Rationing social contact during the COVID-19 pandemic: Transmission risk and social benefits of US locations

Benzell, Seth G.; Collis, Avinash; Nicolaides, Christos

doi:10.1073/pnas.2008025117

Cited by 118 publications

(86 citation statements)

References 4 publications

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“…Understanding how COVID-19 spreads is of paramount importance for managing the crisis and informing effective policies (27). The current work highlights the role of regional personality in a setting of high ecological validity by combining regional personality estimates derived from over 3.4 million participants in the US and Germany with official regional COVID-19 prevalence data and geo-tagged mobility data of approximately 29 million individuals.…”

Section: Discussionmentioning

confidence: 99%

Regional personality differences predict variation in COVID-19 infections and social distancing behavior

Peters¹,

Matz²

2020

Preprint

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Social behaviors play a key role in the spread of COVID-19. Consequently, regional variation in personality traits that capture individual differences in these social behaviors may offer new insight into regional variation in COVID-19 cases. Here we combine self-reported personality data (N ≈ 3.5 million people), official COVID-19 prevalence rates, and behavioral observations (N ≈ 29 million people) to show that regional personality differences in the US and Germany predict the regional onset and growth of COVID-19 cases above and beyond a highly conservative set of socio-demographic, socio-economic, and pandemic-related control variables. Openness and Extraversion were consistently related to earlier COVID-19 onsets and steeper growth rates. The opposite pattern was found for Neuroticism. We also shed light on regional responses to the outbreak, showing that regional personality (i) is associated with objective indicators of regional social distancing, and (ii) predicts self-reported social distancing beyond the effects of individual-level personality.

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Section: Discussionmentioning

confidence: 99%

Regional personality differences predict variation in COVID-19 infections and social distancing behavior

Peters¹,

Matz²

2020

Preprint

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“…Multiple research studies have found that population activities are important in predicting the growth of future cases. Benzell et al [44] identified risks associated with visits to venues at which people would be placed in proximity and showed that particularly high risk was associated with visits to restaurants, grocery stores, fast food establishments, cafes, and gyms. Also, Lai et al [45] found that there is high risk of small disease outbreaks within residential facilities for elderly, indicating that examining the number of visits to such facilities could be helpful in forecasting the growth of infection.…”

Section: A Feature Collectionmentioning

confidence: 99%

“…We consider visits to the following types of places in our model: amusement parks and arcades, colleges/universities/professional schools, living facilities for elderly, department stores, hospitals, merchandise stores/supercenters, grocery stores, and restaurants/eating places. As discussed in III-A, visits to grocery stores, restaurants/other eating places, living facilities for elderly, and hospitals could be particularly helpful in forecasting the growth of cases since these places could facilitate virus transmission [44] , is defined formally as [32]: where s i , s j represents the population activity intensity in cell i and j respectively and d ij represents the distance between the two cells. In our analysis, we define a cell to be a 4 km 2 area.…”

Section: B Population Activitiesmentioning

confidence: 99%

DeepCOVIDNet: An Interpretable Deep Learning Model for Predictive Surveillance of COVID-19 Using Heterogeneous Features and Their Interactions

2020

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In this paper, we propose a deep learning model to forecast the range of increase in COVID-19 infected cases in future days and we present a novel method to compute equidimensional representations of multivariate time series and multivariate spatial time series data. Using this novel method, the proposed model can both take in a large number of heterogeneous features, such as census data, intra-county mobility, inter-county mobility, social distancing data, past growth of infection, among others, and learn complex interactions between these features. Using data collected from various sources, we estimate the range of increase in infected cases seven days into the future for all U.S. counties. In addition, we use the model to identify the most influential features for prediction of the growth of infection. We also analyze pairs of features and estimate the amount of observed second-order interaction between them. Experiments show that the proposed model obtains satisfactory predictive performance and fairly interpretable feature analysis results; hence, the proposed model could complement the standard epidemiological models for nationallevel surveillance of pandemics, such as COVID-19. The results and findings obtained from the deep learning model could potentially inform policymakers and researchers in devising effective mitigation and response strategies. To fast-track further development and experimentation, the code used to implement the proposed model has been made fully open source.

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“…Our work is also related to a number of more empirical studies exploring the role of heterogeneity across sectors and across workers in the spreading of the infection and in designing efficient containment policies, such as Benzell et al (2020), Dingel and Neiman (2020), Kaplan et al (2020), Leibovici et al (2020), and Mongey et al (2020).…”

Section: Connection To Existing Literaturementioning

confidence: 99%

Pandemic Control in Econ-Epi Networks

Fogli

Perri

Ponder

et al. 2020

Preprint

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We develop an ECON-EPI network model to evaluate policies designed to improve health and economic outcomes during a pandemic. Relative to the standard epidemiological SIR set-up, we explicitly model social contacts among individuals and allow for heterogeneity in their number and stability. In addition, we embed the network in a structural economic model describing how contacts generate economic activity. We calibrate it to the New York metro area during the 2020 COVID-19 crisis and show three main results. First, the ECON-EPI network implies patterns of infections that better match the data compared to the standard SIR. The switching during the early phase of the pandemic from unstable to stable contacts is crucial for this result. Second, the model suggests the design of smart policies that reduce infections and at the same time boost economic activity. Third, the model shows that reopening sectors characterized by numerous and unstable contacts (such as large events or schools) too early leads to fast growth of infections.

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Rationing social contact during the COVID-19 pandemic: Transmission risk and social benefits of US locations

Cited by 118 publications

References 4 publications

Regional personality differences predict variation in COVID-19 infections and social distancing behavior

Regional personality differences predict variation in COVID-19 infections and social distancing behavior

DeepCOVIDNet: An Interpretable Deep Learning Model for Predictive Surveillance of COVID-19 Using Heterogeneous Features and Their Interactions

Pandemic Control in Econ-Epi Networks

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