Dataset Information

Mathematical model of COVID-19 intervention scenarios for Sao Paulo-Brazil.

ABSTRACT: 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.

SUBMITTER: Pinto Neto O

PROVIDER: S-EPMC7814036 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Mathematical model of COVID-19 intervention scenarios for São Paulo-Brazil.

Pinto Neto Osmar O Kennedy Deanna M DM Reis José Clark JC Wang Yiyu Y Brizzi Ana Carolina Brisola ACB Zambrano Gustavo José GJ de Souza Joabe Marcos JM Pedroso Wellington W de Mello Pedreiro Rodrigo Cunha RC de Matos Brizzi Bruno B Abinader Ellysson Oliveira EO Zângaro Renato Amaro RA

Nature communications 20210118 1

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 ...[more]

PMID: 33462211

Similar Datasets

Project description:The presence of a large number of infected individuals with few or no symptoms is an important epidemiological difficulty and the main mathematical feature of COVID-19. The A-SIR model, i.e. a SIR (Susceptible-Infected-Removed) model with a compartment for infected individuals with no symptoms or few symptoms was proposed by Giuseppe Gaeta, Mathematics in Engineering, 3(2): 1–39 (2020). In this paper we investigate a slightly generalized version of the same model and propose a scheme for fitting the parameters of the model to real data using the time series only of the deceased individuals. The scheme is applied to the concrete cases of Lombardy, Italy and São Paulo state, Brazil, showing different aspects of the epidemics. In both cases we see strong evidence that the adoption of social distancing measures contributed to a slower increase in the number of deceased individuals when compared to the baseline of no reduction in the infection rate. Both for Lombardy and São Paulo we show that we may have good fits to the data up to the present, but with very large differences in the future behavior. The reasons behind such disparate outcomes are the uncertainty on the value of a key parameter, the probability that an infected individual is fully symptomatic, and on the intensity of the social distancing measures adopted. This conclusion enforces the necessity of trying to determine the real number of infected individuals in a population, symptomatic or asymptomatic. Highlights • COVID-19 epidemic previsions must take into account a large class of asymptomatic cases.• Disparate outcomes in previsions are consequence of the uncertainty in the number of asymptomatic individuals.• Necessity of determining the real number of infected individuals, symptomatic or asymptomatic.• Parameters of the model fitted to real data using only time series of deceased individuals.• Social distancing measures contributed to slower increase in the number of deceased individuals.

Dataset Information

Mathematical model of COVID-19 intervention scenarios for Sao Paulo-Brazil.

Publications

Mathematical model of COVID-19 intervention scenarios for São Paulo-Brazil.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets