key: cord-0922074-d7at4wp4
authors: Ochoa, Yaset Caicedo; Sanchez, David E Rebellón; Peñaloza, Marcela; Motta, Hector F Cortes; Méndez-Fandiño, Yardany R
title: Effective Reproductive Number estimation for initial stage of COVID-19 pandemic in Latin American Countries
date: 2020-04-30
journal: International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases
DOI: 10.1016/j.ijid.2020.04.069
sha: 70edf01179e7228e161e51fc5347d3c7862e03ca
doc_id: 922074
cord_uid: d7at4wp4

Abstract Objectives The coronavirus disease 2019 (COVID-19) has become pandemic and turn in a challenge for Latin America. Understanding the dynamics of the epidemic is essential for decision making, and to reduce the health, economic, and social impacts of the pandemic. The present study aimed to estimate the effective reproductive number (Rt) of Severe Acute Respiratory Syndrome coronavirus 2 (SARS-Cov2) infection during the first 10 days of the outbreak in seven Latin American countries with the highest incidence of cases as of March 23, 2020. Furthermore, we chose to compare the seven countries with Spain and Italy given their history with the virus. Methods Incidence data retrieved from the COVID-19 data repository by Johns Hopkins University were analyzed. The Rt was calculated for the first 10 days of the epidemic in Brazil, Ecuador, Chile, Colombia, Panama, Mexico, and Peru. Rt estimations were compared with Spain and Italy values for the same interval. Results The median Rt for the first 10 days of the COVID-19 epidemic were 2.90 (2.67-3.14) for Spain and 2.83 (2.7-2.96) for Italy. Latin American Rt estimations were higher in Ecuador (3.95(3.7-4.21)), Panama (3.95(3.7-4.21)), and Brazil (3.95(3.7-4.21)). The smallest one was observed in Peru (2.36(2.11-2.63)). All Latin American countries had Rt greater than 2. Conclusions The initial stages of the COVID-19 epidemic in Latin America suggested a high Rt. Interventions such as domestic and international travel restrictions, educational institutions closure, social distancing, and intensified case surveillance should be adopted to prevent the collapse of the health systems.

In December 2019, Severe Acute Respiratory Syndrome (SARS) with an unknown etiology was reported in Wuhan City, Hubei Province, China. A novel coronavirus disease 2019 (COVID-19) was rapidly identified and characterized (Zhu et al. 2020) . In a three-month lapse and despite intense containment measures, COVID-19 cases worldwide have continually increased. On March 11, 2020, the WHO declared the coronavirus a pandemic. At present, more than two million people have been infected worldwide, with the highest incidences appearing in Asia, Europe, and North America. Health systems around the world have collapsed, and mortality rates have reached overwhelming numbers in high income countries (World Health Organization 2020). J o u r n a l P r e -p r o o f 4 mitigate the effects of COVID-19 and make appropriate decisions related to the health, economic, and social system, it is crucial to understand the nature of the outbreak.

Modeling studies are useful in understanding epidemics and evaluating the potential impact of interventions in the early stages of pandemics. The Effective Reproductive number (Rt) is a parameter that can be used to follow-up the epidemics (de Silva et al. 2009; Nishiura et al. 2016; Koo et al. 2020) . R t is different from the basic reproductive number (R 0 ). The R 0 is the expected number of secondary cases from a primary case in a population where everyone is susceptible. During the course of the epidemic, some individuals become infected or immune and are no longer susceptible; therefore, the estimation of Rt is more appropriate. R t is calculated as a function of time and is the actual average number of secondary cases per primary case (Wallinga 2004) . The R t number will usually be smaller than the R o number because it takes into account the impact of control measures and non-susceptible individuals in the population (de Silva et al. 2009; Cori et al. 2013; Delamater et al. 2019 ).

Currently, governments in Latin America have taken measures to mitigate the spread of COVID-19 primarily based on world health organization recommendations. However, the potential impact of the virus in Latin America is still unknown. The first cases in Latin America were reported months after the virus spread in China, Italy, Spain, and North America. Given the urgency in these countries, governments need more accurate estimates of what could happen in Latin America in order to make informed decisions. The present study estimated the Rt of SARS-Cov2 during the first 10 days of the outbreak in seven Latin American countries and compared it to the R t in Spain and Italy.

Time-dependent incidence data were retrieved from the 2019 Novel Coronavirus COVID-19 Johns Hopkins University data repository (Dong et al. 2020) . Brazil, Ecuador, Chile, Colombia, Panama, Mexico, and Peru were selected because they had been through 10 days of the outbreak, as well as having the highest incidence of cases in Latin America as of March 23, 2020. We chose to compare the Latin American R t values with Spain and Italy because they had similar population numbers to the Latin An evaluation version of novaPDF was used to create this PDF file. Purchase a license to generate PDF files without this notice.

Page 5 of 10 J o u r n a l P r e -p r o o f 5 American countries. Additionally, Spain and Italy were among the most affected countries in Europe due to COVID-19 (Yuan et al. 2020 ).

To calculate Rt values, we used the same methodology of Cori et al (Cori et al. 2013) . R t was estimated by the ratio of the number of new infections generated in step t, I t , to the total infectiousness of infected individuals at time t, given by , the sum of infection incidence up to the time step t -1, weighted by the infectivity function . R t values equal or below one indicates that there will be a decline in the number of cases. R t values higher than one indicate that the number of cases will increase. Contact traces and transmissibility can change over time in regard to the outbreak progression. R t values can be adjusted to include a serial interval (SI) as the infectivity function, under the assumption of a gamma distribution (Wallinga 2004; Cori et al. 2013 ).

We estimated two scenarios: a) An Rt estimated using the SI calculated by ) for the early transmission in the Wuhan's outbreak (mean SI of 7.5 days, and standard deviation (SD) of 3.4 days). b)

An Rt estimated using the SI calculated by (Nishiura et al. 2020 ) through Bayesian statistics of exponential growth rate adjusted by the growth of curve during the initial stage of the outbreak (Mean SI: 4.7 days. SD SI: 2.9 days).

Analytical estimates of the R t were obtained within a Bayesian framework. R t was estimated in a 10 days interval from the date that a cumulative incidence of 25 or more cases of COVID-19 was reached in each country. The choice of the 25 cases of cumulative incidence is to goal a coefficient of variation of 0.2 in each time window (Cori et al. 2013) . We reported the median and 95% credible interval (CI).

Additionally, we report how many days it took to reach the incidence of 25 cases in each country.

Statistical analysis was performed using R language version 3.6.3 (R Core Team 2019).

An evaluation version of novaPDF was used to create this PDF file. Purchase a license to generate PDF files without this notice. Rt estimates in the first 10 days after reach the 25 cumulative incidence are described in 1 ).

Important variations were observed when the Rt was calculated using the parameters of or (Nishiura et al. 2020 ). The differences between both serial intervals are related to the infectious probability time, which was higher for Li et al. To analyze the initial phases of the outbreak, we preferred to use Nishiuria et al.'s (2020) parameters. The findings using these parameters show more conservative scenarios and less variation than previous studies made in other regions of the world (Yuan et al. 2020; Zhang et al. 2020 ).

The Rt estimates in the first ten days of the COVID-19 pandemic were higher in some of the Latin

American countries compared to the European referents. The aggressive dynamics of the outbreak in countries such as Brazil, Ecuador and Panama should be considered by surveillance systems in order to provide a quick response and stricter containment strategies. Despite the fact that the estimated Rt for Colombia, Peru, Chile and Mexico were lower than the European ones, the variation with respect to them is minimal. All Latin American countries had R t greater than 2, indicating an exponential growth. There is a high probability that the capacity of the Latin American healthcare systems is overwhelmed.

Furthermore, the COVID-19 pandemic could have serious repercussions in the region, given the context of politically divided countries, social inequality, economic limitations, internal conflicts, and social protests (Rodriguez-Morales et al. 2020 ).

An evaluation version of novaPDF was used to create this PDF file. Purchase a license to generate PDF files without this notice. An evaluation version of novaPDF was used to create this PDF file. Purchase a license to generate PDF files without this notice.

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