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Strange weather: What shapes coronavirus transmission patterns?

We now know government COVID-19 policy should not be based on meteorological conditions

A minor is inoculated with the first dose of the Pfizer-BioNtech vaccine against the coronavirus - © Alfredo Estrella/AFP
A minor is inoculated with the first dose of the Pfizer-BioNtech vaccine against the coronavirus - © Alfredo Estrella/AFP

In 2020 it was speculated that weather patterns play a role in the transmission rates of the coronavirus responsible for COVID-19. New data, based on investigations into over 400 cities, suggests that weather may not be a significant factor for explaining viral transmission rates.

The original premise was based on rates of influenza peaking in the drier and colder winter months in temperate climates around the world.

The study, which is reported on by The Daily Telegraph, suggests that human behaviour and government interventions play a far larger role in the spread of the virus. This relates to data gathered from the onset of the pandemic’s first wave.

This additional means that the parameters of temperature and humidity are unlikely to have been a dominant transmission risk factor. This is because the data sets are very variable, with some showing a positive correlation, others showing a negative correlation, and others indicating no relationship between coronavirus transmission and variations in weather patterns whatsoever.

The transmission rates for the virus were based on the reproduction number (the oft quoted R-number), with data drawn 11 January and 28 April 2020 in 409 locations. The key weather parameters examined included: Daily mean temperature, relative and absolute humidity, solar radiation, wind speed and precipitation. Also included within the analysis were pollution levels (such as particulate matter, PM2.5).

In terms of temperature patterns, across the review period, 35 cities experienced low temperatures, 64 medium temperatures and 34 high temperatures. Data was examined by using a multilevel meta-regression model.  To normalize the data, socio-economic and demographic factors were accounted for.

The results breakdown was:

  • A modest, non-linear association of mean temperature.
  • A modest relationship for absolute humidity.
  • Less strong evidence of association was found for relative humidity.
  • No association was found for solar radiation, wind speed and precipitation.

The research appears in the journal Nature Communications, and it is titled “A cross-sectional analysis of meteorological factors and SARS-CoV-2 transmission in 409 cities across 26 countries.”

The research concludes that governmental COVID-19 policy should not be based on meteorological conditions. Instead, a whole range of factors needs to be modelled, including government restrictions, socio-economic indicators, population density and age structure, alongside weather patterns.

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Written By

Dr. Tim Sandle is Digital Journal's Editor-at-Large for science news. Tim specializes in science, technology, environmental, business, and health journalism. He is additionally a practising microbiologist; and an author. He is also interested in history, politics and current affairs.

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