Lockdown as an Intervention Measure to Mitigate the Spread of COVID-19: a modeling study.

Aédson Nascimento Góis,Eduesley Santana-Santos,David Da Silva Santos,Daniel Eduardo Sánchez,Estevão Esmi Laureano,Jussiely Cunha Oliveira,Luiz Fernando Souza,Rita De Cássia Almeida Vieira

doi:10.1590/0037-8682-0417-2020

Abstract

INTRODUCTION: This work aims to develop a biomathematical transmission model of COVID-19, in the State of Sergipe, Brazil, to estimate the distribution of cases over time and project the impact on the spread of the epidemic outbreak due to interventions and control measures over the local population. METHODS: This is an epidemiological mathematical modeling study conducted to analyze the dynamics of the accumulated cases of COVID-19, which used a logistic growth model that adds a term of withdrawal of individuals as a control measure. Three possible COVID-19 propagation scenarios were simulated based on three different rates of withdrawal of individuals. They were adjusted with real data of the infected and measures of control over the population. RESULTS: The lockdown would be the best scenario, with a lower incidence of infected people, when compared to the other measures. The number of infected people would grow slowly over the months, and the number of symptomatic individuals in this scenario would be 40,265 cases. We noticed that the State of Sergipe is still in the initial stage of the disease in the scenarios. It was possible to observe that the peak of cases and the equilibrium, in the current situation of social isolation, will occur when reaching the new support capacity, at the end of August in approximately 1,171,353 infected individuals. CONCLUSIONS: We established that lockdown is the intervention with the highest ability to mitigate the spread of the virus among the population.

Highlights

Introduction: This work aims to develop a biomathematical transmission model of COVID-19, in the State of Sergipe, Brazil, to estimate the distribution of cases over time and project the impact on the spread of the epidemic outbreak due to interventions and control measures over the local population. This is an epidemiological mathematical modeling study conducted to analyze the dynamics of the accumulated cases of COVID-19, which used a logistic growth model that adds a term of withdrawal of individuals as a control measure
The following Initial Value Problem (IVP) satisfactorily models our problem: In this equation I(t) represents the number of individuals infected by the coronavirus at time t, given in days, r refers to the rate of contagion with free mobility between people, K is the carrying capacity of the environment and p; the percentage of individuals removed from social life via isolation
It is possible to observe the evolution of infection day by day in four different scenarios: (1) the red curve represents the isolation-free scenario; (2) the yellow curve refers to the growth of the infection based on the measure of social isolation, instituted in Sergipe; (3) the green curve estimates the situation in the State if a lockdown was decreed and; (4) the blue curve represents the real cases

Summary

Introduction

This work aims to develop a biomathematical transmission model of COVID-19, in the State of Sergipe, Brazil, to estimate the distribution of cases over time and project the impact on the spread of the epidemic outbreak due to interventions and control measures over the local population. Three possible COVID-19 propagation scenarios were simulated based on three different rates of withdrawal of individuals. They were adjusted with real data of the infected and measures of control over the population. On February 11, 2020, the World Health Organization (WHO) recognized the virus as SARS-CoV-2, responsible for Until this moment, it is known that the primary means of transmission are droplets and aerosols (droplet nuclei) from the air and respiratory tracts and can be transmitted by indirect contact through the surfaces or objects on which the infected come into contact[4]. The worst prognosis is directly related to the presence of comorbidities, such as advanced age, immunosuppression, diabetes mellitus, respiratory and cardiac diseases[8]

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