Spatio-Temporal Random Effects Modeling of Malaria Incidence and Mortality in Kenya

Polycarp Okiagera Nyabuto; Anthony Wanjoya; Thomas Mageto; Anthony Ngunyi

doi:doi:10.11648/j.ajtas.20261501.11

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Spatio-Temporal Random Effects Modeling of Malaria Incidence and Mortality in Kenya

Polycarp Okiagera Nyabuto^*

, Anthony Wanjoya

, Thomas Mageto

, Anthony Ngunyi

Published in American Journal of Theoretical and Applied Statistics (Volume 15, Issue 1)

Received: 22 October 2025 Accepted: 5 December 2025 Published: 16 January 2026

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Abstract

Malaria is a major public health challenge in sub-Saharan Africa, with transmission patterns that vary significantly across space and time due to environmental, socioeconomic, and epidemiological factors. These variations complicate efforts to design effective and targeted interventions, making it crucial to understand the dynamics of disease spread. This study employed Bayesian spatio-temporal random effects modeling framework to analyze malaria incidence and mortality ratio across Kenya. The approach incorporated spatial and temporal dependencies to provide a detailed understanding of malaria incidence and mortality risk patterns. Spatial random effects were modeled using conditional autoregressive (CAR) priors to account for correlations among neighboring counties, while temporal dependence was captured using autoregressive processes of order two (AR2), reflecting trends over multiple time periods. An evaluation was on the performance of Spatio-Temporal Poisson Linear Trend Model (STPLM), Spatio-Temporal Poisson ANOVA Model (STPAM), Spatio-Temporal Poisson Separable Model (STPSM) and Poisson Temporal Model for Spatio-Temporal Effects (PTSTN)using the Deviance Information Criterion (DIC), the effective number of parameters (p.d) and the Log Marginal Pseudo-Likelihood (LMPL). The Spatio-Temporal Poisson ANOVA Model (STPAM) was found as the best Poissson Spatial-Temporal Model and was used to develop a multivariate spatio-temporal model for the joint modeling of malaria incidence and mortality. Using the developed model, the study identified significant spatial clustering of malaria, with persistent high-risk zones in western and coastal counties. Temporal trends indicated an overall decline in transmission, though progress was uneven across counties, reflecting differences in intervention coverage, healthcare access, and local epidemiology. These findings underscored the value of multivariate spatio-temporal modeling of malaria incidence and mortality for guiding malaria control strategies. This study thus demonstrates that Bayesian Spatial-Temporal modeling is essential for understanding heterogeneous malaria incidence and mortality risk and informing strategies aimed at reducing disease burden and advancing toward malaria elimination in Kenya.

Published in	American Journal of Theoretical and Applied Statistics (Volume 15, Issue 1)
DOI	10.11648/j.ajtas.20261501.11
Page(s)	1-11
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2026. Published by Science Publishing Group

Keywords

Spatio-temporal Modeling, Malaria Incidence, Bayesian Hierarchical Models, Spatial Random Effects

References

[1]	Aheto, J. M. K., Menezes, L. J., Takramah, W., & Cui, L.(2024). Modelling spatiotemporal variation in under-five malaria risk in Ghana in 2016-2021. Malaria Journal, 23(1), 102. https://doi.org/10.1186/s12936-024-04918-x
[2]	Apeagyei, A. E., Patel, N. K., Cogswell, I., O’Rourke, K., Tsakalos, G., & Dieleman, J. (2024). Examining geographical inequalities for malaria outcomes and spending on malaria in 40 malaria-endemic countries, 2010-2020. Malaria Journal, 23(1), 206. https://doi.org/10.1186/s12936-024-05028-4
[3]	Armando, C. J., Rocklv, J., Sidat, M., Tozan, Y., Mavume,A. F., Bunker, A., & Sewes, M. O. (2023). Climate variability, socio-economic conditions and vulnerability to malaria infections in Mozambique 2016-2018: A spatial temporal analysis. Frontiers in Public Health, 11. https://doi.org/10.3389/fpubh.2023.1162535
[4]	Ateba, F. F., Sagara, I., Sogoba, N., Tour, M., Konat, D., Diawara, S. I., Diakit, S. A. S., Diarra, A., Coulibaly,M. D., Dolo, M., Dolo, A., Sacko, A., Thiam, S. M., Sissako, A., Sangar, L., Diakit, M., Koita, O. A., Cissoko, M., Traore, S. F., & Gaudart, J. (2020). Spatio-temporal dynamic of malaria incidence: A comparison of two ecological zones in Mali. International Journal of Environmental Research and Public Health, 17(13), Article 13. https://doi.org/10.3390/ijerph17134698
[5]	Bationo, C. S., Gaudart, J., Dieng, S., Cissoko, M., Taconet, P., Ouedraogo, B., Som, A., Zongo, I., Soma,D. D., Tougri, G., Dabir, R. K., Koffi, A., Pennetier, C., & Moiroux, N. (2021). Spatio-temporal analysis and prediction of malaria cases using remote sensing meteorological data in Dibougou health district, Burkina Faso, 2016-2017. Scientific Reports, 11, 20027. https://doi.org/10.1038/s41598-021-99457-9
[6]	Bisanzio, D., Mutuku, F., LaBeaud, A. D., Mungai,P. L., Muinde, J., Busaidy, H., Mukoko, D., King, C. H., & Kitron, U. (2015). Use of prospective hospital surveillance data to define spatiotemporal heterogeneity of malaria risk in coastal Kenya. Malaria Journal, 14(1), 482. https://doi.org/10.1186/s12936-015-1006-7
[7]	Gopal, S., Ma, Y., Xin, C., Pitts, J., & Were, L. (2019). Characterizing the spatial determinants and prevention of malaria in Kenya. International Journal of Environmental Research and Public Health, 16. https://doi.org/10.3390/ijerph16245078
[8]	Gwitira, I., Mukonoweshuro, M., Mapako, G., Shekede,M. D., Chirenda, J., & Mberikunashe, J. (2020). Spatial and spatio-temporal analysis of malaria cases in Zimbabwe. Infectious Diseases of Poverty, 9(5), 45-58. https://doi.org/10.1186/s40249-020-00764-6
[9]	Kabaghe, A. N., Chipeta, M. G., Gowelo, S., Mburu, M., Truwah, Z., McCann, R. S., van Vugt, M., Grobusch, M. P., & Phiri, K. S. (2018). Fine-scale spatial and temporal variation of clinical malaria incidence and associated factors in children in rural Malawi: A longitudinal study. Parasites & Vectors, 11(1), 129. https://doi.org/10.1186/s13071-018-2730-y
[10]	Kamau, A., Mtanje, G., Mataza, C., Bejon, P., & Snow, R. W. (2021). Spatial and temporal clustering of malaria using routinely collected health facility data on the Kenyan Coast. Malaria Journal, 20(1), 227. https://doi.org/10.1186/s12936-021-03758-3
[11]	Kang, S. Y., Battle, K. E., Gibson, H. S., Ratsimbasoa, A., Randrianarivelojosia, M., Ramboarina, S., Zimmerman,P. A., Weiss, D. J., Cameron, E., Gething, P. W., & Howes, R. E. (2018). Spatio-temporal mapping of Madagascar’s Malaria Indicator Survey results to assess Plasmodium falciparum endemicity trends between 2011 and 2016. BMC Medicine, 16(1), 71. https://doi.org/10.1186/s12916-018-1060-4
[12]	Katale, R. N., & Gemechu, D. B. (2023). Spatio-temporal analysis of malaria incidence and its risk factors in North Namibia. Malaria Journal, 22(1), 149. https://doi.org/10.1186/s12936-023-04577-4
[13]	Katile, A., Sagara, I., Cissoko, M., Bationo, C. S., Dolo, M., Thera, I., Traore, S., Kone, M., Dembele, P., Bocoum, D., Sidibe, I., Simaga, I., Sissoko, M. S., Landier, J., & Gaudart, J. (2022). Spatio-temporal variability of malaria incidence in the health district of Kati, Mali, 2015-2019. International Journal of Environmental Research and Public Health, 19(21), Article 21. https://doi.org/10.3390/ijerph192114361
[14]	Kimuyu, J. S. (2021). Comparative spatial-temporal analysis and predictive modeling of climate change-induced malaria vectors’ invasion in new hotspots in Kenya. SN Applied Sciences, 3(8), 741. https://doi.org/10.1007/s42452-021-04722-1
[15]	Lacey, H., Jain, N., Sugimoto, M., Shimato, M., Reine, I., & Oria, K. (2023). Combating malaria in Kenya through collaborative population health education: A systematic review and pilot case study. Infectious Diseases, 55(10), 664-683. https://doi.org/10.1080/23744235.2023.2231082
[16]	Liu, H., Dong, X., Lin, L., Luo, H., & Zhang, Y. (2023). Exploring spatiotemporal patterns of malaria incidence and its socio-environmental factors in Hainan, China. Frontiers in Public Health, 11, 1203145. https://doi.org/10.3389/fpubh.2023.1203145
[17]	Macharia, P. M., Giorgi, E., Noor, A. M., Snow, R. W., & Okiro, E. A. (2018). A geostatistical framework for combining data from multiple sources to map malaria risk. Malaria Journal, 17(1), 422. https://doi.org/10.1186/s12936-018-2592-9
[18]	Munyenyembe, A., Nkhoma, P., Chipeta, M., & Kazembe, L. N. (2023). Bayesian spatio-temporal modeling of malaria incidence in Malawi, 2018-2020. Malaria Journal, 22(1), 171. https://doi.org/10.1186/s12936-023-04590-7
[19]	Noor, A. M., Uusiku, P., Kamwi, R. N., Katokele, S., Ntomwa, B., Alegana, V. A., Snow, R. W., & Noor, A.M. (2013). The changing risk of malaria in northern Namibia: A spatial and temporal analysis of malaria case data from 2001-2010. Malaria Journal, 12(1), 393. https://doi.org/10.1186/1475-2875-12-393
[20]	Okello, G., Izugbara, C. O., Ofware, P., Hamel, M. J., & Bauni, E. (2018). Examining spatial and temporal variation of malaria transmission in rural western Kenya. BMC Infectious Diseases, 18(1), 486. https://doi.org/10.1186/s12879-018-3408-6
[21]	Omondi, C. J., Odero, J. O., & Onyango, E. A. (2023). Spatial and temporal analysis of malaria incidence in Kisumu County, Kenya. BMC Public Health, 23(1), 1132. https://doi.org/10.1186/s12889-023-15716-3
[22]	Otambo, W. O., Wanjala, C. L., Zhou, G., Afrane,Y. A., Nyindo, M., Githeko, A. K., & Yan, G.(2021). Spatio-temporal distribution of Anopheles gambiae s.l. and Anopheles funestus mosquitoes in western Kenya highlands. Parasites & Vectors, 14(1), 384. https://doi.org/10.1186/s13071-021-04871-2
[23]	Ouma, P. O., Snow, R. W., & Noor, A. M. (2017). Quantifying the temporal relationship between malaria and rainfall in Kenya using a cross-wavelet approach. Malaria Journal, 16(1), 133. https://doi.org/10.1186/s12936-017-1767-3
[24]	Paaijmans, K. P., Wandago, M. O., Githeko, A. K., & Takken, W. (2007). Unexpected high losses of Anopheles gambiae larvae due to rainfall. PLoS ONE, 2(11), e1146. https://doi.org/10.1371/journal.pone.0001146
[25]	Rutto, J. J., Nyamongo, D. O., & Mwaura, P. M. (2024). Spatio-temporal modelling of malaria risk and its climatic drivers in Kenya, 2000-2022. Environmental Modelling & Software, 173, 106086. https://doi.org/10.1016/j.envsoft.2024.106086
[26]	Ssempiira, J., Nambuusi, B., Kissa, J., Agaba, B., Kasasa, S., Vounatsou, P., & the Uganda Malaria Indicator Survey Working Group. (2017). The contribution of malaria control interventions on spatio-temporal changes of parasitaemia risk in Uganda during 2009-2014. Parasites & Vectors, 10(1), 450. https://doi.org/10.1186/s13071-017-2393-0
[27]	Tchouakui, M., Riveron, J. M., Djonabaye, D., Tchapga, W., Irving, H., Takam, P. S., Wondji, C. S., & Wondji, C. S. (2021). Distribution and frequency of insecticide resistance mutations in Anopheles gambiae and Anopheles coluzzii in Cameroon. Parasites & Vectors, 14(1), 82. https://doi.org/10.1186/s13071-021-04588-2
[28]	Tizifa, T. A., Kabaghe, A. N., McCann, R. S., van Vugt, M., & Phiri, K. S. (2018). Prevention efforts for malaria in Africa: A review of recent research. Malaria Journal, 17(1), 34. https://doi.org/10.1186/s12936-018-2191-1
[29]	Uchendu, O. J., Adebayo, A. M., & Umeh, G. N. (2022). Spatio-temporal dynamics of malaria transmission in Nigeria. BMC Public Health, 22(1), 746. https://doi.org/10.1186/s12889-022-13142-7
[30]	Wanjala, C. L., Kweka, E. J., Githeko, A. K., & Mukabana, W. R. (2020). Spatio-temporal variation in malaria transmission in western Kenya highlands. BMC Infectious Diseases, 20(1), 208. https://doi.org/10.1186/s12879-020-4916-3
[31]	Wanjala, C. L., Zhou, G., Mbugi, J. P., Kweka, E. J., Githeko, A. K., & Yan, G. (2022). Spatio-temporal analysis of malaria risk across ecological zones in Kenya, 2015-2020. Scientific Reports, 12(1), 11768. https://doi.org/10.1038/s41598-022-15762-4
[32]	Wiru, K., Oduor, G., & Mugo, R. (2021). Predicting malaria hotspots using geostatistical models in western Kenya. Geospatial Health, 16(1), 37-47. https://doi.org/10.4081/gh.2021.992

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Nyabuto, P. O., Wanjoya, A., Mageto, T., Ngunyi, A. (2026). Spatio-Temporal Random Effects Modeling of Malaria Incidence and Mortality in Kenya. American Journal of Theoretical and Applied Statistics, 15(1), 1-11. https://doi.org/10.11648/j.ajtas.20261501.11

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ACS Style

Nyabuto, P. O.; Wanjoya, A.; Mageto, T.; Ngunyi, A. Spatio-Temporal Random Effects Modeling of Malaria Incidence and Mortality in Kenya. Am. J. Theor. Appl. Stat. 2026, 15(1), 1-11. doi: 10.11648/j.ajtas.20261501.11

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AMA Style

Nyabuto PO, Wanjoya A, Mageto T, Ngunyi A. Spatio-Temporal Random Effects Modeling of Malaria Incidence and Mortality in Kenya. Am J Theor Appl Stat. 2026;15(1):1-11. doi: 10.11648/j.ajtas.20261501.11

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@article{10.11648/j.ajtas.20261501.11,
  author = {Polycarp Okiagera Nyabuto and Anthony Wanjoya and Thomas Mageto and Anthony Ngunyi},
  title = {Spatio-Temporal Random Effects Modeling of Malaria Incidence and Mortality in Kenya
},
  journal = {American Journal of Theoretical and Applied Statistics},
  volume = {15},
  number = {1},
  pages = {1-11},
  doi = {10.11648/j.ajtas.20261501.11},
  url = {https://doi.org/10.11648/j.ajtas.20261501.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20261501.11},
  abstract = {Malaria is a major public health challenge in sub-Saharan Africa, with transmission patterns that vary significantly across space and time due to environmental, socioeconomic, and epidemiological factors. These variations complicate efforts to design effective and targeted interventions, making it crucial to understand the dynamics of disease spread. This study employed Bayesian spatio-temporal random effects modeling framework to analyze malaria incidence and mortality ratio across Kenya. The approach incorporated spatial and temporal dependencies to provide a detailed understanding of malaria incidence and mortality risk patterns. Spatial random effects were modeled using conditional autoregressive (CAR) priors to account for correlations among neighboring counties, while temporal dependence was captured using autoregressive processes of order two (AR2), reflecting trends over multiple time periods. An evaluation was on the performance of Spatio-Temporal Poisson Linear Trend Model (STPLM), Spatio-Temporal Poisson ANOVA Model (STPAM), Spatio-Temporal Poisson Separable Model (STPSM) and Poisson Temporal Model for Spatio-Temporal Effects (PTSTN)using the Deviance Information Criterion (DIC), the effective number of parameters (p.d) and the Log Marginal Pseudo-Likelihood (LMPL). The Spatio-Temporal Poisson ANOVA Model (STPAM) was found as the best Poissson Spatial-Temporal Model and was used to develop a multivariate spatio-temporal model for the joint modeling of malaria incidence and mortality. Using the developed model, the study identified significant spatial clustering of malaria, with persistent high-risk zones in western and coastal counties. Temporal trends indicated an overall decline in transmission, though progress was uneven across counties, reflecting differences in intervention coverage, healthcare access, and local epidemiology. These findings underscored the value of multivariate spatio-temporal modeling of malaria incidence and mortality for guiding malaria control strategies. This study thus demonstrates that Bayesian Spatial-Temporal modeling is essential for understanding heterogeneous malaria incidence and mortality risk and informing strategies aimed at reducing disease burden and advancing toward malaria elimination in Kenya.
},
 year = {2026}
}

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TY  - JOUR
T1  - Spatio-Temporal Random Effects Modeling of Malaria Incidence and Mortality in Kenya

AU  - Polycarp Okiagera Nyabuto
AU  - Anthony Wanjoya
AU  - Thomas Mageto
AU  - Anthony Ngunyi
Y1  - 2026/01/16
PY  - 2026
N1  - https://doi.org/10.11648/j.ajtas.20261501.11
DO  - 10.11648/j.ajtas.20261501.11
T2  - American Journal of Theoretical and Applied Statistics
JF  - American Journal of Theoretical and Applied Statistics
JO  - American Journal of Theoretical and Applied Statistics
SP  - 1
EP  - 11
PB  - Science Publishing Group
SN  - 2326-9006
UR  - https://doi.org/10.11648/j.ajtas.20261501.11
AB  - Malaria is a major public health challenge in sub-Saharan Africa, with transmission patterns that vary significantly across space and time due to environmental, socioeconomic, and epidemiological factors. These variations complicate efforts to design effective and targeted interventions, making it crucial to understand the dynamics of disease spread. This study employed Bayesian spatio-temporal random effects modeling framework to analyze malaria incidence and mortality ratio across Kenya. The approach incorporated spatial and temporal dependencies to provide a detailed understanding of malaria incidence and mortality risk patterns. Spatial random effects were modeled using conditional autoregressive (CAR) priors to account for correlations among neighboring counties, while temporal dependence was captured using autoregressive processes of order two (AR2), reflecting trends over multiple time periods. An evaluation was on the performance of Spatio-Temporal Poisson Linear Trend Model (STPLM), Spatio-Temporal Poisson ANOVA Model (STPAM), Spatio-Temporal Poisson Separable Model (STPSM) and Poisson Temporal Model for Spatio-Temporal Effects (PTSTN)using the Deviance Information Criterion (DIC), the effective number of parameters (p.d) and the Log Marginal Pseudo-Likelihood (LMPL). The Spatio-Temporal Poisson ANOVA Model (STPAM) was found as the best Poissson Spatial-Temporal Model and was used to develop a multivariate spatio-temporal model for the joint modeling of malaria incidence and mortality. Using the developed model, the study identified significant spatial clustering of malaria, with persistent high-risk zones in western and coastal counties. Temporal trends indicated an overall decline in transmission, though progress was uneven across counties, reflecting differences in intervention coverage, healthcare access, and local epidemiology. These findings underscored the value of multivariate spatio-temporal modeling of malaria incidence and mortality for guiding malaria control strategies. This study thus demonstrates that Bayesian Spatial-Temporal modeling is essential for understanding heterogeneous malaria incidence and mortality risk and informing strategies aimed at reducing disease burden and advancing toward malaria elimination in Kenya.

VL  - 15
IS  - 1
ER  -

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Author Information

Polycarp Okiagera Nyabuto

Department of Statistics and Actuarial Science, Jomo Kenyatta University of Science and Technology, Nairobi, Kenya

Contact Email

http://orcid.org/0000-0001-5740-2674
Anthony Wanjoya

Department of Statistics and Actuarial Science, Jomo Kenyatta University of Science and Technology, Nairobi, Kenya

Contact Email

http://orcid.org/0009-0009-7609-4318
Thomas Mageto

Department of Statistics and Actuarial Science, Jomo Kenyatta University of Science and Technology, Nairobi, Kenya

Contact Email

http://orcid.org/0009-0008-6195-041X
Anthony Ngunyi

Department of Statistics and Actuarial Science, Dedan Kimathi University of Technology, Nyeri, Kenya

Contact Email

http://orcid.org/0009-0002-1089-6392

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APA Style

Nyabuto, P. O., Wanjoya, A., Mageto, T., Ngunyi, A. (2026). Spatio-Temporal Random Effects Modeling of Malaria Incidence and Mortality in Kenya. American Journal of Theoretical and Applied Statistics, 15(1), 1-11. https://doi.org/10.11648/j.ajtas.20261501.11

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ACS Style

Nyabuto, P. O.; Wanjoya, A.; Mageto, T.; Ngunyi, A. Spatio-Temporal Random Effects Modeling of Malaria Incidence and Mortality in Kenya. Am. J. Theor. Appl. Stat. 2026, 15(1), 1-11. doi: 10.11648/j.ajtas.20261501.11

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AMA Style

Nyabuto PO, Wanjoya A, Mageto T, Ngunyi A. Spatio-Temporal Random Effects Modeling of Malaria Incidence and Mortality in Kenya. Am J Theor Appl Stat. 2026;15(1):1-11. doi: 10.11648/j.ajtas.20261501.11

Copy | Download

@article{10.11648/j.ajtas.20261501.11,
  author = {Polycarp Okiagera Nyabuto and Anthony Wanjoya and Thomas Mageto and Anthony Ngunyi},
  title = {Spatio-Temporal Random Effects Modeling of Malaria Incidence and Mortality in Kenya
},
  journal = {American Journal of Theoretical and Applied Statistics},
  volume = {15},
  number = {1},
  pages = {1-11},
  doi = {10.11648/j.ajtas.20261501.11},
  url = {https://doi.org/10.11648/j.ajtas.20261501.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20261501.11},
  abstract = {Malaria is a major public health challenge in sub-Saharan Africa, with transmission patterns that vary significantly across space and time due to environmental, socioeconomic, and epidemiological factors. These variations complicate efforts to design effective and targeted interventions, making it crucial to understand the dynamics of disease spread. This study employed Bayesian spatio-temporal random effects modeling framework to analyze malaria incidence and mortality ratio across Kenya. The approach incorporated spatial and temporal dependencies to provide a detailed understanding of malaria incidence and mortality risk patterns. Spatial random effects were modeled using conditional autoregressive (CAR) priors to account for correlations among neighboring counties, while temporal dependence was captured using autoregressive processes of order two (AR2), reflecting trends over multiple time periods. An evaluation was on the performance of Spatio-Temporal Poisson Linear Trend Model (STPLM), Spatio-Temporal Poisson ANOVA Model (STPAM), Spatio-Temporal Poisson Separable Model (STPSM) and Poisson Temporal Model for Spatio-Temporal Effects (PTSTN)using the Deviance Information Criterion (DIC), the effective number of parameters (p.d) and the Log Marginal Pseudo-Likelihood (LMPL). The Spatio-Temporal Poisson ANOVA Model (STPAM) was found as the best Poissson Spatial-Temporal Model and was used to develop a multivariate spatio-temporal model for the joint modeling of malaria incidence and mortality. Using the developed model, the study identified significant spatial clustering of malaria, with persistent high-risk zones in western and coastal counties. Temporal trends indicated an overall decline in transmission, though progress was uneven across counties, reflecting differences in intervention coverage, healthcare access, and local epidemiology. These findings underscored the value of multivariate spatio-temporal modeling of malaria incidence and mortality for guiding malaria control strategies. This study thus demonstrates that Bayesian Spatial-Temporal modeling is essential for understanding heterogeneous malaria incidence and mortality risk and informing strategies aimed at reducing disease burden and advancing toward malaria elimination in Kenya.
},
 year = {2026}
}

Copy | Download

TY  - JOUR
T1  - Spatio-Temporal Random Effects Modeling of Malaria Incidence and Mortality in Kenya

AU  - Polycarp Okiagera Nyabuto
AU  - Anthony Wanjoya
AU  - Thomas Mageto
AU  - Anthony Ngunyi
Y1  - 2026/01/16
PY  - 2026
N1  - https://doi.org/10.11648/j.ajtas.20261501.11
DO  - 10.11648/j.ajtas.20261501.11
T2  - American Journal of Theoretical and Applied Statistics
JF  - American Journal of Theoretical and Applied Statistics
JO  - American Journal of Theoretical and Applied Statistics
SP  - 1
EP  - 11
PB  - Science Publishing Group
SN  - 2326-9006
UR  - https://doi.org/10.11648/j.ajtas.20261501.11
AB  - Malaria is a major public health challenge in sub-Saharan Africa, with transmission patterns that vary significantly across space and time due to environmental, socioeconomic, and epidemiological factors. These variations complicate efforts to design effective and targeted interventions, making it crucial to understand the dynamics of disease spread. This study employed Bayesian spatio-temporal random effects modeling framework to analyze malaria incidence and mortality ratio across Kenya. The approach incorporated spatial and temporal dependencies to provide a detailed understanding of malaria incidence and mortality risk patterns. Spatial random effects were modeled using conditional autoregressive (CAR) priors to account for correlations among neighboring counties, while temporal dependence was captured using autoregressive processes of order two (AR2), reflecting trends over multiple time periods. An evaluation was on the performance of Spatio-Temporal Poisson Linear Trend Model (STPLM), Spatio-Temporal Poisson ANOVA Model (STPAM), Spatio-Temporal Poisson Separable Model (STPSM) and Poisson Temporal Model for Spatio-Temporal Effects (PTSTN)using the Deviance Information Criterion (DIC), the effective number of parameters (p.d) and the Log Marginal Pseudo-Likelihood (LMPL). The Spatio-Temporal Poisson ANOVA Model (STPAM) was found as the best Poissson Spatial-Temporal Model and was used to develop a multivariate spatio-temporal model for the joint modeling of malaria incidence and mortality. Using the developed model, the study identified significant spatial clustering of malaria, with persistent high-risk zones in western and coastal counties. Temporal trends indicated an overall decline in transmission, though progress was uneven across counties, reflecting differences in intervention coverage, healthcare access, and local epidemiology. These findings underscored the value of multivariate spatio-temporal modeling of malaria incidence and mortality for guiding malaria control strategies. This study thus demonstrates that Bayesian Spatial-Temporal modeling is essential for understanding heterogeneous malaria incidence and mortality risk and informing strategies aimed at reducing disease burden and advancing toward malaria elimination in Kenya.

VL  - 15
IS  - 1
ER  -

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