Microsimulation based quantitative analysis of COVID-19 management strategies

Background Pandemic management includes a variety of control measures, such as social distancing, testing/quarantining and vaccination applied to a population where the virus is circulating. The COVID 19 (SARS-CoV-2) pandemic is mitigated by several non-pharmaceutical interventions, but it is hard to predict which of these regulations are the most effective for a given population. Methods We developed a computationally effective and scalable, agent-based microsimulation framework. This unified framework was fitted to realistic data to enable us to test control measures (closures, quarantining, testing, vaccination) in multiple infection waves caused by the spread of a new virus variant in a city-sized societal environment. Our framework is capable of simulating nine billion agent steps per minute, allowing us to model interactions in populations with up to 90 million individuals. Findings 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 increased infection rate. We also found that intensive vaccination and non-pharmaceutical interventions can substantially suppress the spread of the virus, while low levels of vaccination and premature reopening may easily revert the epidemic to an uncontrolled state. Interpretation Our analysis highlights that while vaccination protects the elderly from COVID-19, a large percentage of children will contract and spread the virus, and we also show the benefits and limitations of various quarantine and testing scenarios. Funding This work was carried out within the framework of the Hungarian National Development, Research, and Innovation (NKFIH) Fund 2020-2.1.1-ED-2020-00003.