Forecasting Thailand’s mobility trends using Feature Engineered XGBoost for pandemic crisis movement management

Aritath Siraphatwongkorn; Thanin Methiyothin; Kittisak Onuean; Krisana Chinnasarn; Athita Onuean; Insung Ahn; Suwanna Rasmequan

doi:10.1371/journal.pone.0345547

Peer Review History

Original SubmissionDecember 1, 2025
28 Jan 2026 Decision Letter - Matthew Chin Heng Chua, Editor Dear Dr. Rasmequan, 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 14 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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The use of three distinct, high-quality datasets—the Google Community Mobility Reports, the Oxford Stringency Index, and JHU-CSSE epidemiological data—provides a comprehensive view of Thailand's pandemic dynamics. Furthermore, integrating these via a "Date" primary key ensures temporal consistency. Regarding the model validation, the paper employs a 90:10 train-test split. While the authors justify this as necessary to capture mobility patterns during rapid policy changes, a fixed split in time-series data is often problematic. I recommend implementing time-series cross-validation (such as a rolling origin approach) to demonstrate model stability across different pandemic waves. Comparing a traditional statistical model (ARIMA), a trend-based model (Prophet), and a machine learning model (XGBoost) provides a robust benchmark. However, the nomenclature "X-XGBoost" requires clarification. In machine learning literature, "Extended" often implies an algorithmic modification. The authors should specify whether "X-XGBoost" involves an architectural change or simply refers to a standard XGBRegressor with a specific feature set. If the model relies solely on feature engineering, renaming it "Feature-Engineered XGBoost" would prevent confusion regarding potential algorithmic extensions. While the methods effectively tie to the results, some gaps remain. For instance, although "feature importance" (weight and gain) is mentioned in the methods, these results are only briefly summarized. A detailed table or plot showing which features (e.g., Stringency Index vs. Day of Week) were most influential for each of the six mobility categories would better justify the "X-XGBoost" approach. Finally, to enhance transparency and reproducibility of the machine learning implementation, I strongly recommend that the authors provide the full code or scripts used for data preprocessing, model training, and evaluation. Making these scripts available—either as supplementary material or via a public repository (e.g., GitHub)—would allow other researchers to replicate the results and verify the methodology ****** 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 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? For information about this choice, including consent withdrawal, please see our For information about this choice, including consent withdrawal, please see our Privacy Policy .--> Reviewer #1: No ******** [NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". 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Revision 1
10 Feb 2026 Author Response Reviewers' comments 1. The methodology is well-structured and follows a logical progression from data acquisition to model evaluation. The use of three distinct, high-quality datasets—the Google Community Mobility Reports, the Oxford Stringency Index, and JHU-CSSE epidemiological data—provides a comprehensive view of Thailand's pandemic dynamics. Furthermore, integrating these via a "Date" primary key ensures temporal consistency. Regarding the model validation, the paper employs a 90:10 train-test split. While the authors justify this as necessary to capture mobility patterns during rapid policy changes, a fixed split in time-series data is often problematic. I recommend implementing time-series cross-validation (such as a rolling origin approach) to demonstrate model stability across different pandemic waves. Response : Thank you for your comment. For this comment, we have added a graph depicting the analysis of MAE of the validation results of each forecasting approach using a Rolling-Origin Evaluation (ROE) method in addition to a 90:10 train-test split. We have categorized the validation results into four event waves, as shown below. We hope this will help demonstrate the stability of our proposed method across different pandemic waves. ------------------------------------------------------------------------------------------------------------------------------------- In order to ensure the highest level of predictive accuracy and model stability, this study employed a dual-validation framework. As discussed earlier, the dataset was evaluated using a standard 90:10 training-to-testing ratio to establish baseline performance. Subsequently, a rigorous Rolling-Origin Evaluation (ROE) strategy was applied to all three forecasting models: ARIMA, Prophet, and Feature Engineered XGBoost (the proposed method). The ROE approach, as illustrated in Figure 7, utilizes an expanding window technique where the forecast origin shifts chronologically across four distinct pandemic phases in Thailand: the Wuhan (Wave 1), Alpha-Beta (Wave 2), Delta (Wave 3), and Omicron (Wave 4) waves. Figure 8. Comparison of MAE across 4 COVID-19-Waves using ARIMA, FB-PROPHET and XGBoost based on Rolling-Origin Evaluation Approach Figure 8 compares MAE values across six mobility categories, namely Retail and Recreation, Grocery and Pharmacy, Parks, Transit Stations, Workplaces, and Residential, over four COVID-19 waves in Thailand using ARIMA, FB-PROPHET and Xgboost based on Rolling-Origin Evaluation Approach. Overall, the Feature Engineered XGBoost model consistently achieves the lowest prediction errors across most mobility categories and pandemic phases, particularly for mobility types that are highly sensitive to policy interventions such as Transit Stations, Workplaces, and Residential areas. During the first wave (Wuhan), prediction errors remain relatively moderate across all models, reflecting more uniform mobility restrictions. More pronounced performance differences emerge in subsequent waves as mobility behavior becomes increasingly heterogeneous. In the Alpha-Beta and Delta waves, MAE values increase notably, especially for Parks and Workplaces, indicating heightened volatility and reduced predictability during periods of rapid and stringent policy changes. In contrast, the Omicron wave exhibits lower MAE values in several categories, suggesting partial behavioral adaptation and stabilization despite ongoing policy adjustments. ARIMA performs reasonably well in mobility categories with smoother trends but shows reduced accuracy during highly variable periods, while Prophet records the highest MAE in most categories, particularly for Parks and Workplaces. 2. Comparing a traditional statistical model (ARIMA), a trend-based model (Prophet), and a machine learning model (XGBoost) provides a robust benchmark. However, the nomenclature "X-XGBoost" requires clarification. In machine learning literature, "Extended" often implies an algorithmic modification. The authors should specify whether "X-XGBoost" involves an architectural change or simply refers to a standard XGBRegressor with a specific feature set. If the model relies solely on feature engineering, renaming it "Feature-Engineered XGBoost" would prevent confusion regarding potential algorithmic extensions. Response : Thank you for your comment. We have changed X-XGBoost to Feature Engineered XGBoost and also made change to our title too. 3. While the methods effectively tie to the results, some gaps remain. For instance, although "feature importance" (weight and gain) is mentioned in the methods, these results are only briefly summarized. A detailed table or plot showing which features (e.g., Stringency Index vs. Day of Week) were most influential for each of the six mobility categories would better justify the "X-XGBoost" approach. Response : Thank you for your comment. For this comment, we have added additional analysis using bar-graph to depict the influencing level to the XGBoost model of each feature important and also added the explanation as shown below. Hope, this could help reader to see the significant of each feature better. ------------------------------------------------------------------------------------------------------------------------------------- In addition to the tabular form of information about important features, we have also conducted an analysis using a bar graph, as illustrated in Figure 9, to depict the influencing level of each important feature. This graph shows the frequency of feature usage in trees (weight), the contribution to loss reduction (gain), and the relative number of samples affected (cover), as shown below. Figure 9. Feature importance analysis of the Feature Engineered XGBoost Approach Figure 9 illustrates the feature importance analysis of the Feature Engineered XGBoost model for population mobility forecasting across all mobility categories, including Retail and Recreation, Grocery and Pharmacy, Parks, Transit Stations, Workplaces, and Residential. The analysis is evaluated using three complementary importance metrics: Weight, Gain, and Cover, which respectively represent the frequency of feature usage in tree construction, the contribution of each feature to loss reduction, and the proportion of samples affected by feature-based splits. In the Weight dimension (top panel), which reflects how frequently features are selected during tree construction, avg_all_mobility and temporal features such as day, week, and month consistently exhibit the highest values across all mobility categories. This indicates that historical mobility patterns and temporal structures form the core foundation of the forecasting process. The StringencyIndex plays a secondary but notable role, particularly in policy-sensitive categories such as Workplaces and Transit Stations. In contrast, policy-specific variables such as lockdown_level and is_holiday are used less frequently, suggesting that they function primarily as complementary rather than dominant predictors. The Gain dimension (middle panel), which measures each feature’s contribution to reducing prediction error, reveals clear structural differences across mobility categories. The feature avg_all_mobility provides substantial gain for Retail and Recreation, Parks, and Transit Stations, while year shows a pronounced influence in Grocery and Pharmacy and Residential mobility, reflecting longer-term structural changes across pandemic periods. Additionally, is_holiday and lockdown_level demonstrate relatively high gain in specific categories such as Workplaces and Transit Stations, indicating strong but localized effects associated with policy interventions or special periods. In the Cover dimension (bottom panel), which represents the relative proportion of samples influenced by feature splits, temporal variables including avg_all_mobility, year, week, and day consistently affect a large share of observations across all mobility categories. The StringencyIndex and lockdown_level exhibit moderate coverage, particularly in work and travel related categories. Notably, day_of_week shows high coverage in Parks and Transit Stations, highlighting systematic differences in mobility behavior between weekdays and weekends. 4. Finally, to enhance transparency and reproducibility of the machine learning implementation, I strongly recommend that the authors provide the full code or scripts used for data preprocessing, model training, and evaluation. Making these scripts available—either as supplementary material or via a public repository (e.g., GitHub)—would allow other researchers to replicate the results and verify the methodology Response : Thank you for your comments, which help make our work beneficial to other researchers and increase the transparency and reproducibility of our work. In this regard, we have made our full code, data preprocessing, model training and evaluation available on GitHub at the following address: https://github.com/apisitgo/COVID19-Mobility-Forecasting-TH Attachments Attachment Submitted filename: Response to Reviewers.docx https://doi.org/10.1371/journal.pone.0345547.r002
2 Mar 2026 Decision Letter - Jie Zhang, Editor Dear Dr. Rasmequan, Please submit your revised manuscript by Apr 16 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. A letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'. A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'. 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[Note: HTML markup is below. Please do not edit.] Reviewers' comments: Reviewer's Responses to Questions Comments to the Author Reviewer #1: All comments have been addressed ******** 2. Is the manuscript technically sound, and do the data support the conclusions??> Reviewer #1: Yes ****** 3. Has the statistical analysis been performed appropriately and rigorously? -->?> Reviewer #1: Yes ****** 4. 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.--> Reviewer #1: Yes ****** 5. Is the manuscript presented in an intelligible fashion and written in standard English??> Reviewer #1: Yes ****** Reviewer #1: Overall, the revision substantially improves transparency and model comparison. However, several consistency fixes and editorial corrections are still needed before acceptance. For example: • Duplicate entry — Radečić (items 24 and 32) appears twice; please deduplicate. • Source quality — Replace non‑scholarly/tutorial sources (e.g., Medium, Analytics Vidhya) with primary documentation or peer‑reviewed publications wherever possible. ****** 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 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? For information about this choice, including consent withdrawal, please see our For information about this choice, including consent withdrawal, please see our Privacy Policy .--> Reviewer #1: No ******** [NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.] To ensure your figures meet our technical requirements, please review our figure guidelines: https://journals.plos.org/plosone/s/figures You may also use PLOS’s free figure tool, NAAS, to help you prepare publication quality figures: https://journals.plos.org/plosone/s/figures#loc-tools-for-figure-preparation. NAAS will assess whether your figures meet our technical requirements by comparing each figure against our figure specifications. https://doi.org/10.1371/journal.pone.0345547.r003
Revision 2
5 Mar 2026 Author Response Part 2 : Response to Reviewers: 1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation. Reviewer #1: All comments have been addressed Response : We appreciate the reviewer’s positive assessment. No changes were made to this section of the manuscript. 2. Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: Yes Response : We appreciate the reviewer’s positive assessment. No changes were made to this section of the manuscript. 3. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: Yes Response : We appreciate the reviewer’s positive assessment. No changes were made to this section of the manuscript. 4. 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. Reviewer #1: Yes Response : We appreciate the reviewer’s positive assessment. No changes were made to this section of the manuscript. 5. Is the manuscript presented in an intelligible fashion and written in standard English? PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here. Reviewer #1: Yes Response : We appreciate the reviewer’s positive assessment. No changes were made to this section of the manuscript. 6. Review Comments to the Author Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters) Reviewer #1: Overall, the revision substantially improves transparency and model comparison. However, several consistency fixes and editorial corrections are still needed before acceptance. For example: • Duplicate entry — Radečić (items 24 and 32) appears twice; please deduplicate. • Source quality — Replace non‑scholarly/tutorial sources (e.g., Medium, Analytics Vidhya) with primary documentation or peer‑reviewed publications wherever possible. Response : We appreciate the reviewer’s constructive comments. We carefully reviewed the reference list by updated them with scholarly source and removed those duplication references as follows: 1. To improve source quality, non-scholarly/tutorial sources were replaced with peer-reviewed primary references: 1.1 Reference [24] was replaced with: Bergmeir C, Hyndman RJ, Koo B. A note on the validity of cross-validation for evaluating autoregressive time series prediction. Comput Stat Data Anal. 2018;120:70–83. https://doi.org/10.1016/j.csda.2017.11.003 1.2 Reference [25] was replaced with: Chen T, Guestrin C. XGBoost: A scalable tree boosting system. Proc 22nd ACM SIGKDD Int Conf Knowledge Discovery and Data Mining. 2016:785–794. https://doi.org/10.1145/2939672.2939785 2. Those non-scholarly and duplicate references were removed: 2.1 Former Reference [32] has been removed. 2.2 Former Reference [33] has been removed 3. The in-text citations in Section 3 (Results and Discussion) were updated accordingly, with former references [32, 33] has been replaced with [24, 25]. 7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). 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? For information about this choice, including consent withdrawal, please see our Privacy Policy. Reviewer #1: No Response : Not applicable. This comment concerns reviewer privacy preferences and does not require manuscript modification. Attachments Attachment Submitted filename: Response to Reviewers_Forecasting_Thailand_6Mar2026.docx https://doi.org/10.1371/journal.pone.0345547.r004
9 Mar 2026 Decision Letter - Jie Zhang, Editor Forecasting Thailand’s mobility trends using Feature Engineered XGBoost for pandemic crisis movement management PONE-D-25-63776R2 Dear Dr. Rasmequan, 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. Please make sure your user information is up-to-date by logging into Editorial Manager at Editorial Manager® and clicking the ‘Update My Information' link at the top of the page. For questions related to billing, please contact and clicking the ‘Update My Information' link at the top of the page. For questions related to billing, please contact billing support .. If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org. Kind regards, Jie Zhang Academic Editor PLOS One Additional Editor Comments (optional): Reviewers' comments: https://doi.org/10.1371/journal.pone.0345547.r005
Formally Accepted
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