Impacts of transportation and meteorological factors on the transmission of COVID-19

Abstract

The ongoing pandemic of 2019 novel coronavirus disease (COVID-19) is challenging global public health response system. We aim to identify the risk factors for the transmission of COVID-19 using data on mainland China. We estimated attack rate (AR) at county level. Logistic regression was used to explore the role of transportation in the nationwide spread. Generalized additive model and stratified linear mixed-effects model were developed to identify the effects of multiple meteorological factors on local transmission. The ARs in affected counties ranged from 0.6 to 9750.4 per million persons, with a median of 8.8. The counties being intersected by railways, freeways, national highways or having airports had significantly higher risk for COVID-19 with adjusted odds ratios (ORs) of 1.40 (p = 0.001), 2.07 (p < 0.001), 1.31 (p = 0.04), and 1.70 (p < 0.001), respectively. The higher AR of COVID-19 was significantly associated with lower average temperature, moderate cumulative precipitation and higher wind speed. Significant pairwise interactions were found among above three meteorological factors with higher risk of COVID-19 under low temperature and moderate precipitation. Warm areas can also be in higher risk of the disease with the increasing wind speed. In conclusion, transportation and meteorological factors may play important roles in the transmission of COVID-19 in mainland China, and could be integrated in consideration by public health alarm systems to better prevent the disease.

Social, economic, and environmental factors influencing the basic reproduction number of COVID-19 across countries

“…We identified four factors (youth, city, social media, and GINI inequality) as having strong relationships with COVID-19 R 0 across countries. Environmental factors, which are the most common factors previously identified (temperature (11–24), pollution (13,25–31), precipitation/humidity (18,32,33)), did not have strong relationships with R 0 when other factors are considered simultaneously, although pollution, temperature, and humidity all have positive associations.…”

“…All of these categories have been suggested previously as possible factors for COVID-19 transmission. The most common factors previously studied were temperature (11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24), pollution (13,(25)(26)(27)(28)(29)(30)(31), precipitation/humidity (18,32,33), population density (34,35), age structure (1,36,37), and population size (1,11,31). For these and additional covariates either previously studied or only mentioned in the media, we rely on statistics measured at a national level.…”

An Optimal Nucleic Acid Testing Strategy for COVID-19 during the Spring Festival Travel Rush in Mainland China: A Modelling Study

“…We used published literature on the sensitivity and specificity of PCR assays for SARS-CoV-2, incorporating time-varying estimates of sensitivity based on time since exposure [ 16 ]. We set that each individual had a fixed probability of being infected every day, with a slightly increased risk on the day of travel [ 17 , 18 ]. Based on a previous estimation [ 19 ], the COVID-19 daily incidence was set at 20 per 10,000,000 persons.…”

COVID-19: Optimal Allocation of Ventilator Supply under Uncertainty and Risk

“…Kretzschmar et al (2020) develop a stochastic mathematical model to understand the impact of time delays in testing and isolation on the reproductive number of the COVID-19 transmission. Differential equations have been mainly used to solve compartmental disease models (see, e.g., Ambikapathy and Krishnamurthy (2020), Wei et al (2020), Zeb et al (2020), Tuan et al (2020), and Wang et al (2020)). For example, Roberts et al (2020) come up with a series of differential equations that are designed to compare the two scenarios based on delaying of ICU bed shortage effects of hospitalizing fewer COVID-19 patients versus increasing the ICU bed capacity.…”