A multi-criteria framework for disease surveillance site selection: case study for Plasmodium knowlesi malaria in Indonesia
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 tra...
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| Format: | Article |
| Language: | English |
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The Royal Society
2024-01-01
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| Series: | Royal Society Open Science |
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| Online Access: | https://royalsocietypublishing.org/doi/10.1098/rsos.230641 |
| _version_ | 1827385972167278592 |
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| author | Lucinda E. Harrison Jennifer A. Flegg Ruarai Tobin Inke N. D. Lubis Rintis Noviyanti Matthew J. Grigg Freya M. Shearer David J. Price |
| author_facet | Lucinda E. Harrison Jennifer A. Flegg Ruarai Tobin Inke N. D. Lubis Rintis Noviyanti Matthew J. Grigg Freya M. Shearer David J. Price |
| author_sort | Lucinda E. Harrison |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-08T15:35:19Z |
| format | Article |
| id | doaj.art-24da0593d01b43fa92f7f42a2d5d6808 |
| institution | Directory Open Access Journal |
| issn | 2054-5703 |
| language | English |
| last_indexed | 2024-03-08T15:35:19Z |
| publishDate | 2024-01-01 |
| publisher | The Royal Society |
| record_format | Article |
| series | Royal Society Open Science |
| spelling | doaj.art-24da0593d01b43fa92f7f42a2d5d68082024-01-10T00:05:55ZengThe Royal SocietyRoyal Society Open Science2054-57032024-01-0111110.1098/rsos.230641A multi-criteria framework for disease surveillance site selection: case study for Plasmodium knowlesi malaria in IndonesiaLucinda E. Harrison0Jennifer A. Flegg1Ruarai Tobin2Inke N. D. Lubis3Rintis Noviyanti4Matthew J. Grigg5Freya M. Shearer6David J. Price7School of Mathematics and Statistics, The University of Melbourne, Melbourne, AustraliaSchool of Mathematics and Statistics, The University of Melbourne, Melbourne, AustraliaMelbourne School of Population and Global Health, The University of Melbourne, Melbourne, AustraliaDepartment of Paediatrics, Faculty of Medicine, Universitas Sumatera Utara, Medan, IndonesiaEijkman Institute for Infection and Molecular Biology, Jakarta, IndonesiaMenzies School of Health Research and Charles Darwin University, Darwin, AustraliaMelbourne School of Population and Global Health, The University of Melbourne, Melbourne, AustraliaMelbourne School of Population and Global Health, The University of Melbourne, Melbourne, AustraliaDisease 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.https://royalsocietypublishing.org/doi/10.1098/rsos.230641geospatial modellingdisease surveillancesite selectionPlasmodium knowlesi malariamulti-criteria decision-making |
| spellingShingle | Lucinda E. Harrison Jennifer A. Flegg Ruarai Tobin Inke N. D. Lubis Rintis Noviyanti Matthew J. Grigg Freya M. Shearer David J. Price A multi-criteria framework for disease surveillance site selection: case study for Plasmodium knowlesi malaria in Indonesia Royal Society Open Science geospatial modelling disease surveillance site selection Plasmodium knowlesi malaria multi-criteria decision-making |
| title | A multi-criteria framework for disease surveillance site selection: case study for Plasmodium knowlesi malaria in Indonesia |
| title_full | A multi-criteria framework for disease surveillance site selection: case study for Plasmodium knowlesi malaria in Indonesia |
| title_fullStr | A multi-criteria framework for disease surveillance site selection: case study for Plasmodium knowlesi malaria in Indonesia |
| title_full_unstemmed | A multi-criteria framework for disease surveillance site selection: case study for Plasmodium knowlesi malaria in Indonesia |
| title_short | A multi-criteria framework for disease surveillance site selection: case study for Plasmodium knowlesi malaria in Indonesia |
| title_sort | multi criteria framework for disease surveillance site selection case study for plasmodium knowlesi malaria in indonesia |
| topic | geospatial modelling disease surveillance site selection Plasmodium knowlesi malaria multi-criteria decision-making |
| url | https://royalsocietypublishing.org/doi/10.1098/rsos.230641 |
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