Reconstructing the Effectiveness of Policy Measures to Avoid Next-Wave COVID-19 Infections and Deaths Using a Dynamic Simulation Model: Implications for Health Technology Assessment

Abstract

Objective: The goal of this study was to dynamically model next-wave scenarios to observe the impact of different lockdown measures on the infection rates (IR) and mortality for two different prototype countries, mimicking the 1st year of the COVID-19 pandemic in Europe. Methods: A dynamic simulation SIRD model was designed to assess the effectiveness of policy measures on four next-wave scenarios, each preceded by two different lockdowns. The four scenarios were (1) no-measures, (2) uniform measures, (3) differential measures based on isolating > 60 years of age group, and (4) differential measures with additional contact reduction measures for the 20–60 years of age group. The dynamic simulation model was prepared for two prototype European countries, Northwestern (NW) and Southern (S) country. Both prototype countries were characterized based on age composition and contact matrix. Results: The results show that the outcomes of the next-wave scenarios depend on number of infections of previous lockdowns. All scenarios reduce the incremental deaths compared with a no-measures scenario. Differential measures show lower number of deaths despite an increase of infections. Additionally, prototype S shows overall more deaths compared with prototype NW due to a higher share of older citizens. Conclusion: This study shows that differential measures are a worthwhile option for controlling the COVID-19 epidemic. This may also be the case in situations where relevant parts of the population have taken up vaccination. Additionally, the effectiveness of interventions strongly depends on the number of previously infected individuals. The results of this study may be useful when planning and forecasting the impact of non-pharmacological interventions and vaccination campaigns.