000232073 001__ 232073
000232073 005__ 20190317000850.0
000232073 037__ $$aSTUDENT
000232073 245__ $$aA dynamically forced cholera model for operational forecasting in Haiti
000232073 269__ $$a2017
000232073 260__ $$c2017
000232073 336__ $$aStudent Projects
000232073 520__ $$aWaterborne diseases are among the leading causes of deaths in developing countries. The prompt intervention during an outbreak is crucial to limit the spread of an epidemic and reduce the number of casualties. The epidemiological models developed in the past years at the ECHO lab to study cholera and schistosomiasis epidemics, produce reliable predictions of the infected population. Human mobility fluxes together with the hydrological network constitute the main spatial drivers of the model, as they delineate the possible network along which the epidemic might spread. Due to the lack of available data, human mobility is typically described through a simple gravity-model, constant in time. However, mobility fluxes might highly change, especially during the beginning of the outbreak or after a hurricane The main objective of this project is to perform a sensitivity analysis of the spatially-explicit cholera model developed at the ECHO lab to the possible estimations of human mobility. To achieve this goal, different formulations of human mobility will be considered, including both mathematical estimations via a gravity model and direct estimations derived from mobile-phone data. Depending on the availability of the mobile phone data, the analysis will be performed on the 2010 Haitian outbreak or on the 2016 epidemic dynamics in Haiti after hurricane Matthew.
000232073 6531_ $$amobile-phone data
000232073 6531_ $$ahuman mobility
000232073 6531_ $$acholera model
000232073 700__ $$aKälin, Carolina
000232073 720_2 $$aRinaldo, Andrea$$edir.$$g182281$$0240022
000232073 8564_ $$uhttps://infoscience.epfl.ch/record/232073/files/K%C3%84LIN_PDM%20PRINTEMPS%202017.pdf$$zPublisher's version$$s8397222$$yPublisher's version
000232073 8564_ $$uhttps://infoscience.epfl.ch/record/232073/files/K%C3%84LIN_POSTER%20PDM%2016-17.pdf$$zn/a$$s3459541
000232073 909C0 $$0252601$$pSSIE
000232073 909C0 $$pECHO$$xU10273$$0252014
000232073 909CO $$qGLOBAL_SET$$pENAC$$ooai:infoscience.tind.io:232073
000232073 917Z8 $$x145925
000232073 917Z8 $$x145925
000232073 917Z8 $$x145925
000232073 937__ $$aEPFL-STUDENT-232073
000232073 973__ $$aEPFL
000232073 980__ $$bMASTERS$$aSTUDENT