利用新的计算模型探索 COVID-19 期间公共卫生能力限制的针对性方法

IF 8.8 3区医学 Q1 Medicine

Infectious Disease Modelling Pub Date : 2024-01-12 DOI:10.1016/j.idm.2024.01.001

Ashley N. Micuda , Mark R. Anderson , Irina Babayan , Erin Bolger , Logan Cantin , Gillian Groth , Ry Pressman-Cyna , Charlotte Z. Reed , Noah J. Rowe , Mehdi Shafiee , Benjamin Tam , Marie C. Vidal , Tianai Ye , Ryan D. Martin

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引用次数: 0

摘要

这项工作介绍了女王大学基于代理的疫情结果模型（QUABOOM）。该工具是一种基于代理的蒙特卡罗模拟，用于模拟流行病并为公共卫生政策提供信息。我们通过研究封锁期间的容量限制来说明该模型的使用。我们发现，公共卫生措施的重点应放在杂货店等人流较多的少数地点，而不是小企业等人流较少的众多地点。我们还讨论了一种情况，即模拟结果可以扩展到更大的人口规模，从而提高计算效率。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Exploring a targeted approach for public health capacity restrictions during COVID-19 using a new computational model

This work introduces the Queen's University Agent-Based Outbreak Outcome Model (QUABOOM). This tool is an agent-based Monte Carlo simulation for modelling epidemics and informing public health policy. We illustrate the use of the model by examining capacity restrictions during a lockdown. We find that public health measures should focus on the few locations where many people interact, such as grocery stores, rather than the many locations where few people interact, such as small businesses. We also discuss a case where the results of the simulation can be scaled to larger population sizes, thereby improving computational efficiency.

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来源期刊

Infectious Disease Modelling Mathematics-Applied Mathematics

CiteScore

17.00

自引率

3.40%

发文量

审稿时长

17 weeks

期刊介绍： Infectious Disease Modelling is an open access journal that undergoes peer-review. Its main objective is to facilitate research that combines mathematical modelling, retrieval and analysis of infection disease data, and public health decision support. The journal actively encourages original research that improves this interface, as well as review articles that highlight innovative methodologies relevant to data collection, informatics, and policy making in the field of public health.