Assessing the nationwide impact of COVID-19 mitigation policies on the transmission rate of SARS-CoV-2 in Brazil

Abstract

COVID-19 is now identified in almost all countries in the world, with poorer regions being particularly more disadvantaged to efficiently mitigate the impacts of the pandemic. In the absence of efficient therapeutics or vaccines, control strategies are currently based on non-pharmaceutical interventions, comprising changes in population behavior and governmental interventions, among which the prohibition of mass gatherings, closure of non-essential establishments, quarantine and movement restrictions. In this work we analyzed the effects of 547 published governmental interventions, and population adherence thereof, on the dynamics of COVID-19 cases across all 27 Brazilian states, with emphasis on state capitals and remaining inland cities. A generalized SEIR model with a time-varying transmission rate (TR), that considers transmission by asymptomatic individuals, is presented. Confirmed COVID-19 cases were used to calibrate the model parameters using non-linear least squares methods. We analyze the changes on the TR and effective reproduction number as a function of both the extent of enforced measures across Brazilian states as well as population movement. The social mobility reduction index, a measure of population movement, together with the stringency index, adapted to incorporate the degree of restrictions imposed by governmental regulations, were used in conjunction to quantify and compare the effects of varying degrees of policy strictness across Brazilian states. Our results show that population adherence to social distance recommendations plays an important role for the effectiveness of interventions, and represents a major challenge to the control of COVID-19 in low- and middle-income countries.

COVID-19 no Nordeste brasileiro: sucessos e limitações nas respostas dos governos dos estados

“…The effective reproduction number over time (Rt), the transmission parameter Beta and the observed and predicted curves for the number of cases were calculated using a compartmental model which divides the population among susceptible, exposed (individuals in incubation), asymptomatic infected, symptomatic infected and recovered 24,25 . The daily Rt value is defined as the average number of secondary cases infected by a case with onset of symptoms on day "t", which in the present methodology also takes into account the secondary cases caused by asymptomatic infected individuals.…”

“…Rather, it may be related to weaknesses in the epidemiological surveillance system that did not have access to the first cases of COVID-19 that occurred in Fortaleza and in other parts of the State. It seems that the information about wealthy patients, entering the private health care system, wasn't promptly reported to the surveillance system 24 .…”

População, ambiente e a covid-19

“…Em segundo lugar, constata-se que a diminuição da adesão ao distanciamento físico e o relaxamento em relação aos cuidados preventivos, principalmente considerando o longo tempo da quarentena, é um fator que também contribui para o aumento do número de casos de pessoas contaminadas (JORGE et al, 2020). Os pesquisadores da Rede CoVida -Ciência, Informação e Solidariedade utilizaram geomonitoramento de 60 milhões de smartphones para medir a adesão da população as políticas de distanciamento social em 27 Unidades da Federação do Brasil.…”

“…A pesquisa identificou que os estados que intensificaram as medidas de isolamento através de decretos tiveram melhor adesão da população -como aconteceu nos estados do Ceará, Amapá, Bahia, Pernambuco, Acre e Maranhão, que apresentaram adesão de 40 a 60% da população. Entretanto, quando o estado flexibilizou as medidas de isolamento, a adesão da população se tornou menor do que no começo das primeiras intervenções, como foi o caso de Santa Catarina, que permitiu a reabertura de shoppings (JORGE et al, 2020).…”

One Study of COVID-19 Spreading at The United States - Brazil - Colombia1

“…The objective of this article is to carry out a mathematical study of the possible trends of the epidemic by COVID-19 in these three countries. This article was based on the data provided by the WHO [4] Regarding the adjustment of the parameters of the SIRD model for The United States, Brazil and Colombia, it appears that these parameters change frequently, depending on local political factors (closure of non-essential establishments, quarantine, movement restrictions...) [9], socio-economic factors (social and hygienic behaviors, lower per-capita income...) [10] [11], climatic factors (temperature, humidity, average wind speed, UV index...) [12], among others. In this context, it was decided to adjust the parameters of the SIRD model over time, based on data made available by WHO, using non-linear least squares methods [13] [14].…”