OpenWeather Data in the Fight Against COVID-19
Posted on 17 Dec 2020
The COVID-19 pandemic has disrupted economies, societies, and — most importantly — lives across the world. Behind the scenes, researchers have been working hard to understand SARS-CoV-2, the virus that causes COVID-19, so that effective measures can be taken to control its spread.
One of the first questions many researchers asked about the novel coronavirus was whether it is affected by weather conditions. Needless to say, we received dozens of enquiries from non-profit research groups who needed access to our world-class data. With our established policy of providing free data to students and researchers, we did not hesitate to oblige.
In this article, we explain why weather must be taken into account when modelling the spread of COVID-19, which specific parameters are thought to be most significant, and everything we are doing to help in this collaborative, worldwide research effort.
Why Look at Weather?
Atmospheric parameters like temperature and humidity have long been thought to play a role in the spread of viruses. This is one of several possible explanations for the seasonal nature of the flu: the influenza virus thrives in dry, cold conditions like those in the winter.
Of course, there may be other factors at play in the seasonality of viral diseases. Some believe that colder weather causes individuals to spend more time in close proximity indoors. Additionally, the condition of the immune system (which depends on seasonal factors like sunlight) may also play a role.
As a result of this, weather is a prime suspect when it comes to identifying factors that affect the spread of COVID-19. By analyzing trends in new coronavirus cases and meteorological readings in the areas they are detected, researchers have established that weather and climate must be taken into account to fully understand coronavirus.
Atmospheric Parameters to Consider
There are a variety of atmospheric parameters that may affect coronavirus spread. Here are four commonly cited examples, with a brief description of the mechanism through which they may impact the virus:
Temperature: Higher temperatures correlate to less time spent indoors and stronger immune systems. Additionally, higher temperatures may have a direct effect on coronavirus particles.
UV index: High UV levels also correlate to less time spent indoors and stronger immune systems. Additionally, UV light may have a direct effect on coronavirus particles.
Humidity: Increased humidity may prevent coronavirus particles from remaining in the air for longer periods of time.
Wind speed: High wind speed may prevent coronavirus from particles remaining in the air for longer periods of time.
Choosing an OpenWeather Dataset
OpenWeather offers current and future weather data products that include all four of the atmospheric parameters listed above, and many others! For coronavirus researchers, however, it's the historical products — our History Bulks and Historical Weather API — that are most valuable, as past weather data can be analyzed against new coronavirus cases and/or deaths.
As researchers are able to identify relationships between weather conditions and COVID-19 spread, they may wish to build predictive models that describe these relationships. Then, by feeding future weather data (from our One Call API or Climatic Forecast), researchers may be able to forecast coronavirus trends for the near future.
Does Weather Affect Coronavirus?
Having shared our weather data with several research groups at the start of 2020, we reached out to ask about their findings. Our first question was obvious: do weather conditions really affect the spread of coronavirus?
Volunteer group India Covid, who used OpenWeather data to increase the accuracy of their coronavirus prediction models, did not hesitate to confirm that their research had indeed discovered a correlation:
Our prediction accuracy increased from 85% to 92% once we started using your weather data. We see there is a correlation between the weather parameters and coronavirus but have more analysis to do before we can state exactly which parameters play a role and by how much.
This conclusion reflects the sentiment of other coronavirus research, including a new paper published in Environmental Research as well as a recent report from Goldman Sachs. In both cases, a negative correlation between coronavirus cases and temperature was observed, meaning more coronavirus cases were detected as it got colder.
Another group who requested access to our weather data for coronavirus research earlier in the year — ALT-F1 — refrained from making definitive statements. However, they did stress that future research must continue to take into account weather variables:
Short term prediction of COVID hospitalization is a complex and multivariate forecasting problem. The use of weather variables helped assess the impact of weather on the epidemic evolution. It is essential to keep them in the list of descriptors to deal with potential seasonal or non-stationary effects.
Free for Open-Source
Here at OpenWeather, we're strong believers in keeping access to data open. As such, we support altruistic initiatives such as open source software projects and research studies — like these ones — by providing free, extended access to our products.
Are you a researcher or open source contributor in need of free access to extensive weather datasets? We'd be happy to help support your project — just reach out to us at email@example.com to learn more.