University of Michigan - Ann Arbor

The study aims to determine if there is an association between the number of COVID-19 cases and meteorological parameters in select U.S cities. The contribution of temperature and specific humidity on COVID-19 was evaluated using a case-crossover design with a distributed lag nonlinear model. Each COVID-19 case was evaluated on its own control before and after transmission occurred using case-crossover. Looking at eight U.S cities, 5 cities were found have significant relationships while 3 cities did not display significant associations. Overall, there is a small, but positive relationship between humidity and COVID-19 transmission in four cities. Higher COVID-19 transmission were associated with humidity of around 6 to 9 g/kg. Although these results show that weather can have an impact on COVID-19 transmission, more studies need to be done to learn more about the relationship between different meteorological and number of COVID-19 cases. 

Abstract of Short-term effects of specific humidity and temperature on COVID-19 morbidity in select US cities

Runkle, J. D., Sugg, M. M., Leeper, R. D., Rao, Y., Matthews, J. L., & Rennie, J. J. (2020). Short-term effects of specific humidity and temperature on COVID-19 morbidity in select US cities. The Science of the total environment, 740, 140093. Advance online publication. https://doi.org/10.1016/j.scitotenv.2020.140093

Short-term effects of specific humidity and temperature on COVID-19 morbidity in select US cities

A density map was used to see the association between COVID-19 cases and the combined effect of specific humidity and air temperature. This was done by evaluating the daily COVID-19 reports by the combined mean specific humidity and mean air temperature. More COVID-19 cases were found in conditions with low specific humidity (2-6 g/kg) and low temperature (2-11 C). This association also holds true in different incubation times. 

Association between COVID-19 cases and the combined effect of specific humidity and air temperature

- A condition Poisson regression was used along with the Distributed Lag Nonlinear model (DLNM). A time-stratified case-crossover design was applied in order to evaluate nonlinear and delayed effects, along with cumulative exposure-response between short-term daily average exposure to meteorological parameters and daily COVID-19 cases. All model types for all locations and parameters show the goodness of fit (qAIC) values 
- Humidity seems to be the strongest predictor for COVID-19 cases for all model types and locations. Case crossover models aligned better in Seattle, WA, New York City, NY, Chicago, IL, and New Orleans, LA, showing a strong association between humidity and COVID-19 counts. Detroit, MI, Pittsfield, MA, and Bridgeport, CT did not show significant results based on high qAIC values. Case crossover and DLNM models were better than DLNM models, with the exception a couple of locations including Albany GA. Humidity between 6-9 g/kg demonstrated a two-fold increase in transmission of COVID-19 in certain areas. 
- Temperature and solar radiation did not show to be strongly associated with the number of COVID-19 cases. Other studies showed different results compared to this study. This demonstrates that more studies should be done to fully understand the possible influence temperature, solar radiation, and other meteoerolcial parameters that may effect the transmission of COVID-19.
   

Meteorological parameters, humidity, temperature, and solar radiation, were evaluated to determine significant relationships with COVID-19 transmission.

Learn Before

Related