Journal: Scientific Reports

Article Title: Big-data-driven modeling unveils country-wide drivers of endemic schistosomiasis

doi: 10.1038/s41598-017-00493-1

Figure Lengend Snippet: Schistosomiasis transmission cycle ( a ) and data for model application ( b – e ). ( a ) Paired adult worms within human hosts produce eggs (left to right: S . mansoni , S . japonicum , S . haematobium ) that are shed through feces or urine and hatch into miracidia. Miracidia infect species-specific intermediate snail hosts (left to right: genus Biomphalaria , Bulinus , Oncomelania ), which then shed free-swimming cercariae that can penetrate human skin and eventually develop into reproductive schistosomes. ( b ) High-resolution population density map of Senegal [inhabitants km −2 ]. Black lines indicate administrative boundaries (thick/thin lines are for regions/arrondissements). Regions are numbered as follows: 1–Dakar, 2–Thiès, 3–Diourbel, 4–Fatick, 5–Louga, 6–Kaolack, 7–Kaffrine, 8–Saint-Louis, 9–Kolda, 10–Sédhiou, 11–Ziguinchor, 12–Kédougou, 13–Tambacounda, 14–Matam. ( c ) People living in rural settings [%] (colors) and rivers of Senegal (thick/thin white lines are for perennial/ephemeral rivers). ( d ) Human mobility fluxes in year 2013 [number of people] estimated from anonymous mobile phone traces; the flux between any two arrondissements (say i and j , \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$i\ne j$$\end{document} i ≠ j ) is obtained as K i Q ij (see Table ). Only fluxes ≥100 people are displayed as links between arrondissement-level population centroids. ( e ) Prevalence of urogenital schistosomiasis [% of infected people] according to the national surveys operated by the Senegalese Ministry of Health. Data are shown at the scale of health districts and cover the timespan 1996–2013. See SI for details on data sources. The drawings in panel a are from the Centers for Disease Control and Prevention (CDC, Parasites: Schistosomiasis, http://www.cdc.gov/parasites/schistosomiasis/biology.html ; last date of access: 03/02/2017). The maps in panels b–e have been created with QGIS 2.4 (QGIS Development Team, QGIS: A free and open source geographic information system, http://www.qgis.org/ ; last date of access: 03/02/2017) and MATLAB R2015b (MathWorks, MATLAB, http://www.mathworks.com/products/matlab/ ; last date of access: 03/02/2017).

Article Snippet: The map in the inset of panel (a) has been created with QGIS 2.4 (QGIS Development Team, QGIS: A free and open source geographic information system, http://www.qgis.org/ ; last date of access: 03/02/2017) and MATLAB R2015b (MathWorks, MATLAB, http://www.mathworks.com/products/matlab/ ; last date of access: 03/02/2017).

Techniques: Transmission Assay, Infection

Journal: Scientific Reports

Article Title: Big-data-driven modeling unveils country-wide drivers of endemic schistosomiasis

doi: 10.1038/s41598-017-00493-1

Figure Lengend Snippet: Reference model simulation and comparison with epidemiological evidence. ( a ) Quantitative agreement between simulated disease prevalence at the regional scale and the available data (labels as in Fig. ) for the best-fit model accounting for fine-grained spatial heterogeneity in transmission risk and human mobility estimated from CDRs (M4, reference model). ( b ) Projected schistosomiasis prevalence [% of people infected] at the scale of third-level administrative units as obtained from the reference model. Calibrated parameter values: β 0 = 5.5 · 10 −3 [days −1 ], χ 0 = 2.2 · 10 −6 [days −1 parasites −1 ], \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\varphi =4.3\cdot {10}^{-1}$$\end{document} φ = 4.3 ⋅ 10 − , \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\xi =1.1\cdot {10}^{2}$$\end{document} ξ = 1.1 ⋅ 10 2 . See Table for parameter definitions and Methods for details on the model. The map in panel (b) has been created with QGIS 2.4 (QGIS Development Team, QGIS: A free and open source geographic information system, http://www.qgis.org/ ; last date of access: 03/02/2017) and MATLAB R2015b (MathWorks, MATLAB, http://www.mathworks.com/products/matlab/ ; last date of access: 03/02/2017).

Article Snippet: The map in the inset of panel (a) has been created with QGIS 2.4 (QGIS Development Team, QGIS: A free and open source geographic information system, http://www.qgis.org/ ; last date of access: 03/02/2017) and MATLAB R2015b (MathWorks, MATLAB, http://www.mathworks.com/products/matlab/ ; last date of access: 03/02/2017).

Techniques: Transmission Assay, Infection

Journal: Scientific Reports

Article Title: Big-data-driven modeling unveils country-wide drivers of endemic schistosomiasis

doi: 10.1038/s41598-017-00493-1

Figure Lengend Snippet: Effects of human mobility on schistosomiasis prevalence. ( a ) Differences [%] in regional disease prevalence (labels as in Fig. ) as predicted by the reference model (M4, mobility matrix estimated from CDRs) or by a model with the same parameter values but no mobility (mobility matrix set to be the identity matrix); positive values indicate higher prevalence in the model without mobility (absolute differences sorted in decreasing order). Inset: arrondissement-scale differences in infection prevalence: positive values indicate again higher prevalence in the model without mobility. ( b ) Projected country-scale disease prevalence (black, left axis) and APB (gray, right axis) as a function of the fraction of mobile people (those who leave their home arrondissement at least once a year). The dots indicate infection prevalence and APB corresponding to the mobility level inferred from CDR analysis (26%). Different levels of human mobility have been obtained by artificially manipulating the mobility matrix estimated from data (SI). Parameters as in Fig. . The map in the inset of panel (a) has been created with QGIS 2.4 (QGIS Development Team, QGIS: A free and open source geographic information system, http://www.qgis.org/ ; last date of access: 03/02/2017) and MATLAB R2015b (MathWorks, MATLAB, http://www.mathworks.com/products/matlab/ ; last date of access: 03/02/2017).

Article Snippet: The map in the inset of panel (a) has been created with QGIS 2.4 (QGIS Development Team, QGIS: A free and open source geographic information system, http://www.qgis.org/ ; last date of access: 03/02/2017) and MATLAB R2015b (MathWorks, MATLAB, http://www.mathworks.com/products/matlab/ ; last date of access: 03/02/2017).

Techniques: Infection