Forecasting birth trends in Ethiopia using time-series and machine-learning models: a secondary data analysis of EDHS surveys (2000–2019)
Objective Ethiopia, the second most populous country in Africa, faces significant demographic transitions, with fertility rates playing a central role in shaping economic and healthcare policies. Family planning programmes face challenges due to funding limitations. The recent suspension of the US A...
Saved in:
| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMJ Publishing Group
2025-07-01
|
| Series: | BMJ Open |
| Online Access: | https://bmjopen.bmj.com/content/15/7/e101006.full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850101276179693568 |
|---|---|
| author | Daniel Adane Assefa Andargie Kassa Berhanu Abebaw Mekonnen Rahel Mulatie Anteneh Chalachew Yenew Meron Asmamaw Alemayehu Tilahun Degu Tsega Sintayehu Simie Tsega Almaw Genet Yeshiwas Abathun Temesegen Habitamu Mekonen Amare Genetu Ejigu Abraham Teym Gashaw Melkie Bayeh Getaneh Atikilit Tesfaneh Shimels Wolde Melese Ayele Ahmed Fentaw Ahmed Birhanemaskal Malkamu Wondimnew Desalegn Addis Getasew Yirdaw Chalachew Abiyu Ayalew Kalaab Esubalew Zeamanuel Anteneh Yigzaw |
| author_facet | Daniel Adane Assefa Andargie Kassa Berhanu Abebaw Mekonnen Rahel Mulatie Anteneh Chalachew Yenew Meron Asmamaw Alemayehu Tilahun Degu Tsega Sintayehu Simie Tsega Almaw Genet Yeshiwas Abathun Temesegen Habitamu Mekonen Amare Genetu Ejigu Abraham Teym Gashaw Melkie Bayeh Getaneh Atikilit Tesfaneh Shimels Wolde Melese Ayele Ahmed Fentaw Ahmed Birhanemaskal Malkamu Wondimnew Desalegn Addis Getasew Yirdaw Chalachew Abiyu Ayalew Kalaab Esubalew Zeamanuel Anteneh Yigzaw |
| author_sort | Daniel Adane |
| collection | DOAJ |
| description | Objective Ethiopia, the second most populous country in Africa, faces significant demographic transitions, with fertility rates playing a central role in shaping economic and healthcare policies. Family planning programmes face challenges due to funding limitations. The recent suspension of the US Agency for International Development funding exacerbates these issues, highlighting the need for accurate birth forecasting to guide policy and resource allocation. This study applied time-series and advanced machine-learning models to forecast future birth trends in Ethiopia.Design Secondary data from the Ethiopian Demographic and Health Survey from 2000 to 2019 were used. After data preprocessing steps, including data conversion, filtering, aggregation and transformation, stationarity was checked using the Augmented Dickey-Fuller (ADF) test. Time-series decomposition was then performed, followed by time-series splitting. Seven forecasting models, including Autoregressive Integrated Moving Average, Prophet, Generalised Linear Models with Elastic Net Regularisation (GLMNET), Random Forest and Prophet-XGBoost, were built and compared. The models’ performance was evaluated using key metrics such as root mean square error (RMSE), mean absolute error (MAE) and R-squared value.Results GLMNET emerged as the best model, explaining 77% of the variance with an RMSE of 119.01. Prophet-XGBoost performed reasonably well but struggled to capture the full complexity of the data, with a lower R-squared value of 0.32 and an RMSE of 146.87. Forecasts were made for both average monthly births and average births per woman over a 10-year horizon (2025–2034). The forecast for average monthly births indicated a gradual decline over the projection period. Meanwhile, the average births per woman showed an increasing trend but fluctuated over time, influenced by demographic shifts such as changes in fertility preferences, age structure and migration patterns.Conclusions This study demonstrates the effectiveness of combining time-series models and machine learning, with GLMNET and Prophet XGBoost emerging as the most effective. While average monthly births are expected to decline due to demographic transitions and migration, the average births per woman will remain high, reflecting persistent fertility preferences within certain subpopulations. These findings underscore the need for policies addressing both population trends and sociocultural factors. |
| format | Article |
| id | doaj-art-62dcb320362c45e5bb8f3a90aa59aef5 |
| institution | DOAJ |
| issn | 2044-6055 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | BMJ Publishing Group |
| record_format | Article |
| series | BMJ Open |
| spelling | doaj-art-62dcb320362c45e5bb8f3a90aa59aef52025-08-20T02:40:04ZengBMJ Publishing GroupBMJ Open2044-60552025-07-0115710.1136/bmjopen-2025-101006Forecasting birth trends in Ethiopia using time-series and machine-learning models: a secondary data analysis of EDHS surveys (2000–2019)Daniel Adane0Assefa Andargie Kassa1Berhanu Abebaw Mekonnen2Rahel Mulatie Anteneh3Chalachew Yenew4Meron Asmamaw Alemayehu5Tilahun Degu Tsega6Sintayehu Simie Tsega7Almaw Genet Yeshiwas8Abathun Temesegen9Habitamu Mekonen10Amare Genetu Ejigu11Abraham Teym12Gashaw Melkie Bayeh13Getaneh Atikilit14Tesfaneh Shimels15Wolde Melese Ayele16Ahmed Fentaw Ahmed17Birhanemaskal Malkamu18Wondimnew Desalegn Addis19Getasew Yirdaw20Chalachew Abiyu Ayalew21Kalaab Esubalew22Zeamanuel Anteneh Yigzaw23Department of Midwifery, College of Medicine and Health Sciences, Injibara University, Injibara, EthiopiaDepartment of Public Health, College of Medicine and Health Sciences, Injibara University, Injibara, EthiopiaDepartment of Nutrition and Dietetics, School of Public Health, College of Medicine and Health Sciences, Bahir Dar University, Bahir Dar, Amhara, EthiopiaDepatment of Public Health, College of Health Science, Debre Tabor University, Debre Tabor, EthiopiaDepartment of Environmental Health Sciences, Public Health, Debre Tabor University, Debre Tabor, EthiopiaDepartment of Epidemiology and Biostatistics, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, Amhara, EthiopiaDepartment of Public Health, College of Medicine and Health Sciences, Injibara University, Injibara, EthiopiaDepartment of Medical Nursing, School of Nursing, College of Medicine and Health Science, University of Gondar, Gondar, EthiopiaDepartment of Environmental Health, College of Medicine and Health Sciences, Injibara University, Injibara, EthiopiaDepartment of Environmental Health, College of Medicine and Health Sciences, Injibara University, Injibara, EthiopiaDepartment of Human Nutrition, Debre Markos University, Debre Markos, Amhara, EthiopiaDepartment of Midwifery, College of Medicine and Health Sciences, Injibara University, Injibara, EthiopiaDepartment of Environmental Health, College of Health Sciences, Debre Markos University, Debre Markos, EthiopiaDepartment of Environmental Health, College of Medicine and Health Sciences, Injibara University, Injibara, EthiopiaDepatment of Public Health, College of Health Science, Debre Tabor University, Debre Tabor, EthiopiaDepatment of Public Health, College of Health Science, Debre Tabor University, Debre Tabor, EthiopiaDepartment of Public Health, College of Medicine and Health Sciences, Injibara University, Injibara, EthiopiaDepartment of Public Health, College of Medicine and Health Sciences, Injibara University, Injibara, EthiopiaDepartment of Medical Laboratory Sciences, College of Health Sciences, Debre Tabor University, Debre Tabor, EthiopiaDepatment of Public Health, College of Health Science, Debre Tabor University, Debre Tabor, EthiopiaDepartment of Environmental Health, Debre Markos University, Debre Markos, EthiopiaDepartment of Public Health, College of Medicine and Health Sciences, Injibara University, Injibara, EthiopiaDepartment of Internal Medicine, School of Medicine, College of Medicine and Health Sciences, Injibara University, Injibara, EthiopiaDepartment of Health Promotion and Behavioral Sciences, School of Public Health, College of Medicine and Health Sciences, Bahir Dar University, Bahir Dar, EthiopiaObjective Ethiopia, the second most populous country in Africa, faces significant demographic transitions, with fertility rates playing a central role in shaping economic and healthcare policies. Family planning programmes face challenges due to funding limitations. The recent suspension of the US Agency for International Development funding exacerbates these issues, highlighting the need for accurate birth forecasting to guide policy and resource allocation. This study applied time-series and advanced machine-learning models to forecast future birth trends in Ethiopia.Design Secondary data from the Ethiopian Demographic and Health Survey from 2000 to 2019 were used. After data preprocessing steps, including data conversion, filtering, aggregation and transformation, stationarity was checked using the Augmented Dickey-Fuller (ADF) test. Time-series decomposition was then performed, followed by time-series splitting. Seven forecasting models, including Autoregressive Integrated Moving Average, Prophet, Generalised Linear Models with Elastic Net Regularisation (GLMNET), Random Forest and Prophet-XGBoost, were built and compared. The models’ performance was evaluated using key metrics such as root mean square error (RMSE), mean absolute error (MAE) and R-squared value.Results GLMNET emerged as the best model, explaining 77% of the variance with an RMSE of 119.01. Prophet-XGBoost performed reasonably well but struggled to capture the full complexity of the data, with a lower R-squared value of 0.32 and an RMSE of 146.87. Forecasts were made for both average monthly births and average births per woman over a 10-year horizon (2025–2034). The forecast for average monthly births indicated a gradual decline over the projection period. Meanwhile, the average births per woman showed an increasing trend but fluctuated over time, influenced by demographic shifts such as changes in fertility preferences, age structure and migration patterns.Conclusions This study demonstrates the effectiveness of combining time-series models and machine learning, with GLMNET and Prophet XGBoost emerging as the most effective. While average monthly births are expected to decline due to demographic transitions and migration, the average births per woman will remain high, reflecting persistent fertility preferences within certain subpopulations. These findings underscore the need for policies addressing both population trends and sociocultural factors.https://bmjopen.bmj.com/content/15/7/e101006.full |
| spellingShingle | Daniel Adane Assefa Andargie Kassa Berhanu Abebaw Mekonnen Rahel Mulatie Anteneh Chalachew Yenew Meron Asmamaw Alemayehu Tilahun Degu Tsega Sintayehu Simie Tsega Almaw Genet Yeshiwas Abathun Temesegen Habitamu Mekonen Amare Genetu Ejigu Abraham Teym Gashaw Melkie Bayeh Getaneh Atikilit Tesfaneh Shimels Wolde Melese Ayele Ahmed Fentaw Ahmed Birhanemaskal Malkamu Wondimnew Desalegn Addis Getasew Yirdaw Chalachew Abiyu Ayalew Kalaab Esubalew Zeamanuel Anteneh Yigzaw Forecasting birth trends in Ethiopia using time-series and machine-learning models: a secondary data analysis of EDHS surveys (2000–2019) BMJ Open |
| title | Forecasting birth trends in Ethiopia using time-series and machine-learning models: a secondary data analysis of EDHS surveys (2000–2019) |
| title_full | Forecasting birth trends in Ethiopia using time-series and machine-learning models: a secondary data analysis of EDHS surveys (2000–2019) |
| title_fullStr | Forecasting birth trends in Ethiopia using time-series and machine-learning models: a secondary data analysis of EDHS surveys (2000–2019) |
| title_full_unstemmed | Forecasting birth trends in Ethiopia using time-series and machine-learning models: a secondary data analysis of EDHS surveys (2000–2019) |
| title_short | Forecasting birth trends in Ethiopia using time-series and machine-learning models: a secondary data analysis of EDHS surveys (2000–2019) |
| title_sort | forecasting birth trends in ethiopia using time series and machine learning models a secondary data analysis of edhs surveys 2000 2019 |
| url | https://bmjopen.bmj.com/content/15/7/e101006.full |
| work_keys_str_mv | AT danieladane forecastingbirthtrendsinethiopiausingtimeseriesandmachinelearningmodelsasecondarydataanalysisofedhssurveys20002019 AT assefaandargiekassa forecastingbirthtrendsinethiopiausingtimeseriesandmachinelearningmodelsasecondarydataanalysisofedhssurveys20002019 AT berhanuabebawmekonnen forecastingbirthtrendsinethiopiausingtimeseriesandmachinelearningmodelsasecondarydataanalysisofedhssurveys20002019 AT rahelmulatieanteneh forecastingbirthtrendsinethiopiausingtimeseriesandmachinelearningmodelsasecondarydataanalysisofedhssurveys20002019 AT chalachewyenew forecastingbirthtrendsinethiopiausingtimeseriesandmachinelearningmodelsasecondarydataanalysisofedhssurveys20002019 AT meronasmamawalemayehu forecastingbirthtrendsinethiopiausingtimeseriesandmachinelearningmodelsasecondarydataanalysisofedhssurveys20002019 AT tilahundegutsega forecastingbirthtrendsinethiopiausingtimeseriesandmachinelearningmodelsasecondarydataanalysisofedhssurveys20002019 AT sintayehusimietsega forecastingbirthtrendsinethiopiausingtimeseriesandmachinelearningmodelsasecondarydataanalysisofedhssurveys20002019 AT almawgenetyeshiwas forecastingbirthtrendsinethiopiausingtimeseriesandmachinelearningmodelsasecondarydataanalysisofedhssurveys20002019 AT abathuntemesegen forecastingbirthtrendsinethiopiausingtimeseriesandmachinelearningmodelsasecondarydataanalysisofedhssurveys20002019 AT habitamumekonen forecastingbirthtrendsinethiopiausingtimeseriesandmachinelearningmodelsasecondarydataanalysisofedhssurveys20002019 AT amaregenetuejigu forecastingbirthtrendsinethiopiausingtimeseriesandmachinelearningmodelsasecondarydataanalysisofedhssurveys20002019 AT abrahamteym forecastingbirthtrendsinethiopiausingtimeseriesandmachinelearningmodelsasecondarydataanalysisofedhssurveys20002019 AT gashawmelkiebayeh forecastingbirthtrendsinethiopiausingtimeseriesandmachinelearningmodelsasecondarydataanalysisofedhssurveys20002019 AT getanehatikilit forecastingbirthtrendsinethiopiausingtimeseriesandmachinelearningmodelsasecondarydataanalysisofedhssurveys20002019 AT tesfanehshimels forecastingbirthtrendsinethiopiausingtimeseriesandmachinelearningmodelsasecondarydataanalysisofedhssurveys20002019 AT woldemeleseayele forecastingbirthtrendsinethiopiausingtimeseriesandmachinelearningmodelsasecondarydataanalysisofedhssurveys20002019 AT ahmedfentawahmed forecastingbirthtrendsinethiopiausingtimeseriesandmachinelearningmodelsasecondarydataanalysisofedhssurveys20002019 AT birhanemaskalmalkamu forecastingbirthtrendsinethiopiausingtimeseriesandmachinelearningmodelsasecondarydataanalysisofedhssurveys20002019 AT wondimnewdesalegnaddis forecastingbirthtrendsinethiopiausingtimeseriesandmachinelearningmodelsasecondarydataanalysisofedhssurveys20002019 AT getasewyirdaw forecastingbirthtrendsinethiopiausingtimeseriesandmachinelearningmodelsasecondarydataanalysisofedhssurveys20002019 AT chalachewabiyuayalew forecastingbirthtrendsinethiopiausingtimeseriesandmachinelearningmodelsasecondarydataanalysisofedhssurveys20002019 AT kalaabesubalew forecastingbirthtrendsinethiopiausingtimeseriesandmachinelearningmodelsasecondarydataanalysisofedhssurveys20002019 AT zeamanuelantenehyigzaw forecastingbirthtrendsinethiopiausingtimeseriesandmachinelearningmodelsasecondarydataanalysisofedhssurveys20002019 |