Background: The use of a malaria early warning system (MEWS) to trigger prompt public health interventions is a key step in adding value to the epidemiological data routinely collected by sentinel surveillance systems.
Methods: This study describes a system using various epidemic thresholds and a forecasting component with the support of new technologies to improve the performance of a sentinel MEWS. Malaria-related data from 21 sentinel sites collected by Short Message Service are automatically analysed to detect malaria trends and malaria outbreak alerts with automated feedback reports.
Results: Roll Back Malaria partners can, through a user-friendly web-based tool, visualize potential outbreaks and generate a forecasting model. The system already demonstrated its ability to detect malaria outbreaks in Madagascar in 2014.
Conclusion: This approach aims to maximize the usefulness of a sentinel surveillance system to predict and detect epidemics in limited-resource environments.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5307694 | PMC |
| http://dx.doi.org/10.1186/s12936-017-1728-9 | DOI Listing |
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