Population-level differences in disease transmission: a Bayesian analysis of multiple smallpox epidemics

Area of Science:

• Epidemiology
• Mathematical Biology
• Public Health

Background:

• Accurate estimation of the basic reproductive rate (R0) is crucial for understanding disease outbreaks and planning interventions.
• Current R0 calculations often assume identical transmission rates across host populations, potentially underestimating uncertainty.

Purpose of the Study:

• To develop and apply a Bayesian method for quantifying uncertainty in R0 due to population-specific transmission rates.
• To assess the impact of inter-population differences in R0 on epidemic modeling and public health decision-making.

Main Methods:

• Utilized a Bayesian approach to quantify uncertainty in population-specific basic reproductive rates.
• Fitted spatial and non-spatial susceptible-exposed-infected-recovered (SEIR) models to 13 smallpox outbreaks.
• Employed Bayesian Information Criterion (BIC) to select the best-fitting model.

Main Results:

• The best model incorporated population-specific R0 values, indicating significant inter-population variability.
• Differences in R0 may be attributed to factors like genetic background, social structure, and resource availability.
• The overall uncertainty for the population-average smallpox R0 was larger than previously estimated.

Conclusions:

• Bayesian hierarchical models effectively account for uncertainty in multiple epidemics and provide a clearer understanding of epidemic dynamics.
• Acknowledging population-specific R0 differences is essential for accurate epidemic intensity assessment and effective public health interventions.
• This approach yields a better assessment of risks and consequences for decision-makers during epidemics.