Artificial neural networks for short-term forecasting of cases, deaths, and hospital beds occupancy in the COVID-19 pandemic at the Brazilian Amazon

Marcus de Barros Braga; Rafael da Silva Fernandes; Gilberto Nerino de Souza Jr; Jonas Elias Castro da Rocha; Cícero Jorge Fonseca Dolácio; Ivaldo da Silva Tavares Jr; Raphael Rodrigues Pinheiro; Fernando Napoleão Noronha; Luana Lorena Silva Rodrigues; Rommel Thiago Jucá Ramos; Adriana Ribeiro Carneiro; Silvana Rossy de Brito; Hugo Alex Carneiro Diniz; Marcel do Nascimento Botelho; Antonio Carlos Rosário Vallinoto

doi:10.1371/journal.pone.0248161

Peer Review History

Original SubmissionDecember 10, 2020
12 Jan 2021 Decision Letter - Qiang Zeng, Editor PONE-D-20-38808 Artificial neural networks for short-term forecasting of cases, deaths, and hospital beds occupancy in the COVID-19 pandemic at the Brazilian Amazon PLOS ONE Dear Dr. Braga, 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 Feb 26 2021 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. 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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 Reviewer #2: Yes ******** 2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: N/A Reviewer #2: Yes ****** 3. 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 Reviewer #2: Yes ****** 4. 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 Reviewer #2: Yes ****** 5. 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: This study applies ANNs for short-term forecasting of cases, deaths, and hospital beds occupancy in the COVID-19 pandemic at the Brazilian Amazon. The research topic is worth of investigation. The methods sound reasonable. Some minor revisions are needed before its publication in PLOS ONE. Specifically, More illustration on the strength of ANNs are expected in the Introduction section. Additionally, more references on the application of ANNs and the used criteria for measuring goodness-of-fit should be acknowledged, such as: Modeling nonlinear relationship between crash frequency by severity and contributing factors by neural networks. Analytic Methods in Accident Research, 2016, 10: 12-25. Macro and micro models for zonal crash prediction with application in hot zones identification. Journal of Transport Geography, 2016, 54: 248-256. Bayesian spatial-temporal model for the main and interaction effects of roadway and weather characteristics on freeway crash incidence. Accident Analysis and Prevention, 2019, 132, 105249. In the ANN modeling section, a figure showing how the output and inputs connected would present the model more explicitly. The Broyden-Fletcher-Goldfarb-Shanno iterative algorithm used for ANN training is rare in the previous studies. A detailed description of this algorithm is anticipated. It would be interesting and further demonstrate the advantages of ANNs to compare their performance with that of time series models that are used in the previous studies on forecasting COVID-19 cases. Reviewer #2: This study presents an approach based on artificial neural networks for the daily and cumulative forecasts of cases and deaths caused by COVID-19, and the forecast of demand for hospital beds. The topic is important. The methods sound. The results are meaningful. It is suggested to be published. ****** 6. 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 Reviewer #2: 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
8 Feb 2021 Author Response Response to Reviewers Journal Requirements: 1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf RESPONSE: We have reviewed and edited the manuscript according to the journal's recommendations. 2. We suggest you thoroughly copyedit your manuscript for language usage, spelling, and grammar. If you do not know anyone who can help you do this, you may wish to consider employing a professional scientific editing service. RESPONSE: According to reviewers the manuscript was presented in an intelligible fashion and written in standard English. We have submitted the manuscript to an official translation service at the Federal University of Bahia, Brazil. We will send the translation declaration as an attached file. 3. We note that Figure 1 in your submission contain map images which may be copyrighted. All PLOS content is published under the Creative Commons Attribution License (CC BY 4.0), which means that the manuscript, images, and Supporting Information files will be freely available online, and any third party is permitted to access, download, copy, distribute, and use these materials in any way, even commercially, with proper attribution. For these reasons, we cannot publish previously copyrighted maps or satellite images created using proprietary data, such as Google software (Google Maps, Street View, and Earth). For more information, see our copyright guidelines: http://journals.plos.org/plosone/s/licenses-and-copyright. We require you to either (1) present written permission from the copyright holder to publish these figures specifically under the CC BY 4.0 license, or (2) remove the figures from your submission: RESPONSE: Thank you for the careful revision, but the Figure 1 was created by the manuscript's authors and they own its use rights. Review Comments to the Author - Reviewer #1: 1 - More illustration on the strength of ANNs are expected in the Introduction section RESPONSE: Thank you for the suggestion. We have added in the introduction section the following text (line 70), reinforcing the strength of the ANNs: “The main characteristic of ANN is self-learning without prior knowledge of the complex non-linear relationships that exist between the input and output variables [22]. This is due to the massive and parallel processing of neurons and the tolerance to noise [23]. In addition, this technique captures small distortions in the observed data and transfers them for projections differently from mechanistic models [24]. Another advantage is that this type of approach also makes it possible to use several predictor variables simultaneously, such as demographic data and incidence curves, which helps in capturing the dynamics of virus transmission in the cities over time [25,26].” 2 - Additionally, more references on the application of ANNs and the used criteria for measuring goodness-of-fit should be acknowledged, such as: Modeling nonlinear relationship between crash frequency by severity and contributing factors by neural networks. Analytic Methods in Accident Research, 2016, 10: 12-25. Macro and micro models for zonal crash prediction with application in hot zones identification. Journal of Transport Geography, 2016, 54: 248-256. Bayesian spatial-temporal model for the main and interaction effects of roadway and weather characteristics on freeway crash incidence. Accident Analysis and Prevention, 2019, 132, 105249. RESPONSE: Thank you for the suggestion. The suggested references were added in the materials and methods section, line 212: “For each application and data set observed, it is necessary to choose the most appropriate technique among the many available. In this case, different performance metrics can be used as selection criteria. Zeng and Wen used Mean Absolute Deviance (MAD) and Mean Squared Prediction Error (MSPE) [25,46], and, in an epidemiological context, Chowell used the Root Mean Squared Error (RMSE), Mean Squared Error (MAE), and Mean Absolut Percent Error (MAPE) [15].” 3 - In the ANN modeling section, a figure showing how the output and inputs connected would present the model more explicitly. RESPONSE: We have added the figure (Fig. 3) as suggested by the reviewer in the ANN modeling subsection. 4 - The Broyden-Fletcher-Goldfarb-Shanno iterative algorithm used for ANN training is rare in the previous studies. A detailed description of this algorithm is anticipated. RESPONSE: We have added a brief explanation of the BFGS algorithm and quote a reference that shows the detailed operation of the algorithm in the ANN modeling subsection, at line 178, as follow: “The BFGS memoryless quasi-Newton was successfully used for minimizing errors on artificial neural networks. The Quasi-Newton method is a method that is used when the calculation of the Hessian matrix is difficult or time-consuming. This method has a rapid convergence when compared with the method of gradient descent [44].” 5 - It would be interesting and further demonstrate the advantages of ANNs to compare their performance with that of time series models that are used in the previous studies on forecasting COVID-19 cases. RESPONSE: Thank you for the suggestion. Unfortunately, we did not found studies with similar methodology and proposal to compare. But we have added the information regards the work of Petropoulos F & Makridakis S. [54] that performs an analysis of different scenarios but with different day intervals and without goodness-of-fit analysis, which prevents a proper comparison. Attachments Attachment Submitted filename: Response to Reviewers.docx https://doi.org/10.1371/journal.pone.0248161.r002
22 Feb 2021 Decision Letter - Qiang Zeng, Editor Artificial neural networks for short-term forecasting of cases, deaths, and hospital beds occupancy in the COVID-19 pandemic at the Brazilian Amazon PONE-D-20-38808R1 Dear Dr. Braga, 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 for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org. 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, Qiang Zeng, Ph.D. Academic Editor PLOS ONE Additional Editor Comments (optional): Reviewers' comments: Reviewer's Responses to Questions Comments to the Author 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 Reviewer #2: (No Response) ******** 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: (No Response) Reviewer #2: (No Response) ****** 3. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: (No Response) Reviewer #2: (No Response) ****** 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: (No Response) Reviewer #2: (No Response) ****** 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: (No Response) Reviewer #2: (No Response) ****** 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: (No Response) Reviewer #2: (No Response) ****** 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 Reviewer #2: No https://doi.org/10.1371/journal.pone.0248161.r003
Formally Accepted
2 Mar 2021 Acceptance Letter - Qiang Zeng, Editor PONE-D-20-38808R1 Artificial neural networks for short-term forecasting of cases, deaths, and hospital beds occupancy in the COVID-19 pandemic at the Brazilian Amazon Dear Dr. Braga: I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department. If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. 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. If we can help with anything else, please email us at plosone@plos.org. Thank you for submitting your work to PLOS ONE and supporting open access. Kind regards, PLOS ONE Editorial Office Staff on behalf of Dr. Qiang Zeng Academic Editor PLOS ONE https://doi.org/10.1371/journal.pone.0248161.r004

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