@article{fdi:010069275,
  title = {{A}nalysing trends and forecasting malaria epidemics in {M}adagascar using a sentinel surveillance network : a web-based application},
  author = {{G}irond, {F}. and {R}andrianasolo, {L}. and {R}andriamampionona, {L}. and {R}akotomanana, {F}. and {R}andrianarivelojosia, {M}. and {R}atsitorahina, {M}. and {B}rou, {T}. {Y}. and {H}erbreteau, {V}incent and {M}angeas, {M}organ and {Z}igiumugabe, {S}. and {H}edje, {J}. and {R}ogier, {C}. and {P}iola, {P}.},
  editor = {},
  language = {{ENG}},
  abstract = {{B}ackground : {T}he 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. {M}ethods : {T}his 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}. {M}alaria-related data from 21 sentinel sites collected by {S}hort {M}essage {S}ervice are automatically analysed to detect malaria trends and malaria outbreak alerts with automated feedback reports. {R}esults : {R}oll {B}ack {M}alaria partners can, through a user-friendly web-based tool, visualize potential outbreaks and generate a forecasting model. {T}he system already demonstrated its ability to detect malaria outbreaks in {M}adagascar in 2014. {C}onclusion : {T}his approach aims to maximize the usefulness of a sentinel surveillance system to predict and detect epidemics in limited-resource environments.},
  keywords = {{MADAGASCAR}},
  booktitle = {},
  journal = {{M}alaria {J}ournal},
  volume = {16},
  numero = {},
  pages = {art. 72 [11 p.]},
  ISSN = {1475-2875},
  year = {2017},
  DOI = {10.1186/s12936-017-1728-9},
  URL = {https://www.documentation.ird.fr/hor/fdi:010069275},
}