This research study focuses on the Spatial and temporal Modelling of malaria incidences in Kenya, taking into account Time- dependent covariates. Malaria remains a significant public health concern in Kenya, with varying rates of infection across its 47 counties. Environmental factors such as temperature, rainfall, humidity and elevation play a crucial role in influencing Malaria transmission. Despite numerous malaria control efforts and initiatives the burden of the disease persist. The main objective of this study was to formulate Bayesian Spatio-temporal models for malaria incidence, with a particular emphasis on incorporating time-dependent covariates. The availability of data collected over time from various counties, as provided by the malaria project Atlas, was essential for achieving this goal. The Besag-York-Molli ́e (BYM) Spatio-temporal Model were formulated and implemented using Bayesian approach. Bayesian inference technique, coupled with Markov Chain Monte Carlo (MCMC) algorithms, was used to fit the models to the data. We also conducted convergence diagnostic of MCMC algorithm in order to check if the algorithm has converged and how reliable the posterior estimates are. In the analysis under Bayesian model choice and comparison of spatio-temporal model, spatial model with time dependent covariates and Spatio-temporal model with time dependent covariate were fitted. We found out that Spatio-temporal model with Time Dependent covariates was the best model. The resulting model and maps will be valuable for identifying disease hotspots, allocating resources for disease prevention and mitigation, and guiding policy decisions to reduce the burden of malaria. To ensure the validity of the Bayesian analysis, MCMC diagnostics were applied, including the Geweke Test, Gelman-Rubin statistics, and trace plots. These tests confirmed that the MCMC chains had converged to a common distribution, indicating the reliability of the obtained results.
| Published in | International Journal of Data Science and Analysis (Volume 9, Issue 3) |
| DOI | 10.11648/j.ijdsa.20230903.12 |
| Page(s) | 60-66 |
| 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), 2024. Published by Science Publishing Group |
Spatial Model, Spatio-Temporal Model, MCMC Convergence, Gelman Rubins Statistics, Malaria-Incidences, Geweke Test
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APA Style
Nduvi Musyoka, E., Mwalili, S., Malenje, B. (2023). Bayesian Spatio-Temporal Models for the Incidence of Malaria Using Time Dependent Covariates. International Journal of Data Science and Analysis, 9(3), 60-66. https://doi.org/10.11648/j.ijdsa.20230903.12
ACS Style
Nduvi Musyoka, E.; Mwalili, S.; Malenje, B. Bayesian Spatio-Temporal Models for the Incidence of Malaria Using Time Dependent Covariates. Int. J. Data Sci. Anal. 2023, 9(3), 60-66. doi: 10.11648/j.ijdsa.20230903.12
AMA Style
Nduvi Musyoka E, Mwalili S, Malenje B. Bayesian Spatio-Temporal Models for the Incidence of Malaria Using Time Dependent Covariates. Int J Data Sci Anal. 2023;9(3):60-66. doi: 10.11648/j.ijdsa.20230903.12
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Download@article{10.11648/j.ijdsa.20230903.12,
author = {Evalyne Nduvi Musyoka and Samuel Mwalili and Boniface Malenje},
title = {Bayesian Spatio-Temporal Models for the Incidence of Malaria Using Time Dependent Covariates},
journal = {International Journal of Data Science and Analysis},
volume = {9},
number = {3},
pages = {60-66},
doi = {10.11648/j.ijdsa.20230903.12},
url = {https://doi.org/10.11648/j.ijdsa.20230903.12},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdsa.20230903.12},
abstract = {This research study focuses on the Spatial and temporal Modelling of malaria incidences in Kenya, taking into account Time- dependent covariates. Malaria remains a significant public health concern in Kenya, with varying rates of infection across its 47 counties. Environmental factors such as temperature, rainfall, humidity and elevation play a crucial role in influencing Malaria transmission. Despite numerous malaria control efforts and initiatives the burden of the disease persist. The main objective of this study was to formulate Bayesian Spatio-temporal models for malaria incidence, with a particular emphasis on incorporating time-dependent covariates. The availability of data collected over time from various counties, as provided by the malaria project Atlas, was essential for achieving this goal. The Besag-York-Molli ́e (BYM) Spatio-temporal Model were formulated and implemented using Bayesian approach. Bayesian inference technique, coupled with Markov Chain Monte Carlo (MCMC) algorithms, was used to fit the models to the data. We also conducted convergence diagnostic of MCMC algorithm in order to check if the algorithm has converged and how reliable the posterior estimates are. In the analysis under Bayesian model choice and comparison of spatio-temporal model, spatial model with time dependent covariates and Spatio-temporal model with time dependent covariate were fitted. We found out that Spatio-temporal model with Time Dependent covariates was the best model. The resulting model and maps will be valuable for identifying disease hotspots, allocating resources for disease prevention and mitigation, and guiding policy decisions to reduce the burden of malaria. To ensure the validity of the Bayesian analysis, MCMC diagnostics were applied, including the Geweke Test, Gelman-Rubin statistics, and trace plots. These tests confirmed that the MCMC chains had converged to a common distribution, indicating the reliability of the obtained results.
},
year = {2023}
}
TY - JOUR T1 - Bayesian Spatio-Temporal Models for the Incidence of Malaria Using Time Dependent Covariates AU - Evalyne Nduvi Musyoka AU - Samuel Mwalili AU - Boniface Malenje Y1 - 2023/11/11 PY - 2023 N1 - https://doi.org/10.11648/j.ijdsa.20230903.12 DO - 10.11648/j.ijdsa.20230903.12 T2 - International Journal of Data Science and Analysis JF - International Journal of Data Science and Analysis JO - International Journal of Data Science and Analysis SP - 60 EP - 66 PB - Science Publishing Group SN - 2575-1891 UR - https://doi.org/10.11648/j.ijdsa.20230903.12 AB - This research study focuses on the Spatial and temporal Modelling of malaria incidences in Kenya, taking into account Time- dependent covariates. Malaria remains a significant public health concern in Kenya, with varying rates of infection across its 47 counties. Environmental factors such as temperature, rainfall, humidity and elevation play a crucial role in influencing Malaria transmission. Despite numerous malaria control efforts and initiatives the burden of the disease persist. The main objective of this study was to formulate Bayesian Spatio-temporal models for malaria incidence, with a particular emphasis on incorporating time-dependent covariates. The availability of data collected over time from various counties, as provided by the malaria project Atlas, was essential for achieving this goal. The Besag-York-Molli ́e (BYM) Spatio-temporal Model were formulated and implemented using Bayesian approach. Bayesian inference technique, coupled with Markov Chain Monte Carlo (MCMC) algorithms, was used to fit the models to the data. We also conducted convergence diagnostic of MCMC algorithm in order to check if the algorithm has converged and how reliable the posterior estimates are. In the analysis under Bayesian model choice and comparison of spatio-temporal model, spatial model with time dependent covariates and Spatio-temporal model with time dependent covariate were fitted. We found out that Spatio-temporal model with Time Dependent covariates was the best model. The resulting model and maps will be valuable for identifying disease hotspots, allocating resources for disease prevention and mitigation, and guiding policy decisions to reduce the burden of malaria. To ensure the validity of the Bayesian analysis, MCMC diagnostics were applied, including the Geweke Test, Gelman-Rubin statistics, and trace plots. These tests confirmed that the MCMC chains had converged to a common distribution, indicating the reliability of the obtained results. VL - 9 IS - 3 ER -
Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology (JKUAT), Nairobi, Kenya
Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology (JKUAT), Nairobi, Kenya
Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology (JKUAT), Nairobi, Kenya
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