02947nas a2200433 4500000000100000008004100001260003400042653001500076653002000091653001900111653002700130653001700157100001300174700001600187700001300203700001300216700001100229700001200240700001800252700001200270700001100282700001300293700001300306700001300319700001800332700001500350700001400365700001300379700001200392700001600404700002100420700001200441245011700453856008700570300001400657490000700671520181000678022002502488 2024 d bOxford University Press (OUP)10aEvaluation10aNational Policy10aPublic health 10aMathematical modelling10aEpidemiology1 aPrada JM1 aTouloupou P1 aKebede B1 aGiorgi E1 aSime H1 aSmith M1 aKontoroupis P1 aBrown P1 aCano J1 aFarkas H1 aIrvine M1 aReimer L1 aCaja Rivera R1 ade Vlas SJ1 aMichael E1 aStolk WA1 aPulan R1 aSpencer SEF1 aHollingsworth TD1 aSeife F00aSubnational Projections of Lymphatic Filariasis Elimination Targets in Ethiopia to Support National Level Policy uhttps://academic.oup.com/cid/article-pdf/78/Supplement_2/S117/57334415/ciae072.pdf aS117-S1250 v783 a
Background: Lymphatic filariasis (LF) is a debilitating, poverty-promoting, neglected tropical disease (NTD) targeted for worldwide elimination as a public health problem (EPHP) by 2030. Evaluating progress towards this target for national programmes is challenging, due to differences in disease transmission and interventions at the subnational level. Mathematical models can help address these challenges by capturing spatial heterogeneities and evaluating progress towards LF elimination and how different interventions could be leveraged to achieve elimination by 2030.
Methods: Here we used a novel approach to combine historical geo-spatial disease prevalence maps of LF in Ethiopia with 3 contemporary disease transmission models to project trends in infection under different intervention scenarios at subnational level.
Results: Our findings show that local context, particularly the coverage of interventions, is an important determinant for the success of control and elimination programmes. Furthermore, although current strategies seem sufficient to achieve LF elimination by 2030, some areas may benefit from the implementation of alternative strategies, such as using enhanced coverage or increased frequency, to accelerate progress towards the 2030 targets.
Conclusions: The combination of geospatial disease prevalence maps of LF with transmission models and intervention histories enables the projection of trends in infection at the subnational level under different control scenarios in Ethiopia. This approach, which adapts transmission models to local settings, may be useful to inform the design of optimal interventions at the subnational level in other LF endemic regions.
a1058-4838, 1537-6591