Agent-based Modelling for Ebola Risk Reduction: Understanding and Mitigating the Potential for Global Transportation-fuelled Epidemics (ABMERR)

My fellowship originally proposed to simulate outbreaks of Ebola throughout western Africa; I originally wrote that "humanitarians are in urgent need of operational intelligence in order to fight increasingly large epidemics". This is truer than ever. Even during the flourishing of simulation for Covid-19 response, it was wealthy countries which saw the development of frameworks for disease projection. These frameworks cannot necessarily translate to Low- and Middle-Income Countries (LMIC), as transportation networks and the availability of medical care typically vary drastically with national wealth, and model assumptions do not hold. Data that is routinely collected in some countries and communities may never have been gathered in others. Finally, the skills and targeted tools developed during the pandemic require investments that are simply out of the question for organisations working in LMICs. Humanitarians still need tools suitable for the contexts in which they work. My project renewal seeks to support them in meeting this need. During my fellowship, I have developed an initial agent-based model (ABM) in conjunction with project partners at the World Bank (WB). This partnership emerged during the pandemic, when my colleagues at WB were seeking to develop this kind of platform to support groups such as the Zimbabwe Infectious Disease Consortium (ZIDMC), headed by researchers from Zimbabwe's National Blood service. Both of these are new partners, who join the work going forward. My original project partners, Médecins Sans Frontières (MSF) and the British Red Cross, were intimately involved in responding to the pandemic; as pressures have lessened, I have scaled up my collaboration with them, interfacing with MSF's Manson Unit and periodically working out of their offices in London. We have already used the developed framework to explore assumptions made by other researchers, documenting best practice and cautionary examples of built-in modeller assumptions. In the last year of the original fellowship, I will be working closely with colleagues at MSF's Manson Unit to further refine the model and make it usable for their purposes. They are particularly interested in applying the framework to other diseases and understanding how they spread through travel along transport networks. The framework we have developed can accommodate these expansions, but through the renewal I hope to both expand upon the developed framework and also break new theoretical ground. Humanitarian partners are necessarily working with limited resources. Thus, there is pressure for them to make the best possible use of these, in terms of both space and time. The identification of outbreaks in the first place can be challenging in communities without robust testing infrastructure - but through spatial analysis, we can start to explore where humanitarians could maximise their understanding of outbreaks by testing. The existing framework is suitable for research and exploration of epidemic outbreak scenarios, and can be used in conjunction with a larger analytical workflow to identify testing and treatment targets which maximise the impact of humanitarian resources. The renewal of my fellowship would, once more, provide resources to help humanitarians and governments to understand the options available to them in crisis situations. It would also give me the opportunity to emerge from a challenging first few years as a PI and establish myself more firmly as an internationally recognised leader in the field of ABM for humanitarian modelling.