Differential impact of the COVID-19 pandemic on laboratory reporting of norovirus and Campylobacter in England: A modelling approach

Nikola Ondrikova; Helen E. Clough; Amy Douglas; Miren Iturriza-Gomara; Lesley Larkin; Roberto Vivancos; John P. Harris; Nigel A. Cunliffe

doi:10.1371/journal.pone.0256638

Peer Review History

Original SubmissionApril 7, 2021
25 May 2021 Decision Letter - Shinya Tsuzuki, Editor PONE-D-21-11454 Differential Impact of the COVID-19 Pandemic on Laboratory Reporting of Norovirus and Campylobacter in England: Modelling Approach PLOS ONE Dear Dr. Ondrikova, 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 25th June 2021. 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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When you resubmit, please ensure that you provide the correct grant numbers for the awards you received for your study in the ‘Funding Information’ section. Additional Editor Comments: One of the reviewers raised some concerns about modelling and I agree with his opinion. Please address the comments from both reviewers before further consideration. [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: Partly Reviewer #2: No ******** 2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: No Reviewer #2: No ****** 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: No 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: The study “Differential Impact of the COVID-19 Pandemic on Laboratory Reporting of Norovirus and Campylobacter in England: Modelling Approach” is interesting. The data and methodology parts are well described. I would recommend the authors add some more explanations and also describe the contribution of this work in the introduction section. Moreover, attention should be given to the following highlighted points before resubmitting. 1. Page 10 of 21, “The conditional distribution was chosen as negative binomial to account for over-dispersion, and the link function was logarithmic.” How the conditional distribution is selected any reference or source please provide details. 2. Page 13 of 21, “In particular, changes in the stringency index were associated with a reduction of ~7% (CI 2% - 6%)” check these confidence limits. 3. Table 3, the values of Sars-cov-2 tests is not correct for norovirus. 4. Construct a table that lists the character shorthand. 5. From Figure 1 it seems that the Actual data is following downward trend while the predicted data trend is upward. Is this a real picture of the data or correct modelling? Reviewer #2: The authors present interesting surveillance data on the impact of lockdown restrictions on laboratory reports of norovirus and Campylobacter. The paper is very well written and broadly speaking the modelling approach seems reasonable. My main difficulty with the paper was that I found the results of the modelling hard to interpret in light of the data presented in Figure 2. In the figure, norovirus cases reduce from ~200 per week pre-COVID to <20 cases per week post COVID, which is a >90% reduction. Why are the estimates presented in Table 2 significantly lower, even for delta=1? Similarly, for Campylobacter the decrease is from ~1,000 to 250 which corresponds to a 75% reduction. But again the estimates are lower. I agree with the authors assessment that delta=1 for norovirus and approximately 0.85 for Campylobacter but they do not provide any justification for the later value. You could argue, for example, that the reduction in Campylobacter halves over 4 weeks and 0.85^4=0.52. It is a bit of shame that delta is not estimated from the data. Fitting a transfer function model would be one way to do this - see for example chapter 13 of “Time Series Analysis” by Box, Jenkins , Reinsel and Ljung (a pdf version of the book is available for free online) In summary, I suspect the modelling needs to be revisited or better explained. If this can be addressed, then I think this will be an excellent paper. ****** 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: Yes:** Christian Bottomley [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.0256638.r001
Revision 1
9 Jul 2021 Author Response We appreciate the time and effort that the reviewers have dedicated to providing their valuable feedback. We have been able to incorporate changes to the analysis and the manuscript to reflect the comments provided. Point-by-point response to the reviewers’ comments and concerns. Comments from Reviewer 1 • Comment 1: I would recommend the authors add some more explanations and also describe the contribution of this work in the introduction section Response: Even though, the contribution of the study is described in the discussion, I agree that it is good to mention it more in the introduction too. The highlighted text was added to the introduction. Changes in the manuscript (Introduction, lines 123-127): … To the best of our knowledge, this is the first study to compare, both quantitative and qualitative differences in the pandemic’s impact on the number of norovirus and Campylobacter reports. Additionally, the analysis provides insights into how to account for the pandemic in detection algorithms and predictive models used more broadly in public health. • Comment 2 (Page 10 of 21): “The conditional distribution was chosen as negative binomial to account for over-dispersion, and the link function was logarithmic.” How the conditional distribution is selected any reference or source please provide details. Response: A negative binomial model is a standard approach to dealing with over-dispersion: for background context and to guide the reader we have inserted reference 15 was added (Materials and methods, line 193). • Comment 3 (Page 13 of 21): “In particular, changes in the stringency index were associated with a reduction of ~7% (CI 2% - 6%)” check these confidence limits. Response: Thank you for pointing this out – yes, this was indeed a typo. These values have further changed since the second model was updated in response to Referee 2 (Results, lines 249-254). • Comment 4 (Table 3): The values of Sars-cov-2 tests is not correct for norovirus. Response: The model was updated so this table has now changed. • Comment 5 (Table 3): Construct a table that lists the character shorthand. Response: We have modified the caption to Table 3 to reflect the shorthand, since this seems to be the most parsimonious way to represent this information. • Comment 6 (Figure 1): From Figure 1 it seems that the Actual data is following downward trend while the predicted data trend is upward. Is this a real picture of the data or correct modelling? Response: The Figure 1 (now Fig. 2) shows what would have happened had there been no pandemic. While norovirus in England tends to have winter seasonality and is expected to decrease in spring, Campylobacter reports tend to increase in spring. However, due to the pandemic, this was disrupted and so it is expected that the prediction (what would have happened) and the actual data (what happened) will look different. Changes in the manuscript (Results section, line 228): As illustrated by Fig 2, this is likely because the effect was too short to be detected with higher level of significance. Notes under Fig 2 (lines 239-242): The figure displays the model’s prediction (Fitted) and the actual number of weekly reports (Actual). The section with the ribbon, i.e. confidence interval, highlights what was expected in the absence of a pandemic. Comments from Reviewer 2 • Comment 1 (Figure 2): In the figure, norovirus cases reduce from ~200 per week pre-COVID to <20 cases per week post COVID, which is a >90% reduction. Why are the estimates presented in Table 2 significantly lower, even for delta=1? Response: We agree this needs clarification. The pandemic arrived in the UK at the time of the year when norovirus usually decreases. Since our modelling accounts for the seasonality, some decrease is expected and shouldn’t necessarily be attributed to the pandemic. Similarly, this applies to the increasing air temperature (weaker for Campylobacter) and effect of easter holidays (dip in reporting). Also, the model considers the autoregressive nature of the weekly reporting. Therefore, the observed difference between the lines does not equal the estimated impact of the pandemic. On top of that, the actual difference between the Actual and Fitted lines in the pandemic period is ~86% and since the model’s Mean Absolute Error (MAE) on non-pandemic period is ~20 reports the estimates we provide make sense. Even the model assuming the start of the pandemic on week 13 showed point estimate of 62% (CI 53% - 70%); confirming the stability of the estimate. Changes to the manuscript (Results, lines 265 - 280): To make the models easier to interpret and compare, an impact variable was added to the second model to capture residual effects of the pandemic unexplained by the proxies, i.e. number of Sars-CoV-2 tests conducted and stringency index. The section comparing the models now discusses total effect estimated by both models. This new addition widened the estimates, and these were updated in the abstract and the discussion. However, these still might be conservative and so this has been added to the limitations. (Discussion, line 350) Modelling both pathogens brings many challenges; … Considering all of these challenges, our estimates might be conservative. • Comment 2: Similarly, for Campylobacter the decrease is from ~1,000 to 250 which corresponds to a 75% reduction. But again, the estimates are lower. Response: Similarly, to the response above, there is a lot of uncertainty, e.g. seasonality can differ by region (see Louis et al., 2005). In our paper, the estimate is provided as a mean value over the 32 weeks. The estimated impact at the beginning was between 38% – 55% but then it decreased exponentially. The actual difference between the lines is ~34% during the pandemic and the MAE in the period before the pandemic was ~76 reports. Clarifications were added to the manuscript (see highlights below). Changes in the manuscript (Materials and methods, lines 165-170): … For example, level shift is the same on the first as well as the fifth or tenth week, while for transient shift, the highest impact is assumed at the beginning, but decreases exponentially with time, e.g. (δ = 0.85) in the second week but (δ = 0.52) in the fifth week (see Fig. 1). The estimated coefficient of the indicator is then a relative change in the weekly laboratory reports of a given pathogen, considering the other variables and effects in the model such as air temperature, seasonality and autoregression. • Comment 3: I agree with the authors assessment that delta=1 for norovirus and approximately 0.85 for Campylobacter but they do not provide any justification for the later value. You could argue, for example, that the reduction in Campylobacter halves over 4 weeks and 0.85^4=0.52. Response: The reduction for Campylobacter indeed halves over 4 weeks. As described in the methods, we compare level and transient shifts. The transient shift represents exponential decay. To clarify this more in the paper, a plot visualising the individual effects was added as well as specific examples in the text. Changes in the manuscript (Results, lines 224-228): The decrease in Campylobacter reports was better described by transient shift (δ = .85). The estimated impact was ~46% (CI 38% - 55%) on week 12, ~39% (32% - 47%) on week 13 and so on; the mean weekly reduction across weeks 12 and 43 was ~9% (CI 8% - 11%). This decrease was statistically significant at the 10% but not the 5% level (p = 0.075). • Comment 4: It is a bit of shame that delta is not estimated from the data. Fitting a transfer function model would be one way to do this - see for example chapter 13 of “Time Series Analysis” by Box, Jenkins , Reinsel and Ljung (a pdf version of the book is available for free online) Response: As the paper aims to demonstrate not only quantitative, but also qualitative difference between the impacts of the pandemic on norovirus and Campylobacter, I think the current format of the analysis achieves this better, i.e. it allows for easier communication of the message – Norovirus is more similar to Sars-CoV-2 than Campylobacter and so the reduction in norovirus is related to the actual measures, not only changes to the health seeking behaviour. It is our feeling that a more detailed analysis along the constructive lines suggested is beyond the scope of our paper. Attachments Attachment Submitted filename: Response to Reviewers.docx https://doi.org/10.1371/journal.pone.0256638.r002
30 Jul 2021 Decision Letter - Shinya Tsuzuki, Editor PONE-D-21-11454R1 Differential impact of the COVID-19 pandemic on laboratory reporting of norovirus and Campylobacter in England: a modelling approach PLOS ONE Dear Dr. Ondrikova, 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 13th August 2021. 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. Please include the following items when submitting your revised manuscript: A rebuttal 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'. An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'. If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter. If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. 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Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice. Additional Editor Comments: Both reviewers made positive decisions but one of them raised a minor concern. Please answer the comment before publication. [Note: HTML markup is below. Please do not edit.] 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: Partly 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 Response) Reviewer #2: I would like to thank the authors for addressing my comments. In my view the paper is acceptable for publication. I have one question and a suggestion. Question: I was unsure how to interpret the norovirus shift parameter estimate in the new Table 3. I imagined exponentiating this parameter would give me the measure of impact but it doesn't (exp(-0.792)=0.45 but the reported impact is 59%). Is this an error or how should this parameter be interpreted? Suggestion: Given that seasonality is so important in this analysis it might be worth illustrating it somehow - e.g. by promoting the weekly time series figures from the supplementary information to the main text. ****** 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: Yes:** Christian Bottomley [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.0256638.r003
Revision 2
10 Aug 2021 Author Response We appreciate the extra effort that the reviewers have dedicated to providing additional feedback. We have incorporated changes to the manuscript and one of the figures to reflect the comments provided. Here is a point-by-point response to the comments. Comments from Reviewer 2 • Question: I was unsure how to interpret the norovirus shift parameter estimate in the new Table 3. I imagined exponentiating this parameter would give me the measure of impact but it doesn't (exp(-0.792)=0.45 but the reported impact is 59%). Is this an error or how should this parameter be interpreted? Response: Thank you for spotting this, it needs clarification in the text. Briefly, since three variables represent the impact in the second model, the estimate is a sum of these three variables giving us ~59% decrease. Specifically, all coefficients are transformed by exponentiating and subtracting one – (exp(coefficient) – 1) * 100. So the shift is then (exp(-0.792) – 1) * 100 ~ 55%. In the second model, we have two more variables indicating impact, which are added to the shift. As stringency index and the number of conducted Sars-CoV-2 tests vary over time, we need to multiply the coefficients by the actual values of the respective variable. Additionally, we only consider the period between weeks 12 and 43 to match the timeline of the shift variable. For norovirus, both the conducted tests and stringency index give us a decrease of approximately 2%, which is 4% in total and 55% + 4 % gives 59%. The simpler overall model represents the pandemic only with the shift indicator where (exp(-0.880) -1) 100 ~59%. Changes in the manuscript (Results, lines 271-281): … A simpler (overall) model might have underestimated the impact as the total decrease in norovirus reports determined by the second (specific trends) model has wider confidence intervals ~59% (CI 47% - 79%). On the other hand, the point estimates of both the Overall Impact and Specific Trends models were ~59%. Similarly, the total mean reduction of Campylobacter reports estimated by the Specific Trends model was between ~11% (CI 8% - 17%), which is higher than the simpler Overall Impact model ~9% (8% - 11%). Note that the impact estimate from the Specific Trends models is a sum of all the pandemic related estimates, i.e. conducted Sars-CoV-2 tests, stringency index and the shift determined by the overall impact model. • Suggestion: Given that seasonality is so important in this analysis it might be worth illustrating it somehow - e.g. by promoting the weekly time series figures from the supplementary information to the main text. Response: Thank you for the suggestion. Figure 2 now includes the previous season (2018/19), showing two years in total, and the seasonality is visible there. Attachments Attachment Submitted filename: Response to Reviewers.docx* https://doi.org/10.1371/journal.pone.0256638.r004
12 Aug 2021 Decision Letter - Shinya Tsuzuki, Editor Differential impact of the COVID-19 pandemic on laboratory reporting of norovirus and Campylobacter in England: a modelling approach PONE-D-21-11454R2 Dear Dr. Ondrikova, 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, Shinya Tsuzuki, MD, MSc Academic Editor PLOS ONE Additional Editor Comments (optional): Reviewers' comments: https://doi.org/10.1371/journal.pone.0256638.r005
Formally Accepted
16 Aug 2021 Acceptance Letter - Shinya Tsuzuki, Editor PONE-D-21-11454R2 Differential impact of the COVID-19 pandemic on laboratory reporting of norovirus and Campylobacter in England: a modelling approach Dear Dr. Ondrikova: 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. Shinya Tsuzuki Academic Editor PLOS ONE https://doi.org/10.1371/journal.pone.0256638.r006

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