Abstract
This study describes the dynamics of COVID-19 deaths and infections via a Monte Carlo approach. The analyses include death's data from USA, Brazil, Mexico, UK, India and Russia, which comprise the four countries with the highest number of deaths/confirmed cases, as of Aug 07, 2020, according to the WHO. The Gompertz functions were fitted to the data of weekly averaged confirmed deaths per day by mapping the $\chi^2$ values. The uncertainties, variances and covariances of the model parameters were calculated by propagation. The fitted functions for the average deaths per day for USA and India have an upward trend, with the former having a higher growth rate and quite huge uncertainties. For Mexico, UK and Russia, the fits are consistent with a slope down pattern. For Brazil we found a subtle trend down, but with significant uncertainties. The USA, UK and India data shown a first peak with a higher growth rate when compared to the second one, demonstrating the benefits of non-pharmaceutical interventions of sanitary measures and social distance flattening the curve. For USA, a third peak seems quite plausible, most likely related with the recent relaxation policies. Brazil's data are satisfactorily described by two highly overlapped Gompertz functions with similar growth rates, suggesting a two-steps process for the pandemic spreading. The 95% CI for the total number of deaths ($\times 10^3$) predicted by the model for Aug 31, 2020 are 160 to 220, 110 to 130, 59 to 62, 46.6 to 47.3, 54 to 63 and 16.0 to 16.7 for USA, Brazil, Mexico, UK, India and Russia, respectively. Our estimates for the prevalences of infections are in reasonable agreement with some preliminary reports from serological studies carried out in USA and Brazil. The method represents an effective framework to estimate the line-shape of the infection curves and the uncertainties of the relevant parameters based on the actual data.
Highlights
The outbreak of the new coronavirus disease 2019 (COVID-19) brought a challenging scenario worldwide [1,2,3], urging timely and effective responses from the authorities regarding the availability of intensive care units [4,5], as well as the implementation of nonpharmacological interventions of social distance and protective sanitary measures [6,7,8]
The average elapsed time from symptom onset to death was corrected to 17.8 days in Ref. [35], superseding the previous parameter of 18.8 days used in Ref. [34]
The method was successfully applied for the outbreak of COVID-19, providing a consistent interpretation of the time evolution of deaths in the USA, Brazil, Mexico, the United Kingdom (UK), India, and Russia
Summary
The outbreak of the new coronavirus disease 2019 (COVID-19) brought a challenging scenario worldwide [1,2,3], urging timely and effective responses from the authorities regarding the availability of intensive care units [4,5], as well as the implementation of nonpharmacological interventions of social distance and protective sanitary measures [6,7,8]. Epidemiological models [9,10,11,12,13,14,15,16,17,18] and other statistical approaches [19,20,21] have been very useful to guide actions to manage. This crisis and to shed light on how to safely and gradually resume economics and social activities [22]. Some analyses of the overall mortality during the COVID-19 pandemic have been quite useful to shed light on the outbreak of the disease. Another study based on a time-series analysis [28] found an excess mortality in Italy correlated in time with the official COVID-19 deaths but a
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