Humidity vs relative risk and temperature vs relative risk exhibited a U shape trend, with increases in relative risk at high and low humidity and temperatures. Solar vs relative risk has an inverted U shape relationship, with a higher risk at around 5200-6300 W/m^2.

COVID-19 morbidity vs. temperature, humidity, and solar radiation in New Orleans, LA

Only the DLNM-only model showed significant results. Only humidity vs relative risk association showed significant results, with a significant relative risk observed to be form 6 to 9 g/kg.


COVID-19 morbidity vs. temperature, humidity, and solar radiation in Albany, GA

COVID-19 morbidity vs. temperature, humidity, and solar radiation in Chicago, IL

COVID-19 morbidity vs. temperature, humidity, and solar radiation in New York City, NY

COVID-19 morbidity vs. temperature, humidity, and solar radiation in Seattle, WA

- New Orleans, LA
- Albany, GA
- Chicago, IL
- NYC, NY
- Seattle, WA

University of Michigan - Ann Arbor

- 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.

Table 2: quasi-AIC values demonstrating model fit for DLNM and CCO models for each city.

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.

Humidity seems to be the strongest predictor for COVID-19 cases 

Temperature and solar radiation are not 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.

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