Mobility and Engagement  Following the SARS-Cov-2 Outbreak

Atkinson, Tyler; Dolmas, Jim; Koch, Christoffer; Koenig, E.; Mertens, Karel; Murphy, Anthony T.; Yi, Kei-Mu

doi:10.24149/wp2014

Cited by 26 publications

(20 citation statements)

References 11 publications

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“…Each variable is scaled by the weekday-specific average over January and February prior to the principal component analysis. See Atkinson, Dolmas, Koch, Koenig, Mertens, Murphy, and Yi (2020) for details. 7…”

Section: Data and Stylized Factsmentioning

confidence: 99%

Weather, Social Distancing, and the Spread of COVID-19^*

Wilson

2020

Preprint

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Using high-frequency panel data for U.S. counties, I estimate the full dynamic response of COVID-19 cases and deaths to exogenous movements in mobility and weather. I find several important results. First, holding mobility fixed, temperature is found to have a negative and significant effect on COVID-19 cases from 1 to 8 weeks ahead and on deaths from 2 to 8 weeks ahead. Second, holding weather fixed, mobility is found to have a large positive effect on subsequent growth in COVID-19 cases and deaths. The impact on cases becomes significant 3 to 4 weeks ahead and continues through 8 to 10 weeks ahead. The impact on deaths becomes significant around 4 weeks ahead and persists for at least 10 weeks. Third, I find that the deleterious effects of mobility on COVID-19 outcomes are far greater when the local virus transmission rate is above one -- evidence supportive of public health policies aiming to reduce mobility specifically in places experiencing high transmission rates while relaxing restrictions elsewhere. Fourth, I find that the dynamic effects of mobility on cases are generally similar across counties, but the effects on deaths are higher for counties with older populations and, surprisingly, counties with lower black or hispanic population shares. Lastly, I find that while the marginal impact of mobility changes has been stable over recent weeks for cases, it has come down for deaths.

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Section: Data and Stylized Factsmentioning

confidence: 99%

Weather, Social Distancing, and the Spread of COVID-19^*

Wilson

2020

Preprint

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“…A closely related strand of literature focuses on documenting the labor market disruptions associated with COVID-19 (e.g., Atkinson, Dolmas, Koch, Koenig, Mertens, Murphy, Yi, 2020 , Andersen, Maclean, Pesko, Simon, 2020 , Coibion, Gorodnichenko, Weber, 2020 , Kong, Prinz, 2020 ). These studies document significant increases in unemployment and estimate job loss numbers of up to 20 million.…”

Section: Introductionmentioning

confidence: 99%

Coronavirus infections and deaths by poverty status: The effects of social distancing

Jung

Manley

Shrestha

2021

Journal of Economic Behavior & Organization

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We study the spread of COVID-19 infections and deaths by county poverty level in the US. In the beginning of the pandemic, counties with either very low poverty levels or very high poverty levels reported the highest numbers of cases. A U-shaped relationship prevails for counties with high population density while among counties with low population density, only poorer counties report high incidence rates of COVID-19. Second, we discuss the pattern of infections spreading from higher to lower income counties. Third, we show that stay-at-home mandates caused significantly higher reductions in mobility in high income counties that experienced adverse weather shocks than counties that did not. These effects are not present in counties with high poverty rates. Using weather shocks in combination with stay-at-home mandates as an instrument for social distancing, we find that measures taken to promote social distancing helped curb infections in high income counties but not in low income counties. These results have important policy implications for containing the spread of infectious diseases in the future.

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“…Each variable is scaled by the weekday-specific average over January and February prior to the principal component analysis. SeeAtkinson et al (2020) for details. The data were accessed at https://www.dallasfed.org/~/media/documents/research/mei/MEI_counties_ scaled.csv.6 The data were accessed at https://www.google.com/covid19/mobility/.…”

mentioning

confidence: 99%

Weather, Social Distancing, and the Spread of COVID-19

Wilson¹

2020

ERWP

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This paper develops an econometric panel data model that can be used both to identify the dynamic effects of disease transmission factors and to forecast disease spread.The empirical model is derived from the canonical SIR epidemiological model of infectious disease spread. The model is estimated using near real-time, county-level data on mobility, weather, and COVID-19 cases. Both mobility and weather are found to have significant effects on COVID-19 effects up to 70 days ahead. Predicted values from the estimated model, augmented to incorporate recent vaccinations, provide out-of-sample forecasts of COVID-19 infections at the county and national levels. Prior forecasts are shown to have been fairly accurate, especially in terms of the geographical/crosssectional distribution of COVID-19 infections and in terms of the national aggregate forecast. The latest forecasts, using data through February 19, 2021, predict steep declines in infections in most parts of the country over the next several weeks. Nationally, infections are predicted to fall by 59% over the subsequent 30 days. Decomposing the drivers of the latest forecast, the model indicates that accumulated natural immunity (i.e., cumulative infections to date, a.k.a. "seroprevalence") is the primary factor exerting a strong downward pull on new infections.

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Mobility and Engagement Following the SARS-Cov-2 Outbreak

Cited by 26 publications

References 11 publications

Weather, Social Distancing, and the Spread of COVID-19^*

Weather, Social Distancing, and the Spread of COVID-19^*

Coronavirus infections and deaths by poverty status: The effects of social distancing

Weather, Social Distancing, and the Spread of COVID-19

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Mobility and Engagement Following the SARS-Cov-2 Outbreak

Cited by 26 publications

References 11 publications

Weather, Social Distancing, and the Spread of COVID-19*

Weather, Social Distancing, and the Spread of COVID-19*

Coronavirus infections and deaths by poverty status: The effects of social distancing

Weather, Social Distancing, and the Spread of COVID-19

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Product

Resources

About

Weather, Social Distancing, and the Spread of COVID-19^*

Weather, Social Distancing, and the Spread of COVID-19^*