• The dynamics of measles in sub-Saharan Africa.

      Ferrari, M J; Grais, R; Bharti, N; Conlan, A J K; Bjørnstad, O N; Wolfson, L J; Guerin, P J; Djibo, A; Grenfell, B T; Center for Infectious Disease Dynamics, The Pennsylvania State University, University Park, Pennsylvania 16802, USA. mferrari@psu.edu (Macmillan, 2008-02-07)
      Although vaccination has almost eliminated measles in parts of the world, the disease remains a major killer in some high birth rate countries of the Sahel. On the basis of measles dynamics for industrialized countries, high birth rate regions should experience regular annual epidemics. Here, however, we show that measles epidemics in Niger are highly episodic, particularly in the capital Niamey. Models demonstrate that this variability arises from powerful seasonality in transmission-generating high amplitude epidemics-within the chaotic domain of deterministic dynamics. In practice, this leads to frequent stochastic fadeouts, interspersed with irregular, large epidemics. A metapopulation model illustrates how increased vaccine coverage, but still below the local elimination threshold, could lead to increasingly variable major outbreaks in highly seasonally forced contexts. Such erratic dynamics emphasize the importance both of control strategies that address build-up of susceptible individuals and efforts to mitigate the impact of large outbreaks when they occur.
    • 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.