Forecasting under-five stunting in Ethiopia using classical and machine learning time series models

Rewina Tilahun Gessese; Jenberu Mekurianew Kelkay; Fetlework Gubena Arage; Tigist Kifle Tsegaw; Zinabu Bekele Tadese; Meron Asmamaw Alemayehu; Eliyas Addisu Taye; Eyob Akalewold Alemu

doi:10.1371/journal.pone.0345000

Peer Review History

Original SubmissionNovember 14, 2025
10 Feb 2026 Decision Letter - Zaher Mundher Yaseen, Editor Dear Dr. Gessese, Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. Please submit your revised manuscript by Mar 27 2026 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.. 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Have the authors made all data underlying the findings in their manuscript fully available??> The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.--> Reviewer #1: Yes Reviewer #2: Yes ****** 4. Is the manuscript presented in an intelligible fashion and written in standard English??> Reviewer #1: Yes Reviewer #2: Yes ****** Reviewer #1: The study addresses an important public health issue and demonstrates a sound methodological approach by using reliable secondary data and comparing classical and machine learning time series models; however, the manuscript would benefit from clearer reporting of time series stationarity diagnostics and a more detailed description of hyperparameter tuning for the machine learning models. In addition, the exclusive reliance on annual data and the absence of explanatory socioeconomic or contextual variables limit the analytical depth of the forecasts. The superior performance of the ETS model compared to machine learning approaches warrants further methodological discussion, and the conclusions could be strengthened by more specific, results-driven policy recommendations. These limitations are primarily developmental and do not detract from the overall scientific merit of the study. Reviewer #2: Well-organized paper. The motivation behind this paper is good. I didn't see any major issue regarding the methods or the data analysis. Data analysis was reasonably thorough. The authors performed necessary preprocessing. Both the statistical and the deep learning methods used in this work are known to perform well for time series data. The authors also took care of the issues, e.g., overfitting, that arise when one trains a machine learning model, and provided the details. Presentation of the results was good. Minor editing issues, e.g., space between a wort and punctuation, etc., exist. Another proof-read should be conducted to correct these issues. ****** what does this mean?). If published, this will include your full peer review and any attached files.). If published, this will include your full peer review and any attached files.). If published, this will include your full peer review and any attached files.). If published, this will include your full peer review and any attached files. If you choose “no”, your identity will remain anonymous but your review may still be made public. Do you want your identity to be public for this peer review? 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Revision 1
18 Feb 2026 Author Response Comment / Requirement Response / Revision Reviewer #1: The manuscript would benefit from clearer reporting of time series stationarity diagnostics. We have added a detailed description of the stationarity assessment in the Methods section. Visual inspection of rolling statistics (mean and standard deviation) and formal Augmented Dickey-Fuller (ADF) tests are now reported. The original series was non-stationary (ADF p = 0.62), and stationarity was achieved after first differencing (ADF p < 0.001). Figures illustrating these checks are included as Figure 2 and 3. Reviewer #1: A more detailed description of hyperparameter tuning for the machine learning models is needed. The Methods section has been expanded. For MLP, hyperparameters (hidden layers, activation functions, learning rates, batch sizes, L2 regularization, max iterations) were tuned via randomized search with early stopping. For LSTM, hyperparameters (units, dropout rate, learning rate, batch size, number of epochs) were tuned via manual random search with early stopping and learning rate reduction callbacks. The final selected models and rationale for shallow architectures are explicitly described. Reviewer #1: Exclusive reliance on annual data and absence of explanatory socioeconomic/contextual variables limit analytical depth. We acknowledge this limitation. The Discussion now explicitly notes that forecasts are based on national-level annual data without socioeconomic or contextual covariates, which may limit the ability to account for drivers of stunting trends. Additionally, national-level data may obscure important regional or sub-population disparities. Reviewer #1: The superior performance of the ETS model compared to machine learning approaches warrants further methodological discussion. We added a detailed explanation of why ETS outperformed the machine learning models: 1) Small sample size increases overfitting risk for complex models, whereas ETS’s parsimonious structure provides stable forecasts. 2) ETS is well-suited for univariate forecasting and does not require covariates. 3) Smooth trends in the annual stunting series favor classical exponential smoothing methods. Relevant references have been included. Reviewer #1: Conclusions could be strengthened by more specific, results-driven policy recommendations. We revised the Conclusion section to include results-driven recommendations based on our projections: the projected under-five stunting prevalence (~32% by 2030) exceeds Ethiopia’s national target (19%) and the SDG 2.2 goal. We recommend focused research to identify factors driving high stunting prevalence and evidence-based, context-specific interventions to support achievement of national and global targets. Reviewer #2: Well-organized paper; motivation is good; methods and analysis are thorough; minor editing issues (spacing, punctuation, etc.). We thank the reviewer for the positive feedback. We have conducted an additional thorough proof-read of the manuscript to correct spacing, punctuation, and other typographical errors. Editor: Ensure manuscript meets PLOS ONE style requirements, including file naming. The manuscript has been reformatted according to the PLOS ONE style templates for both main body and title/authors/affiliations. All file names now comply with the journal guidelines. Editor: Provide repository name and/or DOI/accession number or direct link in Data Availability Statement. The Data Availability Statement has been updated to include the WHO Global Health Observatory repository and the direct link for the under-five stunting prevalence data: https://data.who.int/indicators/i/5F8A486?utm_source Editor: Include captions for Supporting Information files and update in-text citations. Captions for all Supporting Information files have been added at the end of the manuscript, and all in-text citations have been updated accordingly. Editor: Review reviewer-recommended citations for relevance. No additional citations were recommended by the reviewers. Editor: Review and ensure references are complete and correct; remove or indicate retracted articles. The reference list has been thoroughly reviewed. Any retracted articles have been removed or replaced, and all references are complete and formatted according to PLOS ONE guidelines. Attachments Attachment Submitted filename: Response to Reviewers.docx https://doi.org/10.1371/journal.pone.0345000.r002
2 Mar 2026 Decision Letter - Zaher Mundher Yaseen, Editor <p>Forecasting Under-Five Stunting in Ethiopia Using Classical and Machine Learning Time Series Models PONE-D-25-57738R1 Dear Dr. Gessese, We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements. Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication. An invoice will be generated when your article is formally accepted. Please note, if your institution has a publishing partnership with PLOS and your article meets the relevant criteria, all or part of your publication costs will be covered. 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The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.--> Reviewer #1: Yes Reviewer #2: Yes ****** 5. Is the manuscript presented in an intelligible fashion and written in standard English??> Reviewer #1: Yes Reviewer #2: Yes ****** Reviewer #1: All comments have been addressed. No further correction needed at this stage. Congratulations for the authors Reviewer #2: I didn't have any major concerns. The minor issues I mentioned have been addressed. This is an well-motivated and well-organized paper. ****** what does this mean?). If published, this will include your full peer review and any attached files.). If published, this will include your full peer review and any attached files.). If published, this will include your full peer review and any attached files.). If published, this will include your full peer review and any attached files. 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Formally Accepted
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