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Environmental and Clinical Predictors Forecast Malaria

´╗┐Environmental and Clinical Predictors Forecast Malaria

Background


Malaria forecasting methods have become more sophisticated since Christophers' early work on forecasting malaria epidemics using rainfall, fever-related deaths, and wheat prices although the intent has remained unchanged: to inform malaria control and prevention by predicting burden or early warning of increasing burden. With the mounting cost of the global fight against malaria and the drive towards elimination in many countries, accurate forecasts of malaria could be a valuable tool for public and clinical health services. Accurate disease predictions and early warning signals of an increase in disease burden can provide the information needed to implement targeted approaches for malaria control and prevention that make effective use of limited resources.

Malaria forecasting models have been developed in many endemic countries, although the accuracy of the models is varied and difficult to interpret across studies given the diversity of forecasting methods used, including the way in which models are evaluated. Typically, these models use data on environmental risk factors, such as weather conditions, to forecast malaria for a specific geographic area over a certain interval of time. Clinical predictors, such as anti-malarial treatment, have not been explored in previous forecasting work. Inappropriate anti-malarial treatment has the potential to be a predictor of future malaria cases, for example, as it could result in the ongoing transmission of malaria within the community, leading to an increase of malaria cases at the health facility.

Uganda experiences one of the highest burdens of malaria in the world, where the disease is endemic in greater than 95% of the country and remains the leading cause of morbidity and mortality. A national household survey in 2009 estimated a 42% malaria prevalence among children less than 5 years old. Furthermore, in 2013, Uganda was ranked first in the world in terms of the number of malaria cases and ninth for malaria-related deaths. The objective of this study was to determine the relevance of environmental and clinical predictors of malaria across different settings in Uganda.

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