Is there an association between the level of ambient air pollution and COVID-19?

Wang, Baoming; Chen, Hui; Chan, Yik Lung; Oliver, Brian G.

doi:10.1152/ajplung.00244.2020

“…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)(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)), did not have strong relationships with R 0 when other factors are considered simultaneously, although pollution, temperature, and humidity all have positive associations.…”

Section: Discussionmentioning

confidence: 86%

“…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.…”

Section: Introductionmentioning

confidence: 99%

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

Kong

¹

,

Tekwa

²

,

Gignoux‐Wolfsohn

³

2021

Preprint

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ObjectiveTo assess whether the basic reproduction number (R0) of COVID-19 is different across countries and what national-level demographic, social, and environmental factors characterize initial vulnerability to the virus.MethodsWe fit logistic growth curves to reported daily case numbers, up to the first epidemic peak. This fitting estimates R0. We then use a generalized additive model to discern the effects, and include 5 random effect covariates to account for potential differences in testing and reporting that can bias the estimated R0.FindingsWe found that the mean R0 is 1.70 (S.D. 0.57), with a range between 1.10 (Ghana) and 3.52 (South Korea). We identified four factors-population between 20-34 years old (youth), population residing in urban agglomerates over 1 million (city), social media use to organize offline action (social media), and GINI income inequality-as having strong relationships with R0. An intermediate level of youth and GINI inequality are associated with high R0, while high city population and high social media use are associated with high R0. Environmental and climate factors were not found to have strong relationships with R0.ConclusionStudies that aim to measure the effectiveness of interventions should account for the intrinsic differences between populations.

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“…We simulate scenarios in which the transmission rate during an encounter between an infectious and a susceptible individual depends on the specific site type. Since environmental factors have shown to exhibit vastly different transmission rates, such as indoor vs. outdoor, room size, ventilation and air quality [25], by way of example, we increase the transmission rate at social sites by a factor k and decrease the transmission rate at bus stops by the same factor k , for k ∈ {2, 10}. While PanCast, using site information, has access to the true transmission rates, we assume for SPECTs to only have access to the average transmission rate.…”

Section: Resultsmentioning

confidence: 99%

Listening to Bluetooth Beacons for Epidemic Risk Mitigation

Barthe

¹

,

Viti

²

,

Druschel

³

et al. 2021

Preprint

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During the ongoing COVID-19 pandemic, there have been burgeoning efforts to develop and deploy smartphone apps to expedite contact tracing and risk notification. Unfortunately, the success of these apps has been limited, partly owing to poor interoperability with manual contact tracing, low adoption rates, and a societally sensitive trade-off between utility and privacy. In this work, we introduce a new privacy-preserving and inclusive system for epidemic risk assessment and notification that aims to address the above limitations. Rather than capturing pairwise encounters between smartphones as done by existing apps, our system captures encounters between inexpensive, zero-maintenance, small devices carried by users, and beacons placed in strategic locations where infection clusters are most likely to originate. Epidemiological simulations using an agent-based model demonstrate several beneficial properties of our system. By achieving bidirectional interoperability with manual contact tracing, our system can help control disease spread already at low adoption. By utilizing the location and environmental information provided by the beacons, our system can provide significantly higher sensitivity and specificity than existing app-based systems. In addition, our simulations also suggest that it is sufficient to deploy beacons in a small fraction of strategic locations for our system to achieve high utility.

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“…We simulate scenarios in which the transmission rate during an encounter between an infectious and a susceptible individual depends on the specific site type. Since environmental factors have shown to exhibit vastly different transmission rates, such as indoor vs. outdoor, room size, ventilation and air quality (24), by way of example, we increase the transmission rate at social sites by a factor k and decrease the transmission rate at bus stops by the same factor k, for k ∈ {2, 10}. While PanCast, using site information, has access to the true transmission rates, we assume for SPECTs to only have access to the average transmission rate.…”

Section: Pancast Outperforms Spects Under Partial Adoptionmentioning

confidence: 99%

Listening to Bluetooth Beacons for Epidemic Risk Mitigation

Barthe

¹

,

Viti

²

,

Druschel

³

et al. 2021

Preprint

View full text Add to dashboard Cite

During the ongoing COVID-19 pandemic, there have been burgeoning efforts to develop and deploy smartphone apps to expedite contact tracing and risk notification. Unfortunately, the success of these apps has been limited, partly owing to poor interoperability with manual contact tracing, low adoption rates, and a societally sensitive trade-off between utility and privacy. In this work, we introduce a new privacy-preserving and inclusive system for epidemic risk assessment and notification that aims to address the above limitations. Rather than capturing pairwise encounters between smartphones as done by existing apps, our system captures encounters between inexpensive, zero-maintenance, small devices carried by users, and beacons placed in strategic locations where infection clusters are most likely to originate. Epidemiological simulations using an agent-based model demonstrate several beneficial properties of our system. By achieving bidirectional interoperability with manual contact tracing, our system can help control disease spread already at low adoption. By utilizing the location and environmental information provided by the beacons, our system can provide significantly higher sensitivity and specificity than existing app-based systems. In addition, our simulations also suggest that it is sufficient to deploy beacons in a small fraction of strategic locations for our system to achieve high utility.

show abstract

Is there an association between the level of ambient air pollution and COVID-19?

Cited by 52 publications

References 57 publications

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

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

Listening to Bluetooth Beacons for Epidemic Risk Mitigation

Listening to Bluetooth Beacons for Epidemic Risk Mitigation

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