The data for the coronavirus disease in many different countries follow a common pattern: once the number of confirmed cases reaches order 10 there is a very rapid subsequent growth, which is well fit by an exponential behavior. The latter is typically a good approximation for the following couple of weeks and, after this stage of free propagation, the exponential growth typically gradually slows down, probably due to other effects, such as: lockdown policies from governments, a higher degree of awareness in the population or the tracking and isolation of the positive cases.
A new study tries to see whether the temperature of the environment has an effect, and for this purpose they choose to analyze the first stage of free propagation in a selected sample of countries.
Researchers collected data for countries that had at least 12 days of data after a starting point, which they fixed to be at the threshold of 30 confirmed cases. Then, they considered two datasets: a base dataset with 42 countries, collected on March 26th, and an extended dataset with a total of 88 countries, collected on April 1st. Researchers fit the data for each country with an exponential and extracted the exponents, for each country. Then they analyzed such exponents as a function of the temperature, using the average temperature for the month of March (or slightly earlier in some cases), for each of the selected countries.
In this way, the decrease at high temperatures is expected, since the same happens also for other coronaviruses. It is unclear instead how to interpret the decrease at low temperature (less than 8ºC), present in the base dataset. This could be a statistical fluctuation, since it is not present in the extended dataset. One possible reason for this decrease, if real, could be the lower degree of interaction among people in countries with very low temperatures, which could slow down the propagation of the virus.
Besides, a general observation is also that a large scatter in the residual data is present, clearly due to many other systematic factors, such as variations in the methods and resources used for collecting data and variations in the amount of social interactions, due to cultural reasons. It is also possible that variations in resources bias the testing procedure (i.e. poorer countries have less intense testing), which might be partially degenerate with effects of temperature. However, further study would be required to assess such factors.
As a final remark, researchers’ findings can be very useful for policy makers, since they support the expectation that with growing temperatures the coronavirus crisis should become milder in the coming few months, for countries in the Northern Hemisphere. As an example the estimated doubling time, with the quadratic fit, at the peak temperature of 7.7ºC is of 2.6 days, while at 26ºC is expected to go to about 4.6 days. The linear fit gives a smaller effect: a doubling time of 2.8 days (or 3.1 days) at 7.7ºC and a doubling time of 4.1 days (or 4 days) at 26ºC, using the estimate from the base (or the extended) dataset.
Moreover, for countries with seasonal variations in the Southern Hemisphere, instead, this should give motivation to implement strong lockdown policies before the arrival of the cold season.
Link to the paper: https://doi.org/10.1101/2020.03.26.20044529
Editorial Disclaimer: information published during the 2020 COVID-19 pandemic may be updated frequently to reflect the dynamic nature of current understanding.
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