000223430 001__ 223430
000223430 005__ 20190317000602.0
000223430 037__ $$aREP_WORK
000223430 245__ $$aPrediction of cholera dynamics in Haiti following the passage of Hurricane Matthew
000223430 269__ $$a2016
000223430 260__ $$c2016
000223430 300__ $$a6
000223430 336__ $$aReports
000223430 520__ $$aFollowing the landfall of Hurricane Matthew in Haiti on October 3, 2016, an increase of suspected cholera cases was reported in both the southern part of the island (with Grande-Anse and Le Sud departments reporting 1349 and 1533 cases respectively between 5 October and 6 November) and also in the capital, Port-au-Prince (438 cases reported over the same period). The hurricane caused the displacement of about 175,000 people, the vast majority of which remained in their department of origin; however, about 10% appear to have displaced to the capital Port-au-Prince. In this context, a mass OCV vaccination campaign was planned, starting on November 8 and targeting 816,999 individuals in Grande-Anse and Le Sud. The aim of this study is to provide additional information to health actors responding to the post-hurricane cholera outbreak in Haiti. To this end, we calibrated a mechanistic model of cholera transmission on currently available data for Haiti in order to forecast the spatio-temporal dynamics of the cholera epidemic at the departmental level from November 2016 to January 2017. Model outputs have been translated into operational recommendations, with a focus on the scheduled OCV campaign.
000223430 700__ $$aCamacho, Anton
000223430 700__ $$aCohuet, Sandra
000223430 700__ $$aGrandesso, Francesco
000223430 700__ $$aLuquero, Francisco
000223430 700__ $$aLynch, Emily
000223430 700__ $$0249184$$g262879$$aPasetto, Damiano
000223430 700__ $$0247079$$g178954$$aFinger, Flavio
000223430 700__ $$g182988$$aBertuzzo, Enrico$$0240021
000223430 700__ $$aRinaldo, Andrea$$g182281$$0240022
000223430 8564_ $$uhttps://infoscience.epfl.ch/record/223430/files/Epicentre-EPFL-modelling-cholera-haiti_20161111.pdf$$zn/a$$s566409$$yn/a
000223430 909C0 $$xU10273$$0252014$$pECHO
000223430 909CO $$ooai:infoscience.tind.io:223430$$qGLOBAL_SET$$pENAC$$preport
000223430 917Z8 $$x262879
000223430 937__ $$aEPFL-REPORT-223430
000223430 973__ $$aOTHER
000223430 980__ $$aREPORT