• Falciparum Malaria and Climate Change in the Northwest Frontier Province of Pakistan.

      Bouma, M J; Dye, C; van der Kaay, H J; Medecins Sans Frontieres-Holland, Amsterdam, The Netherlands. (Published by: American Society of Tropical Medicine and Hygiene, 1996-08)
      Following a striking increase in the severity of autumnal outbreaks of Plasmodium falciparum during the last decade in the Northwest Frontier Province (NWFP) of Pakistan, the role of climatologic variables was investigated. A multivariate analysis showed that during the transmission season of P. falciparum, the amount of rainfall in September and October, the temperature in November and December, and the humidity in December were all correlated (r2 = 0.82) with two measures of P. falciparum, the falciparum rate (percent of slides examined positive for P. falciparum) since 1981 and the annual P. falciparum proportion (percent of all malaria infections diagnosed as P. falciparum) since 1978. Climatologic records since 1876 show an increase in mean November and December temperatures by 2 degrees C and 1.5 degrees C, respectively, and in October rainfall. Mean humidity in December has also been increasing since 1950. These climatologic changes in the area appear to have made conditions for transmission of P. falciparum more favorable, and may account for the increase in incidence observed in the NWFP in recent years.
    • Forecasting malaria incidence based on monthly case reports and environmental factors in Karuzi, Burundi, 1997-2003.

      Gomez-Elipe, A; Otero, A; Van Herp, M; Aguirre-Jaime, A; Public Health Department, Universidad Autónoma de Madrid, C/Arzobispo Morcillo 2, 28029 Madrid, Spain. agomez.elipe@gmail.com (BMC, 2007)
      BACKGROUND: The objective of this work was to develop a model to predict malaria incidence in an area of unstable transmission by studying the association between environmental variables and disease dynamics. METHODS: The study was carried out in Karuzi, a province in the Burundi highlands, using time series of monthly notifications of malaria cases from local health facilities, data from rain and temperature records, and the normalized difference vegetation index (NDVI). Using autoregressive integrated moving average (ARIMA) methodology, a model showing the relation between monthly notifications of malaria cases and the environmental variables was developed. RESULTS: The best forecasting model (R2adj = 82%, p < 0.0001 and 93% forecasting accuracy in the range +/- 4 cases per 100 inhabitants) included the NDVI, mean maximum temperature, rainfall and number of malaria cases in the preceding month. CONCLUSION: This model is a simple and useful tool for producing reasonably reliable forecasts of the malaria incidence rate in the study area.
    • Ranking malaria risk factors to guide malaria control efforts in African highlands

      Protopopoff, Natacha; Van Bortel, Wim; Speybroeck, Niko; Van Geertruyden, Jean-Pierre; Baza, Dismas; D'Alessandro, Umberto; Coosemans, Marc; Department of Parasitology, Prince Leopold Institute of Tropical Medicine, Antwerp, Belgium; Medecins Sans Frontieres Brussels, Belgium; Department of Animal Health, Prince Leopold Institute of Tropical Medicine, Antwerp, Belgium; School of Public Health, Universite Catholique de Louvain, Brussels, Belgium; Programme de Lutte contre les Maladies Transmissibles et Carentielles, Ministry of Health, Bujumbura, Burundi; Department of Biomedical Sciences, Faculty of Pharmaceutical, Veterinary and Biomedical Sciences, University of Antwerp, Antwerp, Belgium (2009-11-25)
      INTRODUCTION: Malaria is re-emerging in most of the African highlands exposing the non immune population to deadly epidemics. A better understanding of the factors impacting transmission in the highlands is crucial to improve well targeted malaria control strategies. METHODS AND FINDINGS: A conceptual model of potential malaria risk factors in the highlands was built based on the available literature. Furthermore, the relative importance of these factors on malaria can be estimated through "classification and regression trees", an unexploited statistical method in the malaria field. This CART method was used to analyse the malaria risk factors in the Burundi highlands. The results showed that Anopheles density was the best predictor for high malaria prevalence. Then lower rainfall, no vector control, higher minimum temperature and houses near breeding sites were associated by order of importance to higher Anopheles density. CONCLUSIONS: In Burundi highlands monitoring Anopheles densities when rainfall is low may be able to predict epidemics. The conceptual model combined with the CART analysis is a decision support tool that could provide an important contribution toward the prevention and control of malaria by identifying major risk factors.