Use of Environmental Variables to Predict SARS-CoV-2 Spread in the U.S.

  1. R. Sterling Haring
  2. Sean Trende
  3. Christina M. Ramirez

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Abstract

Background

The COVID-19 pandemic has challenged even the most robust public health systems world-wide, leaving state and local health departments, hospitals, and physicians with little guidance on planning and resource allocation. Efforts at predicting the virus’ spread have largely failed to capture the nuances presented by national and local geographic, environmental, and sociological variables.

Objective

Using county-level data from the United States, we sought to measure the extent to which these demographic, geographic, and environmental variables correlate with the spread of COVID-19.

Methods

Using demographic data from the US Census Bureau’s American Community Survey, weather station data from the National Oceanic and Atmospheric Administration (NOAA), and COVID-19 case data from the Center for Systems Science and Engineering at Johns Hopkins University and the New York State Department of Health, we employed Bayesian hierarchical modeling with zero-inflated Negative Binomial regression to calculate correlations between these variables, COVID-19 case count, and rate of viral spread. Key predictors were identified and measured during two periods of two weeks each: March and June of 2020. The resultant model was then employed to predict case counts and spread rate for early July 2020.

Results

While demographic and environmental factors explain viral spread well, our findings challenge earlier conclusions about how these factors related to viral progress. Using these factors alone, we were able to predict spread to within 1% in all but 8 counties (99.9%), and within 0.1% in all but 51 counties (98.4%). The model was subsequently able to predict early July viral spread to within 0.5% in 98% of counties. Contrary to earlier findings, temperature had variable effect; as Spring temperatures warmed, cases decreased, but Summer heat increased cases, likely reflecting movement of populations from indoors to outdoors and back in. States varied little in their case rate relative to the model, and much of the variation could be linked to known “superspreader” events.

Conclusion

While environmental and demographic variables can help predict COVID-19 spread rates, some relationships are variable in ways earlier research failed to identify.

Role of Funding Source

There was no funding supporting this work.

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    SciScore for 10.1101/2021.05.19.21257350: (What is this?)

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    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

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  2. Version published to 10.1101/2021.05.19.21257350v1 on medRxiv