Forecasting intensive care unit demand during the COVID-19 pandemic: A spatial age-structured microsimulation model

Background The COVID-19 pandemic poses the risk of overburdening health care systems, and in particular intensive care units (ICUs). Non-pharmaceutical interventions (NPIs), ranging from wearing masks to (partial) lockdowns have been implemented as mitigation measures around the globe. However, especially severe NPIs are used with great caution due to their negative effects on the economy, social life and mental well-being. Thus, understanding the impact of the pandemic on ICU demand under alternative scenarios reflecting different levels of NPIs is vital for political decision-making on NPIs. Objective The aim is to support political decision-making by forecasting COVID-19-related ICU demand under alternative scenarios of COVID-19 progression reflecting different levels of NPIs. Substantial sub-national variation in COVID-19-related ICU demand requires a spatially disaggregated approach. This should not only take sub-national variation in ICU-relevant disease dynamics into account, but also variation in the population at risk including COVID-19-relevant risk characteristics (e.g. age), and factors mitigating the pandemic. The forecast provides indications for policy makers and health care stakeholders as to whether mitigation measures have to be maintained or even strengthened to prevent ICU demand from exceeding supply, or whether there is leeway to relax them. Methods We implement a spatial age-structured microsimulation model of the COVID-19 pandemic by extending the Susceptible-Exposed-Infectious-Recovered (SEIR) framework. The model accounts for regional variation in population age structure and in spatial diffusion pathways. In a first step, we calibrate the model by applying a genetic optimization algorithm against hospital data on ICU patients with COVID-19. In a second step, we forecast COVID-19-related ICU demand under alternative scenarios of COVID 19 progression reflecting different levels of NPIs. We apply the model to Germany and provide state-level forecasts over a 2-month period, which can be updated daily based on latest data on the progression of the pandemic. Results To illustrate the merits of our model, we present here forecasts of ICU demand for different stages of the pandemic during 2020. Our forecasts for a quiet summer phase with low infection rates identified quite some variation in potential for relaxing NPIs across the federal states. By contrast, our forecasts during a phase of quickly rising infection numbers in autumn (second wave) suggested that all federal states should implement additional NPIs. However, the identified needs for additional NPIs varied again across federal states. In addition, our model suggests that during large infection waves ICU demand would quickly exceed supply, if there were no NPIs in place to contain the virus. Conclusion Our results provide evidence for substantial spatial variation in (1) the effect of the pandemic on ICU demand, and (2) the potential and need for NPI adjustments at different stages of the pandemic. Forecasts with our spatial age-structured microsimulation model allow to take this spatial variation into account. The model is programmed in R and can be applied to other countries, provided that reliable data on the number of ICU patients infected with COVID-19 are available at sub-national level.