Inapparent infections shape the transmission heterogeneity of dengue
Transmission heterogeneity, whereby a disproportionate fraction of pathogen transmission events result from a small number of individuals or geographic locations, is an inherent property of many, if not most, infectious disease systems. For vector-borne diseases, transmission heterogeneity is commonly inferred from the distribution of the number of vectors per host, which could lead to significant bias in situations where vector abundance and transmission risk at the household level do not correlate, as is the case with dengue virus (DENV). We used data from a contact tracing study to quantify the distribution of DENV acute infections within human activity spaces (AS), the collection of residential locations an individual routinely visits, and quantified measures of virus transmission heterogeneity from two consecutive dengue outbreaks (DENV-4 and DENV-2) that occurred in the Amazon city of Iquitos, Peru. Negative binomial distributions and Pareto fractions showed evidence of strong overdispersion in the number of DENV infections by AS and identified super-spreading units (SSUs): i.e., the AS where most infections occurred. Approximately 8% of AS were identified as SSUs, contributing to more than 50% of DENV infections. SSU occurrence was associated more with DENV-2 infection than with DENV-4, a predominance of inapparent infections (74% of all infections), households with high Aedes aegypti mosquito abundance, and high host susceptibility to the circulating DENV serotype. Marked heterogeneity in dengue case distribution, and the role of inapparent infections in defining it, highlight major challenges faced by reactive interventions if those transmission units contributing the most to transmission are not identified, prioritized, and effectively treated. Files uploaded: 1)Dataset . Information aggregated at the activity space level with all data used in the manuscript. 2)Dispersion analysis script:.R code for estimating ZAS and fitting it to negative binomial and Poisson distributions 3) ParetoSummary.R function to estimate pareto fraction and plot 4) Pareto.R code to generate figures in manuscript using ParetoSummary.R 5) GAMM.R code to recreate all models run to estimate association between SSU and selected variables (mosquitoes and susceptibility)
Steps to reproduce
Dataset contains ZAS for all activity spaces. From that variable, one can fit NB distributions (DispersionAnalysis_script.R) or estimate the Pareto fraction (Pareto.R, after loading ParetoSummary.R). For the variable SSU, one can run a binomial GAMM to determine key factors using GAMM.R.