Sammanfattning

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.

Nyckelord

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

Publicerad i

Interface
2020, Volym: 17, nummer: 170, artikelnummer: 20200398
Utgivare: ROYAL SOC

SLU författare

• Khalil, Hussein

  • Institutionen för vilt, fisk och miljö, Sveriges lantbruksuniversitet
    
  • Universidade Federal da Bahia (UFBA)

UKÄ forskningsämne

Folkhälsovetenskap, global hälsa, socialmedicin och epidemiologi


Publikationens identifierare

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

Permanent länk till denna sida (URI)

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