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- W3100575009 abstract "We build a parsimonious Crump-Mode-Jagers continuous time branching process of COVID-19 propagation based on a negative binomial process subordinated by a gamma subordinator. By focusing on the stochastic nature of the process in small populations, our model provides decision making insight into mitigation strategies as an outbreak begins. Our model accommodates contact tracing and isolation, allowing for comparisons between different types of intervention. We emphasize a physical interpretation of the disease propagation throughout which affords analytical results for comparison to simulations. Our model provides a basis for decision makers to understand the likely trade-offs and consequences between alternative outbreak mitigation strategies particularly in office environments and confined work-spaces. Combining the asymptotic limit of our model with Bayesian hierarchical techniques, we provide US county level inferences for the reproduction number from cumulative case count data over July and August of this year." @default.
- W3100575009 created "2020-11-23" @default.
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- W3100575009 date "2021-03-01" @default.
- W3100575009 modified "2023-10-18" @default.
- W3100575009 title "A model of COVID-19 propagation based on a gamma subordinated negative binomial branching process" @default.
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- W3100575009 doi "https://doi.org/10.1016/j.jtbi.2020.110536" @default.
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