A Statistical Analysis of Typhoid Fever in Aliade

Tamber Abraham Jighjigh; Tamber Dooshima Deborah; Ikpom Emmanuel Terhemba; Okafor Linus Uchenwe

doi:doi:10.11648/j.ijsda.20251103.12

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A Statistical Analysis of Typhoid Fever in Aliade

Tamber Abraham Jighjigh^*

, Tamber Dooshima Deborah, Ikpom Emmanuel Terhemba, Okafor Linus Uchenwe

Published in International Journal of Statistical Distributions and Applications (Volume 11, Issue 3)

Received: 7 May 2025 Accepted: 21 May 2025 Published: 24 December 2025

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Abstract

Typhoid fever remains a serious public health problem in many underdeveloped regions, with children and young adults (5–19 years) at highest risk. In Benue State, Nigeria, hospital records suggest a rising trend in reported typhoid cases, yet few studies have examined future incidence under current conditions. This research modeled and forecasted daily typhoid fever cases at Saint Vincent Hospital. Aliade (Gwer?East LGA) using an Auto?Regressive Integrated Moving Average (ARIMA) approach, thereby providing policymakers with data-driven projections for resource planning. The research also analyzed 501 daily observations of laboratory?confirmed typhoid cases collected from October 2019 to February 2021. Descriptive statistics (mean = 15.95 cases/day; SD = 8.81) characterized the series, which exhibited non?stationarity (ADF p = 0.301) at level. First and second differencing achieved stationarity (ADF p = 0.01). Autocorrelation (ACF) and partial autocorrelation (PACF) plots guided tentative model selection, and the final ARIMA (2,1,4) model was chosen based on minimum Akaike Information Criterion (AIC = 837.11). Model adequacy was confirmed via residual diagnostics (Ljung–Box test), and forecast accuracy was benchmarked against mean and naïve methods using mean absolute deviation and mean squared error. The ARIMA (2,1,4) model demonstrated strong fit (log?likelihood = –410.56; σ² = 0.2998) and outperformed benchmark forecasts in both MAD and MSE. A 15?year forecast indicates a slight upward trajectory in daily typhoid cases, suggesting continued burden without enhanced interventions. ARIMA modeling provides reliable short? and long?term forecasts of typhoid fever incidence. These projections can inform targeted prevention strategies, resource allocation, and public health planning to mitigate future outbreaks in the region.

Published in	International Journal of Statistical Distributions and Applications (Volume 11, Issue 3)
DOI	10.11648/j.ijsda.20251103.12
Page(s)	98-105
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), 2025. Published by Science Publishing Group

Keywords

Typhoid Fever, Autoregression Integrated Moving Average (ARIMA), Optimal Model, Forecast, Prediction

References

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[2]	Akawu, C., Ikusemoran, M., & Ballah, A. (2018). Analysis of Spatial Patterns of Typhoid Prevalence in Borno State, Nigeria. Academic Research International, 9(2), 24–37.
[3]	Babajide, S., & Perry, B. (2015). Meeting the Millennium Development Goal for typhoid by 2015: A Time Series Analysis of typhoid Cases in Ogun State, Nigeria. African Journal of Malaria and Tropical Diseases, 3(9), 245 – 259.
[4]	Box, G. E., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control.
[5]	Fana, S. A., Bunza, M. D., Anka, S. A., & Imam, A. U. (2015). Prevalence and Risk Factors Associated with Malaria Infection among Pregnant Women in a Semi-Urban Community of North-Western Nigeria. Infectious Diseases of Poverty, 4(24), 1-5.
[6]	Houmsou, R. S., Amuta, E. U., Sar, T. T., & Adagba, A. H. (2011). Malaria Infection among Patients Attending a Nigerian Semi-Urban Based Hospital and Performance of HRP-2 PF Rapid Diagnostic Test (RDT) in Screening Clinical Cases of Plasmodium Falciparum Malaria. Translational Biomedicine, 2(1: 5), 1 – 5.
[7]	Ibor, U. W., Okoronkwo, E. M., & Rotimi, E. M. (2016). Temporal Analysis of Malaria Prevalence in Cross River State, Nigeria. E3 Journal of Medical Research, 5(1), 1 – 7. 56.
[8]	Ishaq, A. A., & Murtala, U. M. (2017). Spatio-Temporal Trends of Typhoid Fever Among Youths Attending Muhammad AbdulahiWase Specialist Hospital in Kano Metropolis, Nigeria. Bayero Journal of Applied Sciences, 10(2), 115 – 121.
[9]	Nigeria Malaria Fact Sheet (2011). Nigeria Malaria Fact Sheet. United States Embassy in Nigeria. http://nigeria.usembassy.gov
[10]	Obimakinde, E. T., & Simon-Oke, I. A. (2017). The Prevalence of Malaria Infection Among Patients Attending the Health Centre of the Federal University of Technology, Akure, Nigeria. International Journal of Tropical Disease & Health, 27(4), 1 – 7.
[11]	Onah, I. E., Adesina, F. P., Uweh, P. O., & Anumba, J. U. (2017). Challenges of Typhoid Elimination in Nigeria; A Review. International Journal of Infections Disease and Therapy, 2(4), 79 – 85.
[12]	Osagi, H. D., Anouche, M. R., & Oluchukwu, H. E. (2015). A Study of the Prevalence of Typhoid Fever Co-Infection in Abakaliki, Nigeria. African Journal of Typhoid and Tropical Diseases, 3(9), 162 – 166.
[13]	Owoeye, D. O., Akinyemi, J. O., & Yusuf, O. B. (2018). Decomposition of Changes in Typhoid Prevalence Amongst Under-Five Children in Nigeria. Typhoid World Journal, 9(3), 1 – 6.
[14]	Ozofor, N. M., &Onos, C. N. (2017). Statistical Analysis of Reported Cases of Typhoid in Parklane Specialist Hospital in Enugu Urban for the Period of 2009 – 2015. Researchjournali’s Journal of Mathematics, 4(3), 1 – 10.
[15]	Sekubia, A. C., &Mensah, E. K. (2019). Analysis of Typhoid Fever Surveillance Data, Cape Coast Metropolis, 2016. Acta Scientific Medical Sciences, 3(2), 2–8.
[16]	Ukaegbu, C. O., Nnachi, A. U., Mawak, J. D., &Igwe, C. C. (2014). Incidence of Concerrent Typhoid Fever Infections in Febrile Patients in Jos, Plateau State Nigeria. International Journal of Scientific & Technology Research, 3(4), 157 – 161.
[17]	World Health Organization. (2013). Global action plan for the prevention and control of noncommunicable diseases 2013–2020. Geneva: World Health Organization.

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APA Style

Jighjigh, T. A., Deborah, T. D., Terhemba, I. E., Uchenwe, O. L. (2025). A Statistical Analysis of Typhoid Fever in Aliade. International Journal of Statistical Distributions and Applications, 11(3), 98-105. https://doi.org/10.11648/j.ijsda.20251103.12

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ACS Style

Jighjigh, T. A.; Deborah, T. D.; Terhemba, I. E.; Uchenwe, O. L. A Statistical Analysis of Typhoid Fever in Aliade. Int. J. Stat. Distrib. Appl. 2025, 11(3), 98-105. doi: 10.11648/j.ijsda.20251103.12

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AMA Style

Jighjigh TA, Deborah TD, Terhemba IE, Uchenwe OL. A Statistical Analysis of Typhoid Fever in Aliade. Int J Stat Distrib Appl. 2025;11(3):98-105. doi: 10.11648/j.ijsda.20251103.12

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@article{10.11648/j.ijsda.20251103.12,
  author = {Tamber Abraham Jighjigh and Tamber Dooshima Deborah and Ikpom Emmanuel Terhemba and Okafor Linus Uchenwe},
  title = {A Statistical Analysis of Typhoid Fever in Aliade},
  journal = {International Journal of Statistical Distributions and Applications},
  volume = {11},
  number = {3},
  pages = {98-105},
  doi = {10.11648/j.ijsda.20251103.12},
  url = {https://doi.org/10.11648/j.ijsda.20251103.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijsda.20251103.12},
  abstract = {Typhoid fever remains a serious public health problem in many underdeveloped regions, with children and young adults (5–19 years) at highest risk. In Benue State, Nigeria, hospital records suggest a rising trend in reported typhoid cases, yet few studies have examined future incidence under current conditions. This research modeled and forecasted daily typhoid fever cases at Saint Vincent Hospital. Aliade (Gwer?East LGA) using an Auto?Regressive Integrated Moving Average (ARIMA) approach, thereby providing policymakers with data-driven projections for resource planning. The research also analyzed 501 daily observations of laboratory?confirmed typhoid cases collected from October 2019 to February 2021. Descriptive statistics (mean = 15.95 cases/day; SD = 8.81) characterized the series, which exhibited non?stationarity (ADF p = 0.301) at level. First and second differencing achieved stationarity (ADF p = 0.01). Autocorrelation (ACF) and partial autocorrelation (PACF) plots guided tentative model selection, and the final ARIMA (2,1,4) model was chosen based on minimum Akaike Information Criterion (AIC = 837.11). Model adequacy was confirmed via residual diagnostics (Ljung–Box test), and forecast accuracy was benchmarked against mean and naïve methods using mean absolute deviation and mean squared error. The ARIMA (2,1,4) model demonstrated strong fit (log?likelihood = –410.56; σ² = 0.2998) and outperformed benchmark forecasts in both MAD and MSE. A 15?year forecast indicates a slight upward trajectory in daily typhoid cases, suggesting continued burden without enhanced interventions. ARIMA modeling provides reliable short? and long?term forecasts of typhoid fever incidence. These projections can inform targeted prevention strategies, resource allocation, and public health planning to mitigate future outbreaks in the region.},
 year = {2025}
}

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TY - JOUR
T1 - A Statistical Analysis of Typhoid Fever in Aliade
AU - Tamber Abraham Jighjigh
AU - Tamber Dooshima Deborah
AU - Ikpom Emmanuel Terhemba
AU - Okafor Linus Uchenwe
Y1 - 2025/12/24
PY - 2025
N1 - https://doi.org/10.11648/j.ijsda.20251103.12
DO - 10.11648/j.ijsda.20251103.12
T2 - International Journal of Statistical Distributions and Applications
JF - International Journal of Statistical Distributions and Applications
JO - International Journal of Statistical Distributions and Applications
SP - 98
EP - 105
PB - Science Publishing Group
SN - 2472-3509
UR - https://doi.org/10.11648/j.ijsda.20251103.12
AB - Typhoid fever remains a serious public health problem in many underdeveloped regions, with children and young adults (5–19 years) at highest risk. In Benue State, Nigeria, hospital records suggest a rising trend in reported typhoid cases, yet few studies have examined future incidence under current conditions. This research modeled and forecasted daily typhoid fever cases at Saint Vincent Hospital. Aliade (Gwer?East LGA) using an Auto?Regressive Integrated Moving Average (ARIMA) approach, thereby providing policymakers with data-driven projections for resource planning. The research also analyzed 501 daily observations of laboratory?confirmed typhoid cases collected from October 2019 to February 2021. Descriptive statistics (mean = 15.95 cases/day; SD = 8.81) characterized the series, which exhibited non?stationarity (ADF p = 0.301) at level. First and second differencing achieved stationarity (ADF p = 0.01). Autocorrelation (ACF) and partial autocorrelation (PACF) plots guided tentative model selection, and the final ARIMA (2,1,4) model was chosen based on minimum Akaike Information Criterion (AIC = 837.11). Model adequacy was confirmed via residual diagnostics (Ljung–Box test), and forecast accuracy was benchmarked against mean and naïve methods using mean absolute deviation and mean squared error. The ARIMA (2,1,4) model demonstrated strong fit (log?likelihood = –410.56; σ² = 0.2998) and outperformed benchmark forecasts in both MAD and MSE. A 15?year forecast indicates a slight upward trajectory in daily typhoid cases, suggesting continued burden without enhanced interventions. ARIMA modeling provides reliable short? and long?term forecasts of typhoid fever incidence. These projections can inform targeted prevention strategies, resource allocation, and public health planning to mitigate future outbreaks in the region.
VL - 11
IS - 3
ER -

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Author Information

Tamber Abraham Jighjigh

Department of Statistics, Joseph Sarwua Tarka University, Makurdi, Nigeria

Contact Email

http://orcid.org/0000-0002-2359-9410
Tamber Dooshima Deborah

Mathematics and Computer Science Department, Benue State University, Makurdi, Nigeria
Ikpom Emmanuel Terhemba

Benue State Health Insurance Agency, Makurdi, Nigeria
Okafor Linus Uchenwe

Mathematics Department, Nigerian Defence Academy, Kaduna, Nigeria

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Plain Text BibTeX RIS

APA Style

Jighjigh, T. A., Deborah, T. D., Terhemba, I. E., Uchenwe, O. L. (2025). A Statistical Analysis of Typhoid Fever in Aliade. International Journal of Statistical Distributions and Applications, 11(3), 98-105. https://doi.org/10.11648/j.ijsda.20251103.12

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ACS Style

Jighjigh, T. A.; Deborah, T. D.; Terhemba, I. E.; Uchenwe, O. L. A Statistical Analysis of Typhoid Fever in Aliade. Int. J. Stat. Distrib. Appl. 2025, 11(3), 98-105. doi: 10.11648/j.ijsda.20251103.12

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AMA Style

Jighjigh TA, Deborah TD, Terhemba IE, Uchenwe OL. A Statistical Analysis of Typhoid Fever in Aliade. Int J Stat Distrib Appl. 2025;11(3):98-105. doi: 10.11648/j.ijsda.20251103.12

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@article{10.11648/j.ijsda.20251103.12,
  author = {Tamber Abraham Jighjigh and Tamber Dooshima Deborah and Ikpom Emmanuel Terhemba and Okafor Linus Uchenwe},
  title = {A Statistical Analysis of Typhoid Fever in Aliade},
  journal = {International Journal of Statistical Distributions and Applications},
  volume = {11},
  number = {3},
  pages = {98-105},
  doi = {10.11648/j.ijsda.20251103.12},
  url = {https://doi.org/10.11648/j.ijsda.20251103.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijsda.20251103.12},
  abstract = {Typhoid fever remains a serious public health problem in many underdeveloped regions, with children and young adults (5–19 years) at highest risk. In Benue State, Nigeria, hospital records suggest a rising trend in reported typhoid cases, yet few studies have examined future incidence under current conditions. This research modeled and forecasted daily typhoid fever cases at Saint Vincent Hospital. Aliade (Gwer?East LGA) using an Auto?Regressive Integrated Moving Average (ARIMA) approach, thereby providing policymakers with data-driven projections for resource planning. The research also analyzed 501 daily observations of laboratory?confirmed typhoid cases collected from October 2019 to February 2021. Descriptive statistics (mean = 15.95 cases/day; SD = 8.81) characterized the series, which exhibited non?stationarity (ADF p = 0.301) at level. First and second differencing achieved stationarity (ADF p = 0.01). Autocorrelation (ACF) and partial autocorrelation (PACF) plots guided tentative model selection, and the final ARIMA (2,1,4) model was chosen based on minimum Akaike Information Criterion (AIC = 837.11). Model adequacy was confirmed via residual diagnostics (Ljung–Box test), and forecast accuracy was benchmarked against mean and naïve methods using mean absolute deviation and mean squared error. The ARIMA (2,1,4) model demonstrated strong fit (log?likelihood = –410.56; σ² = 0.2998) and outperformed benchmark forecasts in both MAD and MSE. A 15?year forecast indicates a slight upward trajectory in daily typhoid cases, suggesting continued burden without enhanced interventions. ARIMA modeling provides reliable short? and long?term forecasts of typhoid fever incidence. These projections can inform targeted prevention strategies, resource allocation, and public health planning to mitigate future outbreaks in the region.},
 year = {2025}
}

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