Modeling latent infection transmissions through biosocial stochastic dynamics

Bosiljka Tadić; Roderick Melnik

doi:10.1371/journal.pone.0241163

Peer Review History

Original SubmissionAugust 1, 2020
22 Sep 2020 Decision Letter - Ning Cai, Editor PONE-D-20-23763 Modeling latent infection transmissions through biosocial stochastic dynamics PLOS ONE Dear Dr. Tadić, 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 Nov 06 2020 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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R.M. is grateful to the NSERC and the CRC Program for their support and he is also acknowledging the support of the BERC 2018-2021 program and Spanish Ministry of Science, Innovation, and Universities through the Agencia Estatal de Investigacion (AEI) BCAM Severo Ochoa excellence accreditation SEV-2017-0718, and the Basque Government fund "AI in BCAM EXP. 2019/00432"" We note that you have provided funding information that is not currently declared in your Funding Statement. However, funding information should not appear in the Acknowledgments section or other areas of your manuscript. We will only publish funding information present in the Funding Statement section of the online submission form. Please remove any funding-related text from the manuscript and let us know how you would like to update your Funding Statement. 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We will update your Data Availability statement to reflect the information you provide in your cover letter. [Note: HTML markup is below. Please do not edit.] Reviewers' comments: Reviewer's Responses to Questions Comments to the Author 1. 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 Reviewer #2: Partly ******** 2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: Yes 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 work develop an agent-based model for simulations of infection transmission in an open system driven by the dynamics of social activity; the model takes into account the personal characteristics of individuals, as well as the survival time of the virus and its potential mutations. The paper is well organized. It can be seen that the proposed model reveal complex feedback mechanisms that shape the dependence of the infection curve on the intensity of social dynamics and other sociobiological factors. Reviewer #2: Thank you for the opportunity to read this interesting manuscript. The manuscript is very easy to read. Authors clearly describe their aims, provide clear operationalization of all variables, and the reasoning of the procedure. In general, I consider this type of simulation to have a contribution over the mean-field theory whose results are biased by the well-mixed assumption and missing the temporal dynamics in real-world epidemic spreading. The authors propose an agent-based model to address the latent infection transmissions by introducing temporal dynamics in simulation. The most impressive and practical thing is the authors treat the human and virus as separate elements in their model. The introduction of exposure time for the human and survival time for the virus can also reflect the latent characteristic of infection transmissions. Besides, the application of parameter gv helps capture the weakening process of the virus due to evolution. The authors used the data collected from online social networks, MySpace, as the input data regarding human dynamical fluctuations over time. However, the disease spreading in the real-world depends on contacts (either face-to-face contacts or indirect face-to-face contacts), which may significantly distinct from online social interactions between people. Rather than data from online social media, I miss a modification of this model using data concerning direct or indirect face-to-face contacts. Or, perhaps, the authors could provide a more solid justification for their using of the online social interactions in the context of infection transmission. As a more specific remark, I would have expected clarification (or supporting materials) of this assumption: “thus, more susceptible agents, who are likely to have severe symptoms, can spread viruses more often than those who are barely ill.” I miss a brief description of the schematic of how virus nodes infect human nodes, or which virus node infects which human node. I also miss I miss a more robust discussion on the limitations of the model. [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.] While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. 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Revision 1
2 Oct 2020 Author Response We have found the editor's suggestions and reviewers' comments very encouraging and instructive, and we have revised the manuscript accordingly. Specifically, we have added a description of the social activity time series, which is selected such that it closely reflects the cumulative activity in off-line social communities. We also stated that it serves as a first proxy, having no real data sets from a specific geographical location. We have also expanded the Discussion section to highlight the absence of physical distance and mobility of the agents that limits some applications of the model, for example, to describe epidemics at specific geographical locations and social groups involved. We also comment the potential impact that changed (more realistic) distributions of the number of contacts as well as temporal variations of the viral load may have on the process that we are simulating. Several concrete extensions of the model mentioned in this context will remain for future work. We believe that the presented model captures the essential features of the stochastic processes underlying SARS CoV-02 epidemics, in which the latent infection transmissions have a substantial contribution. Prompted by your suggestion, we have removed funding information from Acknowledgments. Therefore, we would appreciate it if you could update our Funding Statement by adding the following contents: B.T. work supported by the Slovenian Research Agency (research code funding number P1-0044). R.M. research was funded by the Natural Sciences and Engineering Research Council (NSERC) of Canada and Canada Research Chairs (CRC) Program. All Figures are uploaded as separate .tif files (with required dimension and resolution). We have also uploaded as Supporting Information S2 Text.dat a sample of the simulation output data for a specified set of parameters (mentioned in the main text). We have also moved the program flow from the Appendix to Supporting information as S1 File.pdf. A detailed response to the reviewers is attached. All changes are traced in the PDF file. * 5. Review Comments to the Author Reviewer #1: This work develop an agent-based model for simulations of infection transmission in an open system driven by the dynamics of social activity; the model takes into account the personal characteristics of individuals, as well as the survival time of the virus and its potential mutations. The paper is well organized. It can be seen that the proposed model reveal complex feedback mechanisms that shape the dependence of the infection curve on the intensity of social dynamics and other sociobiological factors. Authors response: We appreciate the Reviewer’s time and a deep understanding of our work. Reviewer #2: Thank you for the opportunity to read this interesting manuscript. The manuscript is very easy to read. Authors clearly describe their aims, provide clear operationalization of all variables, and the reasoning of the procedure. In general, I consider this type of simulation to have a contribution over the mean-field theory whose results are biased by the well-mixed assumption and missing the temporal dynamics in real-world epidemic spreading. Authors response: We thank the Reviewer for his/her time and for very encouraging statements about our work. The authors propose an agent-based model to address the latent infection transmissions by introducing temporal dynamics in simulation. The most impressive and practical thing is the authors treat the human and virus as separate elements in their model. The introduction of exposure time for the human and survival time for the virus can also reflect the latent characteristic of infection transmissions. Besides, the application of parameter gv helps capture the weakening process of the virus due to evolution. The authors used the data collected from online social networks, MySpace, as the input data regarding human dynamical fluctuations over time. However, the disease spreading in the real-world depends on contacts (either face-to-face contacts or indirect face-to-face contacts), which may significantly distinct from online social interactions between people. Rather than data from online social media, I miss a modification of this model using data concerning direct or indirect face-to-face contacts. Or, perhaps, the authors could provide a more solid justification for their using of the online social interactions in the context of infection transmission. Authors response: We thank the Reviewer for pointing out these issues, which, indeed, need to be better clarified, as we do in the revised text. Specifically, in the absence of real-life data sets, we use a first proxy of the social dynamics available. Among the online social networks, the MySpace data sets are such that they most closely reflect the off-line community contacts and their mutually triggered activity, as our previous studies in Refs.[8,46] show. Moreover, these data sets contain temporal correlations (persistent fluctuations that precisely match the off-line activity fluctuations) and circadian cycles, both of which define essential features of human activity patterns. We added a brief explanation in favour of this empirical time series, as compared to the alternatives (a computer-generated time series or a random number). We have expanded the Discussion section to once again stress the absence of geographical location and mobility patterns of the agents, also referring to different approaches in Ref.[35,41,54], where a concrete data of such kind are collected and used within the model, aiming to predict the impact of mobility that provides contacts and transmission among different groups of the population. Furthermore, we pointed out the limitations of our model as well as possibilities to expand the properties of the agents. For example, the model can take into account the heterogeneity in the number of contacts along which the virus could be transmitted, possibly with a changed rate, as well as temporal variations of the viral load, as some recent empirical data indicate. These issues remain to be studied in future work. As a more specific remark, I would have expected clarification (or supporting materials) of this assumption: ‚thus, more susceptible agents, who are likely to have severe symptoms, can spread viruses more often than those who are barely ill.‚Äù I miss a brief description of the schematic of how virus nodes infect human nodes, or which virus node infects which human node. I also miss I miss a more robust discussion on the limitations of the model. Authors response: We thank the Reviewer for pointing out this issue. Different infectiousness of the agents, especially between asymptomatic and symptomatic individuals, needs to be clarified. Recent studies, for example, in Refs. [47,48,55], found that the viral load varies during the infectious time and that the average viral load in the upper respiratory tract of mild and severe symptomatic individuals is proportionally different. Also, a summary in Ref.[18] points out that “80,9% were considered asymptomatic or mild pneumonia but released large amount of viruses at the early phase of infection”. We do not have enough data to adequately introduce another variable for the temporal variations of the viral load of each agent; therefore, we use the susceptibility h^i that effectively differentiates asymptomatic from symptomatic ones. In the Model description, we added a comment regarding different viral loads that motivates the corresponding model rule. Furthermore, we mentioned the occurrence of the temporal patterns of viral loads in the Discussion by pointing out the limitations and potential extension of our model. In the graph, we have precisely the link along which one host infects the other one via its virus. Perhaps, the Reviewer is pointing to potential (social) relationships between the hosts, which we do not take into account in this version of the model, as above described. Thus, we randomly select among currently existing active viruses; each of them is posted by a known active carrier. In the expanded Discussion, we pointed out several limitations of the current version of the model, as stated above. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Attachments Attachment Submitted filename: response_n.pdf https://doi.org/10.1371/journal.pone.0241163.r002
12 Oct 2020 Decision Letter - Ning Cai, Editor Modeling latent infection transmissions through biosocial stochastic dynamics PONE-D-20-23763R1 Dear Dr. Tadić, 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, Ning Cai, 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: All comments have been addressed ******** 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 Reviewer #2: Yes ****** 3. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: Yes Reviewer #2: 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 Reviewer #2: Yes ****** 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 Reviewer #2: Yes ******** 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 comments for the author, including concerns about dual publication, research ethics, or publication ethics. Reviewer #2: I am satisfied with the way the authors have incorporated my comments. Especially, they did great work in explaining the problem of using online social media data instead of real face-to-face contact data. They also described the limitations of their study. https://doi.org/10.1371/journal.pone.0241163.r003
Formally Accepted
15 Oct 2020 Acceptance Letter - Ning Cai, Editor PONE-D-20-23763R1 Modeling latent infection transmissions through biosocial stochastic dynamics Dear Dr. Tadić: 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. Ning Cai Academic Editor PLOS ONE https://doi.org/10.1371/journal.pone.0241163.r004

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