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Lancet ; 399(10339): 1937-1938, 2022 05 21.
Article in English | MEDLINE | ID: covidwho-1821537

COVID-19 , Humans
Spat Spatiotemporal Epidemiol ; 39: 100443, 2021 11.
Article in English | MEDLINE | ID: covidwho-1459135


The study of the impacts of air pollution on COVID-19 has gained increasing attention. However, most of the existing studies are based on a single country, with a high degree of variation in the results reported in different papers. We attempt to inform the debate about the long-term effects of air pollution on COVID-19 by conducting a multi-country analysis using a spatial ecological design, including Canada, Italy, England and the United States. The model allows the residual spatial autocorrelation after accounting for covariates. It is concluded that the effects of PM2.5 and NO2 are inconsistent across countries. Specifically, NO2 was not found to be an important factor affecting COVID-19 infection, while a large effect for PM2.5 in the US is not found in the other three countries. The Population Attributable Fraction for COVID-19 incidence ranges from 3.4% in Canada to 45.9% in Italy, although with considerable uncertainty in these estimates.

Air Pollutants , Air Pollution , COVID-19 , Air Pollutants/analysis , Air Pollutants/toxicity , Air Pollution/analysis , Air Pollution/statistics & numerical data , Environmental Exposure/analysis , Environmental Exposure/statistics & numerical data , Humans , Particulate Matter/analysis , Particulate Matter/toxicity , SARS-CoV-2 , United States/epidemiology
Spat Stat ; : 100540, 2021 Sep 28.
Article in English | MEDLINE | ID: covidwho-1440369


Spatial dependence is usually introduced into spatial models using measure of physical proximity. When analyzing COVID-19 case counts, this makes sense as regions that are close together are more likely to have more people moving between them, spreading the disease. However, using the actual number of trips between each region may explain COVID-19 case counts better than physical proximity. In this paper, we investigate the efficacy of using telecommunications-derived mobility data to induce spatial dependence in spatial models applied to two Spanish communities' COVID-19 case counts. We do this by extending Besag York Mollié (BYM) models to include both a physical adjacency effect, alongside a mobility effect. The mobility effect is given a Gaussian Markov random field prior, with the number of trips between regions as edge weights. We leverage modern parametrizations of BYM models to conclude that the number of people moving between regions better explains variation in COVID-19 case counts than physical proximity data. We suggest that this data should be used in conjunction with physical proximity data when developing spatial models for COVID-19 case counts.

Spat Stat ; 41: 100480, 2021 Mar.
Article in English | MEDLINE | ID: covidwho-899515


Many countries have enforced social distancing to stop the spread of COVID-19. Within countries, although the measures taken by governments are similar, the incidence rate varies among areas (e.g., counties, cities). One potential explanation is that people in some areas are more vulnerable to the coronavirus disease because of their worsened health conditions caused by long-term exposure to poor air quality. In this study, we investigate whether long-term exposure to air pollution increases the risk of COVID-19 infection in Germany. The results show that nitrogen dioxide (NO 2 ) is significantly associated with COVID-19 incidence, with a 1 µ g  m - 3 increase in long-term exposure to NO 2 increasing the COVID-19 incidence rate by 5.58% (95% credible interval [CI]: 3.35%, 7.86%). This result is consistent across various models. The analyses can be reproduced and updated routinely using public data sources and shared R code.