Roles of meteorological conditions in COVID-19 transmission on a worldwide scale

Abstract

The novel coronavirus (SARS-CoV-2/ 2019-nCoV) identified in Wuhan, China, in December 2019 has caused great damage to public health and economy worldwide with over 140,000 infected cases up to date. Previous research has suggested an involvement of meteorological conditions in the spread of droplet-mediated viral diseases, such as influenza. However, as for the recent novel coronavirus, few studies have discussed systematically about the role of daily weather in the epidemic transmission of the virus. Here, we examine the relationships of meteorological variables with the severity of the outbreak on a worldwide scale. The confirmed case counts, which indicates the severity of COVID-19 spread, and four meteorological variables, i.e., air temperature, relative humidity, wind speed, and visibility, were : medRxiv preprint all over China, 21 cities/ provinces in Italy, 21 cities/ provinces in Japan, and 51 other countries around the world. Four different time delays of weather (on the day, 3 days ago, 7 days ago, and 14 days ago) as to the epidemic situation were taken for modeling and we finally chose the weather two weeks ago to model against the daily epidemic situation as its correlated with the outbreak best. Taken Chinese cities as a discovery dataset, it was suggested that temperature, wind speed, and relative humidity combined together could best predict the epidemic situation. The meteorological model could well predict the outbreak around the world with a high correlation (r 2 >0.6) with the real data. Using this model, we further predicted the possible epidemic situation in the future 12 days in several high-latitude cities with potential outbreak. This model could provide more information for government's future decisions on COVID-19 outbreak control.

Multivariate Analysis of Factors Affecting COVID-19 Case and Death Rate in U.S. Counties: The Significant Effects of Black Race and Temperature

“…Several recent reports have also linked higher temperature to decreased virus spread, while others have found no significant effects. [9][10][11][12][13][14] Here, we demonstrate an independent effect of temperature that results in reduced COVID-19 cases, but not mortality, across U.S. counties. If warmer temperatures do, in fact, play a role in mitigating disease spread, it is reasonable to expect a potential seasonal trend in global cases and mortality.…”

“…Recent work has primarily focused on patient demographics, underlying health comorbidities, social disparities in healthcare access and quality, and environmental variables such as pollution to identify potential risk factors and vulnerable populations. [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20] However, while these previous studies have examined the effects of these domains on COVID-19 spread independently, some do not control for the potentially confounding interactions between variables. In this study, we sought to investigate COVID-19 prevalence and mortality in all U.S. counties through a more comprehensive framework that accounts for effects of county-level macroeconomic, demographic, environmental, health status, and healthcare access variables.…”

“…Developing new therapies, identifying risk factors, and minimizing spread through social distancing remain top priorities in the fight against COVID-19. 4,5 Previous studies have examined the effects of various risk factors on spread of COVID-19 including patient demographics, 6,7 social determinants of health, 8 environmental variables, [9][10][11][12][13][14][15] housing, 16,17 and underlying health conditions. [18][19][20] Identifying risk factors allows public health officials to determine populations at greater risk and development targeted public health interventions.…”

Causal empirical estimates suggest COVID-19 transmission rates are highly seasonal

“…These empirical frameworks conflate location-specific characteristics -such as testing capacity, population density, and health services -with temperature variation. [8][9][10] Because temperature is correlated with many (often unobservable) confounding factors, such cross-sectional comparisons may not have a causal interpretation. [11][12][13] For example, countries that are cooler on average also tend to have higher income per capita 14 which may affect the number of new COVID-19 cases by enabling more public health measures such as testing and hospitalizations.…”

Seasonality and uncertainty in COVID-19 growth rates

“…We modeled with a hierarchical Bayesian Gaussian regression with a log link on the weekly in previous studies (11)(12)(13). Based on sensitivity analyses discussed below, we found that 382 maximum daily UV was a considerably better predictor than the mean (delta LOOIC = 4.x) so 383 we used the maximum in our best model.…”