Microsimulation based quantitative analysis of COVID-19 management strategies

Pandemic management requires reliable and efficient dynamical simulation to predict and control disease spreading. The COVID-19 (SARS-CoV-2) pandemic is mitigated by several non-pharmaceutical interventions, but it is hard to predict which of these are the most effective for a given population. We developed the computationally effective and scalable, agent-based microsimulation framework PanSim, allowing us to test control measures in multiple infection waves caused by the spread of a new virus variant in a city-sized societal environment using a unified framework fitted to realistic data. We show that vaccination strategies prioritising occupational risk groups minimise the number of infections but allow higher mortality while prioritising vulnerable groups minimises mortality but implies an increased infection rate. We also found that intensive vaccination along with non-pharmaceutical interventions can substantially suppress the spread of the virus, while low levels of vaccination, premature reopening may easily revert the epidemic to an uncontrolled state. Our analysis highlights that while vaccination protects the elderly from COVID-19, a large percentage of children will contract the virus, and we also show the benefits and limitations of various quarantine and testing scenarios. The uniquely detailed spatio-temporal resolution of PanSim allows the design and testing of complex, specifically targeted interventions with a large number of agents under dynamically changing conditions. Author summaryDecision-makers implement various non-pharmaceutical interventions to mitigate the COVID-19 pandemic. These include the closure of social events, restaurants, the introduction of curfews, elevated testing and quarantining of infected people. Once vaccines became available, decisions had to be made about the vaccination order, i.e. whom to vaccinate first. As the pandemic started to slow down, new decisions were needed on when it is safe to lift restrictions and which of them should be kept for longer. By now, we see how these strategic decisions worked in various countries, but as new virus variants bring new waves, we need to learn from previous examples and need better evaluation systems to find locally optimal interventions. Several modelling platforms have been developed to support specific decisions, but there is a clear need for a unified platform that can simulate the combined effects and potential interactions of all simultaneous interventions with fine-grained time and space resolution. Here we introduce PanSim, a virtual city simulator, which we use to mimic the population of the Hungarian city Szeged. We show how the effects of the interventions mentioned above can be compared by this tool, and it can explain why increased testing alone is not enough to stop the pandemic.