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基于伽马从属负二项分支过程的 COVID-19 传播模型。

A model of COVID-19 propagation based on a gamma subordinated negative binomial branching process.

机构信息

Public Services and Procurement Canada, 270 Albert Street, Ottawa, ON K1P 6N7, Canada; Public Health Agency of Canada, 130 Colonnade Road, Ottawa, ON K1A 0K9, Canada.

Public Services and Procurement Canada, 270 Albert Street, Ottawa, ON K1P 6N7, Canada; Public Health Agency of Canada, 130 Colonnade Road, Ottawa, ON K1A 0K9, Canada.

出版信息

J Theor Biol. 2021 Mar 7;512:110536. doi: 10.1016/j.jtbi.2020.110536. Epub 2020 Nov 10.

DOI:10.1016/j.jtbi.2020.110536
PMID:33186594
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7654309/
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.

摘要

我们基于负二项过程和伽马子过程构建了一个简洁的 COVID-19 传播的 Crump-Mode-Jagers 连续时间分支过程。通过关注小种群中过程的随机性质,我们的模型为缓解策略提供了决策见解,因为疫情开始了。我们的模型允许接触者追踪和隔离,允许对不同类型的干预措施进行比较。我们强调疾病传播的物理解释,这为与模拟进行比较提供了分析结果。我们的模型为决策者提供了一个基础,以了解替代疫情缓解策略之间的可能权衡和后果,特别是在办公室环境和封闭工作场所。我们将模型的渐近极限与贝叶斯分层技术相结合,为今年 7 月和 8 月的累积病例数据提供了美国县级的繁殖数推断。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/36b2/7654309/b42b297b8334/gr6_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/36b2/7654309/9cb884ee47eb/gr1_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/36b2/7654309/ebf0f74768aa/gr2_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/36b2/7654309/bf7e7c779891/gr3_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/36b2/7654309/7967513b113c/gr4_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/36b2/7654309/074b13a8babc/gr5_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/36b2/7654309/b42b297b8334/gr6_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/36b2/7654309/9cb884ee47eb/gr1_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/36b2/7654309/ebf0f74768aa/gr2_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/36b2/7654309/bf7e7c779891/gr3_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/36b2/7654309/7967513b113c/gr4_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/36b2/7654309/074b13a8babc/gr5_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/36b2/7654309/b42b297b8334/gr6_lrg.jpg

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