Dengue modeling in rural Cambodia: Statistical performance versus epidemiological relevance

Epidemics. 2019 Mar;26:43-57. doi: 10.1016/j.epidem.2018.08.004. Epub 2018 Aug 29.

Abstract

Dengue dynamics are shaped by the complex interplay between several factors, including vector seasonality, interaction between four virus serotypes, and inapparent infections. However, paucity or quality of data do not allow for all of these to be taken into account in mathematical models. In order to explore separately the importance of these factors in models, we combined surveillance data with a local-scale cluster study in the rural province of Kampong Cham (Cambodia), in which serotypes and asymptomatic infections were documented. We formulate several mechanistic models, each one relying on a different set of hypotheses, such as explicit vector dynamics, transmission via asymptomatic infections and coexistence of several virus serotypes. Models are confronted with the observed time series using Bayesian inference, through Markov chain Monte Carlo. Model selection is then performed using statistical information criteria, and the coherence of epidemiological characteristics (reproduction numbers, incidence proportion, dynamics of the susceptible classes) is assessed in each model. Our analyses on transmission dynamics in a rural endemic setting highlight that two-strain models with interacting effects better reproduce the long term data, but they are difficult to parameterize when relying on incidence cases only. On the other hand, considering the available data, incorporating vector and asymptomatic components seems of limited added-value when seasonality and underreporting are already accounted for.

Keywords: Cambodia; Dengue; Mathematical model; Model selection; Vector borne disease.

Publication types

  • Comparative Study
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Animals
  • Bayes Theorem
  • Cambodia / epidemiology
  • Dengue / epidemiology*
  • Dengue Virus
  • Disease Vectors
  • Humans
  • Incidence
  • Markov Chains
  • Models, Statistical*
  • Monte Carlo Method
  • Rural Population / statistics & numerical data*
  • Seasons