title: Big-data-driven modeling unveils country-wide drivers of endemic schistosomiasis.
abstract: Schistosomiasis is a parasitic infection that is widespread in sub-Saharan Africa, where it represents a major health problem. We study the drivers of its geographical distribution in Senegal via a spatially explicit network model accounting for epidemiological dynamics driven by local socioeconomic and environmental conditions, and human mobility. The model is parameterized by tapping several available geodatabases and a large dataset of mobile phone traces. It reliably reproduces the observed spatial patterns of regional schistosomiasis prevalence throughout the country, provided that spatial heterogeneity and human mobility are suitably accounted for. Specifically, a fine-grained description of the socioeconomic and environmental heterogeneities involved in local disease transmission is crucial to capturing the spatial variability of disease prevalence, while the inclusion of human mobility significantly improves the explanatory power of the model. Concerning human movement, we find that moderate mobility may reduce disease prevalence, whereas either high or low mobility may result in increased prevalence of infection. The effects of control strategies based on exposure and contamination reduction via improved access to safe water or educational campaigns are also analyzed. To our knowledge, this represents the first application of an integrative schistosomiasis transmission model at a whole-country scale.
authors.affiliation: Politecnico di Milano, Dipartimento di Elettronica, Informazione e Bioingegneria, Milano, IT, 20133, Italy. lorenzo.mari@polimi.it.
authors.forename: Lorenzo
authors.identifier: 0000-0003-1326-9992
authors.initials: L
authors.lastname: Mari
authors.affiliation: Politecnico di Milano, Dipartimento di Elettronica, Informazione e Bioingegneria, Milano, IT, 20133, Italy.
authors.forename: Marino
authors.identifier: 0000-0001-8063-9178
authors.initials: M
authors.lastname: Gatto
authors.affiliation: Politecnico di Milano, Dipartimento di Elettronica, Informazione e Bioingegneria, Milano, IT, 20133, Italy.
authors.forename: Manuela
authors.initials: M
authors.lastname: Ciddio
authors.affiliation: Ministère de la Santé et de l'Action Sociale, Dakar, BP, 4024, Senegal.
authors.forename: Elhadji D
authors.identifier: 0000-0003-2368-4698
authors.initials: ED
authors.lastname: Dia
authors.affiliation: Stanford University, Hopkins Marine Station, Pacific Grove, CA, 93950, USA.|University of California, Marine Science Institute, Santa Barbara, CA, 93106, USA.
authors.forename: Susanne H
authors.identifier: 0000-0002-5111-9322
authors.initials: SH
authors.lastname: Sokolow
authors.affiliation: Stanford University, Hopkins Marine Station, Pacific Grove, CA, 93950, USA.
authors.forename: Giulio A
authors.identifier: 0000-0002-4186-3369
authors.initials: GA
authors.lastname: De Leo
authors.affiliation: Politecnico di Milano, Dipartimento di Elettronica, Informazione e Bioingegneria, Milano, IT, 20133, Italy. renato.casagrandi@polimi.it.
authors.forename: Renato
authors.identifier: 0000-0001-5177-803X
authors.initials: R
authors.lastname: Casagrandi
mesh_terms: D017954:Africa South of the Sahara / Q000453:epidemiology; D057225:Data Mining; D016208:Databases, Factual; D062306:Geography, Medical; D063426:Human Migration; D006801:Humans; D015233:Models, Statistical; D062486:Public Health Surveillance; D012552:Schistosomiasis / Q000453:epidemiology* / Q000469:parasitology / Q000517:prevention & control / Q000635:transmission*; D062206:Spatial Analysis