads to governmental decisions support in implementing effective protective measures and prevention of new outbreaks. To this end, we use the logistic equation and the SIR system of ordinary differential equations to model the spread of the COVID-19 pandemic. For the inverse modeling, we propose Bayesian inversion techniques, which are robust and reliable approaches, in order to estimate the unknown parameters of the epidemiological models. We use an adaptive Markov-chain Monte-Carlo (MCMC) algorithm for the estimation of a posteriori probability distribution and confidence intervals for the unknown model parameters as well as for the reproduction number. Furthermore, we present a fatality analysis for COVID-19 in Austria, which is also of importance for governmental p...
Mathematical Models for COVID-19 Pandemic: A Comparative Analysis
Journal of the Indian Institute of Science, 2020
Introduction The ongoing COVID-19 pandemic is the most significant pandemic since the 1918 Influenza pandemic. It has already caused over 21 Million confirmed cases and 758,000 deaths. 1 The economic impact is already in trillions of dollars. As in other pandemics, researchers and public health policy makers are interested in questions such as, 2 (i) How did it start? (ii) How is it likely to progress and how can we control it? (iii) How can we intervene while balancing public health and economic impact? (iv) Why did some countries do better than other countries thus far into the pandemic? In particular, models and their projections/forecasts have received unprecedented attention. With a multitude of modeling frameworks, underlying assumptions, available datasets and the region/timeframe being modeled, these projections have varied widely, causing confusion among end-users and consumers. We believe an overview (non-exhaustive) of the current modeling landscape will benefit the readers and also serve as a historical record for future efforts.
Epidemiology of Coronavirus Disease (COVID-19) andExactitude of Predictions through Models
EC Pulmonology and Respiratory Medicine, 2021
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Modelling insights into the COVID-19 pandemic
Paediatric Respiratory Reviews
Coronavirus disease 2019 (COVID-19) is a newly emerged infectious disease caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) that was declared a pandemic by the World Health Organization on 11th March, 2020. Response to this ongoing pandemic requires extensive collaboration across the scientific community in an attempt to contain its impact and limit further transmission. Mathematical modelling has been at the forefront of these response efforts by: (1) providing initial estimates of the SARS-CoV-2 reproduction rate, R 0 (of approximately 2-3); (2) updating these estimates following the implementation of various interventions (with significantly reduced, often sub-critical, transmission rates); (3) assessing the potential for global spread before significant case numbers had been reported internationally; and (4) quantifying the expected disease severity and burden of COVID-19, indicating that the likely true infection rate is often orders of magnitude greater than estimates based on confirmed case counts alone. In this review, we highlight the critical role played by mathematical modelling to understand COVID-19 thus far, the challenges posed by data availability and uncertainty, and the continuing utility of modelling-based approaches to guide decision making and inform the public health response. yUnless otherwise stated, all bracketed error margins correspond to the 95% credible interval (CrI) for reported estimates.