This project aims to better-inform future pandemic mitigation policies (e.g., lockdowns), by learning from the COVID-19 pandemic. A key question from this pandemic remains unanswered: what was the right balance between total lockdown, and unfettered community transmission? While lockdowns were successful at reducing lives lost, they took tolls on population health in other ways. Evidence suggests that lockdowns increased mental health issues, interpersonal violence, and weight gain, among other socially determined health outcomes. However, it is not yet clear whether the benefits of lockdowns, such as reductions in COVID-19-related mortality, outweighed the costs of these secondary health issues.
To examine this, simulation modelling may be useful, by enabling the comparison of actual scenarios with hypothetical ones. Agent-based modelling (ABM) may be particularly fruitful, as it allows for interactions among people (agents) and may better-reflect real-world social dynamics. Therefore, the research question of this project is: How did different lockdown policies (actual and hypothetical) impact non-COVID-19-related health outcomes?
To date, no existing studies have examined lockdowns' relationships to non-COVID-19-related health outcomes using ABM. This represents a serious gap: the full complexity of factors and interactions influencing these relationships has likely not yet been captured. This project would help to fill this gap, by generating an initial ABM examining the Netherlands, and one health outcome, namely self-rated mental health. Based on this ABM, at least one paper will be submitted to a peer-reviewed journal. Further, the ABM could be used as an open-source basis and/or inspiration to study other outcomes and/or contexts.