On January 21, the first case of COVID-19 in the United States ¹ was announced in a traveler who had arrived in the country from China 5 days earlier. Wuhan, China is where the health emergency originated ² in December 2019. From late January, the North American continent started to see case reports in different states, with cases arriving in South America on February 26 through Brazil after passing through Central America and the Caribbean ³ . Today, America is the epicentre of the pandemic, with the USA and Brazil being the two largest countries with the most cases. In addition, Peru, Chile, Mexico, Canada, Colombia and Ecuador are among the top 25 countries with the highest incidence worldwide.

Since then, governments across the region have implemented a series of non-pharmacological measures to protect their citizens and contain the spread of COVID-19. All South American countries, including the French overseas region of French Guiana and the Falkland Islands, have reported the presence of coronavirus within their borders, and all took measures at different times of the outbreak.

Non-pharmacological measures of containment (mitigation and suppression) have been implemented from the index case on different dates in each country ^{2, 4– 6} . This is based on projections of contagion and lethality resulting from SIR epidemiological models (susceptible, infected and recovered) ⁷ with data taken from the behavior of the virus in Asia and Europe especially, and with approximations to the basic reproduction number ( R ₀ ) ^{8, 9} , effective reproduction number ( R _e ), and variable time estimation of R _e ( R _t ), among others. R ₀ estimates the speed with which a disease can spread in a population and plays a crucial role in predicting the prevalence of infectious disease, in addition to better understanding an epidemic outbreak and preparing the corresponding public health response. R _t is the reproduction number at time t, which reveals the actual transmission rate of the virus at a given moment. This number will vary depending on the control protocols established in each country.

Mitigation measures are aimed at slowing down the spread of infection by progressively reducing R ₀ ¹⁰ . Suppression measures aim to reduce R ₀ below 1, to progressively decrease the number of cases until they disappear. The greatest challenge that this strategy entails is that it needs to be maintained until the pandemic has disappeared or an effective treatment or vaccine is available ^{6, 10, 11} . The estimation of R ₀ and R _t in different scenarios allows a better prognosis of the trends of the global epidemic ¹² , in addition to evaluating the effectiveness in reducing transmission with the measures taken by the different countries to contain COVID-19 ¹¹ .

While this article was being prepared, the R ₀ and R _t values of some countries were published and modeled in three intervention scenarios; however, they only included the United States and Argentina from the American continent, limiting the comparative analysis for the characteristics of America ¹³ . Previous work has calculated the R ₀ and R _t using the maximum likelihood method and sequential Bayesian method, and found that European and North American countries possessed a higher R ₀ and unsteady R _t fluctuations, whereas some heavily affected Asian countries showed a relatively low R ₀ and declining R _t , with a small number of patients in Africa and Latin America, yet with the potential risk of large outbreaks. Xu et al. ¹³ who determined R _t as an indicator to measure the transmission of SARS-CoV-2 before and after interventions, showed that the change in the rate of transmission of SARS-CoV-2 is associated with reducing social interactions.

This study aimed to estimate R ₀ and R _t in multiple countries of the American continent and evaluate their behavior from the first (index) case in each country, and the time of implementation of the chosen non-pharmacological containment measures.

The for the countries selected in this study was 1.2–1.8, which is much lower compared to China where it was reported to be 2.2, 2.6, 3.8, or even 6.47 according to different research ^{2, 18} . Our study findings agree with Xu et al. ¹³ in the estimated for the United States and Argentina. The R _t for these countries is also identical to that found by Xu et al.

As in the study by Xu et al. ¹³ , there are some limitations to the current study. First, the variation in depends on the series interval. Here, we assume 7.5 days for the 13 countries due to limited information on the disease in different nations ¹³ . In addition, we noticed some errors in the John Hopkins University data when we cross referenced these with official country websites. We were able to correct these by searching for cases directly on the official website of each country. Another drawback is the diversity of RT-PCR (real-time reverse transcription polymerase chain reaction) tests used in the diagnostic process and their sensitivity, which may throw up false negatives, perhaps under-estimating the true impact of the pandemic in various countries. Although data were available from other countries, in the case of Ecuador, the report was not included, because cases were not confirmed by RT-PCR, which is a that s technical criteria.

The epidemic trends in different regions around the world are significantly different due to the great difference in the design and implementation of prevention and control measures ⁵ . Estimates of both R ₀ and R _t in different countries will enable a better forecast of global epidemic trends.

In conclusions, our study shows that there is a close relationship between R _t and non-pharmacological interventions decreed by countries’ governments for the control of the COVID-19 pandemic. Additionally, we showed that there are immediate changes in the behavior of the R _t , and therefore in the progression of the outbreak, when the interventions are implemented closer to the index case for each country.