Capturing Household Transmission in Compartmental Models of Infectious Disease

Bayham, Jude; Fenichel, Eli P.

doi:10.1007/978-3-319-40413-4_20

Cited by 4 publications

(3 citation statements)

References 27 publications

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“…Average contacts changed significantly during the epidemic due to defensive behavior and policy (5, 6). Our measure of contacts includes both POI and in-home contacts, which have been shown to be an important source of infectious-disease transmission (7). Generally, mean contacts fell, and the fraction of individuals staying completely at home increased significantly, but shape parameters spiked between week 10 (starting March 2) and week 13 (ending March 29) (Fig.…”

Section: Resultsmentioning

confidence: 99%

Quarantine fatigue thins fat-tailed coronavirus impacts in U.S. cities by making epidemics inevitable

Conte

Gordon

Sims

et al. 2021

Preprint

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We use detailed location data to show that contacts between individuals in most U.S. cities and counties are fat tailed, suggesting that the fat tails documented in a small number of superspreading clusters are widespread. We integrate these results into a stochastic compartmental model to show that COVID-19 cases were also fat tailed for many U.S. cities for several weeks in the spring and summer. Due to epidemiological thresholds, fat-tailed cases would have been more prevalent if not for the gradual increase in contact rates throughout the summer that made outbreaks more certain.

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Section: Resultsmentioning

confidence: 99%

Quarantine fatigue thins fat-tailed coronavirus impacts in U.S. cities by making epidemics inevitable

Conte

Gordon

Sims

et al. 2021

Preprint

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“…We modify the standard SEIR (susceptible, exposed, infectious, and recovered) compartmental modelling structure in two ways to model social distancing. First, the model needs to account for in-home transmission, we do so by simplifying the age-and-household-structured model of (Bayham and Fenichel, 2016). Second, we modify the contact structure to allow for time varying time allocation and conditional proportional mixing (Fenichel, 2013; Fenichel et al, 2011).…”

Section: Methods: Model Structure and Calibrationmentioning

confidence: 99%

“…Time use provides a measuring contact risk with a recognizable unit that has metric properties unlike arbitrary distancing indexes. Here we build on prior epidemiological time use modeling (Bayham and Fenichel, 2016; Bayham et al, 2015; Berry et al, 2018) to adapt the common SEIR framework to a dynamic time use structure that enables differential behavior by health status in order to incorporate smartphone tracking data into a model of the COVID-19 epidemic for every county in the United States. We use these results to characterize the variation in the epidemic and suggest prioritization for testing and targeting for a “back to work” strategy across the United States.…”

Section: Introductionmentioning

confidence: 99%

A cell phone data driven time use analysis of the COVID-19 epidemic

Fenichel

Berry

Bayham

et al. 2020

Preprint

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Transmission of the SAR-COV-2 virus that causes COVID-19 is largely driven by human behavior and person-to-person contact. By staying home, people reduce the probability of contacting an infectious individual, becoming infected, and passing on the virus. One of the most promising sources of data on time use is smartphone location data. We develop a time use driven proportional mixing SEIR model that naturally incorporates time spent at home measured using smartphone location data and allows people of different health statuses to behave differently. We simulate epidemics in almost every county in the United States. The model suggests that Americans' behavioral shifts have reduced cases in 55%-86% of counties and for 71%-91% of the population, depending on modeling assumptions. Resuming pre-epidemic behavior would lead to a rapid rise in cases in most counties. Spatial patterns of bending and flattening the curve are robust to modeling assumptions. Depending on epidemic history, county demographics, and behavior within a county, returning those with acquired immunity (assuming it exists) to regular schedules generally helps reduce cumulative COVID-19 cases. The model robustly identifies which counties would experience the greatest share of case reduction relative to continued distancing behavior. The model occasionally mischaracterizes epidemic patterns in counties tightly connected to larger counties that are experiencing large epidemics. Understanding these patterns is critical for prioritizing testing resources and back-to-work planning for the United States.

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