This paper provides statistical guidance on the development and application of model-
based geostatistical methods for disease prevalence mapping. We illustrate the different …

In low-resource settings, prevalence mapping relies on empirical prevalence data from a
finite, often spatially sparse, set of surveys of communities within the region of interest …

A national HIV/AIDS and malaria parasitological survey was carried out in Tanzania in 2007–
2008. In this study the parasitological data were analyzed: i) to identify …

In this paper, we set out general principles and develop geostatistical methods for the
analysis of data from spatio‐temporally referenced prevalence surveys. Our objective is to …

Background Outbreaks of vector-borne diseases (VBDs) impose a heavy burden on
vulnerable populations. Despite recent progress in eradication and control, malaria remains …

Progress has been made in mapping and predicting the risk of schistosomiasis using
Bayesian geostatistical inference. Applications primarily focused on risk profiling of …

Bayesian spatial models are widely used to analyse data that arise in scientific disciplines
such as health, ecology, and the environment. Traditionally, Markov chain Monte Carlo …

Background: The prevalence of malaria infection in time and space provides important
information on the likely sub-national epidemiology of malaria burdens and how this has …

Objectives Malaria risk maps have re‐emerged as an important tool for appropriately
targeting the limited resources available for malaria control. In Sub‐Saharan Africa …

Abstract Background The Zambia Malaria Indicator Survey (ZMIS) of 2006 was the first
nation-wide malaria survey, which combined parasitological data with other malaria …