02163nas a2200253   4500000000100000008004100001653002600042653002600068653002100094653002300115653002600138653003000164653003900194100000900233700001300242700001000255700001800265700001200283700001200295245015900307856016700466520126200633022001401895       2018                            d10aVector-borne diseases10aSpatial heterogeneity10aDisease dynamics10aCommuting networks10aAgent-based modelling10aLymphatic filariasis (LF)10aNeglected tropical diseases (NTDs)1 aXu Z1 aGraves P1 aLau C1 aClements AC A1 aGeard N1 aGlass K00aGEOFIL: A spatially-explicit agent-based modelling framework for predicting the long-term transmission dynamics of lymphatic filariasis in American Samoa.  uhttps://reader.elsevier.com/reader/sd/pii/S1755436518301270?token=A38C871962839FD9DFD46F9FDB9A7B76E485DFA8BB105A8FA7AA39EC3E0C79B1F96838622E164B32893CF769184DD5953 a<p>In this study, a spatially-explicit agent-based modelling framework GEOFIL was developed to predict lymphatic filariasis (LF) transmission dynamics in American Samoa. GEOFIL included individual-level information on age, gender, disease status, household location, household members, workplace/school location and colleagues/schoolmates at each time step during the simulation. In American Samoa, annual mass drug administration from 2000 to 2006 successfully reduced LF prevalence dramatically. However, GEOFIL predicted continual increase in microfilaraemia prevalence in the absence of further intervention. Evidence from seroprevalence and transmission assessment surveys conducted from 2010 to 2016 indicated a resurgence of LF in American Samoa, corroborating GEOFIL's predictions. The microfilaraemia and antigenaemia prevalence in 6-7-yo children were much lower than in the overall population. Mosquito biting rates were found to be a critical determinant of infection risk. Transmission hotspots are likely to disappear with lower biting rates. GEOFIL highlights current knowledge gaps, such as data on mosquito abundance, biting rates and within-host parasite dynamics, which are important for improving the accuracy of model predictions.</p>
  a1878-0067