Understanding uncertainty in temperature effects on vector-borne disease: a Bayesian approach

Area of Science:

• Ecology
• Epidemiology
• Environmental Science

Background:

• Environmental factors critically influence organism distribution and population dynamics, impacting human health and agriculture.
• Vector-borne diseases like malaria are significantly affected by environmental conditions, posing major global health challenges.
• Accurate prediction of climate change impacts on disease transmission and effective intervention strategies rely on understanding these environmental links.

Purpose of the Study:

• To investigate the mechanistic links between temperature and the basic reproductive number (R0) for vector-borne diseases.
• To identify critical uncertainties in R0 components and their propagation into overall R0 estimates.
• To guide empirical research for improving R0 predictions under varying temperature regimes.

Main Methods:

• Utilized a Bayesian approach to analyze R0 components and quantify uncertainty.
• Examined the influence of temperature on key parameters such as bite rate, fecundity, mortality, and parasite development rate.
• Assessed how uncertainty in individual parameters affects the overall R0 estimate across different temperatures.

Main Results:

• Different R0 parameters exhibit dominant uncertainty at distinct temperature ranges.
• Bite rate uncertainty is highest between 15-25°C.
• Fecundity uncertainty is significant across all temperatures, peaking around 25-32°C.
• Mortality uncertainty is prominent between 20-30°C.
• Parasite development rate uncertainty is notable at 15-16°C and 33-35°C.

Conclusions:

• Targeted empirical studies on temperature-sensitive R0 parameters can most efficiently improve disease transmission models.
• The Bayesian framework effectively identifies and quantifies uncertainty in R0 estimation.
• The methodology is broadly applicable to various process-based models, including epidemiological and species distribution models.