分析现实生活中的疫情爆发模拟器对疫情缓解的影响：一项流行病学建模研究。

Analyzing the impact of a real-life outbreak simulator on pandemic mitigation: An epidemiological modeling study.

作者信息

Specht Ivan, Sani Kian, Loftness Bryn C, Hoffman Curtis, Gionet Gabrielle, Bronson Amy, Marshall John, Decker Craig, Bailey Landen, Siyanbade Tomi, Kemball Molly, Pickett Brett E, Hanage William P, Brown Todd, Sabeti Pardis C, Colubri Andrés

机构信息

The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.

Harvard College, Faculty of Arts and Sciences, Harvard University, Cambridge, MA 02138, USA.

出版信息

Patterns (N Y). 2022 Aug 12;3(8):100572. doi: 10.1016/j.patter.2022.100572.

DOI:10.1016/j.patter.2022.100572

PMID:36033592

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9403368/

Abstract

An app-based educational outbreak simulator, Operation Outbreak (OO), seeks to engage and educate participants to better respond to outbreaks. Here, we examine the utility of OO for understanding epidemiological dynamics. The OO app enables experience-based learning about outbreaks, spreading a virtual pathogen via Bluetooth among participating smartphones. Deployed at many colleges and in other settings, OO collects anonymized spatiotemporal data, including the time and duration of the contacts among participants of the simulation. We report the distribution, timing, duration, and connectedness of student social contacts at two university deployments and uncover cryptic transmission pathways through individuals' second-degree contacts. We then construct epidemiological models based on the OO-generated contact networks to predict the transmission pathways of hypothetical pathogens with varying reproductive numbers. Finally, we demonstrate that the granularity of OO data enables institutions to mitigate outbreaks by proactively and strategically testing and/or vaccinating individuals based on individual social interaction levels.

摘要

一款基于应用程序的教育性疫情模拟工具“疫情行动”（Operation Outbreak，简称OO）旨在让参与者参与其中并接受教育，以便更好地应对疫情。在此，我们考察了OO在理解流行病学动态方面的效用。OO应用程序能让用户通过基于经验的学习来了解疫情，通过蓝牙在参与的智能手机之间传播虚拟病原体。OO在许多大学及其他场所进行了部署，收集匿名的时空数据，包括模拟参与者之间接触的时间和时长。我们报告了在两所大学部署OO时学生社交接触的分布、时间、时长和连通性，并通过个人的二级接触发现了隐匿的传播途径。然后，我们基于OO生成的接触网络构建流行病学模型，以预测具有不同繁殖数的假设病原体的传播途径。最后，我们证明，OO数据的精细程度使各机构能够根据个人社交互动水平，通过主动且策略性地对个人进行检测和/或接种疫苗来缓解疫情。