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Tracking Health Outcomes in Space and Time: Spatial and Spatio-temporal Methods

  • Peter Diggle*
  • , Emanuele Giorgi
  • , Michael Chipeta
  • , Sarah B. Macfarlane
  • *Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Diggle, Giorgi, Chipeta and Macfarlane describe how researchers can assist public health planners by collecting and analysing data on the occurrence of health outcomes in space and over time. They introduce spatial and spatio-temporal modelling to describe, predict and map the distribution of health outcomes. Drawing on extensive experience, the authors show how researchers, with public health practitioners, have used these methods to map snake bite incidence in Sri Lanka and Loa loa prevalence in Cameroon, and to maintain real-time surveillance systems to predict outbreaks of foodborne disease in the United Kingdom, legionellosis in New York and malaria in Malawi. The authors caution that the methods, while increasingly useful, require sophisticated understanding of statistics, and advise researchers to explain probability maps carefully to decision makers.

Original languageEnglish
Title of host publicationThe Palgrave Handbook of Global Health Data Methods for Policy and Practice
PublisherPalgrave Macmillan
Pages383-401
Number of pages19
ISBN (Electronic)9781137549846
ISBN (Print)9781137549839
DOIs
Publication statusPublished - 1 Jan 2019

Bibliographical note

Publisher Copyright:
© The Editor(s) (if applicable) and The Author(s) 2019.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

ASJC Scopus subject areas

  • General Medicine

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