Estimating individual risks of COVID-19-associated hospitalization and death using publicly available data

Rajiv Bhatia; Jeffrey Klausner

doi:10.1371/journal.pone.0243026

Peer Review History

Original SubmissionJune 29, 2020
14 Aug 2020 Decision Letter - Jeffrey Shaman, Editor PONE-D-20-19975 Estimation of Individual Probabilities of COVID-19 Infection, Hospitalization, and Death From A County-level Contact of Unknown infection Status PLOS ONE Dear Dr. Bhatia, 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 respond to the reviewer comments on a point-by-point basis and revise the manuscript accordingly. Please submit your revised manuscript by Sep 28 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. 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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 ******** 2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: Yes ****** 3. Have the authors made all data underlying the findings in their manuscript fully available? 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Reviewer #1: 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: # PONE-D-20-19975 ## Overall comments I would like to thank the editor for sending me an interesting paper to review with such a concise yet useful model for estimating the risk to residents of US cities of contracting SARS-CoV-2 and subsequent hospitalisation and death. Better informing the public about the risks of infection is vital to ensuring proportional public health responses and adherence to guidelines by the public. Overall I am satisfied that the authors have thought through their work and its implications. The requested changes to the model above are meant to help the reader understand how much variability there is likely to be in the results based on uncertainty in published estimates used by the authors. ## Methods In Table 1 there are no subscripts on the CHR and CFRs indicating which age group they belong to. Given that they are indeed age-dependent, it should be noted that equations (3) and (4) are age dependent. Additionally, the probability of an individual in any age group considered becoming infected is likely to be related to the total number of contacts that they have, the age group of those contacts (Prem et al., 2017), and the prevalence of current infection within each age group (although I understand this may be difficult to obtain). Contacts per day are given in the Results section (Table 3) but it is not clear in the Methods where these values are from. Line 107: "can be both symptomatic and asymptomatic" should be "can be either symptomatic or asymptomatic". Regarding the asymptomatic fraction, Buitrago-Garcia et al. (2020) provide a living systematic review of asymptomatic fraction that provides an estimate of 31% (95% prediction interval: 24%-38%), twice that which is used by the authors here. As asymptomatic infection may play an important role in ongoing transmission (e.g. Rivett et al., 2020) it would be worth the authors investigating the effect of a doubling of the asymptomatic fraction, particularly as asymptomatic infections will never have the chance to self-isolate on onset of symptoms. Issues in case reporting are a major source of uncertainty in the estimation of prevalence and therefore risk of transmission. Russell et al. (2020) provide estimates of the reporting rate in the United States of America of 99% based on the method of Golding et al. (2020). The authors should be more explicit in how they came to the value of 75% which represents both under-reporting in symptomatic cases and undetected asymptomatic cases. An assumption of 100% of infections having unknown contacts doesn't match the assumption of household transmission that the authors use. I appreciate that the authors have included this parameter in their model for future work. The discussion should highlight this as a limitation and it may be worth considering varying this assumption for sensitivity analysis. In terms of culture and socioeconomics, I am skeptical that Israel provides a reasonable comparison to the United States of America for adherence to isolation guidelines. While financial compensation has been found to improve adherence there, an assumption that 75% of confirmed cases will voluntarily self-isolate is akin to assuming half of cases are having their income supported, given the numbers the authors provide. In contrast to income support in the United Kingdom and Singapore, income support in the United States of America is both low and spotty. A review earlier this year (Webster et al., 2020) indicated that trust in government and other sociocultural factors may play a role, and while much of the studies cited are related to Ebola in Africa and H1N1 pandemic influenza in Australia, studies of SARS in Canada indicated that a sense of "civic duty" and a belief in the importance of "following the law" were associated with increased adherence. A recent survey indicates association between adherence and in a belief in a moral imperative to comply (van Rooij et al., 2020). There is evidence in the USA that a county's political demographics play a role in adherence (Painter and Qiu, 2020), particularly viewership of Fox News (Simonov et al., 2020), and poverty (Wright et al., 2020). Given that the authors are considering county-level reported cases it may be worth estimating county-level rates of adherence to self-isolation guidelines. ## Results I am satisfied, for the most part, with the presentation of results and their discussion. There are no uncertainties or sensitivity analyses presented in the results, due to the lack of uncertainty in the parameter estimates in the authors' model. I would suggest that estimates of incidence should have uncertainty due to uncertainty in under-reporting rates, CHR and CFR, and average days infectious - where the authors use 8 days without providing a source, despite indications that duration of infectivity may have a great deal of uncertainty due to differences in viral load (Wölfel et al., 2020). Figure 1 is useful but I would also appreciate if the authors provided a figure with faceting by Event and colouring the lines by age group (colours to be consistent with the current Figure 2) in order to more easily see how the probability of each event varies with age group. The x axis is difficult to interpret, and the authors may want to consider converting from fractions to cases per 100,000. ## Discussion The authors' discussion does a reasonable job of explaining the limitations of the data available to them and they compare their results appropriately. At line 309 the authors the authors discuss mediation of risk perceptions based on restrictions on community action. I suggest they consider Webster et al. (2020) and their discussion on social norms and perception of risk. ## References Prem et al. (2017) https://doi.org/10.1371/journal.pcbi.1005697 Rivett et al. (2020) https://doi.org/10.7554/eLife.58728 Russell et al. (2020) https://cmmid.github.io/topics/covid19/global_cfr_estimates.html Golding et al. (2020) https://doi.org/10.1101/2020.07.07.20148460 Buitrago-Garcia et al. (2020) https://doi.org/10.1101/2020.04.25.20079103 Webster et al. (2020) https://doi.org/10.1016/j.puhe.2020.03.007 van Rooij et al. (2020) https://dx.doi.org/10.2139/ssrn.3582626 Painter and Qiu (2020) https://dx.doi.org/10.2139/ssrn.3569098 Simonov et al. (2020) https://doi.org/10.3386/w27237 Wright et al. (2020) https://dx.doi.org/10.2139/ssrn.3573637 Wölfel et al. (2020) https://doi.org/10.1038/s41586-020-2196-x ****** 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: Yes:** Samuel Clifford [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. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step. https://doi.org/10.1371/journal.pone.0243026.r001
Revision 1
26 Oct 2020 Author Response Authors’ responses to reviewers’ comments C1. I would like to thank the editor for sending me an interesting paper to review with such a concise yet useful model for estimating the risk to residents of US cities of contracting SARS-CoV-2 and subsequent hospitalization and death. Better informing the public about the risks of infection is vital to ensuring proportional public health responses and adherence to guidelines by the public. R1. No response indicated C2. Overall I am satisfied that the authors have thought through their work and its implications. The requested changes to the model above are meant to help the reader understand how much variability there is likely to be in the results based on uncertainty in published estimates used by the authors. R2. No response indicated C3. In Table 1 there are no subscripts on the CHR and CFRs indicating which age group they belong to. R3. We have specified the age-specific CHR and CFR estimates in the revision. C4. Given that they are indeed age-dependent, it should be noted that equations (3) and (4) are age dependent. R4. We have revised the description of formulae to note their age specificity. C5. Additionally, the probability of an individual in any age group considered becoming infected is likely to be related to the total number of contacts that they have, the age group of those contacts (Prem et al., 2017), and the prevalence of current infection within each age group (although I understand this may be difficult to obtain). Contacts per day are given in the Results section (Table 3) but it is not clear in the Methods where these values are from. R5. In the revision, we now use age and setting specific pre-pandemic contact rates from the analysis of Prem at al for our risk estimates. Given the lack of publicly available age-specific data on infection prevalence at the county level, we could not apply the age structure of contacts. We note the limitation and the potential value obtaining and applying age-structured incidence data in our revision C6. Line 107: "can be both symptomatic and asymptomatic" should be "can be either symptomatic or asymptomatic". R6. Corrected C7. Regarding the asymptomatic fraction, Buitrago-Garcia et al. (2020) provide a living systematic review of asymptomatic fraction that provides an estimate of 31% (95% prediction interval: 24%-38%), twice that which is used by the authors here. As asymptomatic infection may play an important role in ongoing transmission (e.g. Rivett et al., 2020) it would be worth the authors investigating the effect of a doubling of the asymptomatic fraction, particularly as asymptomatic infections will never have the chance to self-isolate on onset of symptoms. R7. The revision applies the estimate of the clinical fraction from Buitrago-Garcia et al and applies a lower estimate from the work of Poletti et al in a sensitivity analysis. C8. Issues in case reporting are a major source of uncertainty in the estimation of prevalence and therefore risk of transmission. Russell et al. (2020) provide estimates of the reporting rate in the United States of America of 99% based on the method of Golding et al. (2020). The authors should be more explicit in how they came to the value of 75% which represents both under-reporting in symptomatic cases and undetected asymptomatic cases. R8. We have modified the methodology in the revision no longer apply or require a ratio between confirmed and unconfirmed infections. We have made and noted an assumption that the relationship between confirmed infections and all infections will be simply multiplicative within a time and place. C9. An assumption of 100% of infections having unknown contacts doesn't match the assumption of household transmission that the authors use. I appreciate that the authors have included this parameter in their model for future work. The discussion should highlight this as a limitation and it may be worth considering varying this assumption for sensitivity analysis. R9. Changes to the methodology do not apply a parameter for unknown contacts. C10. In terms of culture and socioeconomics, I am skeptical that Israel provides a reasonable comparison to the United States of America for adherence to isolation guidelines. While financial compensation has been found to improve adherence there, an assumption that 75% of confirmed cases will voluntarily self-isolate is akin to assuming half of cases are having their income supported, given the numbers the authors provide. In contrast to income support in the United Kingdom and Singapore, income support in the United States of America is both low and spotty. A review earlier this year (Webster et al., 2020) indicated that trust in government and other sociocultural factors may play a role, and while much of the studies cited are related to Ebola in Africa and H1N1 pandemic influenza in Australia, studies of SARS in Canada indicated that a sense of "civic duty" and a belief in the importance of "following the law" were associated with increased adherence. A recent survey indicates association between adherence and in a belief in a moral imperative to comply (van Rooij et al., 2020). There is evidence in the USA that a county's political demographics play a role in adherence (Painter and Qiu, 2020), particularly viewership of Fox News (Simonov et al., 2020), and poverty (Wright et al., 2020). Given that the authors are considering county-level reported cases it may be worth estimating county-level rates of adherence to self-isolation guidelines. R 10. We agree that data on adherence is insufficient. We were not able to find a reliable source for this parameter for the United States. We test the sensitivity of our estimates to this parameter. C11. I am satisfied, for the most part, with the presentation of results and their discussion. There are no uncertainties or sensitivity analyses presented in the results, due to the lack of uncertainty in the parameter estimates in the authors' model. I would suggest that estimates of incidence should have uncertainty due to uncertainty in under-reporting rates, CHR and CFR, and average days infectious - where the authors use 8 days without providing a source, despite indications that duration of infectivity may have a great deal of uncertainty due to differences in viral load (Wölfel et al., 2020). R11. We have added sensitivity analyses varying individual parameter estimates. We removed the parameter for duration of infectiousness as the SIR estimates include follow up duration that encompasses this interval. C12. Figure 1 is useful but I would also appreciate if the authors provided a figure with faceting by Event and colouring the lines by age group (colours to be consistent with the current Figure 2) in order to more easily see how the probability of each event varies with age group. The x axis is difficult to interpret, and the authors may want to consider converting from fractions to cases per 100,000. R12. The figure has been revised. C13. The authors' discussion does a reasonable job of explaining the limitations of the data available to them and they compare their results appropriately. R13. No response indicated C14. At line 309 the authors the authors discuss mediation of risk perceptions based on restrictions on community action. I suggest they consider Webster et al. (2020) and their discussion on social norms and perception of risk. R14. We reference the review of Webster in the introduction to the revision. Attachments Attachment Submitted filename: covidrisks responsetoreviewers.docx https://doi.org/10.1371/journal.pone.0243026.r002
16 Nov 2020 Decision Letter - Jeffrey Shaman, Editor Estimating individual risks of COVID-19-associated hospitalization and death using publicly available data PONE-D-20-19975R1 Dear Dr. Bhatia, 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, Jeffrey Shaman 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 ******** 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 ****** 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? 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) ****** 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: All my comments have been addressed and I have no further changes to request to the manuscript. Thank you. ****** 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: Yes:** Sam Clifford https://doi.org/10.1371/journal.pone.0243026.r003
Formally Accepted
19 Nov 2020 Acceptance Letter - Jeffrey Shaman, Editor PONE-D-20-19975R1 Estimating individual risks of COVID-19-associated hospitalization and death using publicly available data Dear Dr. Bhatia: 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 Prof. Jeffrey Shaman Academic Editor PLOS ONE https://doi.org/10.1371/journal.pone.0243026.r004

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