Estimating the impact of interventions against COVID-19: from lockdown to vaccination

Abstract

Coronavirus disease 2019 (COVID-19) is an infectious disease of humans caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Since the first case was identified in China in December 2019 the disease has spread worldwide, leading to an ongoing pandemic. In this article, we present a detailed agent-based model of COVID-19 in Luxembourg, and use it to estimate the impact, on cases and deaths, of interventions including testing, contact tracing, lockdown, curfew and vaccination. Our model is based on collation, with agents performing activities and moving between locations accordingly. The model is highly heterogeneous, featuring spatial clustering, over 2000 behavioural types and a 10 minute time resolution. The model is validated against COVID-19 clinical monitoring data collected in Luxembourg in 2020. Our model predicts far fewer cases and deaths than the equivalent equation-based SEIR model. In particular, with $R_0 = 2.45$, the SEIR model infects 87% of the resident population while our agent-based model results, on average, in only around 23% of the resident population infected. Our simulations suggest that testing and contract tracing reduce cases substantially, but are much less effective at reducing deaths. Lockdowns appear very effective although costly, while the impact of an 11pm-6am curfew is relatively small. When vaccinating against a future outbreak, our results suggest that herd immunity can be achieved at relatively low levels, with substantial levels of protection achieved with only 30% of the population immune. When vaccinating in midst of an outbreak, the challenge is more difficult. In this context, we investigate the impact of vaccine efficacy, capacity, hesitancy and strategy. We conclude that, short of a permanent lockdown, vaccination is by far the most effective way to suppress and ultimately control the spread of COVID-19.

The impact of vaccination on the spread of COVID-19: Studying by a mathematical model

“…In [21] , a mathematical model is presented to study the impacts of drugs (vaccination with perfect efficacy) and non-drug prevention measures on the spread of COVID-19 in South Africa. A detailed agent-based model is proposed to study the impact of various prevention and control measures on the spread of COVID-19 in Luxembourg, including testing, contact tracing, lockdown, curfew and vaccination [22] . In [23] , utilizing a modified SIR model and using relevant data of COVID-19 in Ontario, Canada, different vaccination strategies are simulated and compared, including without vaccination.…”

Why a Globally Fair COVID-19 Vaccination? An Analysis based on Agent-Based Simulation

“…But the system dynamic model again does not cater for spatial considerations and mobility. Another sound research is published in [11], in which the authors combines the real data with the agent-based model to estimate the impact of lockdown and vaccination against COVID-19. But, this model is about one country only and does not take nonavailability of vaccine as an option.…”