Hot spot or not: a comparison of spatial statistical methods to predict prospective malaria infections

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Authors

Jacklin F Mosha · National Institute for Medical Research

Hugh J. W. Sturrock · University of California, San Francisco

Brian Greenwood · London School of Hygiene & Tropical Medicine

Colin J. Sutherland · London School of Hygiene & Tropical Medicine

Nahla B. Gadalla · London School of Hygiene & Tropical Medicine

Sharan Atwal · London School of Hygiene & Tropical Medicine

Abstract

Clusters of AMA-1 seroprevalence or parasite prevalence that are predictive of infection a year later can be identified using geospatial models. Kernel smoothing using a 1 km window and spatial scan statistics both provided accurate prediction of future infection.

Topics & Keywords

Malaria Research and Control Data-Driven Disease Surveillance Vaccine Coverage and Hesitancy

UN Sustainable Development Goals

Good health and well-being

Publication Details

Published in: Malaria Journal

Volume 13, Issue 1, pp. 53-53

DOI: 10.1186/1475-2875-13-53

Field-Weighted Citation Impact: 9.63