Modeling transmission dynamics of severe acute respiratory syndrome coronavirus 2 in São Paulo, Brazil

• Autor(a) principal: Cruz,Pedro Alexandre da
• Data de Publicação: 2021
• Outros Autores: Crema-Cruz,Leandra Cristina, Campos,Fabrício Souza
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Revista da Sociedade Brasileira de Medicina Tropical
• Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0037-86822021000100304
• topic: SARS-CoV-2; COVID-19; SEIR model transmission dynamics; Mathematical modelling

Abstract INTRODUCTION Severe acute respiratory syndrome coronavirus 2 has been transmitted to more than 200 countries, with 92.5 million cases and 1,981,678 deaths. METHODS This study applied a mathematical model to estimate the increase in the number of cases in São Paulo state, Brazil during four epidemic periods and the subsequent 300 days. We used different types of dynamic transmission models to measure the effects of social distancing interventions, based on local contact patterns. Specifically, we used a model that incorporated multiple transmission pathways and an environmental class that represented the pathogen concentration in the environmental reservoir and also considered the time that an individual may sustain a latent infection before becoming actively infectious. Thus, this model allowed us to show how the individual quarantine and active monitoring of contacts can influence the model parameters and change the rate of exposure of susceptible individuals to those who are infected. RESULTS The estimated basic reproductive number, R o , was 3.59 (95% confidence interval [CI]: 3.48 - 3.72). The mathematical model data prediction coincided with the real data mainly when the social distancing measures were respected. However, a lack of social distancing measures caused a significant increase in the number of infected individuals. Thus, if social distancing measures are not respected, we estimated a difference of at least 100,000 cases over the next 300 days. CONCLUSIONS: Although the predictive capacity of this model was limited by the accuracy of the available data, our results showed that social distancing is currently the best non-pharmacological measure.