![]() In Madagascar, malaria burden has decreased in recent decades, mainly due to successful malaria control interventions. By automating the analysis and visualization of outbreak detection, the need for time-consuming data preparation and analysis is reduced. The development of methods providing lead time benefits before outbreaks is necessary to allow time for preparedness and to save lives. This system records clinical data on several diseases, including malaria cases confirmed by a rapid diagnostic test (RDT), at 34 sites throughout the country (Fig. ![]() The absence of a gold standard in past epidemics and the lack of consensus on outbreak characterization has serious operational implications and can become a stumbling block for EWS implementation.įever sentinel surveillance (FSS) using mobile health (mHealth) technology has been implemented in Madagascar since 2007. Īn increasing number of statistical methods for detecting changes in trends have been developed, but there is not yet a single reference standard. Monitoring of the epidemic risk of malaria may integrate sequential and complementary components, such as an early detection system (EDS), an EWS and long-range forecasting (LRF). The World Health Organization (WHO) supports the strengthening of existing infectious disease surveillance systems by developing such EWSs. The implementation of an automated early warning system (EWS) is a key step in adding value to the epidemiological data routinely collected by surveillance systems to improve the timeliness of detection of diseases outbreaks. Early detection of outbreaks and rapid control actions are essential to prevent and contain the spread of infectious diseases to reduce morbidity and death. ![]()
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