Reconstructing the Effectiveness of Policy Measures to Avoid Next-Wave COVID-19 Infections and Deaths Using a Dynamic...

Area of Science:

• Epidemiology
• Public Health Modeling

Background:

• The COVID-19 pandemic necessitated rapid policy responses, including lockdowns, to mitigate infection spread and mortality.
• Understanding the differential impact of various non-pharmacological interventions is crucial for future pandemic preparedness.

Purpose of the Study:

• To dynamically model next-wave scenarios for COVID-19.
• To assess the impact of different lockdown measures on infection rates and mortality in prototype European countries.

Main Methods:

• A dynamic simulation SIRD (Susceptible-Infected-Recovered-Deceased) model was employed.
• Four scenarios were simulated: no measures, uniform measures, differential measures (age-based isolation), and differential measures with contact reduction.
• The model was calibrated for two distinct European country prototypes (Northwestern and Southern) based on demographics and contact patterns.

Main Results:

• Scenario outcomes were influenced by the number of infections from prior lockdowns.
• All simulated measures reduced deaths compared to a no-measures approach.
• Differential measures, particularly those isolating individuals over 60, showed lower mortality despite potential infection increases.
• Prototype Southern country experienced higher mortality due to a larger elderly population.

Conclusions:

• Differential public health measures are effective for controlling COVID-19 epidemics, even with vaccination.
• Intervention effectiveness is significantly influenced by the proportion of previously infected individuals.
• These findings aid in planning non-pharmacological interventions and vaccination campaigns for future outbreaks.