Mosquito-borne transmission in urban landscapes: the missing link between vector abundance and human density

Abstract

With escalating urbanization, the environmental, demographic, and socio-economic heterogeneity of urban landscapes poses a challenge to mathematical models for the transmission of vector-borne infections. Classical coupled vector–human models typically assume that mosquito abundance is either independent from, or proportional to, human population density, implying a decreasing force of infection, or per capita infection rate with host number. We question these assumptions by introducing an explicit dependence between host and vector densities through different recruitment functions, whose dynamical consequences we examine in a modified model formulation. Contrasting patterns in the force of infection are demonstrated, including in particular increasing trends when recruitment grows sufficiently fast with human density. Interaction of these patterns with seasonality in temperature can give rise to pronounced differences in timing, relative peak sizes, and duration of epidemics. These proposed dependencies explain empirical dengue risk patterns observed in the city of Delhi where socio-economic status has an impact on both human and mosquito densities. These observed risk trends with host density are inconsistent with current standard models. A better understanding of the connection between vector recruitment and host density is needed to address the population dynamics of mosquito-transmitted infections in urban landscapes.

Predicting re-emergence times of dengue epidemics at low reproductive numbers: DENV1 in Rio de Janeiro, 1986–1990

“…Micro-scale spatial heterogeneity in transmission intensity and the effects of human movement between neighbourhoods could also explain the rapid re-emergence. Small-scale differences in socioeconomic status and population density between neighbourhoods in a large city can result in different relationships between mosquito and human population sizes, resulting in widespread heterogeneity in R 0 across neighbourhoods [55]. Previous studies of mosquito trap data in the city have demonstrated that neighbourhoods with differing socioeconomic characteristics have different vector population patterns [43].…”

A Perspective on Inhabited Urban Space: Land Use and Occupation, Heat Islands, and Precarious Urbanization as Determinants of Territorial Receptivity to Dengue in the City of Rio De Janeiro

“…These indicators are associated with how the territory is occupied by human populations. Hypothesized that areas with higher percentage of human occupation and demographic density are areas with higher risk for the spread of dengue due to the greater proximity between hosts and greater number of breeding sites available for Aedes vectors [27].…”

Rough infection fronts in a random medium

“…A well defined statistical analysis of the front fluctuations can be then performed by comparing it with a perfectly flat reference. 2 Since the disorder is totally uncorrelated, the front feels different disorder realizations as it moves. This assures the property of self-averaging.…”

“…The representation of population heterogeneity in spatially explicit epidemic models was listed as one of the most important challenges [1]. Very recent results show that spatial transmission variability is essential to reproduce spatio-temporal propagation patterns emerging from some epidemic data sets [2]. One of the simplest epidemic models for infectious diseases is the deterministic Susceptible -Infected -Recovered (SIR) originally formulated by Kermack and McKendrick [3] in which the individuals are removed from the population either because they die or because they acquire lifelong immunity.…”