Understanding the spatial non-stationarity in the relationships between malaria incidence and environmental risk factors using Geographically Weighted Random Forest: A case study in Rwanda

Submitted: 9 January 2023
Accepted: 28 April 2023
Published: 25 May 2023
Abstract Views: 1026
PDF: 644
HTML: 15
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.


As found in the health studies literature, the levels of climate association between epidemiological diseases have been found to vary across regions. Therefore, it seems reasonable to allow for the possibility that relationships might vary spatially within regions. We implemented the geographically weighted random forest (GWRF) machine learning method to analyze ecological disease patterns caused by spatially non-stationary processes using a malaria incidence dataset for Rwanda. We first compared the geographically weighted regression (WGR), the global random forest (GRF), and the geographically weighted random forest (GWRF) to examine the spatial non-stationarity in the non-linear relationships between malaria incidence and their risk factors. We used the Gaussian areal kriging model to disaggregate the malaria incidence at the local administrative cell level to understand the relationships at a fine scale since the model goodness of fit was not satisfactory to explain malaria incidence due to the limited number of sample values. Our results show that in terms of the coefficients of determination and prediction accuracy, the geographical random forest model performs better than the GWR and the global random forest model. The coefficients of determination of the geographically weighted regression (R2), the global RF (R2), and the GWRF (R2) were 4.74, 0.76, and 0.79, respectively. The GWRF algorithm achieves the best result and reveals that risk factors (rainfall, land surface temperature, elevation, and air temperature) have a strong non-linear relationship with the spatial distribution of malaria incidence rates, which could have implications for supporting local initiatives for malaria elimination in Rwanda.



PlumX Metrics


Download data is not yet available.


Alonso D, Bouma MJ, Pascual M, 2011. Epidemic malaria and warmer temperatures in recent decades in an East African highland. Proceedings of the Royal Society B 278:1661–9. DOI: https://doi.org/10.1098/rspb.2010.2020
Anselin L, Sergio JR, 2014. Modern Spatial Econometrics in Practice: A Guide to GeoDa, GeoDaSpace and PySAL. GeoDa Press LLC. Available from: https://www.amazon.com/Modern-Spatial-Econometrics-Practice-GeoDaSpace/dp/0986342106
Anselin, L. (1995). Local Indicators of Spatial Association—LISA. Geograph Anal 27:93–115. DOI: https://doi.org/10.1111/j.1538-4632.1995.tb00338.x
Ayanlade A, Nwayor IJ, Sergi C, Ayanlade OS, Di Carlo P, Jeje OD, Jegede MO, 2020. Early warning climate indices for malaria and meningitis in tropical ecological zones. Sci Rep 10:1–13. DOI: https://doi.org/10.1038/s41598-020-71094-8
Beck LR, Lobitz BM, Wood BL, 2000. Remote sensing and human health: new sensors and new opportunities. Emerg Infect Dis 6:217–27. DOI: https://doi.org/10.3201/eid0603.000301
Bishop RA, Litch JA, 2000. Malaria at high altitude. J Travel Med 7:157–8. DOI: https://doi.org/10.2310/7060.2000.00049
Bizimana JP, Kienberger S, Hagenlocher M, Twarabamenye E, 2016. Modelling homogeneous regions of social vulnerability to malaria in Rwanda. Geospatial Health 11:129–46. DOI: https://doi.org/10.4081/gh.2016.404
Bizimana JP, Nduwayezu G, 2021. Spatio-temporal patterns of malaria incidence in Rwanda. Transactions in GIS 25:751–67. DOI: https://doi.org/10.1111/tgis.12711
Bizimana JP, Twarabamenye E, Kienberger S, 2015. Assessing the social vulnerability to malaria in Rwanda. Malaria J 14:1–21. DOI: https://doi.org/10.1186/1475-2875-14-2
Breiman L, 1996a. Bagging predictors. Machine Learning 26:123–40. DOI: https://doi.org/10.1007/BF00058655
Breiman L, 1996b. Out-of-bag estimation. REFERENCE INCOMPLETE
Breiman L, 2001. Random Forests. Machine Learning 45:5–32. DOI: https://doi.org/10.1023/A:1010933404324
Brunsdon C, Fotheringham AS, Charlton ME, 1996. Geographically weighted regression: a method for exploring spatial nonstationarity. Geographical Anal 28:281–98. DOI: https://doi.org/10.1111/j.1538-4632.1996.tb00936.x
Chaves LF, Koenraadt CJM, 2010. Climate change and highland malaria: Fresh air for a hot debate. Quarterly Rev Biol 85:27–55. DOI: https://doi.org/10.1086/650284
Cheng L, Chen X, De Vos J, Lai X, Witlox F, 2019. Applying a random forest method approach to model travel mode choice behavior. Travel Behav Soc 14:1–10. DOI: https://doi.org/10.1016/j.tbs.2018.09.002
Chirebvu E, Chimbari MJ, Ngwenya BN, Sartorius B, 2016. Clinical Malaria Transmission Trends and Its Association with Climatic Variables in Tubu Village, Botswana: A Retrospective Analysis. PLoS One 2016;11:e0139843. DOI: https://doi.org/10.1371/journal.pone.0139843
Cianci D, Hartemink N, Ibáñez-Justicia A, 2015. Modelling the potential spatial distribution of mosquito species using three different techniques. Int J Health Geograph 14:1–10. DOI: https://doi.org/10.1186/s12942-015-0001-0
Clay DC, Johnson NE, 1992. Size of farm or size of family: Which comes first? Population Studies, 46:491–505. DOI: https://doi.org/10.1080/0032472031000146476
Cohen JM, Dlamini S, Novotny JM, Kandula D, Kunene S, Tatem AJ, 2013. Rapid case-based mapping of seasonal malaria transmission risk for strategic elimination planning in Swaziland. Malaria J 12:61. DOI: https://doi.org/10.1186/1475-2875-12-61
Cohen JM, Ernst KC, Lindblade KA, Vulule JM, John CC, Wilson ML, 2008. Topography-derived wetness indices are associated with household-level malaria risk in two communities in the western Kenyan highlands. Malaria J 7:40. DOI: https://doi.org/10.1186/1475-2875-7-40
Colón-González FJ, Tompkins AM, Biondi R, Bizimana JP, Namanya DB, 2016. Assessing the effects of air temperature and rainfall on malaria incidence: an epidemiological study across Rwanda and Uganda. Geospatial Health 11:379. DOI: https://doi.org/10.4081/gh.2016.379
Comber A, Zeng W, 2019. Spatial interpolation using areal features: A review of methods and opportunities using new forms of data with coded illustrations. Geography Compass 13:e12465. DOI: https://doi.org/10.1111/gec3.12465
Cotter C, Sturrock HJW, Hsiang MS, Liu J, Phillips AA, Hwang J, Gueye CS, Fullman N, Gosling RD, Feachem RGA, 2013. The changing epidemiology of malaria elimination: new strategies for new challenges. Lancet 382:900–11. DOI: https://doi.org/10.1016/S0140-6736(13)60310-4
Drisya J, Kumar DS, Roshni T, 2018. Spatiotemporal Variability of Soil Moisture and Drought Estimation Using a Distributed Hydrological Model. In Integrating Disaster Science and Management Global Case Studies in Mitigation and Recovery, 2018, p. 451-460 DOI: https://doi.org/10.1016/B978-0-12-812056-9.00027-0
Flowerdew R, Green M, Kehris E, 1991. Using areal interpolation methods in geographic information systems. Papers Regional Sci 70;303–315. DOI: https://doi.org/10.1007/BF01434424
Fotheringham AS, Brunsdon C, Charlton M, 2002. Geographically weighted regression: the analysis of spatially varying relationships. Wiley.
Fotheringham AS, Crespo R, Yao J, 2015. Geographical and Temporal Weighted Regression (GTWR). Geographical Analysis 47:431–52. DOI: https://doi.org/10.1111/gean.12071
Garnham PCC, 1945. Malaria epidemics at exceptionally high altitudes in kenya. Br Med J 2:45–47. DOI: https://doi.org/10.1136/bmj.2.4410.45
Gasana J, Cailas MD, Brenniman GR, Hallenbeck WH, 1996. Environmental variables involved in the endemicity of malaria in the valley of the Nyabarongo river in Rwanda. Epidemiology 7:78. DOI: https://doi.org/10.1097/00001648-199607001-00239
Gaudart J, Touré O, Dessay N, Dicko AL, Ranque S, Forest L, Demongeot J, Doumbo OK, 2009. Modelling malaria incidence with environmental dependency in a locality of Sudanese savannah area, Mali. Malaria J 8:9. DOI: https://doi.org/10.1186/1475-2875-8-61
Genuer R, Poggi J-M, Tuleau-Malot C, 2010. Variable selection using Random Forests. Pattern Recognition Letters 31:2225–2236. DOI: https://doi.org/10.1016/j.patrec.2010.03.014
Georganos S, Brousse O, Dujardin S, Linard C, Casey D, Milliones M, Parmentier B, Van Lipzig NPM, Demuzere M, Grippa T, Vanhuysse S, Mboga N, Andreo V, Snow RW, Lennert M, 2020. Modelling and mapping the intra-urban spatial distribution of Plasmodium falciparum parasite rate using very-high-resolution satellite derived indicators. Int J Health Geograph 19:1–18. DOI: https://doi.org/10.1186/s12942-020-00232-2
Georganos S, Grippa T, Niang Gadiaga A, Linard C, Lennert M, Vanhuysse S, Mboga N, Wolff E, Kalogirou S, 2019. Geographical random forests: a spatial extension of the random forest algorithm to address spatial heterogeneity in remote sensing and population modelling. Geocarto Int 36:121–36. DOI: https://doi.org/10.1080/10106049.2019.1595177
Georganos S, Kalogirou S, 2022. A Forest of Forests: A Spatially Weighted and Computationally Efficient Formulation of Geographical Random Forests. ISPRS Internat J Geo-Inform 11:471. DOI: https://doi.org/10.3390/ijgi11090471
Githeko AK, 2007. Malaria, Climate Change and Possible Impacts on Populations in Africa. International Studies in Population book series (ISIP, volume 6). DOI: https://doi.org/10.1007/978-1-4020-6174-5_4
Goovaerts P, 2006. Geostatistical analysis of disease data: accounting for spatial support and population density in the isopleth mapping of cancer mortality risk using area-to-point Poisson kriging. Int J Health Geograph 5:52. DOI: https://doi.org/10.1186/1476-072X-5-7
Grekousis G, Feng Z, Marakakis I, Lu Y, Wang R, 2022. Ranking the importance of demographic, socioeconomic, and underlying health factors on US COVID-19 deaths: A geographical random forest approach. Health Place 74:102744. DOI: https://doi.org/10.1016/j.healthplace.2022.102744
Grömping U, 2009). Variable importance assessment in regression: Linear regression versus random forest. American Statistician 63:308–19. DOI: https://doi.org/10.1198/tast.2009.08199
Gubler DJ, Reiter P, Ebi KL, Yap W, Nasci R, Patz JA, 2001. Climate variability and change in the United States: Potential impacts on vector- and Rodent-Borne diseases. Environ Health Perspect 109:223–233. DOI: https://doi.org/10.1289/ehp.109-1240669
Habyarimana F, Ramroop S, 2020. Prevalence and Risk Factors Associated with Malaria among Children Aged Six Months to 14 Years Old in Rwanda. Int J Environ Res Public Health 17:1–13. DOI: https://doi.org/10.3390/ijerph17217975
Hakizimana E, Karema C, Munyakanage D, Githure J, Mazarati JB, Tongren JE, Takken W, Binagwaho A, Koenraadt CJM, 2018. Spatio-temporal distribution of mosquitoes and risk of malaria infection in Rwanda. Acta Tropica 182:149–57. DOI: https://doi.org/10.1016/j.actatropica.2018.02.012
Hammerich A, Campbell OMR, Chandramohan D, 2002. Unstable malaria transmission and maternal mortality experiences from Rwanda. Trop Med Int Health 7:573–6. DOI: https://doi.org/10.1046/j.1365-3156.2002.00898.x
Harvey D, Valkenburg W, Amara A, 2021. Predicting malaria epidemics in Burkina Faso with machine learning. PLoS ONE 16:0253302. DOI: https://doi.org/10.1371/journal.pone.0253302
Hasyim H, Nursafingi A, Haque U, Montag D, Groneberg DA, Dhimal M, Kuch U, Müller R, 2018. Spatial Modeling of Malaria Cases Associated with Environmental Factors in South Sumatra, Indonesia. Malaria J 17:1–15. DOI: https://doi.org/10.1186/s12936-018-2230-8
Hay SI, Guerra CA, Tatem AJ, Atkinson PM, Snow RW, 2005. Urbanization, malaria transmission and disease burden in Africa. Nature Reviews. Microbiology 3:81–90. DOI: https://doi.org/10.1038/nrmicro1069
Kabaria CW, Gilbert M, Noor AM, Snow RW, Linard C, 2017. The impact of urbanization and population density on childhood Plasmodium falciparum parasite prevalence rates in Africa. Malaria J 16:1–10. DOI: https://doi.org/10.1186/s12936-017-1694-2
Kalogirou S, 2003. The statistical analysis and modelling of internal migration flows within England and Wales. Newcastle University.
Kapwata T, Gebreslasie MT, 2016. Random forest variable selection in spatial malaria transmission modelling in Mpumalanga Province, South Africa. Geospatial Health 11:251–262. DOI: https://doi.org/10.4081/gh.2016.434
Karekezi P, Nzabakiriraho JD, Gayawan E, 2021. Modelling the Shared Risks of Malaria and Anemia in Rwanda. Electronic J https://doi.org/10.2139/SSRN.3986223 DOI: https://doi.org/10.2139/ssrn.3986223
Kateera F, Mens PF, Hakizimana E, Ingabire CM, Muragijemariya L, Karinda P, Grobusch MP, Mutesa L, Van Vugt M, 2015. Malaria parasite carriage and risk determinants in a rural population: A malariometric survey in Rwanda. Malaria J 14:1–11. DOI: https://doi.org/10.1186/s12936-014-0534-x
Kibret S, Glenn Wilson G, Ryder D, Tekie H, Petros B, 2019. Environmental and meteorological factors linked to malaria transmission around large dams at three ecological settings in Ethiopia. Malaria J 18:1–16. DOI: https://doi.org/10.1186/s12936-019-2689-y
Kotepui M, Kotepui KU, 2018. Impact of weekly climatic variables on weekly malaria incidence throughout Thailand: A country-based six-year retrospective study. J Environ Public Health, 2018:8397815 DOI: https://doi.org/10.1155/2018/8397815
Krivoruchko K, Gribov A, Krause E, 2011. Multivariate areal interpolation for continuous and count data. Procedia Environ Sci 3:14–19. DOI: https://doi.org/10.1016/j.proenv.2011.02.004
Kundrick A, Huang Z, Carran S, Kagoli M, Grais RF, Hurtado N, Ferrari M, 2018. Sub-national variation in measles vaccine coverage and outbreak risk: A case study from a 2010 outbreak in Malawi. BMC Public Health 18:1–10. DOI: https://doi.org/10.1186/s12889-018-5628-x
Lam NSN, 1983. Spatial Interpolation Methods: A Review. American Cartographer 10:129-149 DOI: https://doi.org/10.1559/152304083783914958
Lindsay S, Martens W, 1988. Malaria in the African highlands: Past, present and future. Bulletin of the World Health Organization, 76. Available from: https://booksc.org/book/63786001/dbc423
Lindsay SW, Birley MH, 1996. Climate change and malaria transmission. Ann Trop Med Parasitol 90:573–88. DOI: https://doi.org/10.1080/00034983.1996.11813087
Loevinsohn ME, 1994. Climatic warming and increased malaria incidence in Rwanda. Lancet, 343:714–718. DOI: https://doi.org/10.1016/S0140-6736(94)91586-5
Macharia PM, Odhiambo JN, Mumo E, Maina A, Giorgi E, Okiro EA, 2022. Approaches to defining health facility catchment areas in sub-Saharan Africa. MedRxiv, 2022.08.18.22278927. DOI: https://doi.org/10.1101/2022.08.18.22278927
Maiti A, Zhang Q, Sannigrahi S, Pramanik S, Chakraborti S, Cerda A, Pilla F, 2021. Exploring spatiotemporal effects of the driving factors on COVID-19 incidences in the contiguous United States. Sustainable Cities Soc 68:102784. DOI: https://doi.org/10.1016/j.scs.2021.102784
McCann RS, Messina JP, MacFarlane DW, Bayoh MN, Vulule JM, Gimnig JE, Walker ED, 2014. Modeling larval malaria vector habitat locations using landscape features and cumulative precipitation measures. Int J Health Geograph 13:1–12. DOI: https://doi.org/10.1186/1476-072X-13-17
McMahon A, Mihretie A, Ahmed AA, Lake M, Awoke W, Wimberly MC, 2021. Remote sensing of environmental risk factors for malaria in different geographic contexts. Int J Health Geograph 20:1–15. DOI: https://doi.org/10.1186/s12942-021-00282-0
Melville A, Wilson DB, Glasgow J, Hocking K, 1945. Malaria in Abyssinia. East Afr Med J 22:285–294.
Meyus H, Caubergh ML, H,1962. L’ état actuel du problème du paludisme d’altitude au Ruanda-Urundi. Annales de La Société Belge de Médecine Tropicale 5:771–782.
Midekisa A, Beyene B, Mihretie A, Bayabil E, Wimberly MC, 2015. Seasonal Associations of Climatic Drivers and Malaria in the Highlands of Ethiopia. Parasites and Vectors. DOI: https://doi.org/10.1186/s13071-015-0954-7
Molnar C, 2022. Interpretable Machine Learning:A Guide for Making Black Box Models Explainable. Independently published.
Mordecai EA, Paaijmans KP, Johnson LR, Balzer C, Ben-Horin T, de Moor E, Mcnally A, Pawar S, Ryan SJ, Smith TC, Lafferty KD, 2013. Optimal temperature for malaria transmission is dramatically lower than previously predicted. Ecology Letters 16:22–30. DOI: https://doi.org/10.1111/ele.12015
Mordecai EA, Ryan SJ, Caldwell JM, Shah MM, LaBeaud AD, 2020. Climate change could shift disease burden from malaria to arboviruses in Africa. Lancet Plan Health 4:e416–e423. DOI: https://doi.org/10.1016/S2542-5196(20)30178-9
Murindahabi MM, Hoseni A, Vreugdenhil LCC, Van Vliet AJH, Umupfasoni J, Mutabazi A, Hakizimana E, Poortvliet PM, Mutesa L, Takken W, Koenraadt CJM, 2021. Citizen science for monitoring the spatial and temporal dynamics of malaria vectors in relation to environmental risk factors in Ruhuha, Rwanda. Malaria J 20:453. DOI: https://doi.org/10.1186/s12936-021-03989-4
Murindahabi MM, Takken W, Hakizimana E, van Vliet AJH, Marijn Poortvliet P, Mutesa L, Koenraadt CJM, 2022. A handmade trap for malaria mosquito surveillance by citizens in Rwanda. PloS One 17:e0266714 DOI: https://doi.org/10.1371/journal.pone.0266714
NISR, 2018. Rwandan Integrated Household Living Conditions Survey (EICV5) 2007/2018. Main Indicators Report.
Nyasa RB, Awatboh F, Kwenti TE, Titanji VPK, Lucy N, Ayamba M, 2022. The effect of climatic factors on the number of malaria cases in an inland and a coastal setting from 2011 to 2017 in the equatorial rain forest of Cameroon. BMC Infect Dis 22:1–11. DOI: https://doi.org/10.1186/s12879-022-07445-9
Ohmer M, Liesch T, Goeppert N, Goldscheider N, 2017. On the optimal selection of interpolation methods for groundwater contouring: an example of propagation of uncertainty regarding inter-aquifer exchange. Adv Water Resour 109:121–132. DOI: https://doi.org/10.1016/j.advwatres.2017.08.016
Pampana E, 1969. A textbook of malaria eradication (2nd ed.).
Pattnaik A, Mohan D, Tsui A, Chipokosa S, Katengeza H, Ndawala J, Marx MA, 2021. The aggregate effect of implementation strength of family planning programs on modern contraceptive use at the health systems level in rural Malawi. PLoS ONE 16:e0232504. DOI: https://doi.org/10.1371/journal.pone.0232504
Patz J, Githeko A, McCarty J, Hussein S, Confalonieri U, Wet NDe, 2003. Climate change and infectious diseases. In Climate change and human health: risks and responses; pp. 103–137.
Peng Y, Li W, Luo X, Li H, 2019. A Geographically and Temporally Weighted Regression Model for Spatial Downscaling of MODIS Land Surface Temperatures over Urban Heterogeneous Regions. IEEE Transact Geosci Remote Sensing 57:5012–27. DOI: https://doi.org/10.1109/TGRS.2019.2895351
Quiñones S, Goyal A, Ahmed ZU, 2021. Geographically weighted machine learning model for untangling spatial heterogeneity of type 2 diabetes mellitus (T2D) prevalence in the USA. Sci Rep 11:1–13. DOI: https://doi.org/10.1038/s41598-021-85381-5
Rhodes CG, Loaiza JR, Romero LM, Alvarado JMG, Delgado G, Salas OR, Rojas MR, Aguilar-Avendaño C, Maynes E, Cordero JAV, Mora AS, Rigg CA, Zardkoohi A, Prado M, Friberg, MD, Bergmann LR, Rodríguez RM, Hamer GL, Chaves LF, 2022. Anopheles albimanus (Diptera: Culicidae) Ensemble Distribution Modeling: Applications for Malaria Elimination. Insects 13:13030221 DOI: https://doi.org/10.3390/insects13030221
Rosenshein L, 2010. The Local Nature of a National Epidemic: Childhood Overweight and the Accessibility of Healthy Food. Masters dissertation. George Mason University, Fairfax, Virginia, USA.
Rudasingwa G, Cho Sil, 2020. Determinants of the persistence of malaria in Rwanda. Malaria J 19:1–9. DOI: https://doi.org/10.1186/s12936-020-3117-z
Rulisa S, Kateera F, Bizimana JP, Agaba S, Dukuzumuremyi J, Baas L, de Dieu Harelimana J, Mens PF, Boer KR, de Vries PJ, 2013. Malaria prevalence, spatial clustering and risk factors in a low endemic area of Eastern Rwanda: a cross sectional study. PloS One 8:e0069443 DOI: https://doi.org/10.1371/journal.pone.0069443
Santos-Vega M, Martinez PP, Vaishnav KG, Kohli V, Desai V, Bouma MJ, Pascual M, 2022. The neglected role of relative humidity in the interannual variability of urban malaria in Indian cities. Nature Communications 13:1–9. DOI: https://doi.org/10.1038/s41467-022-28145-7
Schwetz J, 1942. Recherches sur la limite altimetrique du paludisme dans le Congo Orientale et sur la cause de cette limite. Annales de La Societe Belge de Medecine Tropicale, 183–209.
Semakula M, Niragire F, Faes C, 2020. Bayesian spatio-temporal modeling of malaria risk in Rwanda. PLoS One 15:e0238504. DOI: https://doi.org/10.1371/journal.pone.0238504
Sewe MO, Ahlm C, Rocklöv J, 2016. Remotely Sensed Environmental Conditions and Malaria Mortality in Three Malaria Endemic Regions in Western Kenya. PLoS ONE 11:e0154204. DOI: https://doi.org/10.1371/journal.pone.0154204
Stresman GH, Stevenson JC, Owaga C, Marube E, Anyango C, Drakeley C, Bousema T, Cox J, 2014. Validation of three geolocation strategies for health-facility attendees for research and public health surveillance in a rural setting in western Kenya. Epidemiol Infection 142:1978. DOI: https://doi.org/10.1017/S0950268814000946
Sullivan W, 2017. Machine Learning For Beginners: Algorithms, Decision Tree & Random Forest Introduction. https://1lib.sk/book/3428860/bec3d9
Tatem AJ, Guerra CA, Kabaria CW, Noor AM, Hay SI, 2008. Human population, urban settlement patterns and their impact on Plasmodium falciparum malaria endemicity. Malaria J 7:9 DOI: https://doi.org/10.1186/1475-2875-7-218
Taylor P, Mutambu SL, 1986. A review of the malaria situation in Zimbabwe with special reference to the period 1972-1981. Transactions of the Royal Society of Tropical Medicine and Hygiene, 80:12–19. DOI: https://doi.org/10.1016/0035-9203(86)90185-9
Tompkins AM, Ermert V, 2013. A regional-scale, high resolution dynamical malaria model that accounts for population density, climate and surface hydrology. Malaria J 12:13 DOI: https://doi.org/10.1186/1475-2875-12-65
Wang M, Wang H, Wang J, Liu H, Lu R, Duan T, Gong X, Feng S, Liu Y, Cui Z, Li C, Ma J, 2019. A novel model for malaria prediction based on ensemble algorithms. PLoS ONE 14:e0226910. DOI: https://doi.org/10.1371/journal.pone.0226910
Yamana TK, Eltahir EAB, 2013. Incorporating the effects of humidity in a mechanistic model of Anopheles gambiae mosquito population dynamics in the Sahel region of Africa. Parasites Vectors 6:1–10. DOI: https://doi.org/10.1186/1756-3305-6-235
Zeng W, Comber A, 2020. Using household counts as ancillary information for areal interpolation of population: Comparing formal and informal, online data sources. Computers, Environment and Urban Systems 80:101440. DOI: https://doi.org/10.1016/j.compenvurbsys.2019.101440
Zhao X, Chen F, Feng Z, Li X, Zhou XH, 2014. Characterizing the effect of temperature fluctuation on the incidence of malaria: An epidemiological study in south-west China using the varying coefficient distributed lag non-linear model. Malaria J 13:192. DOI: https://doi.org/10.1186/1475-2875-13-192

How to Cite

Nduwayezu, G., Zhao, P., Kagoyire, C., Eklund, L., Bizimana, J. P., Pilesjo, P., & Mansourian, A. (2023). Understanding the spatial non-stationarity in the relationships between malaria incidence and environmental risk factors using Geographically Weighted Random Forest: A case study in Rwanda. Geospatial Health, 18(1). https://doi.org/10.4081/gh.2023.1184