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Prediction of a Rift Valley fever outbreak.


ABSTRACT: El Niño/Southern Oscillation related climate anomalies were analyzed by using a combination of satellite measurements of elevated sea-surface temperatures and subsequent elevated rainfall and satellite-derived normalized difference vegetation index data. A Rift Valley fever (RVF) risk mapping model using these climate data predicted areas where outbreaks of RVF in humans and animals were expected and occurred in the Horn of Africa from December 2006 to May 2007. The predictions were subsequently confirmed by entomological and epidemiological field investigations of virus activity in the areas identified as at risk. Accurate spatial and temporal predictions of disease activity, as it occurred first in southern Somalia and then through much of Kenya before affecting northern Tanzania, provided a 2 to 6 week period of warning for the Horn of Africa that facilitated disease outbreak response and mitigation activities. To our knowledge, this is the first prospective prediction of a RVF outbreak.

SUBMITTER: Anyamba A 

PROVIDER: S-EPMC2626607 | biostudies-literature | 2009 Jan

REPOSITORIES: biostudies-literature

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Prediction of a Rift Valley fever outbreak.

Anyamba Assaf A   Chretien Jean-Paul JP   Small Jennifer J   Tucker Compton J CJ   Formenty Pierre B PB   Richardson Jason H JH   Britch Seth C SC   Schnabel David C DC   Erickson Ralph L RL   Linthicum Kenneth J KJ  

Proceedings of the National Academy of Sciences of the United States of America 20090114 3


El Niño/Southern Oscillation related climate anomalies were analyzed by using a combination of satellite measurements of elevated sea-surface temperatures and subsequent elevated rainfall and satellite-derived normalized difference vegetation index data. A Rift Valley fever (RVF) risk mapping model using these climate data predicted areas where outbreaks of RVF in humans and animals were expected and occurred in the Horn of Africa from December 2006 to May 2007. The predictions were subsequently  ...[more]

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