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A multi-criteria framework for disease surveillance site selection: case study for Plasmodium knowlesi malaria in Indonesia.
Harrison, Lucinda E; Flegg, Jennifer A; Tobin, Ruarai; Lubis, Inke N D; Noviyanti, Rintis; Grigg, Matthew J; Shearer, Freya M; Price, David J.
Afiliación
  • Harrison LE; School of Mathematics and Statistics, The University of Melbourne, Melbourne, Australia.
  • Flegg JA; School of Mathematics and Statistics, The University of Melbourne, Melbourne, Australia.
  • Tobin R; Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia.
  • Lubis IND; Department of Paediatrics, Faculty of Medicine, Universitas Sumatera Utara, Medan, Indonesia.
  • Noviyanti R; Eijkman Institute for Infection and Molecular Biology, Jakarta, Indonesia.
  • Grigg MJ; Menzies School of Health Research and Charles Darwin University, Darwin, Australia.
  • Shearer FM; Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia.
  • Price DJ; Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia.
R Soc Open Sci ; 11(1): 230641, 2024 Jan.
Article en En | MEDLINE | ID: mdl-38204787
ABSTRACT
Disease surveillance aims to collect data at different times or locations, to assist public health authorities to respond appropriately. Surveillance of the simian malaria parasite, Plasmodium knowlesi, is sparse in some endemic areas and the spatial extent of transmission is uncertain. Zoonotic transmission of Plasmodium knowlesi has been demonstrated throughout Southeast Asia and represents a major hurdle to regional malaria elimination efforts. Given an arbitrary spatial prediction of relative disease risk, we develop a flexible framework for surveillance site selection, drawing on principles from multi-criteria decision-making. To demonstrate the utility of our framework, we apply it to the case study of Plasmodium knowlesi malaria surveillance site selection in western Indonesia. We demonstrate how statistical predictions of relative disease risk can be quantitatively incorporated into public health decision-making, with specific application to active human surveillance of zoonotic malaria. This approach can be used in other contexts to extend the utility of modelling outputs.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies / Screening_studies Idioma: En Revista: R Soc Open Sci Año: 2024 Tipo del documento: Article País de afiliación: Australia Pais de publicación: Reino Unido
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies / Screening_studies Idioma: En Revista: R Soc Open Sci Año: 2024 Tipo del documento: Article País de afiliación: Australia Pais de publicación: Reino Unido