About: We study the effects of three types of variables on the early pace of spread of Covid-19: weather variables, temperature and absolute humidity; population density; the timeline of Covid-19 infection, as outbreak of disease occurs in different dates for different regions. The regions considered were all 50 U.S. states and 110 countries (those which had enough data available by April 10th. We looked for associations between the above variables and an estimate of the growth rate of cases, the exponential coefficient, computed using data for 10 days starting when state/country reached 100 confirmed cases. The results for U.S. states indicate that one cannot expect that higher temperatures and higher levels of absolute humidity would translate into slower pace of Covid-19 infection rate, at least in the ranges of those variables during the months of February and March of 2020 (-2.4 to 24C and 2.3 to 15g/m3). In fact, the opposite is true: the higher the temperature and the absolute humidity, the faster the Covid-19 has expanded in the U.S. states, in the early stages of the outbreak. Secondly, using the highest county population density for each state, there is strong positive association between population density and (early) faster spread of Covid-19. Finally, there is strong negative association between the date when a state reached 100 accumulated cases and the speed of Covid-10 outbreak (the later, the lower the estimate of growth rate). When these variables are considered together, only population density and the timeline variable show statistical significance. We also develop the basic models for the collection of countries, without the demographic variable. Despite the evidence, in that case, that warmer and more humid countries have shown lower rates of Covid-19 expansion, the weather variables lose statistical significance when the timeline variable is added.   Goto Sponge  NotDistinct  Permalink

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  • We study the effects of three types of variables on the early pace of spread of Covid-19: weather variables, temperature and absolute humidity; population density; the timeline of Covid-19 infection, as outbreak of disease occurs in different dates for different regions. The regions considered were all 50 U.S. states and 110 countries (those which had enough data available by April 10th. We looked for associations between the above variables and an estimate of the growth rate of cases, the exponential coefficient, computed using data for 10 days starting when state/country reached 100 confirmed cases. The results for U.S. states indicate that one cannot expect that higher temperatures and higher levels of absolute humidity would translate into slower pace of Covid-19 infection rate, at least in the ranges of those variables during the months of February and March of 2020 (-2.4 to 24C and 2.3 to 15g/m3). In fact, the opposite is true: the higher the temperature and the absolute humidity, the faster the Covid-19 has expanded in the U.S. states, in the early stages of the outbreak. Secondly, using the highest county population density for each state, there is strong positive association between population density and (early) faster spread of Covid-19. Finally, there is strong negative association between the date when a state reached 100 accumulated cases and the speed of Covid-10 outbreak (the later, the lower the estimate of growth rate). When these variables are considered together, only population density and the timeline variable show statistical significance. We also develop the basic models for the collection of countries, without the demographic variable. Despite the evidence, in that case, that warmer and more humid countries have shown lower rates of Covid-19 expansion, the weather variables lose statistical significance when the timeline variable is added.
subject
  • Zoonoses
  • United States
  • Viral respiratory tract infections
  • COVID-19
  • Population density
  • Human geography
  • Occupational safety and health
  • Population ecology
  • Environmental controversies
  • Human overpopulation
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