During the battle against COVID-19, sudden-onset disasters keep happening. These two consecutive events are different in nature and sometimes ask for counter-productive policy interventions: where the epidemic can be contained by ensuring social distancing practices, reducing the number of contacts, and offering hygienic conditions, the response to a natural hazard requires evacuation, which often leads to overcrowded shelters with a questionable hygiene. An exploratory agent-based model is constructed where the dynamics of these socio-technical systems are integrated to find high-level emergent behaviour over time during the response phase of a sudden-onset disaster. The study describes a stylistic agent-based model that combines three socio-technical systems, their interactions and system behaviour to find general trends and interdependencies. The three sub systems comprise of a COVID-19 component, based on the SEIR approach, a livelihood component, which mimics a microeconomic marketing mechanism and accounts for among others difference in occupation, and a hazard component, which is responsible for the evacuation process. The following research question is answered: What robust policy interventions can be identified that balance livelihood of rural communities and their exposure to COVID-19 during the response to a sudden-onset disaster in developing countries? The goal is to find robust policy interventions that can be executed by either local governments or humanitarian organizations and are suitable for poor rural communities in developing countries. Four policy interventions were compared: cash transfers, an awareness campaign, increasing the number of shelters, and varying the moment of imposing a lockdown. With the use of Exploratory Modelling and Analysis, an experimental design was constructed to identify uncertainties and model behaviour. Results show that using direct and unconditional cash transfers in combination with increased awareness have most beneficial effects for the average livelihood and also positively influence the COVID-19 trajectory. Increasing awareness shows best results for containing COVID-19, but is dependent on context factors such as regular testing and implementation before the the virus spirals out of control. Awareness also negatively influences the average livelihood. This research is a first step in modelling compound risk of COVID-19 and sudden-onset disasters. The modular way of building makes this research useful not only for searching trade-offs in developing countries, but can be used in other contexts as well.

One of the substantial causes for food insecurity in an area is drought. Drought is a very insidious disaster. Unlike rapid onset disasters, like an earthquake or flood, it increases its grasp over an area over time. The longer the drought lasts, the more an area is destroyed leaving devastating effects that can last many years. The impacts of droughts are even bigger on areas that are predominantly dependent upon agriculture. One way to assist poor households facing chronic food insecurity and increase their resilience to shocks is through unconditional direct cash transfers. This approach differs from traditional aid, such as food aid, because it gives the power of decision-making back to the people who know best their needs. this study aims to explore different cash transfer policies as a humanitarian response to food insecurity caused by and the influence on household food security.

Responding rapidly to epidemic outbreaks presents significant challenges, due to resource, capacity and time limitations. Anticipatory Action (AA) is a newly emerging strategy in the field of humanitarian aid, designed to preemptively address potential crises. By taking impact-reducing actions before a disaster strikes, AA seeks to minimize human loss. However, AA frameworks currently use static prepared-in-advance plans. As a result, AA is not sufficiently able to deal with the uncertainty levels in the onset and spread of epidemics. Effective epidemic control requires plans that can adapt to a constantly changing environment and incoming information, such as the number and location of suspected cases, weather forecasts and population movement, while balancing flexibility with an effective management approach. We show how the (DAPP) framework for decisionmaking under deep uncertainty can be adapted to enhance the common anticipatory action approach with flexibility and effective management for epidemic control. More specifically, we show how DAPP allows to take into account newly available information and change the strategy to minimize human loss. We illustrate it with a case study of cholera in Cameroon, for which the French, Netherlands, and Cameroon Red Cross, supported by EHESP, are developing an early action protocol and a model that assesses the cost-effectiveness of actions for different risk levels and external shocks. Our results suggest that DAPP increase flexibility and coordination in anticipatory action for epidemics and helps optimizing early action strategies, which could have larger implications for global disease control.