ArticleJournal of environmental management2022
The impact of weather condition and social activity on COVID-19 transmission in the United States.
Article in Journal of environmental management, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Article
- Can past variants of SARS-CoV-2 predict the impact of future variants? Machine learning for early warning of US counties at risk.Health care management science · 2025Article
- Natural and socio-environmental factors in the transmission of COVID-19: a comprehensive analysis of epidemiology and mechanisms.BMC public health · 2024Review
- Asymptomatic Children as a Missing Link in Preventing COVID-19 Transmission.Journal of research in health sciences · 2024Article
- Rain, rain, go away, come again another day: do climate variations enhance the spread of COVID-19?Globalization and health · 2024Review
- Online Handwritten Signature Verification Method Based on Uni-Feature Correlation Coefficient between Signatures.Sensors (Basel, Switzerland) · 2023Article
- Role of Trust, Risk Perception, and Perceived Benefit in COVID-19 Vaccination Intention of the Public.Healthcare (Basel, Switzerland) · 2023Article
- The complex interplay between weather, social activity, and COVID-19 in the US.SSM - population health · 2023Article
- Assessment of natural ventilation strategy to decrease the risk of COVID 19 infection at a rural elementary school.Heliyon · 2023Article
- The impact of built and socio-economic environment factors on Covid-19 transmission at the ZIP-code level in Florida.Journal of environmental management · 2023Article
- Is the virus-laden standing water change the transmission intensity of SARS-CoV-2 after precipitation? A framework for empirical studies.Environmental research · 2022Article
- Article
- Biological Properties of SARS-CoV-2 Variants: Epidemiological Impact and Clinical Consequences.Vaccines · 2022Review
- COVID-19 pandemic over 2020 (withlockdowns) and 2021 (with vaccinations): similar effects for seasonality and environmental factors.Environmental research · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The coronavirus disease 2019 (COVID-19) has been first reported in December 2019 and rapidly spread worldwide. As other severe acute respiratory syndromes, it is a widely discussed topic whether seasonality affects the COVID-19 infection spreading. This study presents two different approaches to analyse the impact of social activity factors and weather variables on daily COVID-19 cases at county level over the Continental U.S. (CONUS). The first one is a traditional statistical method, i.e., Pearson correlation coefficient, whereas the second one is a machine learning algorithm, i.e., random forest regression model. The Pearson correlation is analysed to roughly test the relationship between COVID-19 cases and the weather variables or the social activity factor (i.e. social distance index). The random forest regression model investigates the feasibility of estimating the number of county-level daily confirmed COVID-19 cases by using different combinations of eight factors (county population, county population density, county social distance index, air temperature, specific humidity, shortwave radiation, precipitation, and wind speed). Results show that the number of daily confirmed COVID-19 cases is weakly correlated with the social distance index, air temperature and specific humidity through the Pearson correlation method. The random forest model shows that the estimation of COVID-19 cases is more accurate with adding weather variables as input data. Specifically, the most important factors for estimating daily COVID-19 cases are the population and population density, followed by the social distance index and the five weather variables, with temperature and specific humidity being more critical than shortwave radiation, wind speed, and precipitation. The validation process shows that the general values of correlation coefficients between the daily COVID-19 cases estimated by the random forest model and the observed ones are around 0.85.
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