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

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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.