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)

传播异质性，即病原体传播事件中不成比例地由少数个体或地理区域引起的现象，是许多（如果不是大多数）传染病系统固有的属性。对于宿主携带的疾病而言，传播异质性通常可通过宿主携带媒介数量的分布来推断，这可能导致在媒介丰富度和家庭层面的传播风险不相关的情况下出现显著的偏差，如登革热病毒（DENV）的情况。我们利用接触追踪研究的数据，量化了在亚马逊地区伊基托斯市两次连续登革热疫情（DENV-4 和 DENV-2）中，登革热急性感染在人类活动空间（AS）中的分布，即个人常规访问的居住地点集合，并量化了病毒传播异质性的指标。负二项分布和帕累托分数显示出在AS中DENV感染数量存在强烈的过度分散，并识别出超级传播单元（SSU）：即大多数感染发生的活动空间。大约8%的活动空间被认定为SSU，贡献了超过50%的DENV感染。SSU的发生与DENV-2感染比DENV-4更为相关，与显性感染（所有感染中的74%）的普遍存在、携带高数量埃及伊蚊的家庭以及宿主对循环DENV血清型的较高易感性有关。登革热病例分布的显著异质性和隐性感染在定义其分布中的作用，突显了在未识别、优先考虑并有效治疗对传播贡献最大的传播单元时，反应性干预措施所面临的重大挑战。