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Publications Scientifiques

[ Livre ] Modeling and predicting the spread of COVID-19: a continental analysis.

Date de soumission: 21-02-2022
Année de Publication: 2020
Entité/Laboratoire Laboratoire de Biomathématiques et d'Estimations Forestières (LABEF)
Document type : Livre
Discipline(s) : Epidémiologie
Titre Modeling and predicting the spread of COVID-19: a continental analysis.
Auteurs Ojokoh B.A. [1], Sarumi O.A. [2], SALAKO Kolawolé Valère [3], Gabriel A.J. [4], Taiwo A.E. [5], Johnson O.V. [6], Adegun I.P. [7], Babalola O.T. [8],
Editeur: Kose U., Gupta D., de Albuquerque V.H.C., Khanna A.
ISBN: 978-0-323-90769-9
Resume The world is currently overwhelmed with the perils of the outbreak of the coronavirus disease 2019 (COVID-19) pandemic. As of May 18, 2020, there were 4,819,102 confirmed cases, of which there were 316,959 deaths worldwide. The devastating effects of the COVID-19 pandemic on the world economy are more grievous than many natural disasters like earthquakes and tsunamis in history. Understanding the spread pattern of COVID-19 and predicting the disease dynamics have been essential to assist policymakers and health practitioners in the public and private health sector in providing an efficient way of alleviating the effects of the pandemic across continents. Scholars have steadily worked to provide timely information. Nevertheless, there is a lack of information on which insights can be derived from all these endeavors, especially with regard to modeling and prediction techniques. In this study, we used a literature synthesis approach to provide a narrative review of the current research efforts geared toward predicting the spread of COVID-19 across continents. Such information is useful to provide a global perspective of the virus particularly with regard to modeling and prediction techniques and their outcomes. A total of 69 peer-reviewed articles were reviewed. We found that most articles were from Asia (34.8%) and Europe (23.2%), followed by North America (14.5%), and very few emanated from other continents including Africa and Australia (6.8% each), while no study was reported in Antarctica. Most of the modeling and predictions were based on compartmental epidemiologic models and a few used advanced machine learning techniques. While some models have accurately predicted the end of the epidemic in some countries, other predictions strongly deviate from reality. Interestingly, some studies showed that combining artificial intelligence with classical compartmental models provides a better prediction of the disease spread. Assumptions made when parameterizing the models might be wrong and might not suit the local contexts and might partly explain the observed deviation from the reality on the ground. Furthermore, lack of publicly available key data such as age, gender, comorbidity, and historical medical data of cases and deaths in some continents could limit researchers in addressing some essential aspects of the virus spread and its consequences.

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