Using Agent Based Modelling and Mobility Data to Predict and Respond to the Outbreak and Spread of Infectious Diseases: A Case Study of SARS-CoV-2 in

研究背景SARS-CoV-2大流行继续破坏国家卫生系统和经济，给世界上最脆弱的人群带来最大的风险。在撰写本报告时，全世界已有超过2.03亿例病例，430万例报告死亡（JHU）。这些后果对低收入和中等收入国家构成了特别严重的威胁，由于社会混合模式和家庭规模大，这些国家可能更有可能广泛传播。此外，由于严重缺乏重症监护能力，他们面对大流行的准备最少（步行者等人，2020）。世界银行最近估计，COVID-19可能在2020年内将7100万人推向极端贫困（Mahler et al，2020）。目的本研究的目的有三：1。回顾现有的疾病建模方法，以及基于代理的建模的潜力。建立一个基于Agent的模型，根据人口数据、风险因素和流动性数据预测SARS-CoV-2的时空传播和轨迹以及COVID-193的风险。在空间建模和行为研究相结合的基础上，提出应对未来疾病爆发和快速遏制的政策建议。方法研究将开始，审查迄今为止基于代理的建模用于疾病建模的方式，包括但不限于SARS-CoV-2。它还将评估ABM与更传统的基于方程的模型的相对优点，并调查有关COVID-19已知特征的文献。智能体在模型中移动的方式将由移动性数据告知。研究人员可以访问由于正在进行的工作而概述的所有类型的数据源。数据点告知模型（1）传播风险或（2）严重疾病风险。案例数据在相关区域内被播种，然后模拟模型被设置为运行。这使我们能够了解该国每个地区的传播风险最大的时间和地点。虽然该模型能够告诉我们人们如何应对疫情，但它不会告诉我们原因。因此，为了为最终政策建议的设计提供信息，研究人员将在以往研究的基础上进行实地调查，以更好地了解职业和年龄组之间的社会接触网络的差异。政策建议将寻求解决有关封锁和限制的问题，但在可能的情况下更具本地化的针对性。研究的贡献由于正在进行的参与，研究将为津巴布韦国家卫生研究所和医学研究理事会提供直接产出。该方法一旦开发出来，就有可能适用于其他传染病暴发。这项工作将寻求解决流动性数据在多大程度上可以解释传染病的传播;强调反弹道导弹方法的相对效用;并确定对疾病爆发采取更本地化的政策反应的优点。

Background of the research The SARS-CoV-2 pandemic continues to devastate health systems and economies, placing the largest risk on the world's most vulnerable populations. At the time of writing, there have been over 203 million cases worldwide and 4.3 million reported deaths (JHU). These consequences pose a particularly serious threat to Low and Middle Income Countries (LMICs), which may have higher likelihood of widespread transmission due to social mixing patterns and large household sizes. Furthermore, they are least prepared to face the pandemic due to severe lack of intensive care capacity (Walker et al. 2020). The World Bank recently estimated that COVID-19 could push 71 million into extreme poverty within 2020 (Mahler et al, 2020). AimsThe aims of this study are three fold: 1. Review existing approaches to disease modelling, and the potential that Agent Based Modelling holds2. Build an Agent Based Model informed by population data, risk factors and mobility data to predict the spatial and temporal spread and trajectory of SARS-CoV-2 and the risk of COVID-193. Based on a combination of spatial modelling and behavioural research, propose policy recommendations for tackling future disease outbreaks and rapid containmentMethodologyThe research will begin by reviewing the ways in which Agent Based Modelling has been used thus far for disease modelling, including but not limited to SARS-CoV-2. It will also assess the relative merits of ABM versus more traditional Equation Based Models and survey the literature on the known characteristics of COVID-19. The way the agents move around the model will be informed by mobility data. The researcher has access to all types of data sources outlined due to ongoing work. Data points inform the models in either (1) risk of transmission or (2) risk of severe disease. Case data is seeded within the relevant districts and then the simulation model is set to run. This enables us to understand when and where each part of the country is at greatest risk of transmission. Though the model will be able to tell us how people are behaving in response to the outbreak, it will not tell us why. Therefore, to inform the design of the final policy recommendations, the researcher will conduct fieldwork to better understand how social contact networks differ between occupation and age groups, building on previous research. Policy recommendations will seek to address the questions around lockdowns and quarantines but with a more localized targeting wherever possible. Contribution of ResearchThe research will provide immediate outputs to the National Institute of Health Research and the Medical Research Council's of Zimbabwe due to an on-going engagement. Once developed, the methodology has scope for application to other infectious disease outbreaks. The work will seek to address the extent to which mobility data can explain the spread of infectious disease; highlight the relative utility of ABM approaches; and identify the merits of applying more localized policy responses to disease outbreaks.