Analysing trends and forecasting malaria epidemics in Madagascar using a sentinel surveillance network: a web-based application

Malar J. 2017 Feb 13;16(1):72. doi: 10.1186/s12936-017-1728-9.


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.

Publication types

  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Adolescent
  • Adult
  • Aged
  • Aged, 80 and over
  • Child
  • Child, Preschool
  • Epidemics*
  • Female
  • Forecasting
  • Humans
  • Infant
  • Infant, Newborn
  • Internet
  • Madagascar / epidemiology
  • Malaria / epidemiology*
  • Male
  • Middle Aged
  • Prospective Studies
  • Retrospective Studies
  • Sentinel Surveillance
  • Software
  • Text Messaging
  • Young Adult