Background: Malaria in pregnancy is considered a significant public health problem, for which over 25 million are at risk of the infection each year in sub-Sahara Africa including Nigeria. This is despite interventions and improvement in the diagnosis, and treatment techniques. This study, therefore, forecasted cases of malaria in pregnancy in Rivers State Nigeria in 2021 to 2024. Methods: The total number of reported malaria-in-pregnancy (MIP) cases from 2003 to 2020 was extracted from National Bureau of Statistics (NBS) database. Descriptive statistics was obtained for the series plots, monthly mean plot, and yearly mean plot. The Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model was used to forecast the monthly number of MIP for 2021 to 2024. Results: The time series plot showed that there was a high volatility in the year 2020 in the malaria data. Also, the result shown that GARCH (0,1) and GARCH (1,1) parameters were all significant at 5% significance level. GARCH (1,1) model have least AIC value and log likelihood ratio among the several models. The study revealed an increasing trend in the number of MIP from 2021 to 2024. Conclusion: The study showed an expected increase for the forecasted period. The forecasted malaria cases will help Government and its health agencies, and critical stakeholders to plan and implement interventions to prevent the disease and mitigate its negative effects on mothers and fetus.
| Published in | Biomedical Statistics and Informatics (Volume 7, Issue 4) |
| DOI | 10.11648/j.bsi.20220704.11 |
| Page(s) | 60-68 |
| 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 |
Malaria in Pregnancy, Generalized Autoregressive Conditional Heteroskedasticity Model, Akaike Information Criterion (AIC), Forecasts
| [1] | Menda DM, Nawa M, Zimba RK, Mulikita CM, Mwandia J, Mwaba H, & Sichinga K (2021). Forecasting Confirmed Malaria Cases in Northwestern Province of Zambia: A Time Series Analysis Using 2014–2020 Routine Data. Advances in Public Health. Article ID 6522352, https://doi.org/10.1155/2021/6522352 |
| [2] | Bernard, J., Brabin, B. Marian, W., & Jenny, H. (2008). Monitoring and evaluation of malaria in pregnancy and developing a rational basis for control. Malaria journal, (10), 2875-2877. |
| [3] | World Health Organization (2003). ITS Informal Consultation on Recent Advances in Diagnostic Techniques and Vaccines for Malaria. A rapid dipstick antigen capture assay for the diagnosis of falciparum malaria. Bull World Health Organ. 74, 47–54. |
| [4] | WHO 2020, 20 Years of Global Progress and Challenges World Health Organization, Geneva, 2020. |
| [5] | Agu, A. P., & Nwojiji, J. O. (2005). Childhood malaria: mothers' perception and treatment seeking behaviour in a community in Ebonyi State, South East Nigeria. Journal of Community Medicine and Primary Health Care, 17 (1), 45-50. |
| [6] | Ayanda, O. (2009). Relative abundance of adult female anophelines mosquitoes in Ugah, Nasarawa State, Nigeria. Journal of Parasitology and Vector Biology, 1 (1), 005-008. |
| [7] | World Health Organisation (2019). World Malaria Report 2019. |
| [8] | National Malaria Indicator Survey (NMIS), 2015; p 96 & 99. |
| [9] | Gontie GB, Wolde HF & Baraki AG (2020). Prevalence and associated factors of malaria among pregnant women in Sherkole district, Benishangul Gumuz regional state, West Ethiopia. BMC Infectious Diseases, 20: 573 https://doi.org/10.1186/s12879-020-05289-9 |
| [10] | Simon-Oke IA, Ogunseem M, Afolabi O, Awosolu O (2019). Prevalence of Malaria Parasites among Pregnant Women and Children under Five years in Ekiti State, Southwest Nigeria. Journal of Biomedicine and Translational Research; 5 (1): 5-10. https://doi.org/10.14710/jbtr.v5i1.3711. |
| [11] | Habibu, K., & Sharaihu, S. (2021). Prevalence of Malaria Parasite Infections in Pregnant and Non-pregnant Women Attending Uduth and FMC Gusau in Northern Nigeria. Bakolori Journal of General Studies, 12, 3440-3456. |
| [12] | Fievet, N., Cot, M., Ringwald, P., Bickii, J., Dubois, B., & Le Hesran, J. (1997). Immune response to Plasmodium falciparum antigens in Cameroonian primigravidae: evolution after delivery and during second pregnancy. Clin. Exp. Immunol., 107 (3) 462–7. |
| [13] | Bakika, M. (1994). Management of malaria within households in Mpigi district, women behaviour, attitudes and practice in initial management of malaria. Trans. R. Soc. Trop. Med. Hyg., (87), 648–654. |
| [14] | Greenwood, B. M., Bojang, K., Whitty, C., & Targett, G. (2007). Malaria in pregnancy. Lancet, 365 (9469), 1474-1480. |
| [15] | Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, (31), 307-327. |
APA Style
Lucky Wobodo Alerechi, Anthony Ike Wegbom, Clement Kevin Edet, Emmanuel Oyinebifun Biu. (2022). Forecasting Malaria Cases Among Pregnant Women in Rivers State Nigeria: A GARCH Time Series Analysis Modelling Using Reported Data. Biomedical Statistics and Informatics, 7(4), 60-68. https://doi.org/10.11648/j.bsi.20220704.11
ACS Style
Lucky Wobodo Alerechi; Anthony Ike Wegbom; Clement Kevin Edet; Emmanuel Oyinebifun Biu. Forecasting Malaria Cases Among Pregnant Women in Rivers State Nigeria: A GARCH Time Series Analysis Modelling Using Reported Data. Biomed. Stat. Inform. 2022, 7(4), 60-68. doi: 10.11648/j.bsi.20220704.11
AMA Style
Lucky Wobodo Alerechi, Anthony Ike Wegbom, Clement Kevin Edet, Emmanuel Oyinebifun Biu. Forecasting Malaria Cases Among Pregnant Women in Rivers State Nigeria: A GARCH Time Series Analysis Modelling Using Reported Data. Biomed Stat Inform. 2022;7(4):60-68. doi: 10.11648/j.bsi.20220704.11
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Download@article{10.11648/j.bsi.20220704.11,
author = {Lucky Wobodo Alerechi and Anthony Ike Wegbom and Clement Kevin Edet and Emmanuel Oyinebifun Biu},
title = {Forecasting Malaria Cases Among Pregnant Women in Rivers State Nigeria: A GARCH Time Series Analysis Modelling Using Reported Data},
journal = {Biomedical Statistics and Informatics},
volume = {7},
number = {4},
pages = {60-68},
doi = {10.11648/j.bsi.20220704.11},
url = {https://doi.org/10.11648/j.bsi.20220704.11},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.bsi.20220704.11},
abstract = {Background: Malaria in pregnancy is considered a significant public health problem, for which over 25 million are at risk of the infection each year in sub-Sahara Africa including Nigeria. This is despite interventions and improvement in the diagnosis, and treatment techniques. This study, therefore, forecasted cases of malaria in pregnancy in Rivers State Nigeria in 2021 to 2024. Methods: The total number of reported malaria-in-pregnancy (MIP) cases from 2003 to 2020 was extracted from National Bureau of Statistics (NBS) database. Descriptive statistics was obtained for the series plots, monthly mean plot, and yearly mean plot. The Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model was used to forecast the monthly number of MIP for 2021 to 2024. Results: The time series plot showed that there was a high volatility in the year 2020 in the malaria data. Also, the result shown that GARCH (0,1) and GARCH (1,1) parameters were all significant at 5% significance level. GARCH (1,1) model have least AIC value and log likelihood ratio among the several models. The study revealed an increasing trend in the number of MIP from 2021 to 2024. Conclusion: The study showed an expected increase for the forecasted period. The forecasted malaria cases will help Government and its health agencies, and critical stakeholders to plan and implement interventions to prevent the disease and mitigate its negative effects on mothers and fetus.},
year = {2022}
}
TY - JOUR T1 - Forecasting Malaria Cases Among Pregnant Women in Rivers State Nigeria: A GARCH Time Series Analysis Modelling Using Reported Data AU - Lucky Wobodo Alerechi AU - Anthony Ike Wegbom AU - Clement Kevin Edet AU - Emmanuel Oyinebifun Biu Y1 - 2022/12/15 PY - 2022 N1 - https://doi.org/10.11648/j.bsi.20220704.11 DO - 10.11648/j.bsi.20220704.11 T2 - Biomedical Statistics and Informatics JF - Biomedical Statistics and Informatics JO - Biomedical Statistics and Informatics SP - 60 EP - 68 PB - Science Publishing Group SN - 2578-8728 UR - https://doi.org/10.11648/j.bsi.20220704.11 AB - Background: Malaria in pregnancy is considered a significant public health problem, for which over 25 million are at risk of the infection each year in sub-Sahara Africa including Nigeria. This is despite interventions and improvement in the diagnosis, and treatment techniques. This study, therefore, forecasted cases of malaria in pregnancy in Rivers State Nigeria in 2021 to 2024. Methods: The total number of reported malaria-in-pregnancy (MIP) cases from 2003 to 2020 was extracted from National Bureau of Statistics (NBS) database. Descriptive statistics was obtained for the series plots, monthly mean plot, and yearly mean plot. The Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model was used to forecast the monthly number of MIP for 2021 to 2024. Results: The time series plot showed that there was a high volatility in the year 2020 in the malaria data. Also, the result shown that GARCH (0,1) and GARCH (1,1) parameters were all significant at 5% significance level. GARCH (1,1) model have least AIC value and log likelihood ratio among the several models. The study revealed an increasing trend in the number of MIP from 2021 to 2024. Conclusion: The study showed an expected increase for the forecasted period. The forecasted malaria cases will help Government and its health agencies, and critical stakeholders to plan and implement interventions to prevent the disease and mitigate its negative effects on mothers and fetus. VL - 7 IS - 4 ER -
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