Exposure to air pollution and COVID‐19 severity: A review of current insights, management, and challenges

Ali, Nurshad; Fariha, Khandaker Atkia; Islam, Farjana; Mishu, Moshiul Alam; Mohanto, Nayan Chandra; Hossain, Khaled

doi:10.1002/ieam.4435

Cited by 26 publications

(11 citation statements)

References 81 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Approximately 140,000 total deaths per year were attributable to total outdoor air pollution exposure in the U.S. from 2000 to 2010 ( Lelieveld et al 2015 ). There is evidence for both short and long-term exposure of air pollution to increase SARS-CoV-2 infections (transmission) as well as COVID-19 severity and mortality ( Ali et al 2021 ; Woodby, Arnold, and Valacchi 2021 ). Chronic exposure to air pollution results in increases in all-cause, cardiovascular, and influenza mortality ( Jiang, Mei, and Feng 2016 ; Pope et al 2004 ).…”

Section: Discussionmentioning

confidence: 99%

Association between long-term exposure to particulate air pollution with SARS-CoV-2 infections and COVID-19 deaths in California, U.S.A.

English

Behren

Balmes

et al. 2022

Environmental Advances

View full text Add to dashboard Cite

Section: Discussionmentioning

confidence: 99%

Association between long-term exposure to particulate air pollution with SARS-CoV-2 infections and COVID-19 deaths in California, U.S.A.

English

Behren

Balmes

et al. 2022

Environmental Advances

View full text Add to dashboard Cite

“…Moreover, it is important to point out that the effect of exposure to air pollutants on COVID‐19 outcomes varied within regions. Several factors can explain the divergences, such as differences in lockdown policies and strategies, socioeconomic indicators, quality of healthcare services, geographical location, and meteorological parameters (Ali et al, 2021 ). For instance, a comparative study among China, England, Germany, and Japan indicated that the population density and absolute humidity influence both the SARS-COV-2 spread and the decay duration of the pandemic (Diao et al, 2021 ).…”

Section: Discussionmentioning

confidence: 99%

Does air pollution explain COVID-19 fatality and mortality rates? A multi-city study in São Paulo state, Brazil

Leirião

Debone

Miraglia

2022

Environ Monit Assess

View full text Add to dashboard Cite

Since air pollution compromise the respiratory system and COVID-19 disease is caused by a respiratory virus, it is expected that air pollution plays an important role in the current COVID-19 pandemic. Exploratory studies have observed positive associations between air pollution and COVID-19 cases, deaths, fatality, and mortality rate. However, no study focused on Brazil, one of the most affected countries by the pandemic. Thus, this study aimed to understand how long-term exposure to PM 10 , PM 2.5 , and NO 2 contributed to COVID-19 fatality and mortality rates in São Paulo state in 2020. Air quality data between 2015 and 2019 in 64 monitoring stations within 36 municipalities were considered. The COVID-19 fatality was calculated considering cases and deaths from the government’s official data and the mortality rate was calculated considering the 2020 population. Linear regression models were well-fitted for PM 2.5 concentration and fatality ( R 2 = 0.416; p = 0.003), NO 2 concentration and fatality ( R 2 = 0.232; p = 0.005), and NO 2 concentration and mortality ( R 2 = 0.273; p = 0.002). This study corroborates other authors’ findings and enriches the discussion for having considered a longer time series to represent long-term exposure to the pollutants and for having considered one of the regions with the highest incidence of COVID-19 in the world. Thus, it reinforces measures to reduce the concentration of air pollutants which are essential for public health and will increase the chance to survive in future respiratory disease epidemics.

show abstract

“… Vaccinations reduce the population’s susceptible fraction [ 16 ]. Air pollution may enhance COVID-19 severity [ 17 ]. …”

Section: Methodsmentioning

confidence: 99%

Data-driven prediction of COVID-19 cases in Germany for decision making

Lorenz

Riedlinger

Taubenböck

et al. 2022

BMC Med Res Methodol

View full text Add to dashboard Cite

Background The COVID-19 pandemic has led to a high interest in mathematical models describing and predicting the diverse aspects and implications of the virus outbreak. Model results represent an important part of the information base for the decision process on different administrative levels. The Robert-Koch-Institute (RKI) initiated a project whose main goal is to predict COVID-19-specific occupation of beds in intensive care units: Steuerungs-Prognose von Intensivmedizinischen COVID-19 Kapazitäten (SPoCK). The incidence of COVID-19 cases is a crucial predictor for this occupation. Methods We developed a model based on ordinary differential equations for the COVID-19 spread with a time-dependent infection rate described by a spline. Furthermore, the model explicitly accounts for weekday-specific reporting and adjusts for reporting delay. The model is calibrated in a purely data-driven manner by a maximum likelihood approach. Uncertainties are evaluated using the profile likelihood method. The uncertainty about the appropriate modeling assumptions can be accounted for by including and merging results of different modelling approaches. The analysis uses data from Germany describing the COVID-19 spread from early 2020 until March 31st, 2021. Results The model is calibrated based on incident cases on a daily basis and provides daily predictions of incident COVID-19 cases for the upcoming three weeks including uncertainty estimates for Germany and its subregions. Derived quantities such as cumulative counts and 7-day incidences with corresponding uncertainties can be computed. The estimation of the time-dependent infection rate leads to an estimated reproduction factor that is oscillating around one. Data-driven estimation of the dark figure purely from incident cases is not feasible. Conclusions We successfully implemented a procedure to forecast near future COVID-19 incidences for diverse subregions in Germany which are made available to various decision makers via an interactive web application. Results of the incidence modeling are also used as a predictor for forecasting the need of intensive care units.

show abstract

Exposure to air pollution and COVID‐19 severity: A review of current insights, management, and challenges

Abstract: Data availability statement:The data that support the findings of this study are available in the published literature also cited in the present manuscript.

Cited by 26 publications

References 81 publications

Association between long-term exposure to particulate air pollution with SARS-CoV-2 infections and COVID-19 deaths in California, U.S.A.

Association between long-term exposure to particulate air pollution with SARS-CoV-2 infections and COVID-19 deaths in California, U.S.A.

Does air pollution explain COVID-19 fatality and mortality rates? A multi-city study in São Paulo state, Brazil

Data-driven prediction of COVID-19 cases in Germany for decision making

Contact Info

Product

Resources

About