A global model of malaria climate sensitivity: Comparing malaria response to historic climate data based on simulation and officially reported malaria incidence

Stefan Edlund, Matthew Davis, Judith V. Douglas, Arik Kershenbaum, Narongrit Waraporn, Justin T Lessler, James H. Kaufman

Research output: Contribution to journalArticle

Abstract

Background: The role of the Anopheles vector in malaria transmission and the effect of climate on Anopheles populations are well established. Models of the impact of climate change on the global malaria burden now have access to high-resolution climate data, but malaria surveillance data tends to be less precise, making model calibration problematic. Measurement of malaria response to fluctuations in climate variables offers a way to address these difficulties. Given the demonstrated sensitivity of malaria transmission to vector capacity, this work tests response functions to fluctuations in land surface temperature and precipitation. Methods. This study of regional sensitivity of malaria incidence to year-to-year climate variations used an extended Macdonald Ross compartmental disease model (to compute malaria incidence) built on top of a global Anopheles vector capacity model (based on 10years of satellite climate data). The predicted incidence was compared with estimates from the World Health Organization and the Malaria Atlas. The models and denominator data used are freely available through the Eclipse Foundations Spatiotemporal Epidemiological Modeller (STEM). Results: Although the absolute scale factor relating reported malaria to absolute incidence is uncertain, there is a positive correlation between predicted and reported year-to-year variation in malaria burden with an averaged root mean square (RMS) error of 25% comparing normalized incidence across 86 countries. Based on this, the proposed measure of sensitivity of malaria to variations in climate variables indicates locations where malaria is most likely to increase or decrease in response to specific climate factors. Bootstrapping measures the increased uncertainty in predicting malaria sensitivity when reporting is restricted to national level and an annual basis. Results indicate a potential 20x improvement in accuracy if data were available at the level ISO 3166-2 national subdivisions and with monthly time sampling. Conclusions: The high spatial resolution possible with state-of-the-art numerical models can identify regions most likely to require intervention due to climate changes. Higher-resolution surveillance data can provide a better understanding of how climate fluctuations affect malaria incidence and improve predictions. An open-source modelling framework, such as STEM, can be a valuable tool for the scientific community and provide a collaborative platform for developing such models.

Original languageEnglish (US)
Article number331
JournalMalaria Journal
Volume11
DOIs
StatePublished - 2012

Fingerprint

Climate
Malaria
Incidence
Anopheles
Climate Change
Atlases
Calibration
Uncertainty

Keywords

  • Anopheles
  • Climate data
  • High-resolution data
  • Incidence
  • Macdonald Ross compartmental disease models
  • Malaria
  • Simulation

ASJC Scopus subject areas

  • Infectious Diseases
  • Parasitology

Cite this

A global model of malaria climate sensitivity : Comparing malaria response to historic climate data based on simulation and officially reported malaria incidence. / Edlund, Stefan; Davis, Matthew; Douglas, Judith V.; Kershenbaum, Arik; Waraporn, Narongrit; Lessler, Justin T; Kaufman, James H.

In: Malaria Journal, Vol. 11, 331, 2012.

Research output: Contribution to journalArticle

Edlund, Stefan ; Davis, Matthew ; Douglas, Judith V. ; Kershenbaum, Arik ; Waraporn, Narongrit ; Lessler, Justin T ; Kaufman, James H. / A global model of malaria climate sensitivity : Comparing malaria response to historic climate data based on simulation and officially reported malaria incidence. In: Malaria Journal. 2012 ; Vol. 11.
@article{445f084bb2874080b27b26ebe22dab75,
title = "A global model of malaria climate sensitivity: Comparing malaria response to historic climate data based on simulation and officially reported malaria incidence",
abstract = "Background: The role of the Anopheles vector in malaria transmission and the effect of climate on Anopheles populations are well established. Models of the impact of climate change on the global malaria burden now have access to high-resolution climate data, but malaria surveillance data tends to be less precise, making model calibration problematic. Measurement of malaria response to fluctuations in climate variables offers a way to address these difficulties. Given the demonstrated sensitivity of malaria transmission to vector capacity, this work tests response functions to fluctuations in land surface temperature and precipitation. Methods. This study of regional sensitivity of malaria incidence to year-to-year climate variations used an extended Macdonald Ross compartmental disease model (to compute malaria incidence) built on top of a global Anopheles vector capacity model (based on 10years of satellite climate data). The predicted incidence was compared with estimates from the World Health Organization and the Malaria Atlas. The models and denominator data used are freely available through the Eclipse Foundations Spatiotemporal Epidemiological Modeller (STEM). Results: Although the absolute scale factor relating reported malaria to absolute incidence is uncertain, there is a positive correlation between predicted and reported year-to-year variation in malaria burden with an averaged root mean square (RMS) error of 25{\%} comparing normalized incidence across 86 countries. Based on this, the proposed measure of sensitivity of malaria to variations in climate variables indicates locations where malaria is most likely to increase or decrease in response to specific climate factors. Bootstrapping measures the increased uncertainty in predicting malaria sensitivity when reporting is restricted to national level and an annual basis. Results indicate a potential 20x improvement in accuracy if data were available at the level ISO 3166-2 national subdivisions and with monthly time sampling. Conclusions: The high spatial resolution possible with state-of-the-art numerical models can identify regions most likely to require intervention due to climate changes. Higher-resolution surveillance data can provide a better understanding of how climate fluctuations affect malaria incidence and improve predictions. An open-source modelling framework, such as STEM, can be a valuable tool for the scientific community and provide a collaborative platform for developing such models.",
keywords = "Anopheles, Climate data, High-resolution data, Incidence, Macdonald Ross compartmental disease models, Malaria, Simulation",
author = "Stefan Edlund and Matthew Davis and Douglas, {Judith V.} and Arik Kershenbaum and Narongrit Waraporn and Lessler, {Justin T} and Kaufman, {James H.}",
year = "2012",
doi = "10.1186/1475-2875-11-331",
language = "English (US)",
volume = "11",
journal = "Malaria Journal",
issn = "1475-2875",
publisher = "BioMed Central",

}

TY - JOUR

T1 - A global model of malaria climate sensitivity

T2 - Comparing malaria response to historic climate data based on simulation and officially reported malaria incidence

AU - Edlund, Stefan

AU - Davis, Matthew

AU - Douglas, Judith V.

AU - Kershenbaum, Arik

AU - Waraporn, Narongrit

AU - Lessler, Justin T

AU - Kaufman, James H.

PY - 2012

Y1 - 2012

N2 - Background: The role of the Anopheles vector in malaria transmission and the effect of climate on Anopheles populations are well established. Models of the impact of climate change on the global malaria burden now have access to high-resolution climate data, but malaria surveillance data tends to be less precise, making model calibration problematic. Measurement of malaria response to fluctuations in climate variables offers a way to address these difficulties. Given the demonstrated sensitivity of malaria transmission to vector capacity, this work tests response functions to fluctuations in land surface temperature and precipitation. Methods. This study of regional sensitivity of malaria incidence to year-to-year climate variations used an extended Macdonald Ross compartmental disease model (to compute malaria incidence) built on top of a global Anopheles vector capacity model (based on 10years of satellite climate data). The predicted incidence was compared with estimates from the World Health Organization and the Malaria Atlas. The models and denominator data used are freely available through the Eclipse Foundations Spatiotemporal Epidemiological Modeller (STEM). Results: Although the absolute scale factor relating reported malaria to absolute incidence is uncertain, there is a positive correlation between predicted and reported year-to-year variation in malaria burden with an averaged root mean square (RMS) error of 25% comparing normalized incidence across 86 countries. Based on this, the proposed measure of sensitivity of malaria to variations in climate variables indicates locations where malaria is most likely to increase or decrease in response to specific climate factors. Bootstrapping measures the increased uncertainty in predicting malaria sensitivity when reporting is restricted to national level and an annual basis. Results indicate a potential 20x improvement in accuracy if data were available at the level ISO 3166-2 national subdivisions and with monthly time sampling. Conclusions: The high spatial resolution possible with state-of-the-art numerical models can identify regions most likely to require intervention due to climate changes. Higher-resolution surveillance data can provide a better understanding of how climate fluctuations affect malaria incidence and improve predictions. An open-source modelling framework, such as STEM, can be a valuable tool for the scientific community and provide a collaborative platform for developing such models.

AB - Background: The role of the Anopheles vector in malaria transmission and the effect of climate on Anopheles populations are well established. Models of the impact of climate change on the global malaria burden now have access to high-resolution climate data, but malaria surveillance data tends to be less precise, making model calibration problematic. Measurement of malaria response to fluctuations in climate variables offers a way to address these difficulties. Given the demonstrated sensitivity of malaria transmission to vector capacity, this work tests response functions to fluctuations in land surface temperature and precipitation. Methods. This study of regional sensitivity of malaria incidence to year-to-year climate variations used an extended Macdonald Ross compartmental disease model (to compute malaria incidence) built on top of a global Anopheles vector capacity model (based on 10years of satellite climate data). The predicted incidence was compared with estimates from the World Health Organization and the Malaria Atlas. The models and denominator data used are freely available through the Eclipse Foundations Spatiotemporal Epidemiological Modeller (STEM). Results: Although the absolute scale factor relating reported malaria to absolute incidence is uncertain, there is a positive correlation between predicted and reported year-to-year variation in malaria burden with an averaged root mean square (RMS) error of 25% comparing normalized incidence across 86 countries. Based on this, the proposed measure of sensitivity of malaria to variations in climate variables indicates locations where malaria is most likely to increase or decrease in response to specific climate factors. Bootstrapping measures the increased uncertainty in predicting malaria sensitivity when reporting is restricted to national level and an annual basis. Results indicate a potential 20x improvement in accuracy if data were available at the level ISO 3166-2 national subdivisions and with monthly time sampling. Conclusions: The high spatial resolution possible with state-of-the-art numerical models can identify regions most likely to require intervention due to climate changes. Higher-resolution surveillance data can provide a better understanding of how climate fluctuations affect malaria incidence and improve predictions. An open-source modelling framework, such as STEM, can be a valuable tool for the scientific community and provide a collaborative platform for developing such models.

KW - Anopheles

KW - Climate data

KW - High-resolution data

KW - Incidence

KW - Macdonald Ross compartmental disease models

KW - Malaria

KW - Simulation

UR - http://www.scopus.com/inward/record.url?scp=84866296560&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84866296560&partnerID=8YFLogxK

U2 - 10.1186/1475-2875-11-331

DO - 10.1186/1475-2875-11-331

M3 - Article

C2 - 22988975

AN - SCOPUS:84866296560

VL - 11

JO - Malaria Journal

JF - Malaria Journal

SN - 1475-2875

M1 - 331

ER -