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 betweenrattinessand 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 identifyrattinesshotspots. 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.

Keywords

epidemiology; abundance indices; zoonotic and vector-borne diseases; multivariate model-based geostatistics; leptospirosis; Norway rat

Published in

Interface
2020, Volume: 17, number: 170, article number: 20200398
Publisher: ROYAL SOC

SLU Authors

• Khalil, Hussein

  • Department of Wildlife, Fish and Environmental Studies, Swedish University of Agricultural Sciences
    
  • Federal University of Bahia (UFBA)

UKÄ Subject classification

Public Health, Global Health, Social Medicine and Epidemiology


Publication identifier

DOI: https://doi.org/10.1098/rsif.2020.0398

Permanent link to this page (URI)

https://res.slu.se/id/publ/108074