Predicting malaria in an highly endemic country using clinical and environmental data
Date
2014-04
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Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montréal, QC, CA
Abstract
Accurate disease predictions and forecasting can provide public health and clinical health services with critical information. The aim of this thesis work was to develop evidence to guide the selection and use of malaria forecasting methods that use environmental and clinical predictors across different settings in a highly endemic country. Two forecasting models were developed for the Uganda Malaria Surveillance Project (UMSP) sites, short-term (4 weeks) and long-term (52 weeks) models for a total of 12 models. Short-term models were able to predict variations in malaria counts whereas the intermediate and long-term models were more useful in predicting cumulative cases.
Description
Includes abstract in French
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MALARIA, EPIDEMIOLOGY, SOUTH OF SAHARA, MATHEMATICAL MODELS, FORECASTING TECHNIQUES, STATISTICAL INFERENCE, UGANDA, CLIMATE SENSITIVE DISEASES, DISEASE PREVENTION, ENVIRONMENTAL ASPECTS, RAINFALL, STATISTICS