Predicting malaria infection in Gambian children from satellite data and bed net use surveys

The importance of spatial correlation in the interpretation of results

Madeleine C. Thomson, Stephen J. Connor, Umberto D'Alessandro, Barry Rowlingson, Peter Diggle, Mark Cresswell, Brian Greenwood

Research output: Contribution to journalArticle

Abstract

In line with the renewed World Health Organization Global Malaria Control Strategy, we have advocated the use of satellite imagery by control services to provide environmental information for malaria stratification, monitoring, and early warning. To achieve this operationally, appropriate methodologies must be developed for integrating environmental and epidemiologic data into models that can be used by decision-makers for improved resource allocation. Using methodologies developed for the Famine Early Warning Systems and spatial statistics, we show a significant association between age related malaria infection in Gambian children and the amount of seasonal environmental greenness as measured using the normalized difference vegetation index derived from satellite data. The resulting model is used to predict changes in malaria prevalence rates in children resulting from different bed net control scenarios.

Original languageEnglish (US)
Pages (from-to)2-8
Number of pages7
JournalAmerican Journal of Tropical Medicine and Hygiene
Volume61
Issue number1
StatePublished - Jul 1999
Externally publishedYes

Fingerprint

Malaria
Infection
Satellite Imagery
Resource Allocation
Starvation
Surveys and Questionnaires

ASJC Scopus subject areas

  • Parasitology
  • Infectious Diseases

Cite this

Predicting malaria infection in Gambian children from satellite data and bed net use surveys : The importance of spatial correlation in the interpretation of results. / Thomson, Madeleine C.; Connor, Stephen J.; D'Alessandro, Umberto; Rowlingson, Barry; Diggle, Peter; Cresswell, Mark; Greenwood, Brian.

In: American Journal of Tropical Medicine and Hygiene, Vol. 61, No. 1, 07.1999, p. 2-8.

Research output: Contribution to journalArticle

Thomson, Madeleine C. ; Connor, Stephen J. ; D'Alessandro, Umberto ; Rowlingson, Barry ; Diggle, Peter ; Cresswell, Mark ; Greenwood, Brian. / Predicting malaria infection in Gambian children from satellite data and bed net use surveys : The importance of spatial correlation in the interpretation of results. In: American Journal of Tropical Medicine and Hygiene. 1999 ; Vol. 61, No. 1. pp. 2-8.
@article{bbc311e13020400b87bed57c34e2f063,
title = "Predicting malaria infection in Gambian children from satellite data and bed net use surveys: The importance of spatial correlation in the interpretation of results",
abstract = "In line with the renewed World Health Organization Global Malaria Control Strategy, we have advocated the use of satellite imagery by control services to provide environmental information for malaria stratification, monitoring, and early warning. To achieve this operationally, appropriate methodologies must be developed for integrating environmental and epidemiologic data into models that can be used by decision-makers for improved resource allocation. Using methodologies developed for the Famine Early Warning Systems and spatial statistics, we show a significant association between age related malaria infection in Gambian children and the amount of seasonal environmental greenness as measured using the normalized difference vegetation index derived from satellite data. The resulting model is used to predict changes in malaria prevalence rates in children resulting from different bed net control scenarios.",
author = "Thomson, {Madeleine C.} and Connor, {Stephen J.} and Umberto D'Alessandro and Barry Rowlingson and Peter Diggle and Mark Cresswell and Brian Greenwood",
year = "1999",
month = "7",
language = "English (US)",
volume = "61",
pages = "2--8",
journal = "American Journal of Tropical Medicine and Hygiene",
issn = "0002-9637",
publisher = "American Society of Tropical Medicine and Hygiene",
number = "1",

}

TY - JOUR

T1 - Predicting malaria infection in Gambian children from satellite data and bed net use surveys

T2 - The importance of spatial correlation in the interpretation of results

AU - Thomson, Madeleine C.

AU - Connor, Stephen J.

AU - D'Alessandro, Umberto

AU - Rowlingson, Barry

AU - Diggle, Peter

AU - Cresswell, Mark

AU - Greenwood, Brian

PY - 1999/7

Y1 - 1999/7

N2 - In line with the renewed World Health Organization Global Malaria Control Strategy, we have advocated the use of satellite imagery by control services to provide environmental information for malaria stratification, monitoring, and early warning. To achieve this operationally, appropriate methodologies must be developed for integrating environmental and epidemiologic data into models that can be used by decision-makers for improved resource allocation. Using methodologies developed for the Famine Early Warning Systems and spatial statistics, we show a significant association between age related malaria infection in Gambian children and the amount of seasonal environmental greenness as measured using the normalized difference vegetation index derived from satellite data. The resulting model is used to predict changes in malaria prevalence rates in children resulting from different bed net control scenarios.

AB - In line with the renewed World Health Organization Global Malaria Control Strategy, we have advocated the use of satellite imagery by control services to provide environmental information for malaria stratification, monitoring, and early warning. To achieve this operationally, appropriate methodologies must be developed for integrating environmental and epidemiologic data into models that can be used by decision-makers for improved resource allocation. Using methodologies developed for the Famine Early Warning Systems and spatial statistics, we show a significant association between age related malaria infection in Gambian children and the amount of seasonal environmental greenness as measured using the normalized difference vegetation index derived from satellite data. The resulting model is used to predict changes in malaria prevalence rates in children resulting from different bed net control scenarios.

UR - http://www.scopus.com/inward/record.url?scp=0032799063&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0032799063&partnerID=8YFLogxK

M3 - Article

VL - 61

SP - 2

EP - 8

JO - American Journal of Tropical Medicine and Hygiene

JF - American Journal of Tropical Medicine and Hygiene

SN - 0002-9637

IS - 1

ER -