Optimal sampling strategies for detecting zoonotic disease epidemics

Area of Science:

• Epidemiology
• Mathematical Biology
• Disease Ecology

Background:

• Early detection of epidemics is vital for limiting spread and impact.
• Zoonotic host-vector systems present unique challenges for disease surveillance.
• Understanding disease dynamics is key to designing effective control strategies.

Purpose of the Study:

• To develop an optimal sampling strategy framework for zoonotic host-vector systems during epidemic emergence.
• To identify conditions under which sampling should focus on hosts versus vectors.
• To provide practical guidance for designing disease surveillance programs.

Main Methods:

• Development of a mathematical framework for determining optimal sampling strategies.
• Analysis of time-dependent and time-independent sampling strategies.
• Validation of analytical results using nonlinear simulation models and West Nile virus as a case study.

Main Results:

• Optimal sampling strategies can shift abruptly between host and vector populations if introduction time is known.
• Time-independent strategies may involve simultaneous host and vector sampling for periodic surveillance.
• Sampling rules derived from linearized models show robustness in nonlinear simulations.

Conclusions:

• The study provides actionable rules for designing surveillance programs in zoonotic systems.
• Effective surveillance requires knowledge of epidemiological transition rates, initial infection source, and sampling costs.
• The framework offers a valuable tool for public health practitioners managing emerging infectious diseases.