An archetypes approach to malaria intervention impact mapping: a new framework and example application

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  • Amelia Bertozzi-Villa
  • Caitlin A. Bever
  • Jaline Gerardin
  • Joshua L. Proctor
  • Meikang Wu
  • Dennis Harding
  • T. Deirdre Hollingsworth
  • Bhatt, Samir
  • Peter W. Gething
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.

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.

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.

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.
TidsskriftMalaria Journal
Udgave nummer1
Antal sider22
StatusUdgivet - 2023

Bibliografisk note

Funding Information:
This work was supported, in whole or in part, by the Bill & Melinda Gates Foundation (OPP1197730). Under the grant conditions of the Foundation, a Creative Commons Attribution 4.0 Generic License has already been assigned to the Author Accepted Manuscript version that might arise from this submission. This work was also supported by the Telethon Trust, Western Australia.

Funding Information:
This publication is based on research conducted by the Malaria Atlas Project and funded in whole or in part by the Bill & Melinda Gates Foundation, including models and data analysis performed by the Institute for Disease Modeling at the Bill & Melinda Gates Foundation.

Funding Information:
The authors would like to thank all members of the WHO Strategic Advisory Group for Malaria Elimination and the Lancet Commission on Malaria Eradication for their support, commentary, and feedback on this project.

Publisher Copyright:
© 2023, The Author(s).

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