High-resolution spatio-temporal risk mapping for malaria in Namibia: a comprehensive analysis

• Published in: Malaria journal 2024-10, Vol.23 (1), p.297-10, Article 297
• Main Authors: Zhang, Song, Amratia, Punam, Symons, Tasmin L, Rumisha, Susan F, Kang, Su Yun, Connell, Mark, Uusiku, Petrina, Katokele, Stark, Hamunyela, Jerobeam, Ntusi, Nelly, Soroses, Wilma, Moyo, Ernest, Lukubwe, Ophilia, Maponga, Chivimbiso, Lucero, Dominic, Gething, Peter W, Cameron, Ewan
• Format: Article
• Language: English
• Subjects: Bayes Theorem; Hierarchical model; Humans; Incidence; Malaria; Malaria - epidemiology; Malaria - prevention & control; Malaria control; Methods; Namibia - epidemiology; Prevention; Risk Assessment - methods; Risk factors; Risk maps; Seasons; Social service; Spatial analysis (Statistics); Spatio-temporal; Spatio-Temporal Analysis
• ISSN: 1475-2875; 1475-2875
• DOI: 10.1186/s12936-024-05103-w

Namibia, a low malaria transmission country targeting elimination, has made substantial progress in reducing malaria burden through improved case management, widespread indoor residual spraying and distribution of insecticidal nets. The country's diverse landscape includes regions with varying population densities and geographical niches, with the north of the country prone to periodic outbreaks. As Namibia approaches elimination, malaria transmission has clustered into distinct foci, the identification of which is essential for deployment of targeted interventions to attain the southern Africa Elimination Eight Initiative targets by 2030. Geospatial modelling provides an effective mechanism to identify these foci, synthesizing aggregate routinely collected case counts with gridded environmental covariates to downscale case data into high-resolution risk maps. This study introduces innovative infectious disease mapping techniques to generate high-resolution spatio-temporal risk maps for malaria in Namibia. A two-stage approach is employed to create maps using statistical Bayesian modelling to combine environmental covariates, population data, and clinical malaria case counts gathered from the routine surveillance system between 2018 and 2021. A fine-scale spatial endemicity surface was produced for annual average incidence, followed by a spatio-temporal modelling of seasonal fluctuations in weekly incidence and aggregated further to district level. A seasonal profile was inferred across most districts of the country, where cases rose from late December/early January to a peak around early April and then declined rapidly to a low level from July to December. There was a high degree of spatial heterogeneity in incidence, with much higher rates observed in the northern part and some local epidemic occurrence in specific districts sporadically. While the study acknowledges certain limitations, such as population mobility and incomplete clinical case reporting, it underscores the importance of continuously refining geostatistical techniques to provide timely and accurate support for malaria elimination efforts. The high-resolution spatial risk maps presented in this study have been instrumental in guiding the Namibian Ministry of Health and Social Services in prioritizing and targeting malaria prevention efforts. This two-stage spatio-temporal approach offers a valuable tool for identifying hotspots and monitoring malaria risk patterns, ultimately contributing