Modelling Seasonal Variation and Lassa Fever Outbreak in Nigeria: A Predictive Approach

Adesola Musa; Kazeem Osuolale; Dayo Lawal; Abideen Salako; Fewajesuyan Aponinuola; Wakilat Tijani; Abass Adigun; Babatunde Salako

doi:doi:10.11648/j.ijdsa.20241005.12

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Modelling Seasonal Variation and Lassa Fever Outbreak in Nigeria: A Predictive Approach

Adesola Musa^*

, Kazeem Osuolale

, Dayo Lawal

, Abideen Salako

, Fewajesuyan Aponinuola

, Wakilat Tijani

, Abass Adigun, Babatunde Salako

Published in International Journal of Data Science and Analysis (Volume 10, Issue 5)

Received: 14 October 2024 Accepted: 4 November 2024 Published: 29 November 2024

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Abstract

Background: Lassa fever, a severe viral hemorrhagic fever caused by the Lassa virus, is a significant public health concern in West Africa, particularly in Nigeria. First identified in the 1950s, Lassa fever has been a persistent threat, causing outbreaks annually. This study investigates the temporal patterns and trends of Lassa fever outbreaks in Nigeria between 2017 and 2023, leveraging a comprehensive dataset from the Nigerian Centre for Disease Control (NCDC). Objective: The goal of this study is to analyze the seasonal variations and predict future occurrences of Lassa fever outbreaks in Nigeria. By employing the Box-Jenkins time series analysis and geo-spatial analysis, we aim to: Identify temporal patterns by Examining monthly and annual trends in Lassa fever case numbers, Forecast future outbreaks by utilizing an ARIMA model to predict future incidence rates and inform public health strategies by providing evidence-based recommendations to improve Lassa fever prevention and control efforts. Methods: This study utilized a secondary dataset comprising over 60 data points collected from the NCDC portal between 2017 and 2023. The Box-Jenkins time series analysis, specifically the ARIMA model, was employed to analyze the temporal patterns and forecast future trends. The model's adequacy was assessed using the Ljung-Box test. Additionally, geo-spatial analysis was conducted to visualize the spatial distribution of Lassa fever cases. Results: The analysis revealed distinct seasonal patterns in Lassa fever incidence, influenced by Nigeria's climatic and environmental conditions. Monthly fluctuations in confirmed cases were observed, with peak periods aligning with specific seasons. The ARIMA (0, 1, 1)(0, 1, 1)₁₂ model demonstrated a strong fit to the data, providing reliable forecasts for future outbreaks. Conclusion: This study underscores the importance of strengthening surveillance systems for early detection and rapid response to Lassa fever outbreaks, particularly during peak seasons. Implementing effective rodent control measures, promoting good hygiene practices, and improving environmental sanitation are crucial for reducing the risk of Lassa fever transmission. Furthermore, enhancing collaboration between government agencies, healthcare providers, and research institutions is essential for optimizing Lassa fever prevention and control efforts.

Published in	International Journal of Data Science and Analysis (Volume 10, Issue 5)
DOI	10.11648/j.ijdsa.20241005.12
Page(s)	100-108
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

Keywords

Lassa Fever, Time Series, ARIMA, Box-Jenkins, Temporal Patterns, Seasonal Variation, Forecasting

References

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[2]	Papageorgiou N, Spiliopoulou M, Nguyen TV, et al. Brothers in Arms: Structure, Assembly and Function of Arenaviridae Nucleoprotein. Viruses. 2020; 12(7): 772. https://doi.org/10.3390/v12070772
[3]	Gibb R, Moses LM, Redding DW, Jones KE. Understanding the cryptic nature of Lassa fever in West Africa. Pathog Glob Health. 2017; 111(6): 276-88. https://doi.org/10.1080/20477724.2017.1369643
[4]	Richmond JK, Baglole DJ. Lassa fever: epidemiology, clinical features, and social consequences. BMJ. 2003; 327(7426): 1271-5. https://doi.org/10.1136/bmj.327.7426.1271
[5]	Panning M, Emmerich P, Olschläger S, et al. Laboratory diagnosis of Lassa fever, Liberia. Emerg Infect Dis. 2010; 16(6): 1041-3. https://doi.org/10.3201/eid1606.100040
[6]	Bond N, Schieffelin JS, Moses LM, Bennett AJ, Bausch DG. A historical look at the first reported cases of Lassa fever: IgG antibodies 40 years after acute infection. Am J Trop Med Hyg. 2013; 88(2): 241-4. https://doi.org/10.4269/ajtmh.2012.12-0466
[7]	Ijarotimi IT, Ilesanmi OS, Aderinwale A, et al. Knowledge of Lassa fever and use of infection prevention and control facilities among health care workers during Lassa fever outbreak in Ondo State, Nigeria. Pan Afr Med J. 2018; 30: 56. https://doi.org/10.11604/pamj.2018.30.56.13125
[8]	Lupi O, Tyring SK. Tropical dermatology: viral tropical diseases. J Am Acad Dermatol. 2003; 49(6): 979-1000. https://doi.org/10.1016/s0190-9622(03)02727-0
[9]	Barua S, Dénes A, Ibrahim MA. A seasonal model to assess intervention strategies for preventing periodic recurrence of Lassa fever. Heliyon. 2021; 7(8): e07760. https://doi.org/10.1016/j.heliyon.2021.e07760
[10]	Ilori EA, Furuse Y, Ipadeola OB, et al. Epidemiologic and Clinical Features of Lassa Fever Outbreak in Nigeria, January 1-May 6, 2018. Emerg Infect Dis. 2019; 25(6): 1066-74. https://doi.org/10.3201/eid2506.181035
[11]	Cadmus S, Taiwo OJ, Akinseye V, et al. Ecological correlates and predictors of Lassa fever incidence in Ondo State, Nigeria 2017-2021: an emerging urban trend. Sci Rep. 2023; 13(1): 20855. https://doi.org/10.1038/s41598-023-47820-3
[12]	Andersen KG, Shapiro BJ, Matranga CB, et al. Clinical manifestations and response to Ribavirin among patients with Lassa fever during a hospital outbreak in Nigeria in 2012. Clin Infect Dis. 2015; 61(5): e1-9.
[13]	Zhao S, Musa SS, Fu H, He D, Qin J. Large-scale Lassa fever outbreaks in Nigeria: quantifying the association between disease reproduction number and local rainfall. Epidemiol Infect. 2020; 148: e4. https://doi.org/10.1017/S0950268819002267
[14]	Nasiru MO, Olanrewaju SO. Forecasting airline fatalities in the world using a univariate time series model. Int J Stat Appl. 2015; 5(5): 223-30. https://doi.org/10.5923/j.statistics.20150505.06

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APA Style

Musa, A., Osuolale, K., Lawal, D., Salako, A., Aponinuola, F., et al. (2024). Modelling Seasonal Variation and Lassa Fever Outbreak in Nigeria: A Predictive Approach. International Journal of Data Science and Analysis, 10(5), 100-108. https://doi.org/10.11648/j.ijdsa.20241005.12

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ACS Style

Musa, A.; Osuolale, K.; Lawal, D.; Salako, A.; Aponinuola, F., et al. Modelling Seasonal Variation and Lassa Fever Outbreak in Nigeria: A Predictive Approach. Int. J. Data Sci. Anal. 2024, 10(5), 100-108. doi: 10.11648/j.ijdsa.20241005.12

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AMA Style

Musa A, Osuolale K, Lawal D, Salako A, Aponinuola F, et al. Modelling Seasonal Variation and Lassa Fever Outbreak in Nigeria: A Predictive Approach. Int J Data Sci Anal. 2024;10(5):100-108. doi: 10.11648/j.ijdsa.20241005.12

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@article{10.11648/j.ijdsa.20241005.12,
  author = {Adesola Musa and Kazeem Osuolale and Dayo Lawal and Abideen Salako and Fewajesuyan Aponinuola and Wakilat Tijani and Abass Adigun and Babatunde Salako},
  title = {Modelling Seasonal Variation and Lassa Fever Outbreak in Nigeria: A Predictive Approach
},
  journal = {International Journal of Data Science and Analysis},
  volume = {10},
  number = {5},
  pages = {100-108},
  doi = {10.11648/j.ijdsa.20241005.12},
  url = {https://doi.org/10.11648/j.ijdsa.20241005.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdsa.20241005.12},
  abstract = {Background: Lassa fever, a severe viral hemorrhagic fever caused by the Lassa virus, is a significant public health concern in West Africa, particularly in Nigeria. First identified in the 1950s, Lassa fever has been a persistent threat, causing outbreaks annually. This study investigates the temporal patterns and trends of Lassa fever outbreaks in Nigeria between 2017 and 2023, leveraging a comprehensive dataset from the Nigerian Centre for Disease Control (NCDC). Objective: The goal of this study is to analyze the seasonal variations and predict future occurrences of Lassa fever outbreaks in Nigeria. By employing the Box-Jenkins time series analysis and geo-spatial analysis, we aim to: Identify temporal patterns by Examining monthly and annual trends in Lassa fever case numbers, Forecast future outbreaks by utilizing an ARIMA model to predict future incidence rates and inform public health strategies by providing evidence-based recommendations to improve Lassa fever prevention and control efforts. Methods: This study utilized a secondary dataset comprising over 60 data points collected from the NCDC portal between 2017 and 2023. The Box-Jenkins time series analysis, specifically the ARIMA model, was employed to analyze the temporal patterns and forecast future trends. The model's adequacy was assessed using the Ljung-Box test. Additionally, geo-spatial analysis was conducted to visualize the spatial distribution of Lassa fever cases. Results: The analysis revealed distinct seasonal patterns in Lassa fever incidence, influenced by Nigeria's climatic and environmental conditions. Monthly fluctuations in confirmed cases were observed, with peak periods aligning with specific seasons. The ARIMA (0, 1, 1)(0, 1, 1)12 model demonstrated a strong fit to the data, providing reliable forecasts for future outbreaks. Conclusion: This study underscores the importance of strengthening surveillance systems for early detection and rapid response to Lassa fever outbreaks, particularly during peak seasons. Implementing effective rodent control measures, promoting good hygiene practices, and improving environmental sanitation are crucial for reducing the risk of Lassa fever transmission. Furthermore, enhancing collaboration between government agencies, healthcare providers, and research institutions is essential for optimizing Lassa fever prevention and control efforts.
},
 year = {2024}
}

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TY  - JOUR
T1  - Modelling Seasonal Variation and Lassa Fever Outbreak in Nigeria: A Predictive Approach

AU  - Adesola Musa
AU  - Kazeem Osuolale
AU  - Dayo Lawal
AU  - Abideen Salako
AU  - Fewajesuyan Aponinuola
AU  - Wakilat Tijani
AU  - Abass Adigun
AU  - Babatunde Salako
Y1  - 2024/11/29
PY  - 2024
N1  - https://doi.org/10.11648/j.ijdsa.20241005.12
DO  - 10.11648/j.ijdsa.20241005.12
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  - 100
EP  - 108
PB  - Science Publishing Group
SN  - 2575-1891
UR  - https://doi.org/10.11648/j.ijdsa.20241005.12
AB  - Background: Lassa fever, a severe viral hemorrhagic fever caused by the Lassa virus, is a significant public health concern in West Africa, particularly in Nigeria. First identified in the 1950s, Lassa fever has been a persistent threat, causing outbreaks annually. This study investigates the temporal patterns and trends of Lassa fever outbreaks in Nigeria between 2017 and 2023, leveraging a comprehensive dataset from the Nigerian Centre for Disease Control (NCDC). Objective: The goal of this study is to analyze the seasonal variations and predict future occurrences of Lassa fever outbreaks in Nigeria. By employing the Box-Jenkins time series analysis and geo-spatial analysis, we aim to: Identify temporal patterns by Examining monthly and annual trends in Lassa fever case numbers, Forecast future outbreaks by utilizing an ARIMA model to predict future incidence rates and inform public health strategies by providing evidence-based recommendations to improve Lassa fever prevention and control efforts. Methods: This study utilized a secondary dataset comprising over 60 data points collected from the NCDC portal between 2017 and 2023. The Box-Jenkins time series analysis, specifically the ARIMA model, was employed to analyze the temporal patterns and forecast future trends. The model's adequacy was assessed using the Ljung-Box test. Additionally, geo-spatial analysis was conducted to visualize the spatial distribution of Lassa fever cases. Results: The analysis revealed distinct seasonal patterns in Lassa fever incidence, influenced by Nigeria's climatic and environmental conditions. Monthly fluctuations in confirmed cases were observed, with peak periods aligning with specific seasons. The ARIMA (0, 1, 1)(0, 1, 1)12 model demonstrated a strong fit to the data, providing reliable forecasts for future outbreaks. Conclusion: This study underscores the importance of strengthening surveillance systems for early detection and rapid response to Lassa fever outbreaks, particularly during peak seasons. Implementing effective rodent control measures, promoting good hygiene practices, and improving environmental sanitation are crucial for reducing the risk of Lassa fever transmission. Furthermore, enhancing collaboration between government agencies, healthcare providers, and research institutions is essential for optimizing Lassa fever prevention and control efforts.

VL  - 10
IS  - 5
ER  -

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Author Information

Adesola Musa

Grant (Biostatistics Subunit), Nigerian Institute of Medical Research, Lagos, Nigeria

Contact Email

http://orcid.org/0000-0002-6533-2984
Kazeem Osuolale

Grant (Biostatistics Subunit), Nigerian Institute of Medical Research, Lagos, Nigeria

Contact Email

http://orcid.org/0000-0002-4759-2432
Dayo Lawal

Grant (Biostatistics Subunit), Nigerian Institute of Medical Research, Lagos, Nigeria

Contact Email

http://orcid.org/0009-0006-4155-8395
Abideen Salako

Department of Clinical Sciences, Nigerian Institute of Medical Research, Lagos, Nigeria

Contact Email

http://orcid.org/0000-0002-3018-3712
Fewajesuyan Aponinuola

Internal Medicine Department, Federal Medical Center, Owo, Nigeria

Contact Email

http://orcid.org/0009-0007-5623-5529
Wakilat Tijani

Grant (Biostatistics Subunit), Nigerian Institute of Medical Research, Lagos, Nigeria

Contact Email

http://orcid.org/0009-0003-9667-288X
Abass Adigun

Information and Communications Technology Department, National Centre for Remote Sensing, Nabatum, Nigeria

Contact Email
Babatunde Salako

Department of Clinical Sciences, Nigerian Institute of Medical Research, Lagos, Nigeria

Contact Email

http://orcid.org/0000-0002-0963-7302

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APA Style

Musa, A., Osuolale, K., Lawal, D., Salako, A., Aponinuola, F., et al. (2024). Modelling Seasonal Variation and Lassa Fever Outbreak in Nigeria: A Predictive Approach. International Journal of Data Science and Analysis, 10(5), 100-108. https://doi.org/10.11648/j.ijdsa.20241005.12

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ACS Style

Musa, A.; Osuolale, K.; Lawal, D.; Salako, A.; Aponinuola, F., et al. Modelling Seasonal Variation and Lassa Fever Outbreak in Nigeria: A Predictive Approach. Int. J. Data Sci. Anal. 2024, 10(5), 100-108. doi: 10.11648/j.ijdsa.20241005.12

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AMA Style

Musa A, Osuolale K, Lawal D, Salako A, Aponinuola F, et al. Modelling Seasonal Variation and Lassa Fever Outbreak in Nigeria: A Predictive Approach. Int J Data Sci Anal. 2024;10(5):100-108. doi: 10.11648/j.ijdsa.20241005.12

Copy | Download

@article{10.11648/j.ijdsa.20241005.12,
  author = {Adesola Musa and Kazeem Osuolale and Dayo Lawal and Abideen Salako and Fewajesuyan Aponinuola and Wakilat Tijani and Abass Adigun and Babatunde Salako},
  title = {Modelling Seasonal Variation and Lassa Fever Outbreak in Nigeria: A Predictive Approach
},
  journal = {International Journal of Data Science and Analysis},
  volume = {10},
  number = {5},
  pages = {100-108},
  doi = {10.11648/j.ijdsa.20241005.12},
  url = {https://doi.org/10.11648/j.ijdsa.20241005.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdsa.20241005.12},
  abstract = {Background: Lassa fever, a severe viral hemorrhagic fever caused by the Lassa virus, is a significant public health concern in West Africa, particularly in Nigeria. First identified in the 1950s, Lassa fever has been a persistent threat, causing outbreaks annually. This study investigates the temporal patterns and trends of Lassa fever outbreaks in Nigeria between 2017 and 2023, leveraging a comprehensive dataset from the Nigerian Centre for Disease Control (NCDC). Objective: The goal of this study is to analyze the seasonal variations and predict future occurrences of Lassa fever outbreaks in Nigeria. By employing the Box-Jenkins time series analysis and geo-spatial analysis, we aim to: Identify temporal patterns by Examining monthly and annual trends in Lassa fever case numbers, Forecast future outbreaks by utilizing an ARIMA model to predict future incidence rates and inform public health strategies by providing evidence-based recommendations to improve Lassa fever prevention and control efforts. Methods: This study utilized a secondary dataset comprising over 60 data points collected from the NCDC portal between 2017 and 2023. The Box-Jenkins time series analysis, specifically the ARIMA model, was employed to analyze the temporal patterns and forecast future trends. The model's adequacy was assessed using the Ljung-Box test. Additionally, geo-spatial analysis was conducted to visualize the spatial distribution of Lassa fever cases. Results: The analysis revealed distinct seasonal patterns in Lassa fever incidence, influenced by Nigeria's climatic and environmental conditions. Monthly fluctuations in confirmed cases were observed, with peak periods aligning with specific seasons. The ARIMA (0, 1, 1)(0, 1, 1)12 model demonstrated a strong fit to the data, providing reliable forecasts for future outbreaks. Conclusion: This study underscores the importance of strengthening surveillance systems for early detection and rapid response to Lassa fever outbreaks, particularly during peak seasons. Implementing effective rodent control measures, promoting good hygiene practices, and improving environmental sanitation are crucial for reducing the risk of Lassa fever transmission. Furthermore, enhancing collaboration between government agencies, healthcare providers, and research institutions is essential for optimizing Lassa fever prevention and control efforts.
},
 year = {2024}
}

Copy | Download

TY  - JOUR
T1  - Modelling Seasonal Variation and Lassa Fever Outbreak in Nigeria: A Predictive Approach

AU  - Adesola Musa
AU  - Kazeem Osuolale
AU  - Dayo Lawal
AU  - Abideen Salako
AU  - Fewajesuyan Aponinuola
AU  - Wakilat Tijani
AU  - Abass Adigun
AU  - Babatunde Salako
Y1  - 2024/11/29
PY  - 2024
N1  - https://doi.org/10.11648/j.ijdsa.20241005.12
DO  - 10.11648/j.ijdsa.20241005.12
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  - 100
EP  - 108
PB  - Science Publishing Group
SN  - 2575-1891
UR  - https://doi.org/10.11648/j.ijdsa.20241005.12
AB  - Background: Lassa fever, a severe viral hemorrhagic fever caused by the Lassa virus, is a significant public health concern in West Africa, particularly in Nigeria. First identified in the 1950s, Lassa fever has been a persistent threat, causing outbreaks annually. This study investigates the temporal patterns and trends of Lassa fever outbreaks in Nigeria between 2017 and 2023, leveraging a comprehensive dataset from the Nigerian Centre for Disease Control (NCDC). Objective: The goal of this study is to analyze the seasonal variations and predict future occurrences of Lassa fever outbreaks in Nigeria. By employing the Box-Jenkins time series analysis and geo-spatial analysis, we aim to: Identify temporal patterns by Examining monthly and annual trends in Lassa fever case numbers, Forecast future outbreaks by utilizing an ARIMA model to predict future incidence rates and inform public health strategies by providing evidence-based recommendations to improve Lassa fever prevention and control efforts. Methods: This study utilized a secondary dataset comprising over 60 data points collected from the NCDC portal between 2017 and 2023. The Box-Jenkins time series analysis, specifically the ARIMA model, was employed to analyze the temporal patterns and forecast future trends. The model's adequacy was assessed using the Ljung-Box test. Additionally, geo-spatial analysis was conducted to visualize the spatial distribution of Lassa fever cases. Results: The analysis revealed distinct seasonal patterns in Lassa fever incidence, influenced by Nigeria's climatic and environmental conditions. Monthly fluctuations in confirmed cases were observed, with peak periods aligning with specific seasons. The ARIMA (0, 1, 1)(0, 1, 1)12 model demonstrated a strong fit to the data, providing reliable forecasts for future outbreaks. Conclusion: This study underscores the importance of strengthening surveillance systems for early detection and rapid response to Lassa fever outbreaks, particularly during peak seasons. Implementing effective rodent control measures, promoting good hygiene practices, and improving environmental sanitation are crucial for reducing the risk of Lassa fever transmission. Furthermore, enhancing collaboration between government agencies, healthcare providers, and research institutions is essential for optimizing Lassa fever prevention and control efforts.

VL  - 10
IS  - 5
ER  -

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