2021
DOI: 10.1371/journal.pone.0251263
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External quality assessment of Rift Valley fever diagnosis in countries at risk of the disease: African, Indian Ocean and Middle-East regions

Abstract: Rift Valley fever virus (RVFV), an arbovirus belonging to the Phlebovirus genus of the Phenuiviridae family, causes the zoonotic and mosquito-borne RVF. The virus, which primarily affects livestock (ruminants and camels) and humans, is at the origin of recent major outbreaks across the African continent (Mauritania, Libya, Sudan), and in the South-Western Indian Ocean (SWIO) islands (Mayotte). In order to be better prepared for upcoming outbreaks, to predict its introduction in RVFV unscathed countries, and to… Show more

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Cited by 6 publications
(4 citation statements)
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“…Due to its threat to public health and the high risk of epidemics, the WHO identifies RVFV as a priority pathogen for its research and development blueprint [ 23 ]. Therefore, improving RVF diagnostic capacity in endemic countries and countries at risk of epidemic emergence is crucial to prevent further emergence and help in responding to outbreaks [ 22 , 24 ].…”
Section: Discussionmentioning
confidence: 99%
“…Due to its threat to public health and the high risk of epidemics, the WHO identifies RVFV as a priority pathogen for its research and development blueprint [ 23 ]. Therefore, improving RVF diagnostic capacity in endemic countries and countries at risk of epidemic emergence is crucial to prevent further emergence and help in responding to outbreaks [ 22 , 24 ].…”
Section: Discussionmentioning
confidence: 99%
“…Tunisian laboratories have also participated in several 'external quality assessment' tests for RVF diagnosis. These trials certainly reinforced capacity building and improved RVF diagnosis capacities of involved Tunisian laboratories (Monaco et al, 2015;Pedarrieu et al, 2021).…”
Section: ■ Discussionmentioning
confidence: 88%
“…This involves developing more sensitive and specific diagnostic tests, including rapid and point-of-care tools. Integrating surveillance data with advanced modeling techniques can improve prediction and forecasting capabilities (Aurélie et al 2021).…”
Section: Enhanced Surveillancementioning
confidence: 99%