2024
How Does the Proportion of Never Treatment Influence the Success of Mass Drug Administration Programs for the Elimination of Lymphatic Filariasis?
Kura K, Stolk W, Basáñez M, Collyer B, de Vlas S, Diggle P, Gass K, Graham M, Hollingsworth T, King J, Krentel A, Anderson R, Coffeng L. How Does the Proportion of Never Treatment Influence the Success of Mass Drug Administration Programs for the Elimination of Lymphatic Filariasis? Clinical Infectious Diseases 2024, 78: s93-s100. PMID: 38662701, PMCID: PMC11045024, DOI: 10.1093/cid/ciae021.Peer-Reviewed Original ResearchConceptsMass drug administrationElimination of lymphatic filariasisEfficacious treatment regimensLymphatic filariasisLevels of NTMass drug administration programmesYears of annual treatmentTreatment regimensDrug combinationsTransmission settingsMass drug administration programsDrug AdministrationTreatment coveragePrevalenceTransmission areasMDA coverageBaselineProportion of peopleTreatmentPrevalence thresholdImpact of NTAlbendazoleDiethylcarbamazineHighest infection prevalenceHighest proportionA Comparison of Markov and Mechanistic Models for Soil-Transmitted Helminth Prevalence Projections in the Context of Survey Design
Eyre M, Bulstra C, Johnson O, de Vlas S, Diggle P, Fronterrè C, Coffeng L. A Comparison of Markov and Mechanistic Models for Soil-Transmitted Helminth Prevalence Projections in the Context of Survey Design. Clinical Infectious Diseases 2024, 78: s146-s152. PMID: 38662703, PMCID: PMC11045013, DOI: 10.1093/cid/ciae022.Peer-Reviewed Original ResearchConceptsOptimal survey designImpact assessment surveysSurvey designProjected prevalenceGeostatistical methodsPrevalence surveySampling designSchool-aged childrenPreventive chemotherapySoil-transmitted helminthsPrevalence projectionsControl programsWorld Health OrganizationAssessment surveyPrevalence dataSub-Saharan AfricaMechanistic modelTarget of PCSoutheast AsiaHealth OrganizationMechanistic methodsPrevalenceBillion peopleSub-SaharanInadequate sanitationIntegrating wastewater and randomised prevalence survey data for national COVID surveillance
Li G, Diggle P, Blangiardo M. Integrating wastewater and randomised prevalence survey data for national COVID surveillance. Scientific Reports 2024, 14: 5124. PMID: 38429366, PMCID: PMC10907376, DOI: 10.1038/s41598-024-55752-9.Peer-Reviewed Original ResearchConceptsPrevalence dataPrevalence surveySurveillance systemCollection of health dataDisease prevalenceMeasures of prevalenceLocal disease prevalenceWastewaterSpatial resolutionWastewater dataPrevalence survey dataHealth dataReduced scaleDisease metricsCoarser spatial resolutionDisease-agnosticPrevalenceCOVID-19 pandemicSurveillance toolPost-pandemic settingEarly detectionNon-epidemic periodsCost-effective mannerSurveyDetect outbreaks
2023
Using a model-based geostatistical approach to design and analyse the prevalence of schistosomiasis in Kenya
Okoyo C, Minnery M, Orowe I, Owaga C, Wambugu C, Olick N, Hagemann J, Omondi W, Gichuki P, McCracken K, Montresor A, Fronterre C, Diggle P, Mwandawiro C. Using a model-based geostatistical approach to design and analyse the prevalence of schistosomiasis in Kenya. Frontiers In Tropical Diseases 2023, 4: 1240617. DOI: 10.3389/fitd.2023.1240617.Peer-Reviewed Original ResearchTreatment strategy changesSchool-based deworming programmePublic health problemPrevalence of schistosomiasisWorld Health Organization guidelinesCross-sectional surveyHealth Organization guidelinesPredictive probabilitySCH prevalenceDeworming programsSchistosoma haematobiumHealth problemsSchistosoma mansoniOrganization guidelinesStudy designCounties of KenyaPrevalenceSchistosomiasisHighest predictive probabilityParasitic wormsTreatment requirementsTreatmentGuidelinesMorbidityHaematobium