Optimal timing of one-shot interventions for epidemic control

Area of Science:

• Epidemiology
• Public Health Policy
• Mathematical Modeling

Background:

• The COVID-19 pandemic presents varied interventions with societal costs and harms.
• Optimizing public health policy requires evaluating numerous intervention scenarios, especially for non-sustainable measures.

Purpose of the Study:

• To investigate the optimal timing of short-term, non-repeated public health interventions during an epidemic.
• To analyze the impact of intervention timing on epidemic dynamics, including total infections, peak prevalence, and infection delay.

Main Methods:

• Modeling the impact of a single, short-term, non-repeated intervention ('one-shot intervention') on epidemic spread.
• Analyzing different intervention timings relative to the epidemic peak.

Main Results:

• To minimize total infections, interventions should commence near the epidemic peak to limit rebound.
• To minimize peak prevalence, earlier intervention initiation is beneficial, creating a smaller peak after a temporary reduction.
• Earlier interventions effectively delay infections, which is advantageous if better treatments are anticipated.
• Synchronized interventions across distinct population subgroups are less effective than targeted, separate interventions.

Conclusions:

• The optimal timing of epidemic interventions is context-dependent, balancing different public health objectives.
• Understanding intervention timing is crucial for effective, short-term public health strategies during pandemics.
• Targeted interventions within subgroups enhance effectiveness compared to broad, synchronized approaches.