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Statistical methods for linking geostatistical maps and transmission models: Application to lymphatic filariasis in East Africa.
Touloupou, Panayiota; Retkute, Renata; Hollingsworth, T Déirdre; Spencer, Simon E F.
Afiliação
  • Touloupou P; School of Mathematics, University of Birmingham, Birmingham, UK. Electronic address: P.Touloupou@bham.ac.uk.
  • Retkute R; Department of Plant Sciences, University of Cambridge, Cambridge, UK. Electronic address: rr614@cam.ac.uk.
  • Hollingsworth TD; Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK. Electronic address: Deirdre.Hollingsworth@bdi.ox.ac.uk.
  • Spencer SEF; Department of Statistics, University of Warwick, Coventry, UK; Zeeman Institute, University of Warwick, Coventry, UK. Electronic address: S.E.F.Spencer@warwick.ac.uk.
Spat Spatiotemporal Epidemiol ; 41: 100391, 2022 06.
Article em En | MEDLINE | ID: mdl-35691660
Infectious diseases remain one of the major causes of human mortality and suffering. Mathematical models have been established as an important tool for capturing the features that drive the spread of the disease, predicting the progression of an epidemic and hence guiding the development of strategies to control it. Another important area of epidemiological interest is the development of geostatistical methods for the analysis of data from spatially referenced prevalence surveys. Maps of prevalence are useful, not only for enabling a more precise disease risk stratification, but also for guiding the planning of more reliable spatial control programmes by identifying affected areas. Despite the methodological advances that have been made in each area independently, efforts to link transmission models and geostatistical maps have been limited. Motivated by this fact, we developed a Bayesian approach that combines fine-scale geostatistical maps of disease prevalence with transmission models to provide quantitative, spatially-explicit projections of the current and future impact of control programs against a disease. These estimates can then be used at a local level to identify the effectiveness of suggested intervention schemes and allow investigation of alternative strategies. The methodology has been applied to lymphatic filariasis in East Africa to provide estimates of the impact of different intervention strategies against the disease.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Filariose Linfática Idioma: En Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Filariose Linfática Idioma: En Ano de publicação: 2022 Tipo de documento: Article