Weather Conditions and COVID-19 Transmission: Estimates and Projections

  1. Ran Xu
  2. Hazhir Rahmandad
  3. Marichi Gupta
  4. Catherine DiGennaro
  5. Navid Ghaffarzadegan
  6. Mohammad S. Jalali

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Abstract

Background

Understanding and projecting the spread of COVID-19 requires reliable estimates of how weather components are associated with the transmission of the virus. Prior research on this topic has been inconclusive. Identifying key challenges to reliable estimation of weather impact on transmission we study this question using one of the largest assembled databases of COVID-19 infections and weather.

Methods

We assemble a dataset that includes virus transmission and weather data across 3,739 locations from December 12, 2019 to April 22, 2020. Using simulation, we identify key challenges to reliable estimation of weather impacts on transmission, design a statistical method to overcome these challenges, and validate it in a blinded simulation study. Using this method and controlling for location-specific response trends we estimate how different weather variables are associated with the reproduction number for COVID-19. We then use the estimates to project the relative weather-related risk of COVID-19 transmission across the world and in large cities.

Results

We show that the delay between exposure and detection of infection complicates the estimation of weather impact on COVID-19 transmission, potentially explaining significant variability in results to-date. Correcting for that distributed delay and offering conservative estimates, we find a negative relationship between temperatures above 25 degrees Celsius and estimated reproduction number ( Ȓ ), with each degree Celsius associated with a 3.1% (95% CI, 1.5% to 4.8%) reduction in Ȓ . Higher levels of relative humidity strengthen the negative effect of temperature above 25 degrees. Moreover, one millibar of additional pressure increases Ȓ by approximately 0.8 percent (95% CI, 0.6% to 1%) at the median pressure (1016 millibars) in our sample. We also find significant positive effects for wind speed, precipitation, and diurnal temperature on Ȓ . Sensitivity analysis and simulations show that results are robust to multiple assumptions. Despite conservative estimates, weather effects are associated with a 43% change in Ȓ between the 5 th and 95 th percentile of weather conditions in our sample.

Conclusions

These results provide evidence for the relationship between several weather variables and the spread of COVID-19. However, the (conservatively) estimated relationships are not strong enough to seasonally control the epidemic in most locations.

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  1. ScreenIT

    SciScore for 10.1101/2020.05.05.20092627: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: Thank you for sharing your code and data.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Other limitations include: the lack of reliable transmission data in some regions of the world; oversampling from U.S. locations; limited data with high temperature and UV in our estimation sample, which reduce confidence for projections when either is very high; use of last year’s weather data to project next year’s outcomes; and use of correlational evidence to inform out-of-sample projections. Despite these limitations, consistent results using various conservative specifications and placebo and validation tests provide promising indications of the true impacts of weather conditions on transmission. The estimated impacts suggest summer may offer partial relief to some regions of the world. However, given a highly susceptible population, the estimated impact of summer weather on transmission risk is not large enough in most places to quell the epidemic in 2020, indicating that policymakers and the public should remain vigilant in their responses to the pandemic. In fact, much of the variation in reproduction number in our sample is explained by location-specific fixed effects and responses, not weather; and most regions that can expect reduced risk in summer will face increased risks in the fall. Ultimately, weather more likely plays a secondary role in the control of the pandemic.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

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  2. Version published to 10.1101/2020.05.05.20092627v3 on medRxiv
  3. Version published to 10.1101/2020.05.05.20092627v2 on medRxiv
  4. Version published to 10.1101/2020.05.05.20092627v1 on medRxiv