ArticleGlobal health action2009
Local scale prediction of Plasmodium falciparum malaria transmission in an endemic region using temperature and rainfall.
Article in Global health action, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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26 citing papers in PubMed, 41 citations in OpenAlex.
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- Inference and dynamic simulation of malaria using a simple climate-driven entomological model of malaria transmission.PLoS computational biology · 2022Article
- Falciparum Malaria in Febrile Patients at Sentinel Sites for Influenza Surveillance in the Central African Republic from 2015 to 2018.Interdisciplinary perspectives on infectious diseases · 2020Article
- Socioeconomic and environmental factors associated with malaria hotspots in the Nanoro demographic surveillance area, Burkina Faso.BMC public health · 2019Article
- Scoping review on vector-borne diseases in urban areas: transmission dynamics, vectorial capacity and co-infection.Infectious diseases of poverty · 2018Article
- Temporal dynamic of malaria in a suburban area along the Niger River.Malaria journal · 2017Article
- Modelling the influence of temperature and rainfall on the population dynamics of Anopheles arabiensis.Malaria journal · 2016Article
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- Climate, environmental and socio-economic change: weighing up the balance in vector-borne disease transmission.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2015Review
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- Temporal and micro-spatial heterogeneity in the distribution of Anopheles vectors of malaria along the Kenyan coast.Parasites & vectors · 2013Article
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- Weather and mortality: a 10 year retrospective analysis of the Nouna Health and Demographic Surveillance System, Burkina Faso.Global health action · 2012Article
- A time series analysis of weather variability and all-cause mortality in the Kasena-Nankana Districts of Northern Ghana, 1995-2010.Global health action · 2012Article
- A malaria transmission-directed model of mosquito life cycle and ecology.Malaria journal · 2011Article
- Modelling the impact of vector control interventions on Anopheles gambiae population dynamics.Parasites & vectors · 2011Article
- Mathematical models of malaria--a review.Malaria journal · 2011Review
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTo support malaria control strategies, prior knowledge of disease risk is necessary. Developing a model to explain the transmission of malaria, in endemic and epidemic regions, is of high priority in developing health system interventions. We develop, fit and validate a non-spatial dynamic model driven by meteorological conditions that can capture seasonal malaria transmission dynamics at the village level in a malaria holoendemic area of north-western Burkina Faso.
methodsA total of 676 children aged 6-59 months took part in this study. Trained interviewers visited children at home weekly from December 2003 to November 2004 for Plasmodium falciparum malaria infection detection. Anopheles daily biting rate, mortality rate and growth rate were evaluated. Digital meteorological stations measured ambient temperature, humidity and rainfall in each site.
resultsThe overall P. falciparum malaria infection incidence was 1.1 episodes per person year. There was strong seasonal variation in P. falciparum malaria infection incidence with a peak observed in August and September, corresponding to the rainy season and a high number of mosquitoes. The model estimates of monthly mosquito abundance and the incidence of malaria infection correlated well with observed values. The fit was sensitive to daily mosquito survival and daily human parasite clearance.
conclusionThe model has demonstrated potential for local scale seasonal prediction of P. falciparum malaria infection. It could therefore be used to understand malaria transmission dynamics using meteorological parameters as the driving force and to help district health managers in identifying high-risk periods for more focused interventions.
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