Discrete COVID-19 Transmission Models and Preliminary Publications in Science: A Systematic review

• Autor(a) principal: Catano-Lopez, Alexandra
• Data de Publicação: 2020
• Outros Autores: Rojas-Diaz, Daniel
• Tipo de documento: preprint
• Idioma: spa
• Título da fonte: SciELO Preprints
• Texto Completo: https://preprints.scielo.org/index.php/scielo/preprint/view/1076
• topic: Discrete models; control; forecast; Covid-19; modelos discretos; control; pronóstico

In the Chinese city of Wuhan at the end of 2019, a new respiratory disease known as COVID-19 emerged, caused by the SARS-CoV-2 virus. This disease spreads rapidly worldwide and presents numerous infections and deaths; therefore, the World Health Organization upgraded its category from epidemic to pandemic because of alarming levels of spread, severity, and inaction. Given this situation, different areas of science have approached the study of this disease, among them is mathematical epidemiology through the modeling of the phenomenon; therefore, in this document, we performed a systematic review related to transmission models of COVID-19, specifically discrete models because of the daily report of infection cases around the world. We identified different important disease features implemented in the models, e.g., metapopulations, migration, quarantine, inclusion of latency, and incubation periods, among others. Also, we identified its basic structure, and we found that many papers directly used SIR and SEIR models with no modification, being an excessive simplification of the COVID-19 transmission phenomenon. Likewise, some authors highlighted an important problem during the application of mathematical models: the quality or absence of the daily case data in some affected countries. Finally, the mathematical models should be constantly updated together with the publication of research related to virology and epidemiology of the disease.