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Professor Adjunct of Biostatistics
Biography
Professor Peter Diggle is currently an EPSRC Senior Fellow, leading a research programme in Spatial and Longitudinal Data Analysis at the University of Lancaster.
Current methodological themes include: geostatistical analysis; spatial and spatio-temporal point processes; joint modelling of repeated measurement and time-to-event outcomes in longitudinal studies. Current areas of application include: real-time disease surveillance; environmental exposure measurement; tropical disease prevalence mapping.
Diggle is founding co-editor of the journal "Biostatistics" and a trustee for the Biometrika Trust.
Last Updated on April 07, 2025.
Appointments
Biostatistics
Professor AdjunctPrimary
Other Departments & Organizations
Education & Training
- PhD
- Newcastle University (1977)
- MSc
- Oxford College (1973)
Research
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Overview
Medical Research Interests
Statistics as Topic
Public Health Interests
Modeling
ORCID
0000-0003-3521-5020
Research at a Glance
Yale Co-Authors
Frequent collaborators of Peter Diggle's published research.
Publications Timeline
A big-picture view of Peter Diggle's research output by year.
Federico Costa, PhD
31Publications
219Citations
Publications
2023
Basic urban services fail to neutralise environmental determinants of ‘rattiness’, a composite metric of rat abundance
Carvalho-Pereira T, Eyre M, Zeppelini C, Espirito Santo V, Santiago D, Santana R, Palma F, Reis M, Lustosa R, Khalil H, Diggle P, Giorgi E, Costa F, Begon M. Basic urban services fail to neutralise environmental determinants of ‘rattiness’, a composite metric of rat abundance. Urban Ecosystems 2023, 27: 757-771. DOI: 10.1007/s11252-023-01481-2.Peer-Reviewed Original ResearchCitationsWastewater-based surveillance models for COVID-19: A focused review on spatio-temporal models
Torabi F, Li G, Mole C, Nicholson G, Rowlingson B, Smith C, Jersakova R, Diggle P, Blangiardo M. Wastewater-based surveillance models for COVID-19: A focused review on spatio-temporal models. Heliyon 2023, 9: e21734. PMID: 38053867, PMCID: PMC10694161, DOI: 10.1016/j.heliyon.2023.e21734.Peer-Reviewed Original ResearchCitationsAltmetricConceptsUsing 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 ResearchCitationsAltmetricConceptsTreatment 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
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