How limitations in data of health surveillance impact decision making in the COVID-19 epidemic

• Autor(a) principal: Villela, Daniel
• Data de Publicação: 2020
• Tipo de documento: preprint
• Idioma: por
• Título da fonte: SciELO Preprints
• Texto Completo: https://preprints.scielo.org/index.php/scielo/preprint/view/1313
• topic: COVID-19; Epidemia de 2019 nCov; Modelos matamáticos; Vigilância epidemiológica; COVID-19; 2019-nCov Epidemic; Mathematical Models; Health Surveillance

The pandemic of COVID-19 signaled an alert to all countries about controlling transmission of SARS-Cov-2 to have fewer infected individuals, making less stress to all health systems and saving lives.  As a result, multiple governments, including national and local levels of government, went through several degrees of social distancing measures.  The decision process about when to lift social distancing measures requires evidence of incidence decrease, available capacity in the health systems to absorb eventual epidemic waves, and serological prevalence studies designed to estimate the proportion of individuals with antibody protection. The trend criterium usually given by the effective reproduction number might be misguided if there are significant delays for reporting cases.  For instance, the reproduction number for Niteroi, in the state of Rio de Janeiro, went down from a value of approximately 3 to little more than 1.  Even with all measures, the reproduction number did not get below R0<1, which would demonstrate a more controlled scenario. Finally, a prediction method permits adjusting the notification delay and analyzing the current status of the epidemics.