A multivariate geostatistical framework for combining multiple indices of abundance for disease vectors and reservoirs: a case study ofrattinessin a low-income urban Brazilian community

Abstract

A key requirement in studies of endemic vector-borne or zoonotic disease is an estimate of the spatial variation in vector or reservoir host abundance. For many vector species, multiple indices of abundance are available, but current approaches to choosing between or combining these indices do not fully exploit the potential inferential benefits that might accrue from modelling their joint spatial distribution. Here, we develop a class of multivariate generalized linear geostatistical models for multiple indices of abundance. We illustrate this novel methodology with a case study on Norway rats in a low-income urban Brazilian community, where rat abundance is a likely risk factor for human leptospirosis. We combine three indices of rat abundance to draw predictive inferences on a spatially continuous latent process, rattiness , that acts as a proxy for abundance. We show how to explore the association between rattiness and spatially varying environmental factors, evaluate the relative importance of each of the three contributing indices and assess the presence of residual, unexplained spatial variation, and identify rattiness hotspots. The proposed methodology is applicable more generally as a tool for understanding the role of vector or reservoir host abundance in predicting spatial variation in the risk of human disease.

Linking rattiness, geography and environmental degradation to spillover Leptospira infections in marginalised urban settings: an eco-epidemiological community-based cohort study in Brazil

“…Residents are consequently often unable to avoid intense exposure with mud and floodwater. These factors result in abundant rat populations [33] and a high estimated annual Leptospira infection rate of 35.4 (95% CI, 30.7, 40.6) infections per 1,000 annual follow-up events [12]. For this reason, Pau da Lima has become an exemplar for investigating urban Leptospira transmission in Brazil over the last 15 years.…”

“…To estimate exposure risk due to local rat contamination between the two serosurveys, a cross-sectional rat ecology study was conducted from October to December 2014. As has been described previously [33], the aim of this study was to collect data on the fine-scale spatial variation in rat reservoir population abundance. Data were collected for five indices of rat abundance: live trapping, track plates, number of active burrows present, presence of faecal droppings and presence of trails.…”

“…There is no gold-standard index of abundance and field teams use a range of imperfect indices, such as traps, infestation surveys and track plates. In our previous work, we developed a multivariate generalized linear geostatistical model for joint spatial modelling of multiple imperfect abundance indices [33]. We use the term ‘abundance’ loosely here to denote all ecological processes that are associated with animal abundance, for example animal presence and activity, and that can be used to quantify exposure to a disease of interest.…”

“…We use the term ‘abundance’ loosely here to denote all ecological processes that are associated with animal abundance, for example animal presence and activity, and that can be used to quantify exposure to a disease of interest. This methodology was then used to model the spatial distribution of ‘rattiness’, our proxy for rat abundance, at a fine scale within a community in Salvador, Brazil [33]. The spatial distribution of rattiness was highly heterogeneous, suggesting that it could be a driver of micro-heterogeneity in infection risk.…”

“…The aim of this study was to develop a flexible modelling framework for zoonotic spillover to explore whether rattiness, acting as a proxy for local leptospiral contamination by Norway rats, can explain spatial heterogeneity in leptospiral transmission in a high-risk urban community in Brazil. We extend the rattiness framework of Eyre et al [33] to include human infection risk. We describe findings from a transdisciplinary eco-epidemiological study which comprises a prospective community-based cohort study with two serosurveys and a fine-scale rat ecology study.…”