COVID-19 spreading in Rio de Janeiro, Brazil: Do the policies of social isolation really work?

The recent Coronavirus (COVID-19) has been spreading through all the world fastly. In this work we focus on the evolution of the COVID-19 in one of the most populous Brazilian states, namely the Rio de Janeiro state. The first case was reported in March 5, 2020, thus we have a considerable amount of available data to make a good analysis. First we study the early evolution of the disease, considering a Susceptible-Infectious-Quarantined-Recovered (SIQR) model. This initial phase shows the usual exponential growth of the number of confirmed cases. In this case, we estimate the parameters of the model based on the data, as well as the epidemic doubling time. After, we analyze all the available data, from March 5, 2020 through April 26, 2020. In this case, we observe a distinct behavior: a sub-exponential growth. In order to capture this change in the behavior of the evolution of the confirmed cases, we consider the implementation of isolation policies. The modified model agrees well with data. Finally, we consider the relaxation of such policies, and discuss about the ideal period of time to release people to return to their activities.

[1]  J. Radcliffe,et al.  The Mathematical Theory of Infectious Diseases and its Applications , 1977 .

[2]  H. Hethcote,et al.  Effects of quarantine in six endemic models for infectious diseases. , 2002, Mathematical biosciences.

[3]  C. Viboud,et al.  Doubling Time of the COVID-19 Epidemic by Chinese Province , 2020, medRxiv.

[4]  Daniel O. Cajueiro,et al.  Modeling and forecasting the early evolution of the Covid-19 pandemic in Brazil , 2020, Scientific reports.

[5]  Christian Drosten,et al.  Severe acute respiratory syndrome-related coronavirus: The species and its viruses – a statement of the Coronavirus Study Group , 2020, bioRxiv.

[6]  G. Zehender,et al.  Early phylogenetic estimate of the effective reproduction number of SARS‐CoV‐2 , 2020, Journal of medical virology.

[7]  Kexin Yang,et al.  Preliminary prediction of the basic reproduction number of the Wuhan novel coronavirus 2019‐nCoV , 2020, Journal of evidence-based medicine.

[8]  J. Xiang,et al.  Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study , 2020, The Lancet.

[9]  Andr'e E. Botha,et al.  A simple iterative map forecast of the COVID-19 pandemic , 2020 .

[10]  N. Crokidakis.  Data analysis and modeling of the evolution of COVID-19 in Brazil , 2020, 2003.12150.

[11]  Nuno R. Faria,et al.  The effect of human mobility and control measures on the COVID-19 epidemic in China , 2020, Science.

[12]  G. L. Vasconcelos,et al.  Modelling fatality curves of COVID-19 and the effectiveness of intervention strategies , 2020, medRxiv.

[13]  R. May,et al.  Infectious Diseases of Humans: Dynamics and Control , 1991, Annals of Internal Medicine.

[14]  Luca Zanotto,et al.  Proximity: a recipe to break the outbreak , 2020, ArXiv.

[15]  C. Whittaker,et al.  Report 9: Impact of non-pharmaceutical interventions (NPIs) to reduce COVID19 mortality and healthcare demand , 2020 .

[16]  N. Ling.  The Mathematical Theory of Infectious Diseases and its applications , 1978 .

[17]  S. Soubeyrand,et al.  Mechanistic-statistical SIR modelling for early estimation of the actual number of cases and mortality rate from COVID-19 , 2020 .

[18]  T. Parisini,et al.  On Fast Multi-Shot Epidemic Interventions for Post Lock-Down Mitigation: Implications for Simple Covid-19 Models , 2020, ArXiv.

[19]  Mikhail Prokopenko,et al.  Modelling transmission and control of the COVID-19 pandemic in Australia , 2020, Nature communications.

[20]  Ruiyun Li,et al.  Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus (SARS-CoV-2) , 2020, Science.

[21]  D. Brockmann,et al.  Effective containment explains subexponential growth in recent confirmed COVID-19 cases in China , 2020, Science.

[22]  J. Rocklöv,et al.  The reproductive number of COVID-19 is higher compared to SARS coronavirus , 2020, Journal of travel medicine.

[23]  Changchuan Yin.  Genotyping coronavirus SARS-CoV-2: methods and implications , 2020, Genomics.

[24]  L. Yang,et al.  Preliminary estimation of the basic reproduction number of novel coronavirus (2019-nCoV) in China, from 2019 to 2020: A data-driven analysis in the early phase of the outbreak , 2020, bioRxiv.

[25]  Jamil Civitarese.  Social Distancing under Epistemic Distress , 2020, SSRN Electronic Journal.

[26]  P. Sen,et al.  Covid-19 spread: Reproduction of data and prediction using a SIR model on Euclidean network. , 2020, 2003.07063.

[27]  Logistic approximations used to describe new outbreaks in the 2020 COVID-19 pandemic , 2020, 2003.11149.

[28]  A. Rădulescu,et al.  Management strategies in a SEIR-type model of COVID 19 community spread , 2020, Scientific Reports.

[29]  Pankaj Mehta,et al.  Data-driven modeling reveals a universal dynamic underlying the COVID-19 pandemic under social distancing , 2020, medRxiv.

[30]  E. Avila-Vales,et al.  A data driven analysis and forecast of an SEIARD epidemic model for COVID-19 in Mexico , 2020, Big Data and Information Analytics.

[31]  L. Yang,et al.  Preliminary estimation of the basic reproduction number of novel coronavirus (2019-nCoV) in China, from 2019 to 2020: A data-driven analysis in the early phase of the outbreak , 2020, International Journal of Infectious Diseases.