Authors [GL, NS, CK, DG, EB] are employees of UnitedHealth Group; GL, NS, CK, and EB own stock in the company. DG is employed as the Senior Infectious Disease Fellow at UnitedHealth Group, Inc and serves as the Chief of Infectious Diseases for ProHealth NY an Optum Company. This does not alter our adherence to PLOS ONE policies on sharing data and materials.
COVID-19 test sensitivity and specificity have been widely examined and discussed, yet optimal use of these tests will depend on the goals of testing, the population or setting, and the anticipated underlying disease prevalence. We model various combinations of key variables to identify and compare a range of effective and practical surveillance strategies for schools and businesses.
We coupled a simulated data set incorporating actual community prevalence and test performance characteristics to a susceptible, infectious, removed (SIR) compartmental model, modeling the impact of base and tunable variables including test sensitivity, testing frequency, results lag, sample pooling, disease prevalence, externally-acquired infections, symptom checking, and test cost on outcomes including case reduction and false positives.
Increasing testing frequency was associated with a non-linear positive effect on cases averted over 100 days. While precise reductions in cumulative number of infections depended on community disease prevalence, testing every 3 days versus every 14 days (even with a lower sensitivity test) reduces the disease burden substantially. Pooling provided cost savings and made a high-frequency approach practical; one high-performing strategy, testing every 3 days, yielded per person per day costs as low as $1.32.
A range of practically viable testing strategies emerged for schools and businesses. Key characteristics of these strategies include high frequency testing with a moderate or high sensitivity test and minimal results delay. Sample pooling allowed for operational efficiency and cost savings with minimal loss of model performance.
As schools and businesses re-open and attempt to stay open, promptly detecting people with infectious COVID-19 is essential, especially as the risk of transmission may be expected to increase as contact networks increase in size and complexity [
Throughout the pandemic the number and variety of tests for detecting active infection have steadily increased [
Testing in large cohort settings such as schools and businesses that require continued surveillance can ensure that facilities remain open safely for the greatest number of people. We model various scenarios of test sensitivity and specificity, testing frequency, cost, and pooling to illustrate the range of practical and sustainable surveillance strategies.
To compare the effects of test sensitivity and specificity, test frequency, and the impact of pooling we considered a classical epidemiological susceptible, infectious-asymptomatic, infectious-symptomatic, removed (SIR) compartmental model for the tested population. That is, individuals move from one compartment to another as they transition from susceptible to infectious to removed. To account for the introduction of infections from the surrounding community, we added a time-dependent term which represents the rate (in people/time) of infections from outside interactions continuously in time. With frequent testing, this external forcing drives the behavior of the model (
The model simulates testing for a common group of people who mix continuously in an institution (i.e., in a school or office) and are subject to the introduction of infection from the surrounding unmonitored community. The framework couples regular testing, described by a handful of tunable parameters, to a disease model. The disease model is dynamic in time, and infections may originate both from inside-the-institution mixing and from the surrounding community at varying rates depending on prevalence.
We examine two scenarios for this forcing. The first is a relatively low and more-or-less constant rate of introduced infections, with data from the 7-day rolling average of the case count in Fayette County, Pennsylvania for the 100 days beginning March 26, 2020, as reported in the
Two scenarios for community prevalence corresponding, relatively, to low and high rates of imported infections (Panels (a) and (b)). Testing with a test with 98% sensitivity with 0-day resulting delay amidst high and low community prevalence (Panels (c) and (d)). Testing with a test with 98% sensitivity with 2-day resulting delay amidst high and low community prevalence (Panels (e) and (f)). Testing with a test with 60% sensitivity with 0-day resulting delay amidst high and low community prevalence (Panels (g) and (h)). Purple (dash-dot-dot) corresponds to no testing, orange (solid) to testing every two weeks with daily symptom tracking, green for testing every week with daily symptom tracking (dash-dot), blue (dash) for testing every 3 days with daily symptom tracking, and red (dot) for daily testing and symptom tracking.
Two scenarios for community prevalence corresponding, relatively, to low and high rates of imported infections (Panels (a) and (b)). Testing weekly with a test with 98% sensitivity with 0-day resulting delay with daily symptom tracking amidst high and low community prevalence (Panels (c) and (d)). Testing weekly with a test with 98% sensitivity with 2-day resulting delay with daily symptom tracking amidst high and low community prevalence (Panels (e) and (f)). Testing every 3 days with a test with 98% sensitivity with 2-day resulting delay with daily symptom tracking amidst high and low community prevalence (Panels (g) and (h)). Orange lines (solid) correspond to 30 samples pooled, green (dash-dot) to ten samples pooled, blue (dash) to five samples pooled, and red (dot) to 2 samples pooled.
Use case of a test with 98% sensitivity and 99.5% specificity with a 2-day result delay costing $100 and a 98% sensitive test with 99.5% specificity and a 0 day result delay costing $120 with free daily symptom tracking. In (c, d, g, h) every person in a positive pool is retested for confirmation and in (e, f) no confirmatory testing is done. We assume all confirmatory tests cost $100. Colors correspond to cost per person per day in dollars.
We do not include an “exposed” category as is often done for compartmental models but account for the shorter time a person is infectious rather than the longer period of time they are infected. Our model includes symptomatic and asymptomatic infectious individuals with daily symptom tracking. In the results that follow we assume 40% of infections are asymptomatic [
The initial conditions are chosen from the average of population-scaled new confirmed cases reported by the
Any testing strategy is better than none at all, and as expected, tests with increased sensitivities perform better for a given time frequency. At the most lenient frequency considered, every 14 days, the number of infections is reduced approximately 21–56% (
Daily symptom tracking assumed.
0.98 | 0.995 | 0 | 3 | 5 | 2 | 5 | 255 | $ 7.92 | $ 8.78 | 99.8% |
0.98 | 0.995 | 0 | 3 | 10 | 3 | 5 | 255 | $ 3.96 | $ 5.65 | 99.8% |
0.98 | 0.995 | 0 | 3 | 30 | 5 | 8 | 255 | $ 1.32 | $ 6.20 | 99.7% |
0.98 | 0.995 | 0 | 7 | 2 | 35 | 68 | 112 | $ 8.40 | $ 8.63 | 97.4% |
0.98 | 0.995 | 0 | 7 | 5 | 100 | 180 | 112 | $ 3.36 | $ 4.21 | 92.6% |
0.98 | 0.995 | 0 | 7 | 10 | 241 | 388 | 111 | $ 1.68 | $ 4.23 | 82.0% |
0.98 | 0.995 | 0 | 7 | 30 | 515 | 628 | 110 | $ 0.56 | $ 6.58 | 61.5% |
0.6 | 0.9 | 0 | 3 | 1 | 560 | 536 | 5034 | $ 6.60 | $ 10.22 | 58.2% |
0.98 | 0.995 | 0 | 14 | 1 | 588 | 649 | 58 | $ 8.40 | $ 8.73 | 56.1% |
0.98 | 0.995 | 2 | 3 | 5 | 666 | 446 | 246 | $ 6.60 | $ 8.96 | 50.3% |
0.98 | 0.995 | 2 | 3 | 30 | 706 | 467 | 246 | $ 1.10 | $ 9.87 | 47.3% |
0.8 | 0.9 | 0 | 7 | 1 | 712 | 675 | 2195 | $ 7.00 | $ 8.76 | 46.9% |
0.98 | 0.995 | 2 | 7 | 2 | 877 | 542 | 104 | $ 7.00 | $ 7.66 | 34.5% |
0.98 | 0.995 | 2 | 7 | 5 | 880 | 542 | 104 | $ 2.80 | $ 4.18 | 34.3% |
0.98 | 0.995 | 2 | 7 | 10 | 884 | 543 | 104 | $ 1.40 | $ 3.61 | 34.0% |
0.98 | 0.995 | 2 | 7 | 30 | 899 | 545 | 104 | $ 0.47 | $ 4.52 | 32.9% |
0.6 | 0.9 | 0 | 7 | 1 | 916 | 638 | 2198 | $ 2.80 | $ 4.47 | 31.6% |
0.98 | 0.995 | 2 | 14 | 1 | 924 | 551 | 51 | $ 7.00 | $ 7.26 | 31.1% |
0.8 | 0.9 | 0 | 14 | 1 | 977 | 631 | 1171 | $ 3.50 | $ 4.44 | 27.1% |
0.6 | 0.9 | 0 | 14 | 1 | 1050 | 603 | 1171 | $ 1.40 | $ 2.30 | 21.6% |
0.98 | 0.995 | 0 | 3 | 5 | 97 | 267 | 254 | $ 7.92 | $ 9.56 | 93.2% |
0.98 | 0.995 | 0 | 3 | 10 | 112 | 283 | 254 | $ 3.96 | $ 7.23 | 92.2% |
0.98 | 0.995 | 0 | 3 | 30 | 187 | 366 | 253 | $ 1.32 | $ 11.01 | 87.0% |
0.98 | 0.995 | 0 | 7 | 2 | 442 | 723 | 109 | $ 8.40 | $ 9.31 | 69.2% |
0.98 | 0.995 | 0 | 7 | 5 | 480 | 749 | 109 | $ 3.36 | $ 5.50 | 66.5% |
0.98 | 0.995 | 0 | 7 | 10 | 538 | 781 | 109 | $ 1.68 | $ 5.50 | 62.5% |
0.98 | 0.995 | 0 | 7 | 30 | 704 | 823 | 109 | $ 0.56 | $ 7.51 | 50.9% |
0.6 | 0.9 | 0 | 3 | 1 | 733 | 698 | 5015 | $ 6.60 | $ 10.28 | 48.9% |
0.98 | 0.995 | 0 | 14 | 1 | 853 | 845 | 57 | $ 8.40 | $ 8.79 | 40.5% |
0.8 | 0.9 | 0 | 7 | 1 | 859 | 799 | 2186 | $ 7.00 | $ 8.80 | 40.1% |
0.98 | 0.995 | 2 | 3 | 5 | 863 | 578 | 246 | $ 6.60 | $ 9.43 | 39.9% |
0.98 | 0.995 | 2 | 3 | 30 | 892 | 590 | 245 | $ 1.10 | $ 11.70 | 37.8% |
0.98 | 0.995 | 2 | 7 | 2 | 1006 | 624 | 103 | $ 7.00 | $ 7.72 | 29.9% |
0.98 | 0.995 | 2 | 7 | 5 | 1008 | 624 | 103 | $ 2.80 | $ 4.33 | 29.7% |
0.98 | 0.995 | 2 | 7 | 10 | 1013 | 625 | 103 | $ 1.40 | $ 3.90 | 29.4% |
0.98 | 0.995 | 2 | 7 | 30 | 1031 | 627 | 103 | $ 0.47 | $ 5.24 | 28.1% |
0.6 | 0.9 | 0 | 7 | 1 | 1038 | 720 | 2191 | $ 2.80 | $ 4.50 | 27.7% |
0.8 | 0.9 | 0 | 14 | 1 | 1062 | 750 | 1158 | $ 3.50 | $ 4.50 | 26.0% |
0.98 | 0.995 | 2 | 14 | 1 | 1080 | 645 | 51 | $ 7.00 | $ 7.26 | 24.7% |
0.6 | 0.9 | 0 | 14 | 1 | 1134 | 681 | 1161 | $ 1.40 | $ 2.33 | 21.0% |
* Cost calculation assumes a test with a 98% sensitivity and 0-day delay in returning results costs $120, a 98% sensitive test with a 2-day delay in results costs $100, an 80% sensitive test costs $50, and a 60% sensitive test costs $20. All (true and false) positive tests are confirmed using a $100 test. The distribution of positive tests among pooled samples is uniform as is consistent with the homogeneous mixing assumptions of the SIR model, and we assume everyone in a pool that is positive will undergo a confirmatory test.
Next we looked at testing strategies that incorporate pooling.
Our findings demonstrate that it is not only critical to choose the right test in terms of performance in asymptomatic individuals, but to use the test in the defined population at the optimal frequency to reduce the risk of case escalation. Optimization is further enhanced at the population level by understanding of underlying disease prevalence and utilization of pooling to reduce cost and increase efficiency. The “ideal” test strategy must be balanced with the practicalities of cost per person to ensure sustainability. For example, daily testing with a 60% sensitive test attenuates community spread, but at a cost of $30.11 per person per day with confirmatory testing, or $20.00 without, may not be possible. Using a 60% sensitive test less frequently reduces expense but sacrifices significant performance. A 98% sensitive test with no delay in results administered every 3 days with pools of 30 people, and no confirmatory test offered by the institution costs less than $1.50 per person per day, with high performance. Even with a highly specific (99.5%) test such as a PCR, in a low prevalence community with large pools, false positives may still become an issue. The previous example results in 253 false positives over 100 days, highlighting the importance of confirmatory testing. The model demonstrates that frequency of testing, test sensitivity, turn-around time, and the external community prevalence are all important factors to consider, and there is often more than one testing strategy to achieve the desired level of performance. The computational code is available as an on line supplement, and an easy-to-use web-based simulator to test various scenarios is available at
With these scenarios in hand, institutions can make an informed operational choice, devise pods or cohorts to be tested by pooling and potentially isolated if positive, and create clear communication about a surveillance rationale. Acknowledging a dynamic community prevalence, the model can be re-run, and the testing strategy can be optimized to maximize benefit at the lost cost and least amount of disruption.
The frequency of test usage to minimize amplification of infection and allow schools and worksites to remain open is an important factor. Given the cost of high frequency testing, we demonstrate the value of pooling of samples to increase efficiency, particularly in areas with lower population prevalence. As background prevalence increases, the value of pooling diminishes as the likelihood of a positive pool will rise, but even a pool of two to three samples results in a dramatic reduction in the need for individual sample analysis. As noted above, with an extremely low prevalence, even in the case of a 99.5% specific test, false positives are much more likely than true positives and confirmatory testing may be necessary. A 90% specificity test would result in an untenable number of false positives over the course of 100 days without confirmatory testing. As shown in
This study confirms and extends previous work. Paltiel et al. [
Populations housed in long-term care facilities are especially vulnerable to COVID-19; surveillance programs designed for these settings may have different goals and tolerances for infection risk than those designed to maintain functionality for other institutions. Smith and colleagues [
Since our work focuses on screening and not performing diagnostic testing, the actual sensitivity of the various available COVID tests for this purpose is not entirely clear. The original testing approaches for COVID-19 focused on the high sensitivity required for diagnosis by clinicians in all stages of the acute period of COVID-19 through detection of SARS-CoV-2 RNA performed on patients with a high pretest probability of disease. This paradigm focused on high sensitivity tests with the performance feature of very low NAAT detectable units/mL (NDU/mL) with a goal of diagnosing patients even if past the contagious period. These tests were not optimized nor validated in terms of sensitivity for the detection of infectious individuals that might spread disease in schools, the workplace or other social situations.
Several studies looking at the ability to culture virus from samples collected from infected individuals have established that RNA copy numbers of 1,000,000 RNA copies/ml or higher are required for any consistent success in viral culture [
Our work has a number of limitations. The SIR compartmental model provides a simplified representation of the natural history of the disease. For example, it assumes uniform mixing of the population being tested and a uniform distribution of likelihood of a positive test. The model is formulated at a population level; it does not permit the tracking of individuals. For example, we cannot incorporate the change in test sensitivity with time from infection [
Despite these limitations, sensitivity, pooling, and frequency modeling can guide institutions on best-fit testing strategies that align to their practical constraints. Organizations can apply this model to determine their best testing strategy given current community prevalence and operational and financial resources that enable sustained testing to stay safely open during the pandemic.
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PONE-D-20-36267
Identifying Optimal COVID-19 Testing Strategies for Schools and Businesses: Balancing Testing Frequency, Individual Test Technology, and Cost
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Reviewer #1: This is very good, original, and profound research work done and a well written manuscript paper. My review is therefore limited to the following minor comments only.
In the supplementary material detailing the model used, under testing strategies; for effect of Symptom tracking alone, you mention that you assume 78% of cases entering a symptomatic phase are caught and a fraction b of these cases self isolate. However, in the manuscript in line 144 – 145 you mention that for the results obtained you had assumed that symptom tracking will catch 66% of symptomatic infections. Kindly clarify this disparity in percentages considered. Wouldn’t it give a substantial disparity in the simulation results obtained as well?
For a population as small as the one modelled in this study (1500 subjects), a stochastic model may be more appropriate. Was this considered?
As alluded to in the manuscript, asymptomatic individuals may also reach just as high detectable viral load levels for the various testing technologies. However, when it comes to infectiousness, some corner of available literature on COVI-19 and earlier communications from the United Nations suggest that the asymptomatic individuals may not be as infectious as symptomatic individuals hence their per capita rate of effective contacts I.e (β) may not be the same as for individuals with symptomatic infection. This has not been considered in the model shown in the supplementary material in 3.2 (Equations). It may not be necessary to consider this at this point but chances are that incorporating this may produce different results and this scenario may be a more accurate representation of the natural history of the infection.
The “in text” referencing should be written in square brackets e.g as [14] and not as superscripts e.g as 14, 23.
The sentence between lines 42 and 44 reads’ “As schools and businesses re-open and attempt to stay open, promptly detecting people with infectious COVID-19 is essential, especially as the risk of transmission is expected to increase with colder weather, more time indoors, and closer contact with others….”.
My impression was that as economies open, people interact more outdoors as they are more frequently away from the indoor safety of their homes such as during lock downs. Perhaps just check that statement again on the part that says “more time indoors”.
The sentence between lines 77 and 79 should read as “To compare the effects of test sensitivity and specificity, test frequency, the impact of pooling, and other key factors influencing testing strategy, we considered a classical epidemiological susceptible, infectious-asymptomatic, infectious-symptomatic, removed (SIR) compartmental model for the tested population.
The sentence describing beta (β) is incomplete in the supplementary material detailing the model. It only partly says that beta is the contact rate. I believe beta can be more accurately described as the per capita rate of effective contacts.
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February 24, 2021
Martin Chtolongo Simuunza, PhD
Academic Editor
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Dear Dr. Simuunza:
We appreciate the comments and suggestions of the reviewer and have incorporated their recommendations or provided alternative considerations. Below is our response to their comments in a point-by-point format, with the original reviewer comment in italics.
EDITOR COMMENTS:
Ensure that your referencing style conforms to that prescribed in the PLOS One guidelines.
References have been updated to match PLOS One formatting guidelines.
Provide references for the tests you mention in lines 53 to 56.
Reference added:
A. La Marca, M. Capuzzo, T. Paglia, L. Roli, T. Trenti, and S. M. Nelson, “Testing for SARS-CoV-2 (COVID-19): a systematic review and clinical guide to molecular and serological in-vitro diagnostic assays,” Reproductive BioMedicine Online, vol. 41, no. 3, pp. 483–499, Sep. 2020, doi: 10.1016/j.rbmo.2020.06.001.
Figure 1 title should read " Schematic representation of the model"
Updated.
Tables 2, 3 and 4 are presenting results. Should it be not appropriate that they are presented in the results section instead of results?
There is only one table in the manuscript. We assume this refers to the figures. We have adjusted the placement of the figures so that they are in the Results section.
Include the word "sensitivity" after the percentages in lines 62 and 63.
We added sensitivity in lines 234 and 235, which is what we believe you were referring to.
Thank you for stating the following in the Competing Interests section:
"The authors have declared that no competing interests exist."
We note that one or more of the authors are employed by a commercial company: UnitedHealth Group Inc, ProHealth Care.
2.1. Please provide an amended Funding Statement declaring this commercial affiliation, as well as a statement regarding the Role of Funders in your study. If the funding organization did not play a role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript and only provided financial support in the form of authors' salaries and/or research materials, please review your statements relating to the author contributions, and ensure you have specifically and accurately indicated the role(s) that these authors had in your study. You can update author roles in the Author Contributions section of the online submission form.
Please also include the following statement within your amended Funding Statement.
“The funder provided support in the form of salaries for authors [insert relevant initials], but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the ‘author contributions’ section.
If your commercial affiliation did play a role in your study, please state and explain this role within your updated Funding Statement.
We have updated the funding statement to read:
Authors [GL, NS, CK, DG, EB] are employees of Optum Labs at UnitedHealth Group. Author [DG] also serves as the Chief of Infectious Disease for ProHealth NY, part of Optum. These funders provided support in the form of salaries for authors [GL, NS, CK, DG, EB], but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Please also provide an updated Competing Interests Statement declaring this commercial affiliation along with any other relevant declarations relating to employment, consultancy, patents, products in development, or marketed products, etc.
GL is an employee of UnitedHealth Group and owns stock in the company. DG is employed as the Senior Infectious Disease Fellow at the commercial company, UnitedHealth Group, Inc and serves as the Chief of Infectious Diseases for ProHealth NY an Optum Company.
Please include captions for your Supporting Information files at the end of your manuscript, and update any in-text citations to match accordingly. Please see our Supporting Information guidelines for more information:
We have added a caption for the supplementary file.
REVIEWER COMMENTS
Reviewer #1: This is very good, original, and profound research work done and a well written manuscript paper. My review is therefore limited to the following minor comments only.
In the supplementary material detailing the model used, under testing strategies; for effect of Symptom tracking alone, you mention that you assume 78% of cases entering a symptomatic phase are caught and a fraction b of these cases self isolate. However, in the manuscript in line 144 – 145 you mention that for the results obtained you had assumed that symptom tracking will catch 66% of symptomatic infections. Kindly clarify this disparity in percentages considered. Wouldn’t it give a substantial disparity in the simulation results obtained as well?
The supplemental information has been updated to reflect the assumption that symptom tracking will catch 66% of symptomatic infections. The source of this choice is the Nature Medicine article below:
C. Menni et al., “Real-time tracking of self-reported symptoms to predict potential COVID-19,” Nature Medicine, vol. 26, no. 7, Art. no. 7, Jul. 2020, doi: 10.1038/s41591-020-0916-2.
However, as the reviewer notes, this value certainly has an impact on the simulation results, and there is continuing discussion in the literature about the efficacy of symptom tracking. For this reason, this is an adjustable parameter in the online calculator.
For a population as small as the one modelled in this study (1500 subjects), a stochastic model may be more appropriate. Was this considered?
Our aim in this project was to build the simplest model we could think of that could incorporate pooling and deal with the realities of a dynamic underlying (and unknown!) community prevalence of disease. For this reason, we built the model on basic SIR-type deterministic dynamics. One of the reasons for this was to ensure that the model (and its limitations) could easily be explained to policy makers and others (school superintendents, long-term care facility administrators, parents,…). For this reason, we did not consider variations or other modeling approaches, e.g., stochastic models or agent-based approaches, that might better capture some elements of the situation.
As alluded to in the manuscript, asymptomatic individuals may also reach just as high detectable viral load levels for the various testing technologies. However, when it comes to infectiousness, some corner of available literature on COVI-19 and earlier communications from the United Nations suggest that the asymptomatic individuals may not be as infectious as symptomatic individuals hence their per capita rate of effective contacts I.e (β) may not be the same as for individuals with symptomatic infection. This has not been considered in the model shown in the supplementary material in 3.2 (Equations). It may not be necessary to consider this at this point but chances are that incorporating this may produce different results and this scenario may be a more accurate representation of the natural history of the infection.
It is true that the model does not incorporate a distinction in infectiousness between symptomatic and asymptomatic individuals. In the name of simplicity, we assume, however, asymptomatic infectious individuals are as contagious as symptomatic ones. Notably, this “stacks the deck” against any testing program one might design. Thus, this is a conservative simplifying assumption. While we certainly don’t advocate being wasteful with testing capacity, our objective was to err on the side of overbuilding testing programs.
The “in text” referencing should be written in square brackets e.g as [14] and not as superscripts e.g as 14, 23.
References have been updated to match PLOS One formatting guidelines.
The sentence between lines 42 and 44 reads’ “As schools and businesses re-open and attempt to stay open, promptly detecting people with infectious COVID-19 is essential, especially as the risk of transmission is expected to increase with colder weather, more time indoors, and closer contact with others….”.
My impression was that as economies open, people interact more outdoors as they are more frequently away from the indoor safety of their homes such as during lock downs. Perhaps just check that statement again on the part that says “more time indoors”.
When this draft was originally written, during fall in the northern hemisphere, there was great concern about colder temperatures in winter leading to more indoor activities (and potentially more infections). For example, in some localities in the US, some restaurants stayed open in the fall by utilizing outdoor dining. The reviewer is correct, of course, that re-opening schools and businesses will also lead to the more contacts between individuals from distinct households.
The sentence has been rewritten to read: “As schools and businesses re-open and attempt to stay open, promptly detecting people with infectious COVID-19 is essential, especially as the risk of transmission may be expected to increase as contact networks increase in size and complexity.”
The sentence between lines 77 and 79 should read as “To compare the effects of test sensitivity and specificity, test frequency, the impact of pooling, and other key factors influencing testing strategy, we considered a classical epidemiological susceptible, infectious-asymptomatic, infectious-symptomatic, removed (SIR) compartmental model for the tested population.
The sentence describing beta (β) is incomplete in the supplementary material detailing the model. It only partly says that beta is the contact rate. I believe beta can be more accurately described as the per capita rate of effective contacts.
The description of β in the supplementary material has been updated.
Submitted filename:
Identifying Optimal COVID-19 Testing Strategies for Schools and Businesses: Balancing Testing Frequency, Individual Test Technology, and Cost
PONE-D-20-36267R1
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All my corrections have been sufficiently attended to. The paper is now well written and the methodology scientifically sound.
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- Line 283 – 287 recheck for correct grammar.
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PONE-D-20-36267R1
Identifying optimal COVID-19 testing strategies for schools and businesses: Balancing testing frequency, individual test technology, and cost
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