An archetypes approach to malaria intervention impact mapping: a new framework and example application
Abstract Background As both mechanistic and geospatial malaria modeling methods become more integrated into malaria policy decisions, there is increasing demand for strategies that combine these two methods. This paper introduces a novel archetypes-based methodology for generating high-resolution in...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2023-04-01
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| Series: | Malaria Journal |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12936-023-04535-0 |
| _version_ | 1797836645798510592 |
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| author | Amelia Bertozzi-Villa Caitlin A. Bever Jaline Gerardin Joshua L. Proctor Meikang Wu Dennis Harding T. Deirdre Hollingsworth Samir Bhatt Peter W. Gething |
| author_facet | Amelia Bertozzi-Villa Caitlin A. Bever Jaline Gerardin Joshua L. Proctor Meikang Wu Dennis Harding T. Deirdre Hollingsworth Samir Bhatt Peter W. Gething |
| author_sort | Amelia Bertozzi-Villa |
| collection | DOAJ |
| description | Abstract Background As both mechanistic and geospatial malaria modeling methods become more integrated into malaria policy decisions, there is increasing demand for strategies that combine these two methods. This paper introduces a novel archetypes-based methodology for generating high-resolution intervention impact maps based on mechanistic model simulations. An example configuration of the framework is described and explored. Methods First, dimensionality reduction and clustering techniques were applied to rasterized geospatial environmental and mosquito covariates to find archetypal malaria transmission patterns. Next, mechanistic models were run on a representative site from each archetype to assess intervention impact. Finally, these mechanistic results were reprojected onto each pixel to generate full maps of intervention impact. The example configuration used ERA5 and Malaria Atlas Project covariates, singular value decomposition, k-means clustering, and the Institute for Disease Modeling’s EMOD model to explore a range of three-year malaria interventions primarily focused on vector control and case management. Results Rainfall, temperature, and mosquito abundance layers were clustered into ten transmission archetypes with distinct properties. Example intervention impact curves and maps highlighted archetype-specific variation in efficacy of vector control interventions. A sensitivity analysis showed that the procedure for selecting representative sites to simulate worked well in all but one archetype. Conclusion This paper introduces a novel methodology which combines the richness of spatiotemporal mapping with the rigor of mechanistic modeling to create a multi-purpose infrastructure for answering a broad range of important questions in the malaria policy space. It is flexible and adaptable to a range of input covariates, mechanistic models, and mapping strategies and can be adapted to the modelers’ setting of choice. |
| first_indexed | 2024-04-09T15:12:16Z |
| format | Article |
| id | doaj.art-d6447c2708de4dcebd2810a5f1d12591 |
| institution | Directory Open Access Journal |
| issn | 1475-2875 |
| language | English |
| last_indexed | 2024-04-09T15:12:16Z |
| publishDate | 2023-04-01 |
| publisher | BMC |
| record_format | Article |
| series | Malaria Journal |
| spelling | doaj.art-d6447c2708de4dcebd2810a5f1d125912023-04-30T11:08:05ZengBMCMalaria Journal1475-28752023-04-0122112210.1186/s12936-023-04535-0An archetypes approach to malaria intervention impact mapping: a new framework and example applicationAmelia Bertozzi-Villa0Caitlin A. Bever1Jaline Gerardin2Joshua L. Proctor3Meikang Wu4Dennis Harding5T. Deirdre Hollingsworth6Samir Bhatt7Peter W. Gething8Institute for Disease Modeling, Bill & Melinda Gates FoundationInstitute for Disease Modeling, Bill & Melinda Gates FoundationInstitute for Disease Modeling, Bill & Melinda Gates FoundationInstitute for Disease Modeling, Bill & Melinda Gates FoundationInstitute for Disease Modeling, Bill & Melinda Gates FoundationInstitute for Disease Modeling, Bill & Melinda Gates FoundationBig Data Institute, Nuffield Department of Medicine, Oxford UniversityMRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial CollegeMalaria Atlas Project, Telethon Kids InstituteAbstract Background As both mechanistic and geospatial malaria modeling methods become more integrated into malaria policy decisions, there is increasing demand for strategies that combine these two methods. This paper introduces a novel archetypes-based methodology for generating high-resolution intervention impact maps based on mechanistic model simulations. An example configuration of the framework is described and explored. Methods First, dimensionality reduction and clustering techniques were applied to rasterized geospatial environmental and mosquito covariates to find archetypal malaria transmission patterns. Next, mechanistic models were run on a representative site from each archetype to assess intervention impact. Finally, these mechanistic results were reprojected onto each pixel to generate full maps of intervention impact. The example configuration used ERA5 and Malaria Atlas Project covariates, singular value decomposition, k-means clustering, and the Institute for Disease Modeling’s EMOD model to explore a range of three-year malaria interventions primarily focused on vector control and case management. Results Rainfall, temperature, and mosquito abundance layers were clustered into ten transmission archetypes with distinct properties. Example intervention impact curves and maps highlighted archetype-specific variation in efficacy of vector control interventions. A sensitivity analysis showed that the procedure for selecting representative sites to simulate worked well in all but one archetype. Conclusion This paper introduces a novel methodology which combines the richness of spatiotemporal mapping with the rigor of mechanistic modeling to create a multi-purpose infrastructure for answering a broad range of important questions in the malaria policy space. It is flexible and adaptable to a range of input covariates, mechanistic models, and mapping strategies and can be adapted to the modelers’ setting of choice.https://doi.org/10.1186/s12936-023-04535-0MalariaModelingMapping |
| spellingShingle | Amelia Bertozzi-Villa Caitlin A. Bever Jaline Gerardin Joshua L. Proctor Meikang Wu Dennis Harding T. Deirdre Hollingsworth Samir Bhatt Peter W. Gething An archetypes approach to malaria intervention impact mapping: a new framework and example application Malaria Journal Malaria Modeling Mapping |
| title | An archetypes approach to malaria intervention impact mapping: a new framework and example application |
| title_full | An archetypes approach to malaria intervention impact mapping: a new framework and example application |
| title_fullStr | An archetypes approach to malaria intervention impact mapping: a new framework and example application |
| title_full_unstemmed | An archetypes approach to malaria intervention impact mapping: a new framework and example application |
| title_short | An archetypes approach to malaria intervention impact mapping: a new framework and example application |
| title_sort | archetypes approach to malaria intervention impact mapping a new framework and example application |
| topic | Malaria Modeling Mapping |
| url | https://doi.org/10.1186/s12936-023-04535-0 |
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