High-resolution spatio-temporal risk mapping for malaria in Namibia: a comprehensive analysis.
Hierarchical model
Malaria control
Risk maps
Spatio-temporal
Journal
Malaria journal
ISSN: 1475-2875
Titre abrégé: Malar J
Pays: England
ID NLM: 101139802
Informations de publication
Date de publication:
05 Oct 2024
05 Oct 2024
Historique:
received:
04
03
2024
accepted:
03
09
2024
medline:
5
10
2024
pubmed:
5
10
2024
entrez:
4
10
2024
Statut:
epublish
Résumé
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 to the achievement of national and sub-national elimination goals.
Sections du résumé
BACKGROUND
BACKGROUND
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.
METHODS
METHODS
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.
RESULTS
RESULTS
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.
CONCLUSIONS
CONCLUSIONS
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 to the achievement of national and sub-national elimination goals.
Identifiants
pubmed: 39367414
doi: 10.1186/s12936-024-05103-w
pii: 10.1186/s12936-024-05103-w
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
297Subventions
Organisme : Bill and Melinda Gates Foundation
ID : BMGF:INV-055192
Informations de copyright
© 2024. The Author(s).
Références
Fambirai T, Chimbari MJ, Ndarukwa P. Global cross-border malaria control collaborative initiatives: a scoping review. Int J Environ Res Public Health. 2022;19:12216.
doi: 10.3390/ijerph191912216
White NJ, Day NPJ, Ashley EA, Smithuis FM, Nosten FH. Have we really failed to roll back malaria? Lancet. 2022;399:799–800.
doi: 10.1016/S0140-6736(22)00175-1
Smith Gueye C, Gerigk M, Newby G, Lourenco C, Uusiku P, Liu J. Namibia’s path toward malaria elimination: a case study of malaria strategies and costs along the northern border. BMC Public Health. 2014;14:1190.
doi: 10.1186/1471-2458-14-1190
Chanda E, Ameneshewa B, Angula HA, Iitula I, Uusiku P, Trune D, et al. Strengthening tactical planning and operational frameworks for vector control: the roadmap for malaria elimination in Namibia. Malaria J. 2015;14:302.
doi: 10.1186/s12936-015-0785-1
Chung AM, Love E, Neidel J, Mendai I, Nairenge S, van Wyk LA, et al. Strengthening management, community engagement, and sustainability of the subnational response to accelerate malaria elimination in Namibia. Am J Trop Med Hyg. 2022;106:1646–52.
doi: 10.4269/ajtmh.21-1195
Raman J, Fakudze P, Sikaala CH, Chimumbwa J, Moonasar D. Eliminating malaria from the margins of transmission in Southern Africa through the Elimination 8 Initiative. Trans R Soc South Africa. 2021;76:137–45.
doi: 10.1080/0035919X.2021.1915410
WHO. World malaria report 2023. Geneva: World Health Organization; 2023.
Chanda E, Arshad M, Khaloua A, Zhang W, Namboze J, Uusiku P, et al. An investigation of the Plasmodium falciparum malaria epidemic in Kavango and Zambezi regions of Namibia in 2016. Trans R Soc Trop Med Hyg. 2018;112:546–54.
Cotter C, Sturrock HJ, Hsiang MS, Liu J, Phillips AA, Hwang J, et al. The changing epidemiology of malaria elimination: new strategies for new challenges. Lancet. 2013;382:900–11.
doi: 10.1016/S0140-6736(13)60310-4
National vector-borne diseases control programme. Namibia malaria elimination strategic plan 2023–2027. 2023.
Weiss DJ, Mappin B, Dalrymple U, Bhatt S, Cameron E, Hay SI, et al. Re-examining environmental correlates of Plasmodium falciparum malaria endemicity: a data-intensive variable selection approach. Malaria J. 2015;14:68.
doi: 10.1186/s12936-015-0574-x
Weiss DJ, Nelson A, Gibson HS, Temperley W, Peedell S, Lieber A, et al. A global map of travel time to cities to assess inequalities in accessibility in 2015. Nature. 2018;553:333–6.
doi: 10.1038/nature25181
Trabucco A, Zomer R. Global Aridity Index (Global-Aridity) and global potential evapo-transpiration (Global-PET) geospatial database. CGIAR-CSI GeoPortal. 2009.
Farr TG, Rosen PA, Caro E, Crippen R, Duren R, Hensley S, et al. The shuttle radar topography mission. Rev Geophys. 2007;45:2.
doi: 10.1029/2005RG000183
Huete A, Justice C, Van Leeuwen W. MODIS vegetation index (MOD13). Algorithm Theor Basis Doc. 1999;3:295–309.
Friedl MA, Sulla-Menashe D, Tan B, Schneider A, Ramankutty N, Sibley A, et al. MODIS Collection 5 global land cover: algorithm refinements and characterization of new datasets. Remote Sens Environ. 2010;114:168–82.
doi: 10.1016/j.rse.2009.08.016
Wan Z, Zhang Y, Zhang Q, Li ZL. Validation of the land-surface temperature products retrieved from terra moderate resolution imaging spectroradiometer data. Remote Sens Environ. 2002;83:163–80.
doi: 10.1016/S0034-4257(02)00093-7
Gething PW, Van Boeckel TP, Smith DL, Guerra CA, Patil AP, Snow RW, et al. Modelling the global constraints of temperature on transmission of Plasmodium falciparum and P. vivax. Parasit Vectors. 2011;4:92.
doi: 10.1186/1756-3305-4-92
Elvidge CD, Baugh K, Zhizhin M, Hsu FC, Ghosh T. VIIRS night-time lights. Int J Remote Sens. 2017;38:5860–79.
doi: 10.1080/01431161.2017.1342050
Hansen MC, Potapov PV, Moore R, Hancher M, Turubanova SA, Tyukavina A, et al. High-resolution global maps of 21st-century forest cover change. Science. 2013;342:850–3.
doi: 10.1126/science.1244693
Kauth R, Thomas G. The Tasseled Cap - a graphic description of the spectral-temporal development of agricultural crops as seen by Landsat. Proceedings of the Symposium on Machine Processing of Remotely Sensed Data, West Lafayette, Purdue University. 1976.
Weiss DJ, Atkinson PM, Bhatt S, Mappin B, Hay SI, Gething PW. An effective approach for gap-filling continental scale remotely sensed time-series. ISPRS J Photogramm Remote Sens. 2014;98:106–18.
doi: 10.1016/j.isprsjprs.2014.10.001
Alegana VA, Atkinson PM, Wright JA, Kamwi R, Uusiku P, Katokele S, et al. Estimation of malaria incidence in northern Namibia in 2009 using Bayesian conditional-autoregressive spatial-temporal models. Spatial Spatio Temporal Epidemiol. 2013;7:25–36.
doi: 10.1016/j.sste.2013.09.001
Alegana VA, Atkinson PM, Lourenço C, Ruktanonchai NW, Bosco C, Erbach-Schoenberg EZ, et al. Advances in mapping malaria for elimination: fine resolution modelling of Plasmodium falciparum incidence. Sci Rep. 2016;6:29628.
doi: 10.1038/srep29628
Ministry of Health Social Services - MoHSS/Namibia, ICF International. Namibia Demographic and Health Survey 2013. Windhoek, Namibia: MoHSS/Namibia and ICF International; 2014.
Pfeffer DA, Lucas TC, May D, Harris J, Rozier J, Twohig KA, et al. MalariaAtlas: an R interface to global malariometric data hosted by the Malaria Atlas Project. Malaria J. 2018;17:352.
doi: 10.1186/s12936-018-2500-5
Cameron E, Young AJ, Twohig KA, Pothin E, Bhavnani D, Dismer A, et al. Mapping the endemicity and seasonality of clinical malaria for intervention targeting in Haiti using routine case data. Elife. 2021;10: e62122.
doi: 10.7554/eLife.62122
Kristensen K. Template model builder TMB. J Stat Softw. 2015;70:1–21.
Lindgren F, Rue H. Bayesian spatial modelling with R-INLA. J Stat Softw. 2015;63:1–25.
doi: 10.18637/jss.v063.i19
Eaton JW, Dwyer-Lindgren L, Gutreuter S, O’Driscoll M, Stevens O, Bajaj S, et al. Naomi: a new modelling tool for estimating HIV epidemic indicators at the district level in sub-Saharan Africa. J Int AIDS Soc. 2021;24(Suppl 5): e25788.
doi: 10.1002/jia2.25788
Nguyen M, Howes RE, Lucas TCD, Battle KE, Cameron E, Gibson HS, et al. Mapping malaria seasonality in Madagascar using health facility data. BMC Med. 2020;18:26.
doi: 10.1186/s12916-019-1486-3
Arambepola R, Keddie SH, Collins EL, Twohig KA, Amratia P, Bertozzi-Villa A, et al. Spatiotemporal mapping of malaria prevalence in Madagascar using routine surveillance and health survey data. Sci Rep. 2020;10:18129.
doi: 10.1038/s41598-020-75189-0
Gething PW, Noor AM, Gikandi PW, Ogara EA, Hay SI, Nixon MS, et al. Improving imperfect data from health management information systems in Africa using space-time geostatistics. PLoS Med. 2006;3: e271.
doi: 10.1371/journal.pmed.0030271
Smith JL, Mumbengegwi D, Haindongo E, Cueto C, Roberts KW, Gosling R, et al. Malaria risk factors in northern Namibia: the importance of occupation, age and mobility in characterizing high-risk populations. PLoS ONE. 2021;16: e0252690.
doi: 10.1371/journal.pone.0252690
Smith JL, Auala J, Tambo M, Haindongo E, Katokele S, Uusiku P, et al. Spatial clustering of patent and sub-patent malaria infections in northern Namibia: implications for surveillance and response strategies for elimination. PLoS ONE. 2017;12: e0180845.
doi: 10.1371/journal.pone.0180845
Jacobson JO, Smith JL, Cueto C, Chisenga M, Roberts K, Hsiang M, et al. Assessing malaria risk at night-time venues in a low-transmission setting: a time-location sampling study in Zambezi, Namibia. Malar J. 2019;18:179.
doi: 10.1186/s12936-019-2807-x
Wu L, Hsiang MS, Prach LM, Schrubbe L, Ntuku H, Dufour MK, et al. Serological evaluation of the effectiveness of reactive focal mass drug administration and reactive vector control to reduce malaria transmission in Zambezi region, Namibia: results from a secondary analysis of a cluster randomised trial. EClinicalMedicine. 2022;44: 101272.
doi: 10.1016/j.eclinm.2022.101272
Uushona SI, Sheehama JA, Iita H. Sociocultural factors that influence the prevention of malaria in Ohangwena region, Namibia. Afr J Prim Health Care Fam Med. 2022;14:e1–10.
doi: 10.4102/phcfm.v14i1.3524
Tatem AJ, Huang Z, Narib C, Kumar U, Kandula D, Pindolia DK, et al. Integrating rapid risk mapping and mobile phone call record data for strategic malaria elimination planning. Malaria J. 2014;13:52.
doi: 10.1186/1475-2875-13-52
Cosner C, Beier JC, Cantrell RS, Impoinvil D, Kapitanski L, Potts MD, et al. The effects of human movement on the persistence of vector-borne diseases. J Theor Biol. 2009;258:550–60.
doi: 10.1016/j.jtbi.2009.02.016
Lai S, Erbach-Schoenberg EZ, Pezzulo C, Ruktanonchai NW, Sorichetta A, Steele J, et al. Exploring the use of mobile phone data for national migration statistics. Palgrave Commun. 2019;5:34.
doi: 10.1057/s41599-019-0242-9
Ruktanonchai NW, DeLeenheer P, Tatem AJ, Alegana VA, Caughlin TT, Erbach-Schoenberg E, et al. Identifying malaria transmission foci for elimination using human mobility data. PLoS Comput Biol. 2016;12: e1004846.
doi: 10.1371/journal.pcbi.1004846
Noor AM, Alegana VA, Kamwi RN, Hansford CF, Ntomwa B, Katokele S, et al. Malaria control and the intensity of Plasmodium falciparum transmission in Namibia 1969–1992. PLoS ONE. 2013;8: e63350.
doi: 10.1371/journal.pone.0063350
Noor AM, Uusiku P, Kamwi RN, Katokele S, Ntomwa B, Alegana VA, et al. The receptive versus current risks of Plasmodium falciparum transmission in Northern Namibia: implications for elimination. BMC Infect Dis. 2013;13:184.
doi: 10.1186/1471-2334-13-184
Liu X, Zhou J. Assessment of the continuous extreme drought events in Namibia during the last decade. Water. 2021;13:2942.
doi: 10.3390/w13202942
Gao L, Shi Q, Liu Z, Li Z, Dong X. Impact of the COVID-19 pandemic on malaria control in Africa: a preliminary analysis. Trop Med Infect Dis. 2023;8:67.
doi: 10.3390/tropicalmed8010067