With COVID-19 surging across the world, understanding the effectiveness of intervention strategies on transmission dynamics is of primary global health importance. Here, we develop and analyze an epidemiological compartmental model using multi-objective genetic algorithm design optimization to compare scenarios related to strategy type, the extent of social distancing, time window, and personal protection levels on the transmission dynamics of COVID-19 in São Paulo, Brazil. The results indicate that the optimal strategy for São Paulo is to reduce social distancing over time with a stepping-down reduction in the magnitude of social distancing every 80-days. Our results also indicate that the ability to reduce social distancing depends on a 5–10% increase in the current percentage of people strictly following protective guidelines, highlighting the importance of protective behavior in controlling the pandemic. Our framework can be extended to model transmission dynamics for other countries, regions, states, cities, and organizations.

An epidemiological compartmental model was used to simulate social distancing strategies to contain the COVID-19 pandemic and prevent a second wave in São Paulo, Brazil. Optimization using genetic algorithm was used to determine the optimal solutions. Our results suggest the best-case strategy for São Paulo is to maintain or increase the current magnitude of social distancing for at least 60 more days and increase the current levels of personal protection behaviors by a minimum of 10% (e.g., wearing facemasks, proper hand hygiene and avoid agglomeration). Followed by a long-term oscillatory level of social distancing with a stepping-down approach every 80 days over a period of two years with continued protective behavior.

In December 2019, China reported a series of atypical pneumonia cases caused by a new Coronavirus, called COVID-19. In response to the rapid global dissemination of the virus, on the 11th of Mars, the World Health Organization (WHO) has declared the outbreak a pandemic. In light of this situation, this paper intends to analyze and improve the current SEIR models to better represent the behavior of the COVID-19 and accurately predict the outcome of the pandemic in a given social, economic and political scenario. We present a novel generalized Susceptible-Exposed-Infected-Recovered (SEIR) compartmental model and test it using a global optimization algorithm with data collected from the WHO. Our main results were: (a) our model was able to accurately fit the data of all countries tested (b) it is possible to predict values for one week ahead with errors in the order of 15% for the number of cases and 30% in the number of deaths for all countries; (c) predictions are better for countries where the active cases curve already reached the maximum; the error being in the order of 10% in the number of cases and 20% in the number of deaths; (d) for countries where the active curve is still growing, different optimization solutions can be found that fit the data; so, to predict future behavior in this scenarios some of the model coefficients should be estimated from outside sources or based on generalized results from other countries according to their health policies of social distance, quarantining and case test and tracing.

Introduction In December 2019, China reported a series of atypical pneumonia cases caused by a new Coronavirus, called COVID-19. In response to the rapid global dissemination of the virus, on the 11th of Mars, the World Health Organization (WHO) has declared the outbreak a pandemic. Considering this situation, this paper intends to analyze and improve the current SEIR models to better represent the behavior of the COVID-19 and accurately predict the outcome of the pandemic in each social, economic, and political scenario. Methodology We present a generalized Susceptible-Exposed-Infected-Recovered (SEIR) compartmental model and test it using a global optimization algorithm with data collected from the WHO. Results The main results were: (a) Our model was able to accurately fit the either deaths or active cases data of all tested countries using optimized coefficient values in agreement with recent reports; (b) when trying to fit both sets of data at the same time, fit was good for most countries, but not all. (c) Using our model, large ranges for each input, and optimization we predict death values for 15, 30, 45, and 60 days ahead with errors in the order of 5, 10, 20, and 80%, respectively; (d) sudden changes in the number of active cases cannot be predicted by the model unless data from outside sources are used. Conclusion The results suggest that the presented model may be used to predict 15 days ahead values of total deaths with errors in the order of 5%. These errors may be minimized if social distance data are inputted into the model.

Introdução: Em outubro de 2015 a Organização Mundial de Saúde (OMS) declarou situação de emergência internacional em função do aumento da incidência de microcefalia em zonas endêmicas com a proliferação do Zika vírus. No Brasil foi observado um aumento inesperado do número de casos de nascidos vivos com microcefalia primaria, sendo então a hipótese da existência de uma associação causal da infecção congênita pelo vírus Zika com a microcefalia (1,2) exposta inicialmente no país. Objetivo: Analisar o perfil do cenário epidemiológico da Microcefalia e Zika Vírus do Brasil nos anos de 2015 a 2021. Métodos: Trata-se de um estudo epidemiológico, dedutivo, de natureza básica, descritivo, e com abordagem quantitativa com dados obtidos a partir de dados de recém-nascidos vivos extraídos do SINASC, do Sistema de Vigilância Alimentar e Nutricional (SISVAN Web) e ainda dos Boletins Epidemiológicos do Ministério da Saúde entre os anos 2015 à 2021. Resultados e Discussão: Analisando os dados epidemiológicos coletados no SINASC, pré período da epidemia de Zika vírus e pós, evidencia-se um aumento na prevalência média do Brasil de recém nascidos com microcefalia, com a prevalência registrada até o ano de 2014 de 5,4 /100 mil nascidos vivos, e no ano de 2015 e 2016, no qual o país vivenciou a epidemia, a prevalência foi de 12,8 e 79,6 /100 mil nascidos vivos. A região Nordeste, no ano de 2015, concentrou 76% dos casos de microcefalia, sendo possível evidenciar que nesse ano 71% das mães residiam no Nordeste, 70% tinham até 24 anos de idade e das mães que tinham menos que oito anos de estudo, 78% residiam na região Nordeste, o que elucida a existência de condições ambientais e sociais propicias para a propagação do Zika vírus e a infecção de gestantes na região Nordeste. Conclusão: Os dados epidemiológicos, assim como a gravidade das consequências trazidas pela epidemia do Zika vírus, como a Síndrome Congênita do Zika vírus, são alertas para a importância da tomada de ações coordenadas entre vigilância e atenção à saúde com intuito de fortalecer a prevenção de novos casos assim como a promoção da saúde.