Multiple-serotype models of dengue virus transmission: simulation study and perspectives for the application of inference in epidemiological surveillance

Abstract

AbstractWith around 3 billion people at risk, dengue virus is endemic to many parts of the world. In the Brazilian city of Rio de Janeiro, surveillance measures require notification of new dengue virus cases, and are supplemented by serum collection from patients and sequencing of viral RNA. Phylogenetic analyses have been performed for all serotypes circulating in the country to identify viral genotypes, potentially identify new introductions, and compare viruses presently circulating in the country with those in the past, and of other countries. As a separate type of analysis, a number of mathematical models have been developed to describe dengue virus transmission – particularly qualitative incidence or prevalence patterns – although few have been tested. In this chapter, I show how different mathematical formulations could represent transmission of dengue virus by mosquitoes to humans, how the different model structures entail assumptions about the process, and how these affect outputs qualitatively. Inference from simulated data is used as proof of principle that the kind of data available could be used to accurately estimate all model parameters; however, it is shown that stochasticity may severely hamper efforts to test the models quantitatively. I further implement inference from sequence data for the different models, and compare the performance to that of time series. The methods are applied to the data available for the city of Rio de Janeiro.