Estimating the Attack Ratio of Dengue Epidemics under Time-varying Force of Infection using Aggregated Notification Data

TL;DR

This paper introduces a Bayesian approach to estimate the attack ratio of dengue epidemics using aggregated data, accounting for time-varying infection rates and immunity, demonstrated with 18 years of Rio de Janeiro data.

Contribution

It presents a novel Bayesian method for estimating attack ratios and initial susceptibilities from aggregated incidence data, applicable even without serotype-specific information.

Findings

Able to estimate attack ratio without serotype data

Method applied successfully to 18 years of dengue data

Limited by the need for detailed serological surveys

Abstract

Quantifying the attack ratio of disease is key to epidemiological inference and Public Health planning. For multi-serotype pathogens, however, different levels of serotype-specific immunity make it difficult to assess the population at risk. In this paper we propose a Bayesian method for estimation of the attack ratio of an epidemic and the initial fraction of susceptibles using aggregated incidence data. We derive the probability distribution of the effective reproductive number, R t , and use MCMC to obtain posterior distributions of the parameters of a single-strain SIR transmission model with time-varying force of infection. Our method is showcased in a data set consisting of 18 years of dengue incidence in the city of Rio de Janeiro, Brazil. We demonstrate that it is possible to learn about the initial fraction of susceptibles and the attack ratio even in the absence of serotype specific data. On the other hand, the information provided by this approach is limited, stressing the need for detailed serological surveys to characterise the distribution of serotype-specific immunity in the population.