Small Area Risk Mapping of Under-five Mortality: A District-level Analysis using Parametric Approach

Salyungu Mabula; Robert Too; Gregory Kerich

doi:doi:10.11648/j.mma.20261101.11

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Small Area Risk Mapping of Under-five Mortality: A District-level Analysis using Parametric Approach

Salyungu Mabula^*

, Robert Too

, Gregory Kerich

Published in Mathematical Modelling and Applications (Volume 11, Issue 1)

Received: 30 November 2025 Accepted: 20 December 2025 Published: 20 January 2026

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Abstract

Mortality of under-five children remains a pressing public health concern in Tanzania, with spatial disparities contributed by the intersection of socioeconomic, health and environmental factors. This study employs Bayesian techniques and epidemiological spatial methods to estimate and map district-level under-five mortality, focusing on malaria prevalence, maternal education and source of drinking water. The districts with high risk are identified using georeferenced data of under-five mortality from recent Tanzania Demographic and Health Survey. We estimated the relative risk (RR) of under-five mortality within a hierarchical Bayesian spatial framework using three priors namely Besang-York-Mollie (BYM), Dean & Cressie (DC) and Leroux (BMY2). The model performance was evaluated using Watanabe-Akaike Information criterion (WAIC) to identify the best, from which the relative risk of under-five mortality was estimated and visualized using interactive and static maps. The Leroux (BYM2) model effectively estimated the relative risk compared to BYM and DC. The findings reveal that there are disparities of under-five mortality even across districts of the same region. The top ten districts with higher relative risk of under-five mortality based on model estimates are Ifakara (RR = 3.2), Nyasa ( RR = 3.0), Babati (RR = 2.74), Rungwe (RR = 2.58), Songea (RR = 2.58), Mbarali (RR = 2.55), Rorya (RR = 2.36), Namtumbo (RR = 2.36), Mbulu (RR = 2.11) and Mbogwe (RR = 2.11). The findings advocate for targeted interventions in these districts that may reduce under-five mortality and consequently increase child survival rates in Tanzania.

Published in	Mathematical Modelling and Applications (Volume 11, Issue 1)
DOI	10.11648/j.mma.20261101.11
Page(s)	1-17
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

Under-five Mortality, Bayesian Hierarchical Model, Relative Risk, Small Area Estimation, Tanzania

References

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APA Style

Mabula, S., Too, R., Kerich, G. (2026). Small Area Risk Mapping of Under-five Mortality: A District-level Analysis using Parametric Approach. Mathematical Modelling and Applications, 11(1), 1-17. https://doi.org/10.11648/j.mma.20261101.11

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ACS Style

Mabula, S.; Too, R.; Kerich, G. Small Area Risk Mapping of Under-five Mortality: A District-level Analysis using Parametric Approach. Math. Model. Appl. 2026, 11(1), 1-17. doi: 10.11648/j.mma.20261101.11

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AMA Style

Mabula S, Too R, Kerich G. Small Area Risk Mapping of Under-five Mortality: A District-level Analysis using Parametric Approach. Math Model Appl. 2026;11(1):1-17. doi: 10.11648/j.mma.20261101.11

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@article{10.11648/j.mma.20261101.11,
  author = {Salyungu Mabula and Robert Too and Gregory Kerich},
  title = {Small Area Risk Mapping of Under-five Mortality: A District-level Analysis using Parametric Approach
},
  journal = {Mathematical Modelling and Applications},
  volume = {11},
  number = {1},
  pages = {1-17},
  doi = {10.11648/j.mma.20261101.11},
  url = {https://doi.org/10.11648/j.mma.20261101.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.mma.20261101.11},
  abstract = {Mortality of under-five children remains a pressing public health concern in Tanzania, with spatial disparities contributed by the intersection of socioeconomic, health and environmental factors. This study employs Bayesian techniques and epidemiological spatial methods to estimate and map district-level under-five mortality, focusing on malaria prevalence, maternal education and source of drinking water. The districts with high risk are identified using georeferenced data of under-five mortality from recent Tanzania Demographic and Health Survey. We estimated the relative risk (RR) of under-five mortality within a hierarchical Bayesian spatial framework using three priors namely Besang-York-Mollie (BYM), Dean & Cressie (DC) and Leroux (BMY2). The model performance was evaluated using Watanabe-Akaike Information criterion (WAIC) to identify the best, from which the relative risk of under-five mortality was estimated and visualized using interactive and static maps. The Leroux (BYM2) model effectively estimated the relative risk compared to BYM and DC. The findings reveal that there are disparities of under-five mortality even across districts of the same region. The top ten districts with higher relative risk of under-five mortality based on model estimates are Ifakara (RR = 3.2), Nyasa ( RR = 3.0), Babati (RR = 2.74), Rungwe (RR = 2.58), Songea (RR = 2.58), Mbarali (RR = 2.55), Rorya (RR = 2.36), Namtumbo (RR = 2.36), Mbulu (RR = 2.11) and Mbogwe (RR = 2.11). The findings advocate for targeted interventions in these districts that may reduce under-five mortality and consequently increase child survival rates in Tanzania.
},
 year = {2026}
}

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TY  - JOUR
T1  - Small Area Risk Mapping of Under-five Mortality: A District-level Analysis using Parametric Approach

AU  - Salyungu Mabula
AU  - Robert Too
AU  - Gregory Kerich
Y1  - 2026/01/20
PY  - 2026
N1  - https://doi.org/10.11648/j.mma.20261101.11
DO  - 10.11648/j.mma.20261101.11
T2  - Mathematical Modelling and Applications
JF  - Mathematical Modelling and Applications
JO  - Mathematical Modelling and Applications
SP  - 1
EP  - 17
PB  - Science Publishing Group
SN  - 2575-1794
UR  - https://doi.org/10.11648/j.mma.20261101.11
AB  - Mortality of under-five children remains a pressing public health concern in Tanzania, with spatial disparities contributed by the intersection of socioeconomic, health and environmental factors. This study employs Bayesian techniques and epidemiological spatial methods to estimate and map district-level under-five mortality, focusing on malaria prevalence, maternal education and source of drinking water. The districts with high risk are identified using georeferenced data of under-five mortality from recent Tanzania Demographic and Health Survey. We estimated the relative risk (RR) of under-five mortality within a hierarchical Bayesian spatial framework using three priors namely Besang-York-Mollie (BYM), Dean & Cressie (DC) and Leroux (BMY2). The model performance was evaluated using Watanabe-Akaike Information criterion (WAIC) to identify the best, from which the relative risk of under-five mortality was estimated and visualized using interactive and static maps. The Leroux (BYM2) model effectively estimated the relative risk compared to BYM and DC. The findings reveal that there are disparities of under-five mortality even across districts of the same region. The top ten districts with higher relative risk of under-five mortality based on model estimates are Ifakara (RR = 3.2), Nyasa ( RR = 3.0), Babati (RR = 2.74), Rungwe (RR = 2.58), Songea (RR = 2.58), Mbarali (RR = 2.55), Rorya (RR = 2.36), Namtumbo (RR = 2.36), Mbulu (RR = 2.11) and Mbogwe (RR = 2.11). The findings advocate for targeted interventions in these districts that may reduce under-five mortality and consequently increase child survival rates in Tanzania.

VL  - 11
IS  - 1
ER  -

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Author Information

Salyungu Mabula

Department of Mathematics and Statistics, University of Dodoma, Dodoma, Tanzania; Department of Mathematics, Physics and Computing, Moi University, Eldoret, Kenya

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http://orcid.org/0009-0002-1026-1194
Robert Too

Department of Mathematics, Physics and Computing, Moi University, Eldoret, Kenya

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http://orcid.org/0009-0002-3067-7373
Gregory Kerich

Department of Mathematics, Physics and Computing, Moi University, Eldoret, Kenya

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http://orcid.org/0000-0003-2485-9373

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APA Style

Mabula, S., Too, R., Kerich, G. (2026). Small Area Risk Mapping of Under-five Mortality: A District-level Analysis using Parametric Approach. Mathematical Modelling and Applications, 11(1), 1-17. https://doi.org/10.11648/j.mma.20261101.11

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ACS Style

Mabula, S.; Too, R.; Kerich, G. Small Area Risk Mapping of Under-five Mortality: A District-level Analysis using Parametric Approach. Math. Model. Appl. 2026, 11(1), 1-17. doi: 10.11648/j.mma.20261101.11

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AMA Style

Mabula S, Too R, Kerich G. Small Area Risk Mapping of Under-five Mortality: A District-level Analysis using Parametric Approach. Math Model Appl. 2026;11(1):1-17. doi: 10.11648/j.mma.20261101.11

Copy | Download

@article{10.11648/j.mma.20261101.11,
  author = {Salyungu Mabula and Robert Too and Gregory Kerich},
  title = {Small Area Risk Mapping of Under-five Mortality: A District-level Analysis using Parametric Approach
},
  journal = {Mathematical Modelling and Applications},
  volume = {11},
  number = {1},
  pages = {1-17},
  doi = {10.11648/j.mma.20261101.11},
  url = {https://doi.org/10.11648/j.mma.20261101.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.mma.20261101.11},
  abstract = {Mortality of under-five children remains a pressing public health concern in Tanzania, with spatial disparities contributed by the intersection of socioeconomic, health and environmental factors. This study employs Bayesian techniques and epidemiological spatial methods to estimate and map district-level under-five mortality, focusing on malaria prevalence, maternal education and source of drinking water. The districts with high risk are identified using georeferenced data of under-five mortality from recent Tanzania Demographic and Health Survey. We estimated the relative risk (RR) of under-five mortality within a hierarchical Bayesian spatial framework using three priors namely Besang-York-Mollie (BYM), Dean & Cressie (DC) and Leroux (BMY2). The model performance was evaluated using Watanabe-Akaike Information criterion (WAIC) to identify the best, from which the relative risk of under-five mortality was estimated and visualized using interactive and static maps. The Leroux (BYM2) model effectively estimated the relative risk compared to BYM and DC. The findings reveal that there are disparities of under-five mortality even across districts of the same region. The top ten districts with higher relative risk of under-five mortality based on model estimates are Ifakara (RR = 3.2), Nyasa ( RR = 3.0), Babati (RR = 2.74), Rungwe (RR = 2.58), Songea (RR = 2.58), Mbarali (RR = 2.55), Rorya (RR = 2.36), Namtumbo (RR = 2.36), Mbulu (RR = 2.11) and Mbogwe (RR = 2.11). The findings advocate for targeted interventions in these districts that may reduce under-five mortality and consequently increase child survival rates in Tanzania.
},
 year = {2026}
}

Copy | Download

TY  - JOUR
T1  - Small Area Risk Mapping of Under-five Mortality: A District-level Analysis using Parametric Approach

AU  - Salyungu Mabula
AU  - Robert Too
AU  - Gregory Kerich
Y1  - 2026/01/20
PY  - 2026
N1  - https://doi.org/10.11648/j.mma.20261101.11
DO  - 10.11648/j.mma.20261101.11
T2  - Mathematical Modelling and Applications
JF  - Mathematical Modelling and Applications
JO  - Mathematical Modelling and Applications
SP  - 1
EP  - 17
PB  - Science Publishing Group
SN  - 2575-1794
UR  - https://doi.org/10.11648/j.mma.20261101.11
AB  - Mortality of under-five children remains a pressing public health concern in Tanzania, with spatial disparities contributed by the intersection of socioeconomic, health and environmental factors. This study employs Bayesian techniques and epidemiological spatial methods to estimate and map district-level under-five mortality, focusing on malaria prevalence, maternal education and source of drinking water. The districts with high risk are identified using georeferenced data of under-five mortality from recent Tanzania Demographic and Health Survey. We estimated the relative risk (RR) of under-five mortality within a hierarchical Bayesian spatial framework using three priors namely Besang-York-Mollie (BYM), Dean & Cressie (DC) and Leroux (BMY2). The model performance was evaluated using Watanabe-Akaike Information criterion (WAIC) to identify the best, from which the relative risk of under-five mortality was estimated and visualized using interactive and static maps. The Leroux (BYM2) model effectively estimated the relative risk compared to BYM and DC. The findings reveal that there are disparities of under-five mortality even across districts of the same region. The top ten districts with higher relative risk of under-five mortality based on model estimates are Ifakara (RR = 3.2), Nyasa ( RR = 3.0), Babati (RR = 2.74), Rungwe (RR = 2.58), Songea (RR = 2.58), Mbarali (RR = 2.55), Rorya (RR = 2.36), Namtumbo (RR = 2.36), Mbulu (RR = 2.11) and Mbogwe (RR = 2.11). The findings advocate for targeted interventions in these districts that may reduce under-five mortality and consequently increase child survival rates in Tanzania.

VL  - 11
IS  - 1
ER  -

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