A proposed analytical approach to estimate excess daily mortality rates in Ecuador.

Background: The COVID-19 pandemic has proved deadly all over the globe; however, one of the most lethal outbreaks occurred in Ecuador. Aims: This study aims to highlight the pandemic's impact on the most affected countries worldwide in terms of excess deaths per capita and per day. Methods: An ecological study of all-cause mortality recorded in Ecuador was performed. To calculate the excess deaths relative to the historical average for the same dates in 2017, 2018, and 2019, we developed a bootstrap method based on the central tendency measure of mean. A Poisson fitting analysis was used to identify trends on officially recorded all-cause deaths and COVID-19 deaths. A bootstrapping technique was used to emulate the sampling distribution of our expected deaths estimator µâŒ¢deaths by simulating the data generation and model fitting processes daily since the first confirmed case. Results: In Ecuador, during 2020, 115,070 deaths were reported and 42,453 were cataloged as excess mortality when compared to 2017-2019 period. Ecuador is the country with the highest recorded excess mortality in the world within the shortest timespan. In one single day, Ecuador recorded 1,120 deaths (6/100,000), which represents an additional 408% of the expected fatalities. Conclusion: Adjusting for population size and time, the hardest-hit country due to the COVID-19 pandemic was Ecuador. The mortality excess rate shows that the SARS-CoV-2 virus spread rapidly in Ecuador, especially in the coastal region. Our results and the proposed new methodology could help to address the real situation of the number of deaths during the initial phase of pandemics.

Analysis of Excess Mortality Data at Different Altitudes During the COVID-19 Outbreak in Ecuador.

Ortiz-Prado, Esteban, Raul Patricio Fernandez Naranjo, Eduardo Vasconez, Katherine Simbaña-Rivera, Trigomar Correa-Sancho, Alex Lister, Manuel Calvopiña, and Ginés Viscor. Analysis of excess mortality data at different altitudes during the COVID-19 outbreak in Ecuador. High Alt Med Biol. 22:406-416, 2021. Background: It has been speculated that living at high altitude confers some risk reduction in terms of SARS-CoV-2 infection, reduced transmissibility, and arguable lower COVID-19-related mortality. Objective: We aim to determine the number of excess deaths reported in Ecuador during the first year of the COVID-19 pandemic in relation to different altitude categories among 221 cantons in Ecuador, ranging from sea level to 4,300 m above. Methods: A descriptive ecological country-wide analysis of the excess mortality in Ecuador was performed since March 1, 2020, to March 1, 2021. Every canton was categorized as lower (for altitudes 2,500 m or less) or higher (for altitudes >2,500 m) in a first broad classification, as well as in two different classifications: The one proposed by Imray et al. in 2011 (low altitude <1,500 m, moderate altitude 1,500-2,500 m, high altitude 2,500-3,500 m, or very high altitude 3,500-5,500 m) and the one proposed by Bärtsch et al. in 2008 (near sea level 0-500 m, low altitude 500-2,000 m, moderate altitude 2,000-3,000 m, high altitude 3,000-5,500 m, and extreme altitude 5,500 m). A Poisson fitting analysis was used to identify trends on officially recorded all-caused deaths and those attributed to COVID-19. Results: In Ecuador, at least 120,573 deaths were recorded during the first year of the pandemic, from which 42,453 were catalogued as excessive when compared with the past 3 years of averages (2017-2019). The mortality rate at the lower altitude was 301/100,000 people, in comparison to 242/100,000 inhabitants in elevated cantons. Considering the four elevation categories, the highest excess deaths came from towns located at low altitude (324/100,000), in contrast to the moderate altitude (171/100,000), high-altitude (249/100,000), and very high-altitude (153/100,000) groups. Conclusions: This is the first report on COVID-19 excess mortality in a high-altitude range from 0 to 4,300 m above sea level. We found that absolute COVID-19-related excess mortality is lower both in time and in proportion in the cantons located at high and very high altitude when compared with those cantons located at low altitude.

Statistical data driven approach of COVID-19 in Ecuador: R 0 and R t estimation via new method.

The growth of COVID-19 pandemic throughout more than 213 countries around the world have put a lot of pressures on governments and health services to try to stop the rapid expansion of the pandemic. During 2009, H1N1 Influenza pandemic, statistical and mathematical methods were used to track how the virus spreads around countries. Most of these models that were developed at the beginning of the XXI century are based on the classical susceptible-infected-recovered (SIR) model developed almost a hundred years ago. The evolution of this model allows us to forecast and compute basic and effective reproduction numbers (R t and R 0 ), measures that quantify the epidemic potential of a pathogen and estimates different scenarios. In this study, we present a traditional estimation technique for R 0 with statistical distributions by best fitting and a Bayesian approach based on continuous feed of prior distributions to obtain posterior distributions and computing real time R t . We use data from COVID-19 officially reported cases in Ecuador since the first confirmed case on February 29th. Because of the lack of data, in the case of R 0 we compare two methods for the estimation of these parameters below exponential growth and maximum likelihood estimation. We do not make any assumption about the evolution of cases due to limited information and we use previous methods to compare scenarios about R 0 and in the case of R t we used Bayesian inference to model uncertainty in contagious proposing a new modification to the well-known model of Bettencourt and Ribeiro based on a time window of m days to improve estimations. Ecuadorian R 0 with exponential growth criteria was 3.45 and with the maximum likelihood estimation method was 2.93. The results show that Guayas, Pichincha and Manabí were the provinces with the highest number of cases due to COVID-19. Some reasons explain the increased transmissibility in these localities: massive events, population density, cities dispersion patterns, and the delayed time of public health actions to contain pandemic. In conclusion, this is a novel approach that allow us to measure infection dynamics and outbreak distribution when not enough detailed data is available. The use of this model can be used to predict pandemic distribution and to implement data-based effective measures.