Title |
Analysing trends and forecasting malaria epidemics in Madagascar using a sentinel surveillance network: a web-based application
|
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Published in |
Malaria Journal, February 2017
|
DOI | 10.1186/s12936-017-1728-9 |
Pubmed ID | |
Authors |
Florian Girond, Laurence Randrianasolo, Lea Randriamampionona, Fanjasoa Rakotomanana, Milijaona Randrianarivelojosia, Maherisoa Ratsitorahina, Télesphore Yao Brou, Vincent Herbreteau, Morgan Mangeas, Sixte Zigiumugabe, Judith Hedje, Christophe Rogier, Patrice Piola |
Abstract |
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. 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. 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. This approach aims to maximize the usefulness of a sentinel surveillance system to predict and detect epidemics in limited-resource environments. |
Twitter Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
Mexico | 1 | 17% |
Madagascar | 1 | 17% |
Unknown | 4 | 67% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 3 | 50% |
Science communicators (journalists, bloggers, editors) | 2 | 33% |
Practitioners (doctors, other healthcare professionals) | 1 | 17% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Madagascar | 1 | <1% |
Unknown | 133 | 99% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 22 | 16% |
Researcher | 22 | 16% |
Student > Master | 21 | 16% |
Student > Bachelor | 17 | 13% |
Student > Doctoral Student | 12 | 9% |
Other | 12 | 9% |
Unknown | 28 | 21% |
Readers by discipline | Count | As % |
---|---|---|
Medicine and Dentistry | 26 | 19% |
Computer Science | 12 | 9% |
Nursing and Health Professions | 10 | 7% |
Agricultural and Biological Sciences | 10 | 7% |
Social Sciences | 7 | 5% |
Other | 32 | 24% |
Unknown | 37 | 28% |