Abstract
Simple SummaryRates of viral spread during first and second waves of the COVID-19 pandemic for USA states, and for consecutive nonoverlapping periods of 20 days for the USA and 51 countries across the globe associate with mean temperature, elevation, population density and age. Some associations switch directions when comparing different periods. Even population density, which presumably should always increase viral spread, at some periods seems to decrease spread rates. We also observed systematic inversions between spread rates estimated at 80–100 day intervals. These patterns remain unexplained and suggest difficulties in managing and predicting the pandemic, in particular, negative correlations between population density and spread rates, which were observed in independent samples and at different periods. Putatively, confinements could produce these patterns, by selecting viral strains with longer contagiousness and/or latent periods.We present spread parameters for first and second waves of the COVID-19 pandemic for USA states, and for consecutive nonoverlapping periods of 20 days for the USA and 51 countries across the globe. We studied spread rates in the USA states and 51 countries, and analyzed associations between spread rates at different periods, and with temperature, elevation, population density and age. USA first/second wave spread rates increase/decrease with population density, and are uncorrelated with temperature and median population age. Spread rates are systematically inversely proportional to those estimated 80–100 days later. Ascending/descending phases of the same wave only partially explain this. Directions of correlations with factors such as temperature and median age flip. Changes in environmental trends of the COVID-19 pandemic remain unpredictable; predictions based on classical epidemiological knowledge are highly uncertain. Negative associations between population density and spread rates, observed in independent samples and at different periods, are most surprising. We suggest that systematic negative associations between spread rates 80–100 days apart could result from confinements selecting for greater contagiousness, a potential double-edged sword effect of confinements.
Highlights
IntroductionSpread parameters of daily new confirmed COVID-19 cases (calculated as the slope of their logarithmic regression curve) estimates viral contagiousness
Spread parameters of daily new confirmed COVID-19 cases estimates viral contagiousness
Note that the slopes we evaluate from daily data in daily new cases estimate the acceleration in increase of new cases: in the first differential model proposed for the COVID-19 outbreak [6], the variable is the number of cumulative cases at time t, denoted Xt, the velocity is the number of daily new cases, denoted Yt = (Xt − Xt − 1)/(t − (t − 1)), and the acceleration is the slope of the curve of these daily new cases, denoted Zt = (Yt − Yt − 1)/ (t − (t − 1))
Summary
Spread parameters of daily new confirmed COVID-19 cases (calculated as the slope of their logarithmic regression curve) estimates viral contagiousness. It was shown that first wave spread, when comparing different countries, decreases with mean annual temperature [1], and the opposite trend with temperature occurs for second wave spread parameters [2]. This is in line with observations on variation in spread across different regions of Italy, in March 2020 (first wave period, negative correlation with temperature) and in May 2020 (second wave period, positive correlation with temperature) [3]. First and second wave spreads differ in terms of other factors: The spread of the first wave increases with the median population age and decreases the later the date of its onset. The spread of the second wave decreases with the median population age and increases the later the date of its onset. First and second waves differ because we detected no associations between second wave slopes and mean country elevation, while first wave slopes increase with elevation up to 900 m and decrease beyond that approximate altitude [2]
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