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 ten...
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ftdoajarticles:oai:doaj.org/article:66fee6a7ef534b218803ff472e588fde 2023-05-15T15:17:31+02:00 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 Kaufman James H 2012-09-01T00:00:00Z https://doi.org/10.1186/1475-2875-11-331 https://doaj.org/article/66fee6a7ef534b218803ff472e588fde EN eng BMC http://www.malariajournal.com/content/11/1/331 https://doaj.org/toc/1475-2875 doi:10.1186/1475-2875-11-331 1475-2875 https://doaj.org/article/66fee6a7ef534b218803ff472e588fde Malaria Journal, Vol 11, Iss 1, p 331 (2012) Malaria Macdonald Ross compartmental disease models Simulation Climate data Anopheles High-resolution data Incidence Arctic medicine. Tropical medicine RC955-962 Infectious and parasitic diseases RC109-216 article 2012 ftdoajarticles https://doi.org/10.1186/1475-2875-11-331 2022-12-31T08:43:49Z 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 10 years 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 Foundation’s 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 ... Article in Journal/Newspaper Arctic Climate change Directory of Open Access Journals: DOAJ Articles Arctic Malaria Journal 11 1 331 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
Malaria Macdonald Ross compartmental disease models Simulation Climate data Anopheles High-resolution data Incidence Arctic medicine. Tropical medicine RC955-962 Infectious and parasitic diseases RC109-216 |
spellingShingle |
Malaria Macdonald Ross compartmental disease models Simulation Climate data Anopheles High-resolution data Incidence Arctic medicine. Tropical medicine RC955-962 Infectious and parasitic diseases RC109-216 Edlund Stefan Davis Matthew Douglas Judith V Kershenbaum Arik Waraporn Narongrit Lessler Justin 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 |
topic_facet |
Malaria Macdonald Ross compartmental disease models Simulation Climate data Anopheles High-resolution data Incidence Arctic medicine. Tropical medicine RC955-962 Infectious and parasitic diseases RC109-216 |
description |
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 10 years 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 Foundation’s 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 ... |
format |
Article in Journal/Newspaper |
author |
Edlund Stefan Davis Matthew Douglas Judith V Kershenbaum Arik Waraporn Narongrit Lessler Justin Kaufman James H |
author_facet |
Edlund Stefan Davis Matthew Douglas Judith V Kershenbaum Arik Waraporn Narongrit Lessler Justin Kaufman James H |
author_sort |
Edlund Stefan |
title |
A global model of malaria climate sensitivity: comparing malaria response to historic climate data based on simulation and officially reported malaria incidence |
title_short |
A global model of malaria climate sensitivity: comparing malaria response to historic climate data based on simulation and officially reported malaria incidence |
title_full |
A global model of malaria climate sensitivity: comparing malaria response to historic climate data based on simulation and officially reported malaria incidence |
title_fullStr |
A global model of malaria climate sensitivity: comparing malaria response to historic climate data based on simulation and officially reported malaria incidence |
title_full_unstemmed |
A global model of malaria climate sensitivity: comparing malaria response to historic climate data based on simulation and officially reported malaria incidence |
title_sort |
global model of malaria climate sensitivity: comparing malaria response to historic climate data based on simulation and officially reported malaria incidence |
publisher |
BMC |
publishDate |
2012 |
url |
https://doi.org/10.1186/1475-2875-11-331 https://doaj.org/article/66fee6a7ef534b218803ff472e588fde |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Climate change |
genre_facet |
Arctic Climate change |
op_source |
Malaria Journal, Vol 11, Iss 1, p 331 (2012) |
op_relation |
http://www.malariajournal.com/content/11/1/331 https://doaj.org/toc/1475-2875 doi:10.1186/1475-2875-11-331 1475-2875 https://doaj.org/article/66fee6a7ef534b218803ff472e588fde |
op_doi |
https://doi.org/10.1186/1475-2875-11-331 |
container_title |
Malaria Journal |
container_volume |
11 |
container_issue |
1 |
container_start_page |
331 |
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1766347752019591168 |