PONE-D-20-24209

Using Control Charts to Understand Community Variation in COVID-19

PLOS ONE

Dear Dr. Inkelas,

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.

The manuscript has been evaluated by two reviewers, and their comments are available below.

The reviewers have raised a number of major concerns. They request improvements to the introduction and justification for use of control charts, reporting of methodological aspects of the study, and further discussion of your findings or alternative approaches.

Could you please carefully revise the manuscript to address all comments raised?

Please submit your revised manuscript by January 22, 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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We look forward to receiving your revised manuscript.

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Beryne Odeny

Staff Editor

PLOS ONE

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[National Institutes of Health

/National Center for Advancing Translational Sciences Grant Number UL1TR000124

and Grant Number TL1TR001883. The funders had no role in study design, data

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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: Yes

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

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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

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4. Is the manuscript presented in an intelligible fashion and written in standard English?

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Reviewer #1: Yes

Reviewer #2: Yes

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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: Review PONE-D-20-24209

This is a well written and interesting article illustrating how improved presentation and visualization of data can improve learning and decision-making related to the ongoing Covid pandemic. I have some suggestions that authors may consider when revising and finalizing their manuscript.

The title of the article as well as the abstract focus on the use of control charts, i.e. how data is displayed. I would say that the first strong argument being made in the article is the use of disaggregate data, in particular as more localized decisions are needed to handle existing variations in the pandemic. That data should be presented in the form of control charts, to handle randomness and identify significant changes, is the second argument. I miss explicit references to disaggregated data in the abstract (and perhaps in title). I also miss references to randomness to explain the value of control charts in the abstract (especially when disaggregate data is used, i.e. fewer observations). Also in the introduction, the focus is on “displays of Covid-19 data”. The problem with over-aggregation is mentioned (impairing the ability of decision-makers) but I think this could be presented as a (first) separate issue.

On page 4 it is argued that control charts “are less burdensome” and on page 12 you say that “the analysis show that control charts are easily interpretable” and “have a visual simplicity”. First of all you have not analyzed if control charts are easily interpretable, at least not from a (real) decision-maker point of view. There is no data based on decision-makers views. The views expressed are rather arguments usually put forward by scholars, proposing control charts, funnel plots and similar displays to identify special cause variation. If think (and this is supported by experiments) that real decision-makers need at least initial support to fully understand control charts. Support becomes even more important when decision-making is distributed across local authorities (with more limited access to “experts”). Without initial and continued support (training seminars/webbinars, on-line help etc) it would probably be difficult to develop this into “a community engagement tool”. It would also take time - the Covid pandemic require local actions here and now! In summary, I think the article should take the need for training and support involving local decision-makers more seriously. Continued support could also include sharing of local experiences and local knowledge. This could support engaged discussions and improved decisions further.

Reviewer #2: The authors developed a unique system of C and I-type control charts in order to retrospectively model the first 4 months of the COVID-19 pandemic within California counties and cities/neighborhoods. These charts detected both periods of exponential growth and periods of stable rates for the counties and cities/neighborhoods analyzed. The authors showed how annotated control charts could provide near real-time data to decision makers and the general population, as well as how these charts might provide hypotheses to explain special cause variation.

The manuscript is interesting and well written. As the authors point out, use of control charts to monitor healthcare processes has become more common, but use within the public health arena remains surprisingly uncommon to date.

I do have several comments and suggestions, primarily targeting the methods, that if addressed might improve the readability and impact of this manuscript:

1) Methods. In the first paragraph of the methods, the authors describe the characteristics of the control charts that they used in this analysis. However, these specific characteristics do not apply to all control charts relevant to this type of analysis, and I think this introductory paragraph to control charts should emphasize the wide variation in chart characteristics. Perhaps this paragraph would also be better suited for the introduction rather than the methods?

For example, some control charts estimate the centerline from the mean of past data, but other control charts use an expected external baseline rate or other calculations to estimate the baseline. Furthermore, “freezing” a static baseline is one strategy, but other control charts use a dynamic rolling baseline that updates over time. Finally, defining control limits at +/- 3 standard deviations is a common convention but is not required.

2) Methods – control chart analysis. Exponential outbreak growth was quite common in many locations at the onset of the COVID-19 pandemic. The C-charts in this study estimated a centerline from the first 20 observations. However, as expected for a respiratory virus outbreak, exponential growth often occurred within the first 20 days/data points. Does use of a centerline estimated from the first 20 days of data decrease the ability of these charts to demonstrate timely detection of exponential growth within the first 20 days? Does the 20-day baseline need to be separately analyzed for exponential growth before being used as an “in control” baseline for a process assumed not yet to demonstrate exponential growth? I.e., would the baseline be more effective if it were proven to be “in control” before being used to monitor for special cause variation?

3) Methods/Figure 1.

a) Charts c/d/e/f were deemed not to exhibit exponential growth, but many data points ultimately exceeded the apparent upper control limits for these charts. How were these data indicating special cause variation and possible transition to an exponential growth phase determined not to represent exponential growth?

b) Chart g is labeled as both a hybrid chart and a C-chart – could the authors please clarify this unique feature of the Lancaster control chart?

c) The Figure 1 legend states that county C-charts used a centerline estimated from the midpoint of all observations, but the methods state that C-chart centerlines were estimated from the first 20 data points. Could the authors please reconcile this discrepancy? Also, does “all observations” refer to all observations up to the current observation or all observations for the entire study? I am asking this question because if observations from May were used to calculate the centerline for April, this method of using a global baseline would not be useful for prospective surveillance.

d) Could the authors please label Figure 1? Perhaps a single label for all panels would work well, but the “daily counts,” centerline, UCL, and LCL should be labeled, as in Figure 2.

4) Methods – control chart analysis. The authors defined the end of the exponential phase as when a data point is below the lower limit – when this happens, should an I-Chart be converted back to a C-chart to monitor for re-entry into an exponential phase? If not, it seems that ongoing use of the exponential centerline and control limits is not very useful.

5) Methods – control chart analysis. My comment here is a generalization of comment #4 above. At the bottom of page 6, the authors state that the centerline and control limits remained constant over time, except when an initial transition to epidemic growth was noted. I think this strategy is a major limitation of this analysis that should at least be discussed in more detail or, ideally, addressed by showing how centerlines and control limits could be adjusted overtime as proof of concept for using these charts for prospective surveillance. For example, in Figure 1h, the majority of datapoints from mid-May onwards are above the apparent UCL, and every data point over this broad timeframe is above the centerline. Clearly, a mechanism is needed to update the centerline and control limits over time for COVID-19 monitoring in order for this chart type to remain useful outside of monitoring for exponential growth during the early stage of the outbreak.

6) Results – page 14, just prior to conclusions. The authors state that “the purpose was to draw conclusions about causal events,” but I believe this represents a typo and was intended to state that “the purpose was NOT to draw conclusions…”

7) Overall comment – did the authors explore other chart types and chart characteristics for this analysis? How would other charts perform in certain scenarios for monitoring COVID-19 trends? For example, did the authors experiment with different centerline definitions, out of control data point definitions, control limits, etc.? Per my comment above, would other charts provide more useful monitoring after exponential growth has been confirmed or for locations that continue to see variation in case rates without exponential growth?

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Reviewer #1: No

Reviewer #2: No

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PONE-D-20-24209R1

Using Control Charts to Understand Community Variation in COVID-19

PLOS ONE

Dear Dr. Inkelas,

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.

I participated as a reviewer for the initial evaluation of this manuscript and believe that you have made important improvements to the manuscript in responding to reviewer feedback. If you can make a few additional minor changes to improve readability, I anticipate that your manuscript will fully meet PLOS ONE's publication criteria.

The following three areas require revision or further clarification:

1. Results, page 12. The results state that household crowding at Westlake occurred in 45% of households, but the revised Table 1 lists 45% for Westlake household crowding. The results also describe Westlake as having a large household size with median size of 4.0; however, Table 1 gives median household size of 3.0 for Westlake, which is the second smallest household size among the five cities/neighborhoods studied. Could you please clarify this apparent discrepancy and ensure that all table/manuscript statistics are accurate?

2. Results, page 12. I recommend removing the last paragraph that mentions publicly available stratification of counts. This comment is useful but is better suited for the discussion section. This comment could be folded into the related discussion of general public vs. congregate living populations on page 14.

3. Discussion/Conclusions sections. I appreciate the revised and improved discussion and conclusions sections. Additional copyediting of these sections by the authors would improve readability and impact. While these changes would be stylistic and overall minor, I think editing by the authors at this stage is important because the journal does not provide copyediting. Here are some examples of the type of copyediting that may improve readability of these sections:

a) Page 13, line 3. Could consider: “...tool for daily learning and action that could benefit public health.”

b) Page 13, last sentence of paragraph 1. Could consider: “Use of control charts could reduce overreaction to expected random variation and encourage immediate action when…”

c) Page 14, second paragraph. Could consider: “Data alone do not tell the full story.”

d) Page 17. Could consider: “Use of control charts in public health will require an appreciation of their value and investment of time and resources into their creation and use.”

Please submit your revised manuscript by Apr 08 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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• 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. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols

We look forward to receiving your revised manuscript.

Kind regards,

Arthur Wakefield Baker

Academic Editor

PLOS ONE

[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.]

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Using Control Charts to Understand Community Variation in COVID-19

PONE-D-20-24209R2

Dear Dr. Inkelas,

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.

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PLOS ONE