02939nas a2200265   4500000000100000008004100001260003700042653002400079653005700103100001300160700001700173700001800190700002500208700001400233700001300247700001600260700001600276700001200292245011900304856009900423300001300522490000700535520211700542022001402659       2022                            d  bPublic Library of Science (PLoS)10aInfectious Diseases10aPublic Health, Environmental and Occupational Health1 aLedien J1 aCucunubá ZM1 aParra-Henao G1 aRodríguez-Monguí E1 aDobson AP1 aAdamo SB1 aBasáñez M1 aNouvellet P1 aEkpo UF00aLinear and machine learning modelling for spatiotemporal disease predictions: Force-of-infection of Chagas disease  uhttps://journals.plos.org/plosntds/article/file?id=10.1371/journal.pntd.0010594&type=printable  ae00105940 v163 a<p>Background Chagas disease is a long-lasting disease with a prolonged asymptomatic period. Cumulative indices of infection such as prevalence do not shed light on the current epidemiological situation, as they integrate infection over long periods. Instead, metrics such as the Force-of-Infection (FoI) provide information about the rate at which susceptible people become infected and permit sharper inference about temporal changes in infection rates. FoI is estimated by fitting (catalytic) models to available age-stratified serological (ground-truth) data. Predictive FoI modelling frameworks are then used to understand spatial and temporal trends indicative of heterogeneity in transmission and changes effected by control interventions. Ideally, these frameworks should be able to propagate uncertainty and handle spatiotemporal issues.</p>

<p>Methodology/principal findings We compare three methods in their ability to propagate uncertainty and provide reliable estimates of FoI for Chagas disease in Colombia as a case study: two Machine Learning (ML) methods (Boosted Regression Trees (BRT) and Random Forest (RF)), and a Linear Model (LM) framework that we had developed previously. Our analyses show consistent results between the three modelling methods under scrutiny. The predictors (explanatory variables) selected, as well as the location of the most uncertain FoI values, were coherent across frameworks. RF was faster than BRT and LM, and provided estimates with fewer extreme values when extrapolating to areas where no ground-truth data were available. However, BRT and RF were less efficient at propagating uncertainty. Conclusions/significance The choice of FoI predictive models will depend on the objectives of the analysis. ML methods will help characterise the mean behaviour of the estimates, while LM will provide insight into the uncertainty surrounding such estimates. Our approach can be extended to the modelling of FoI patterns in other Chagas disease-endemic countries and to other infectious diseases for which serosurveys are regularly conducted for surveillance.</p>
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