Adaptive strategies for the deployment of rapid diagnostic tests for COVID-19: a modelling study

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n.callMethod.apply(n,arguments):n.queue.push(arguments)} ;if(!f._fbq)f._fbq=n; n.push=n;n.loaded=!0;n.version='2.0';n.queue=[];t=b.createElement(e);t.async=!0; t.src=v;s=b.getElementsByTagName(e)[0];s.parentNode.insertBefore(t,s)}(window, document,'script','https://connect.facebook.net/en_US/fbevents.js'); fbq('init', '1641728616063202'); fbq('track', "PixelInitialized", {}); Skip to content Gates Open Research file_upload Submit via VeriXiv search clear search menu close clear Search Browse Gateways & Collections How to Publish Submit via VeriXiv My Submissions Article Guidelines Article Guidelines (New Versions) Open Data, Software and Code Guidelines Open Data and Accessible Source Materials Guidelines (HSS) Prepublication Checks Production Process Posters and Slides Guidelines Document Guidelines Publication Charges Finding Article Reviewers About How it Works For Reviewers Our Advisors Policies Glossary FAQs For Developers Contact Blog My Account Submissions Content and Tracking Alerts My Details Sign In Submit via VeriXiv { "@context": "https://schema.org", "@type": "ScholarlyArticle", "mainEntityOfPage": { "@type": "WebPage", "@id": "https://gatesopenresearch.org/articles/7-6" }, "headline": "Adaptive strategies for the deployment of rapid diagnostic tests for COVID-19: a modelling study", "datePublished": "2023-01-27T09:27:20", "dateModified": "2025-05-27T15:06:28", "author": [ { "@type": "Person", "name": "Lucia Cilloni" }, { "@type": "Person", "name": "Emily Kendall" }, { "@type": "Person", "name": "David Dowdy" }, { "@type": "Person", "name": "Nimalan Arinaminpathy" } ], "publisher": { "@type": "Organization", "name": "Gates Open Research", "logo": { "@type": "ImageObject", "url": "https://gatesopenresearch.org/img/AMP/Gates_image.png", "height": 600, "width": 47 } }, "image": { "@type": "ImageObject", "url": "https://gatesopenresearch.org/img/AMP/Gates_image.png", "height": 1200, "width": 94 }, "description": " Background Lateral flow assays (LFAs) for the rapid detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) provide an affordable, rapid and decentralised mean for diagnosing coronavirus disease 2019 (COVID-19). Concentrating on urban areas in low- and middle-income countries, the aim of this analysis was to estimate the degree to which ‘dynamic’ screening algorithms, that adjust the use of confirmatory polymerase chain reaction (PCR) testing based on epidemiological conditions, could reduce cost without substantially reducing the impact of testing. Methods Concentrating on a hypothetical ‘second wave’ of COVID-19 in India, we modelled the potential impact of testing 0.5% of the population per day at random with LFA, regardless of symptom status. We considered dynamic testing strategies where LFA positive cases are confirmed with PCR when LFA positivity rates are below a given threshold (10%, 50% and 90% of the peak positivity rate at the height of the epidemic wave), compared to confirming all positive LFA results or confirming no results. Benefit was estimated based on cumulative incidence of infection, and resource requirements, based on the cumulative number of PCR tests used and the cumulative number of unnecessary isolations. Results A dynamic strategy of discontinuing PCR confirmation when LFA positivity exceeded 50% of the peak positivity rate in an unmitigated epidemic would achieve comparable impact to one employing PCR confirmation throughout (9.2% of cumulative cases averted vs 9.8%), while requiring 35% as many PCR tests. However, the dynamic testing strategy would increase the number of false-positive results substantially, from 0.07% of the population to 1.1%. Conclusions Dynamic diagnostic strategies that adjust to epidemic conditions could help maximise the impact of testing at a given cost. Generally, dynamic strategies reduce the number of confirmatory PCR tests needed, but increase the number of unnecessary isolations. Optimal strategies will depend on whether greater priority is placed on limiting confirmatory testing or false-positive diagnoses. " } { "@context": "http://schema.org", "@type": "BreadcrumbList", "itemListElement": [ { "@type": "ListItem", "position": "1", "item": { "@id": "https://gatesopenresearch.org/", "name": "Home" } }, { "@type": "ListItem", "position": "2", "item": { "@id": "https://gatesopenresearch.org/browse/articles", "name": "Browse" } }, { "@type": "ListItem", "position": "3", "item": { "@id": "https://gatesopenresearch.org/articles/7-6", "name": "Adaptive strategies for the deployment of rapid diagnostic tests for..." } } ] } Home Browse Adaptive strategies for the deployment of rapid diagnostic tests for... ALL Metrics - Views Downloads Get PDF Get XML Cite How to cite this article Cilloni L, Kendall E, Dowdy D and Arinaminpathy N. Adaptive strategies for the deployment of rapid diagnostic tests for COVID-19: a modelling study [version 2; peer review: 2 approved, 1 approved with reservations] . Gates Open Res 2025, 7 :6 ( https://doi.org/10.12688/gatesopenres.14202.2 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. Close Copy Citation Details Export Export Citation Sciwheel EndNote Ref. Manager Bibtex ProCite Sente EXPORT Select a format first Track Share ▬ ✚ Research Article Revised Adaptive strategies for the deployment of rapid diagnostic tests for COVID-19: a modelling study [version 2; peer review: 2 approved, 1 approved with reservations] Lucia Cilloni https://orcid.org/0000-0002-5456-5723 1 , Emily Kendall 1 , David Dowdy 1 , Nimalan Arinaminpathy 2 Lucia Cilloni https://orcid.org/0000-0002-5456-5723 1 , Emily Kendall 1 , David Dowdy 1 , Nimalan Arinaminpathy 2 PUBLISHED 27 May 2025 Author details Author details 1 Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA 2 MRC Center for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, UK Lucia Cilloni Roles: Conceptualization, Data Curation, Formal Analysis, Methodology, Software, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing Emily Kendall Roles: Conceptualization, Investigation, Methodology, Project Administration, Supervision, Writing – Original Draft Preparation, Writing – Review & Editing David Dowdy Roles: Conceptualization, Investigation, Methodology, Project Administration, Supervision, Writing – Original Draft Preparation, Writing – Review & Editing Nimalan Arinaminpathy Roles: Conceptualization, Funding Acquisition, Investigation, Methodology, Project Administration, Supervision, Writing – Original Draft Preparation, Writing – Review & Editing OPEN PEER REVIEW DETAILS REVIEWER STATUS Abstract Background Lateral flow assays (LFAs) for the rapid detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) provide an affordable, rapid and decentralised mean for diagnosing coronavirus disease 2019 (COVID-19). Concentrating on urban areas in low- and middle-income countries, the aim of this analysis was to estimate the degree to which ‘dynamic’ screening algorithms, that adjust the use of confirmatory polymerase chain reaction (PCR) testing based on epidemiological conditions, could reduce cost without substantially reducing the impact of testing. Methods Concentrating on a hypothetical ‘second wave’ of COVID-19 in India, we modelled the potential impact of testing 0.5% of the population per day at random with LFA, regardless of symptom status. We considered dynamic testing strategies where LFA positive cases are confirmed with PCR when LFA positivity rates are below a given threshold (10%, 50% and 90% of the peak positivity rate at the height of the epidemic wave), compared to confirming all positive LFA results or confirming no results. Benefit was estimated based on cumulative incidence of infection, and resource requirements, based on the cumulative number of PCR tests used and the cumulative number of unnecessary isolations. Results A dynamic strategy of discontinuing PCR confirmation when LFA positivity exceeded 50% of the peak positivity rate in an unmitigated epidemic would achieve comparable impact to one employing PCR confirmation throughout (9.2% of cumulative cases averted vs 9.8%), while requiring 35% as many PCR tests. However, the dynamic testing strategy would increase the number of false-positive results substantially, from 0.07% of the population to 1.1%. Conclusions Dynamic diagnostic strategies that adjust to epidemic conditions could help maximise the impact of testing at a given cost. Generally, dynamic strategies reduce the number of confirmatory PCR tests needed, but increase the number of unnecessary isolations. Optimal strategies will depend on whether greater priority is placed on limiting confirmatory testing or false-positive diagnoses. READ ALL READ LESS Keywords COVID-19, lateral flow assays, mathematical modelling Corresponding Author(s) Lucia Cilloni ( [email protected] ) Close Corresponding author: Lucia Cilloni Competing interests: No competing interests were disclosed. Grant information: This work was supported by the Gates Foundation [INV-023013]. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Copyright: © 2025 Cilloni L et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. How to cite: Cilloni L, Kendall E, Dowdy D and Arinaminpathy N. Adaptive strategies for the deployment of rapid diagnostic tests for COVID-19: a modelling study [version 2; peer review: 2 approved, 1 approved with reservations] . Gates Open Res 2025, 7 :6 ( https://doi.org/10.12688/gatesopenres.14202.2 ) First published: 27 Jan 2023, 7 :6 ( https://doi.org/10.12688/gatesopenres.14202.1 ) Latest published: 27 May 2025, 7 :6 ( https://doi.org/10.12688/gatesopenres.14202.2 ) Revised Amendments from Version 1 The major differences between this version of the article and the previously published version include revisions made in response to reviewer feedback. We have edited the manuscript to improve clarity and overall comprehensibility, ensuring that the presentation of our findings is more accessible and organised. Additionally, we have updated and expanded the background section to include more recent references and provide a clearer context for the relevance and importance of our analysis. The major differences between this version of the article and the previously published version include revisions made in response to reviewer feedback. We have edited the manuscript to improve clarity and overall comprehensibility, ensuring that the presentation of our findings is more accessible and organised. Additionally, we have updated and expanded the background section to include more recent references and provide a clearer context for the relevance and importance of our analysis. See the authors' detailed response to the review by Cihan Papan See the authors' detailed response to the review by Flaminia Tomassetti READ REVIEWER RESPONSES Introduction Diagnosis has been a critical component of the global coronavirus disease 2019 (COVID-19) response 1 . In New Delhi, India, during the first wave of the pandemic, the state government implemented a task force in charge of testing, tracing and tracking new infections, with a combination of reverse-transcriptase polymerase chain reaction (RT-PCR) tests, point-of-care molecular tests and rapid antibody tests, in an effort to detect and isolate incident cases of COVID-19 quickly and reduce transmission 2 , 3 . As the numbers of new infections started to drop in September 2020, India loosened the measures that had been put in place to reduce transmission, including lockdowns and social distancing, but in the first quarter of 2021 the surge of the new delta variant of COVID-19, spreading faster than the first wave, took India by surprise 4 . Across megacities like New Delhi and Mumbai, which had seen very high infection rates during the first COVID-19 wave and therefore believed that a large proportion of their population would have gained some antibody protection from further infection, the delta wave spread very quickly, crippling hospitals and health facilities 4 . Despite the introduction of the COVID-19 vaccine in January of 2021, the coverage by March was not high enough to effectively curb the delta wave surge 5 . Although molecular tests such as RT-PCR are highly sensitive 6 , they are costly and require training to operate. Because of these factors, low-and-middle-income countries (LMICs) have limited RT-PCR use 7 , 8 . Rapid diagnostic tests (RDTs), employing lateral flow assays, offer a more affordable approach to detecting SARS-CoV-2, and have been used widely and effectively for community-level testing, as well as for self-testing in households, including in LMICs 9 – 13 . RDTs are, however, less sensitive and specific than PCR which may result in a number diagnoses being missed, as well as more unnecessary isolations of individuals with false-positive test results 13 – 15 . In previous work 16 , we examined the use of RDTs in pandemic response, using modelling to compare different scenarios for their use in cities such as New Delhi, India, and Kampala, Uganda. The results of that analysis suggested that LFAs would affect transmission most efficiently if they were focused on testing symptomatic patients presenting to healthcare facilities, rather than additionally aiming to reach asymptomatic and presymptomatic cases in the community. However, a limitation of that work is that diagnostic algorithms involving LFAs were assumed to remain uniform through time, regardless of the prevalence of SARS-CoV-2. In practice, strategies that can adapt during the course of a pandemic wave – for example, switching to more simplified, rapid algorithms as prevalence increases – may provide an approach for maximising the benefit of LFAs. Here, we sought to examine such strategies using modelling, focusing on the potential impact of dynamic testing strategies during an epidemic consistent with India’s second wave of COVID-19. Although the severity of COVID-19 as a pandemic threat has diminished since 2020, these questions remain relevant for future pandemic response. Methods Model outline We built on a deterministic, compartmental model of a hypothetical second wave of SARS-CoV-2 transmission in New Delhi, originally developed in 16 , with initial conditions and basic reproduction number similar to those relating to the delta wave in India, in 2020–2021. The overall model structure is illustrated schematically in Figure 1 . Briefly, to account for age-dependent severity of infection, and to capture the population structure in New Delhi, the model incorporates three different age groups: <19 years old, 19 – 64 years old, and 65 years old and above. It also captures important features of the natural history of SARS-CoV-2 infection, including presymptomatic infection (cases prior to developing symptoms) and asymptomatic infection (cases who never develop symptoms), both of which are capable of transmission 17 , 18 . We did not model vaccination, because vaccination coverage had not yet reached substantial levels during the first wave in India 19 . Although the originally published model 16 incorporated additional structure for delays in PCR testing, for simplicity we did not incorporate that structure in the present work. Instead, for the current analysis, we modelled challenges in the availability of PCR in LMIC settings by assuming that the delay for PCR confirmatory testing is fixed at three days (amounting to half of the infectious period) at all stages of the pandemic, and that when PCR confirmatory testing is used, isolations are deferred until LFA positive results are confirmed. As described below, we estimated the number of PCR tests that would be needed under different testing strategies, treating this indicator as a quantity to be minimised. Figure 1. Schematic illustration of the model structure. ( A ) Compartments representing natural history of SARS-CoV-2, and processes involved in a test-and-isolate intervention. This structure is stratified into three age groups: children (≤ 19 years old), adults (20 – 64 years old), and older adults (≥ 65 years old). As described in the main text, we assume that asymptomatic and pre-symptomatic individuals are infectious, but potentially to a lesser extent than symptomatic cases. Arrows in blue show isolation through testing, shown in greater detail in the bottom panel. ( B ) Detail of diagnostic testing. We assumed that there is no constraint on the number of LFA tests that can be performed per unit time. To reflect limits on PCR availability in LMICs in a simple way, we assumed that PCR results are only available after three days, and further that individuals are not required to self-isolate during this period. Model parameters were specified as follows: natural history parameters were drawn from the literature, including estimates of the relative infectivity of asymptomatic/pre-symptomatic vs symptomatic infection. The rate of infectivity per symptomatic case was calibrated in order to yield a basic reproduction number, R 0 , of 2.5, consistent with previous work 20 . Finally, we drew from the literature for the sensitivity and specificity of LFAs and PCR 6 , 7 . See Table S1 in the Underlying data , for a full list of model parameters and values 21 . The code itself is available from GitHub and archived with Zenodo 22 . Scenarios modelled We concentrated on community-level testing, which aims to use LFAs to identify infectious cases of SARS-CoV-2, regardless of symptom status, and to isolate all who test positive. While using LFAs alone would lead to rapid isolation of people with SARS-CoV-2 (and thus greater reduction in transmission), previous analysis 16 illustrated that a major limitation of such a strategy is that it would lead to a prohibitive number of false-positive diagnoses. It would therefore be critical to implement confirmatory testing, for example using PCR, following any LFA positive test results. We therefore examined whether there would be stages in a pandemic wave when the requirement to confirm positive LFA results could be lifted, in order to reduce costs and minimise any delays in the isolation of individuals with SARS-CoV-2. The proportion of LFA results that were positive (an indicator of current disease burden that would be readily available in a public testing program, although not in at-home testing) was used to trigger switching between confirmatory testing and LFA alone in the dynamic strategies we evaluated. We determined the peak LFA positivity rate in our model of an unmitigated epidemic wave (i.e., with no isolation of infected individuals), and we defined thresholds relative to this “peak value”. We modelled the following scenarios: (i) A ‘low threshold’ dynamic strategy where PCR is used for confirmation of LFA-positive results as long as the proportion of LFA positive tests is less than 10% of the peak value. We assumed that, for LFA positivity rates above this threshold, all individuals testing positive on LFA would be asked to isolate without need for PCR confirmation. (ii) A ‘medium threshold’ dynamic strategy: the same as (i), but with the LFA positivity threshold set at 50% of the peak value. (iii) A ‘high threshold’ dynamic strategy: the same as (i), but with the LFA positivity threshold set at 90% of the peak value. (iv) ‘LFA only’: A non-dynamic strategy where PCR confirmation is never used for LFA-positive individuals, and (v) ‘LFA+PCR’: A non-dynamic strategy where PCR confirmation is always required for LFA-positive individuals. The latter two scenarios were included for reference; they represent, respectively, a strategy that would create large numbers of false-positive diagnoses (strategy iv), and one that would involve maximum PCR usage (strategy v). For each strategy we estimated the daily and cumulative incidence of symptomatic COVID as a measure of epidemiological impact, assuming that the testing regime was initiated before the onset of the pandemic wave. We also calculated two proxies for trade-offs between impact and resource requirements: number of cases averted per PCR test used, and number of cases averted per unnecessary (false-positive) isolation. Uncertainty All parameters were subject to the uncertainty intervals shown in Table S1 in the supporting information. Uncertainty was estimated by using Latin Hypercube Sampling to obtain 250 samples of model parameters; identifying the value of β that yielded R 0 = 2.5 for each parameter set; and simulating model projections using each of these 250 samples. Uncertainty in model outputs was calculated using 2.5 th and 97.5 th percentiles as the lower and upper 95% uncertainty intervals, while central estimates were obtained using the 50 th percentile. All analyses were performed in MATLAB, R2022a. An open-source alternative that may be able to perform similar functions required to repeat this study is GNU Octave . Results Figure 2 shows the results of each community-level testing strategy on the hypothetical second epidemic wave of COVID-19 in India, with overall cases averted shown in Table 1 . For example, an intervention testing only with LFA, with isolation of all LFA-positive individuals and no need for confirmation of positive LFA results, would avert 9.8% (95% CrI 6.5 – 13.2%) of symptomatic cases, while requiring PCR confirmation for all LFA-positive individuals would reduce this impact by about one-third, to 6% (95% CrI 4 – 8%). Each of the dynamic strategies was projected to have an epidemiological impact intermediate to these two extremes. Figure 2. Simulated daily incidence under different LFA scenarios. Curves show simulations consistent with the ‘second wave’ of COVID-19 in India, under the following testing scenarios: ‘LFA testing’ denotes the sole use of LFA for testing with no follow-up confirmation; ‘LFA+PCR’ denotes the use of PCR to confirm LFA-positive results; ‘Dynamic testing (medium threshold)’ denotes PCR confirmation of LFA-positives as long as LFA positivity results are below 50% of peak positivity in an unmitigated wave (no confirmation otherwise); and ‘low’ and ‘high’ thresholds correspond respectively to 10% and 90%. In all scenarios we assumed that a proportion 0.5% of the population is tested with LFA, at random each day, regardless of symptoms, and that all diagnosed with SARS-CoV-2 are isolated. Solid lines show central estimates, and shaded areas show 95% uncertainty intervals (for clarity, only shown illustratively in the baseline scenario). Table 1. Summary of epidemiological impact and resource use. Entries show values summarising the outcomes in Figure 2 – Figure 3 . Incidence reduction (compared to baseline scenario) [95% CrI] PCR consumption Unnecessary isolations (relative to population size) Dynamic (medium threshold) 9.20% [6.14%-12.36%] 1.8 million 1.10% Dynamic model (low threshold) 9.70% [6.23%-13%] 830,000 1.70% Dynamic model (high threshold) 7.71% [5.28%-10.64%] 1.76 million 0.45% Figure 3 shows two proxies of resource requirement, namely PCR test volume and unnecessary isolations. An LFA+PCR strategy requires the greatest number of PCR tests (green curve, left-hand panel), while also incurring the fewest unnecessary isolations (right-hand panel). On the other hand, while an LFA-only strategy naturally incurs no PCR usage, it also leads to over 5% of the population being unnecessarily isolated (red curve, right-hand panel). In both cases, dynamic strategies can mitigate these costs substantially. For example, a medium-threshold strategy, one that requires PCR confirmation only when the proportion of LFA positivity is below 50% of peak positivity in an unmitigated wave, would require a total of 1.8 million PCR tests (compared to 2.5 million for an LFA+PCR strategy), and would incur unnecessary isolations for 1.1% of the population. Figure 3. Proxies for costs of different testing strategies. The left-hand panel shows the cumulative number of PCR tests used over time, while the right-hand panel shows the cumulative number of unnecessary isolations over time (arising from false-positive diagnoses of SARS-CoV-2), as a proportion of the population. Figure 4 compares strategies in terms of the cases averted per PCR test used (left-hand panel), and cases averted per unnecessary isolation (right-hand panel). In both cases, higher values correspond to more favourable strategies. Results echo the overall trade-off shown in Figure 3 : that in general, strategies that are favourable in terms of cases averted per PCR test used are less favourable in terms of cases averted per unnecessary isolation. Quantitative estimates behind these results are listed in Table 1 . Figure 4. Proxies for incremental cost-effectiveness ratios (ICERs) under the different testing strategies. As a measure of health gains for the denominator for ICER calculations, we estimated the symptomatic cases averted relative to a scenario of no community-level LFA testing. In each plot, the horizontal red line shows median estimates; the upper and lower edges of the blue polygons show 2.5 th and 97.5 th percentiles; and the upper and lower ‘whiskers’ show the extreme values. The left-hand panel shows ICERs in terms of cases averted per PCR (polymerase chain reaction) test used, while the right-hand plot shows them in terms of cases averted per unnecessary isolation. Strategies shown are as follows. LFA: using LFA (lateral flow assay) only, with no PCR confirmation. LFA+PCR: confirming all LFA-positive results with PCR. Dynamic: Strategies where the need for PCR confirmation of LFA-positives is lifted when LFA test positivity exceeds a given threshold, here showing ‘low’, ‘medium’ and ‘high’ thresholds as described in the main text. Discussion In pandemic response, rapid testing at the community level might offer important opportunities to reduce transmission, but only if there are ways to mitigate false-positive results within the bounds of health system constraints. Building on previous work, our analysis illustrates that some options for doing so could be provided by dynamic strategies for the use of PCR testing for confirmation of positive LFA results, during the delta wave in New Delhi, India. In particular, our analysis found that strategies that impose a threshold for LFA positivity, above which PCR confirmation of positive LFA results is no longer necessary, can offer a compromise between the large number of PCR tests required when confirming all LFA positives with PCR, and the large number of unnecessary isolations when using LFA alone ( Figure 3 and Figure 4 ). In any given setting, the specific choice of threshold will depend on the local conditions and constraints; in particular, a key consideration is whether PCR capacity is a more pressing constraint than the need to avoid unnecessary isolations. Where PCR capacity is tightly constrained, a more liberal threshold for removing the requirement for PCR confirmation would be favoured. By contrast, where isolation capacity is tightly constrained, this threshold should be more stringent. In order to translate these findings into an appropriate choice of threshold for any given setting, further work would need to combine the costs of PCR usage and unnecessary isolations on a common footing. A benefit of this analysis is that we have modelled dynamic thresholds depending on the LFA positivity rate at any given point in time. Adapting LFA strategies in response to this rate therefore requires LFA test results to be reported, at least in a representative proportion. While reporting of home-based test results is currently recommended, it is generally not done 23 . In the future, LFAs having the capacity to report test results automatically could improve efforts to monitor the spread of infection and adapt testing strategies accordingly. Importantly, while we have evaluated the role of LFAs in testing for SARS-CoV-2, the principle of this analysis – evaluating tradeoffs between rapid isolation and reduction of false-positives through confirmatory testing – applies to epidemics of other infectious diseases as well. Often, LFAs aim for speed and high sensitivity but may have insufficient specificity to take definitive action without confirmatory testing 24 . As such, analyses such as this one, in which we assess tradeoffs between speed of testing and the cost of false-positive results, will be relevant as novel LFAs are developed for other infectious diseases. As with any modelling analysis, our work has some limitations to note. Our model assumes a simplified, homogenous population structure, whereas in reality, in the first few waves of COVID-19, the spread of SARS-CoV-2 was more extensive in urban slum populations than elsewhere in India 25 . Concentrating on the impact of testing strategies, we do not model the potential impact of other measures such as social distancing and lockdowns 26 or indeed of vaccination. In reality, in the wake of the devastating ‘delta wave’ in India, the rapid rollout of the world’s largest COVID-19 vaccination programme 27 had substantial impact on disease burden 28 . We also do not model relationships between infectivity and LFA detection. While LFAs are not as sensitive as PCR, there is evidence to suggest that those cases of SARS-CoV-2 that are detectable by LFA are also the most infectious 11 , 29 , 30 , and we would expect such variation to increase the epidemiological impact of LFA-only strategies from that estimated here. We only evaluated dynamic strategies that changed the sequence of testing with the probability of a positive test. Other dynamic strategies could also, for example, impose a requirement for isolation while awaiting confirmatory testing, rather than removing the need for a confirmatory test. We have focused on one example of the use of LFAs and PCR tests: identifying conditions where PCR need not be used to confirm LFA positives. For future work, other possible areas relevant to transmission include the use of LFAs for surveillance during periods of low infection activity, and switching to PCR confirmation of LFA negatives during the epidemic peak. In all cases, limiting the requirement for PCR testing will be an important consideration for LMICs. The model was calibrated to reflect transmission dynamics representative of the delta wave in India, rather than the more recent omicron wave, because at the time of the analysis, the omicron wave was still new and developing. Since the omicron variant was found to be more contagious than the previous ones, we believe we believe our results would show higher epidemiological impact from utilizing LFAs without PCR confirmation in order to isolate infected individuals as quickly as possible. In conclusion, rapid tests can play an important role in reducing opportunities for transmission, but their use must be planned carefully in order to avoid undue adverse impacts, either on the population or on the healthcare system. Mathematical modelling can be a helpful tool for weighing these trade-offs, not only for COVID-19, but also for future pandemics. Data availability Underlying data Zenodo: Adaptive strategies for the deployment of rapid diagnostic tests for COVID-19: a modelling study. https://doi.org/10.5281/zenodo.7401171 21 . This project contains the following information: - Model overview - Governing equations - Model execution Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0). Software availability Source code available from: https://github.com/lmcilloni/covid-RDT Archived source code at time of publication: https://doi.org/10.5281/zenodo.7410262 22 License: GNU General Public License v3.0 Faculty Opinions recommended References 1. Botti-Lodovico Y, Rosenberg E, Sabeti PC: Testing in a pandemic — improving access, coordination, and prioritization. N Engl J Med. 2021; 384 (3): 197–199. PubMed Abstract | Publisher Full Text 2. Shemim SS, Shaju SS, Sooraj S, et al. : A study on COVID-19 management strategies of two Indian states. Arch Med Health Sci. 2020; 8 (2): 318–321. Publisher Full Text 3. Ghosh A, Nundy S, Mallick TK: How India is dealing with COVID-19 pandemic. Sens Int. 2020; 1 : 100021. PubMed Abstract | Publisher Full Text | Free Full Text 4. Joshi RK, Mehendale SM: Prevention and control of COVID-19 in India: strategies and options. Med J Armed Forces India. 2021; 77 (Suppl 2): S237–S241. PubMed Abstract | Publisher Full Text | Free Full Text 5. Yang W, Shaman J: COVID-19 pandemic dynamics in India, the SARS-CoV-2 Delta variant and implications for vaccination. J R Soc Interface. 2022; 19 (191): 20210900. PubMed Abstract | Publisher Full Text | Free Full Text 6. Böger B, Fachi MM, Vilhena RO, et al. : Systematic review with meta-analysis of the accuracy of diagnostic tests for COVID-19. Am J Infect Control. 2021; 49 (1): 21–29. PubMed Abstract | Publisher Full Text | Free Full Text 7. Peeling RW, Olliaro PL, Boeras DI, et al. : Scaling up COVID-19 rapid antigen tests: promises and challenges. Lancet Infect Dis. 2021; 21 (9): e290–5. PubMed Abstract | Publisher Full Text | Free Full Text 8. Thakur S, Chauhan V, Galwankar S, et al. : COVID-19 testing strategy of India - Current status and the way forward. J Glob Infect Dis. 2020; 12 (2): 44–46. PubMed Abstract | Publisher Full Text | Free Full Text 9. Stohr JJJM, Zwart VF, Goderski G, et al. : Self-testing for the detection of SARS-CoV-2 infection with rapid antigen tests for people with suspected COVID-19 in the community. Clin Microbiol Infect. 2022; 28 (5): 695–700. PubMed Abstract | Publisher Full Text | Free Full Text 10. Dinnes J, Deeks JJ, Berhane S, et al. : Rapid, point-of-care antigen and molecular-based tests for diagnosis of SARS-CoV-2 infection. Cochrane Database Syst Rev. 2021; 3 (3): CD013705. PubMed Abstract | Publisher Full Text | Free Full Text 11. Lopera TJ, Alzate-Ángel JC, Díaz FJ, et al. : The usefulness of antigen testing in predicting contagiousness in COVID-19. Microbiol Spectr. 2022; 10 (2): e0196221. PubMed Abstract | Publisher Full Text | Free Full Text 12. Katzenschlager S, Brümmer LE, Schmitz S, et al. : Comparing SARS-CoV-2 antigen-detection rapid diagnostic tests for COVID-19 self-testing/self-sampling with molecular and professional-use tests: a systematic review and meta-analysis. Sci Rep. 2023; 13 (1): 21913. PubMed Abstract | Publisher Full Text | Free Full Text 13. Flower B, Brown JC, Simmons B, et al. : Clinical and laboratory evaluation of SARS-CoV-2 lateral flow assays for use in a national COVID-19 seroprevalence survey. Thorax. 2020; 75 (12): 1082–8. PubMed Abstract | Publisher Full Text | Free Full Text 14. Krüttgen A, Cornelissen CG, Dreher M, et al. : Comparison of the SARS-CoV-2 Rapid antigen test to the real star SARS-CoV-2 RT PCR kit. J Virol Methods. 2021; 288 : 114024. PubMed Abstract | Publisher Full Text | Free Full Text 15. Mistry DA, Wang JY, Moeser ME, et al. : A systematic review of the sensitivity and specificity of lateral flow devices in the detection of SARS-CoV-2. BMC Infect Dis. 2021; 21 (1): 828. PubMed Abstract | Publisher Full Text | Free Full Text 16. Baik Y, Cilloni L, Kendall E, et al. : Symptom-based vs asymptomatic testing for controlling SARS-CoV-2 transmission in low- and middle-income countries: a modelling analysis. Epidemics. 2022; 41 : 100631. PubMed Abstract | Publisher Full Text | Free Full Text 17. Huff HV, Singh A: Asymptomatic transmission during the Coronavirus disease 2019 pandemic and implications for public health strategies. Clin Infect Dis. 2020; 71 (10): 2752–6. PubMed Abstract | Publisher Full Text | Free Full Text 18. Johansson MA, Quandelacy TM, Kada S, et al. : SARS-CoV-2 transmission from people without COVID-19 symptoms. JAMA Netw Open. 2021; 4 (1): e2035057. PubMed Abstract | Publisher Full Text | Free Full Text 19. Choudhary OP, Choudhary P, Singh I: India’s COVID-19 vaccination drive: key challenges and resolutions. Lancet Infect Dis. 2021; 21 (11): 1483–1484. PubMed Abstract | Publisher Full Text | Free Full Text 20. Mandal S, Arinaminpathy N, Bhargava B, et al. : Plausibility of a third wave of COVID-19 in India: a mathematical modelling based analysis. Indian J Med Res. 2021; 153 (5&6): 522–532. PubMed Abstract | Publisher Full Text | Free Full Text 21. Cilloni L, Kendall E, Dowdy D, et al. : Adaptive strategies for the deployment of rapid diagnostic tests for COVID-19: a modelling study. [Dataset]. Zenodo. 2022. http://www.doi.org/10.5281/zenodo.7401171 22. Cilloni L: lmcilloni/covid-RDT: covid-RDT (covidRDT.v1.2). Zenodo. 2022. http://www.doi.org/10.5281/zenodo.7410262 23. Rader B, Gertz A, Iuliano AD, et al. : Use of At-Home COVID-19 tests - United States, August 23, 2021-March 12, 2022. MMWR Morb Mortal Wkly Rep. 2022; 71 (13): 489–94. PubMed Abstract | Publisher Full Text | Free Full Text 24. Boehringer HR, O’Farrell BJ: Lateral flow assays in infectious disease diagnosis. Clin Chem. 2022; 68 (1): 52–8. PubMed Abstract | Publisher Full Text | Free Full Text 25. Murhekar MV, Bhatnagar T, Selvaraju S, et al. : SARS-CoV-2 antibody seroprevalence in India, August-September, 2020: findings from the second nationwide household serosurvey. Lancet Glob Heal. 2021; 9 (3): e257–e266. PubMed Abstract | Publisher Full Text | Free Full Text 26. Mandal S, Das H, Deo S, et al. : Combining serology with case-detection, to allow the easing of restrictions against SARS-CoV-2: a modelling-based study in India. Sci Rep. 2021; 11 (1): 1835. PubMed Abstract | Publisher Full Text | Free Full Text 27. Bagcchi S: The world's largest COVID-19 vaccination campaign. Lancet Infect Dis. 2021; 21 (3): 323. PubMed Abstract | Publisher Full Text | Free Full Text 28. Watson OJ, Barnsley G, Toor J, et al. : Global impact of the first year of COVID-19 vaccination: a mathematical modelling study. Lancet Infect Dis. 2022; 22 (9): 1293–1302. PubMed Abstract | Publisher Full Text | Free Full Text 29. Nordgren J, Sharma S, Olsson H, et al. : SARS-CoV-2 rapid antigen test: high sensitivity to detect infectious virus. J Clin Virol. 2021; 140 : 104846. PubMed Abstract | Publisher Full Text | Free Full Text 30. Kessler HH, Prüller F, Hardt M, et al. : Identification of contagious SARS-CoV-2 infected individuals by Roche’s Rapid Antigen Test. Clin Chem Lab Med. 2022; 60 (5): 778–85. PubMed Abstract | Publisher Full Text Comments on this article Comments (0) Version 2 VERSION 2 PUBLISHED 27 Jan 2023 ADD YOUR COMMENT Comment Author details Author details 1 Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA 2 MRC Center for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, UK Lucia Cilloni Roles: Conceptualization, Data Curation, Formal Analysis, Methodology, Software, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing Emily Kendall Roles: Conceptualization, Investigation, Methodology, Project Administration, Supervision, Writing – Original Draft Preparation, Writing – Review & Editing David Dowdy Roles: Conceptualization, Investigation, Methodology, Project Administration, Supervision, Writing – Original Draft Preparation, Writing – Review & Editing Nimalan Arinaminpathy Roles: Conceptualization, Funding Acquisition, Investigation, Methodology, Project Administration, Supervision, Writing – Original Draft Preparation, Writing – Review & Editing Competing interests No competing interests were disclosed. Grant information This work was supported by the Gates Foundation [INV-023013]. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Article Versions (2) version 2 Revised Published: 27 May 2025, 7:6 https://doi.org/10.12688/gatesopenres.14202.2 version 1 Published: 27 Jan 2023, 7:6 https://doi.org/10.12688/gatesopenres.14202.1 Copyright © 2025 Cilloni L et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Download Export To Sciwheel Bibtex EndNote ProCite Ref. Manager (RIS) Sente metrics Views Downloads Gates Open Research - - PubMed Central info_outline Data from PMC are received and updated monthly. - - Citations open_in_new 0 open_in_new 0 open_in_new SEE MORE DETAILS CITE how to cite this article Cilloni L, Kendall E, Dowdy D and Arinaminpathy N. Adaptive strategies for the deployment of rapid diagnostic tests for COVID-19: a modelling study [version 2; peer review: 2 approved, 1 approved with reservations] . Gates Open Res 2025, 7 :6 ( https://doi.org/10.12688/gatesopenres.14202.2 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS track receive updates on this article Track an article to receive email alerts on any updates to this article. TRACK THIS ARTICLE Share Open Peer Review Current Reviewer Status: ? Key to Reviewer Statuses VIEW HIDE Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Version 2 VERSION 2 PUBLISHED 27 May 2025 Revised Views 0 Cite How to cite this report: Che Kamaruddin N. Reviewer Report For: Adaptive strategies for the deployment of rapid diagnostic tests for COVID-19: a modelling study [version 2; peer review: 2 approved, 1 approved with reservations] . Gates Open Res 2025, 7 :6 ( https://doi.org/10.21956/gatesopenres.17750.r39709 ) The direct URL for this report is: https://gatesopenresearch.org/articles/7-6/v2#referee-response-39709 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 18 Jul 2025 Naim Che Kamaruddin , Universiti Malaya, Kuala Lumpur, Federal Territory of Kuala Lumpur, Malaysia; Clinical Research Centre, Sunway Medical Centre (Ringgold ID: 538534), Bandar Sunway, Selangor, Malaysia Approved VIEWS 0 https://doi.org/10.21956/gatesopenres.17750.r39709 Dear Authors, Congratulations on the completion of your study and thank you for sharing your work in this manuscript. I agree the earlier comments, particularly those made by Dr. Lucia, who has outlined several important revisions. I ... Continue reading READ ALL Dear Authors, Congratulations on the completion of your study and thank you for sharing your work in this manuscript. I agree the earlier comments, particularly those made by Dr. Lucia, who has outlined several important revisions. I would like to offer the following additional suggestions to further strengthen the manuscript: 1. To specify the study aim. While the manuscript is generally understandable and the results are adequately presented, the overall readability would benefit from improved sentence structure. I recommend clearly stating the study aim in a concise sentence to support your hypothesis. For example: “To examine and model COVID-19 testing algorithms/strategies to reduce testing costs and minimise unnecessary quarantine or isolation.” 2. To clarify the vaccination Coverage. In the sentence, “Despite the introduction of the COVID-19 vaccine in January of 2021, the coverage by March was not high enough to effectively curb the delta wave surge,” the term “not high enough” is vague. Please consider specifying the vaccination coverage during the study period. Additionally, clarify whether the delta wave was primarily due to insufficient vaccination coverage, or due to the variant’s high transmissibility or a combination of both. 3. To define abbreviations. Please define “LFA” (lateral flow assay) upon first use in the introduction (paragraph 3). Please double check for the whole manuscript. 4. To justify the false positive implications. The discussion around false positives is currently underdeveloped. You mention that the main implication is unnecessary isolation. However, from a public health standpoint, false negatives may have more serious consequences as they could allow for transmission. Please expand your justification regarding the trade-offs between false positives and false negatives, and the broader implications for public health decision-making. 5. To clarify on the rationale against universal PCR testing. An important question arises, which is why not recommend universal PCR testing for exposed or suspected cases, followed by a short quarantine or isolation for the incubation period (e.g., 3–5 days), and release upon a negative PCR and absence of symptoms? This approach could also be cost-effective and reduce transmission. How does your proposed strategy in this manuscript compare in terms of cost-effectiveness and public health benefit? 6. To add information on the LFA test. Please include the sensitivity and specificity of the LFA tests used in your model, as these values are critical for interpreting the results. 7. To rephrase sentences for clarity. Consider rephrasing this sentence for clarity and connections with your content, “In practice, strategies that can adapt during the course of a pandemic wave – for example, switching to more simplified, rapid algorithms as prevalence increases – may provide an approach for maximising the benefit of LFAs.” 8. To clarify feferenced model. In the sentence “We built on a deterministic, compartmental model of a hypothetical second wave of SARS-CoV-2 transmission in New Delhi, originally developed in 16,” please clarify what reference “16” refers to. Is this your previous work or an external study? 9. To define the sample size. How many total individuals were included in the model? How were populations such as uninfected, exposed, asymptomatic, and presymptomatic individuals defined and tracked? I suggest to summarizing key details currently in the supplementary material into the main methodology section for transparency. 10. To clarify figure/table interpretation. The interpretation of Table 3 and the associated figures is somewhat confusing. You state that LFA+PCR results in the highest PCR test volume and the fewest unnecessary isolations. However, wouldn’t the identification of unnecessary isolations require a confirmatory PCR in all cases? If so, how is unnecessary isolation measured in the LFA-only scenario without PCR confirmation? Please clarify this comparison and the underlying assumptions. 11. To refine concluding remark. The current conclusion is too general and does not fully reflect the study’s specific findings. I recommend rephrasing it to synthesise your results more directly. For example: Our modelling suggests that strategic implementation of LFA-based algorithms particularly when combined with PCR confirmation can reduce transmission and testing costs during high-prevalence phases, while minimising unnecessary isolation. These findings support the importance of adaptable, evidence-based testing policies for future pandemic preparedness.” Thank you again for the opportunity to review this interesting manuscript. I hope these comments help strengthen your final submission. Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Partly Are sufficient details of methods and analysis provided to allow replication by others? No If applicable, is the statistical analysis and its interpretation appropriate? Partly Are all the source data underlying the results available to ensure full reproducibility? Partly Are the conclusions drawn adequately supported by the results? Partly Competing Interests: No competing interests were disclosed. Reviewer Expertise: Epidemiology; Infectious Disease Immunology; Tropical Medicine; Respiratory Infections I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Che Kamaruddin N. Reviewer Report For: Adaptive strategies for the deployment of rapid diagnostic tests for COVID-19: a modelling study [version 2; peer review: 2 approved, 1 approved with reservations] . Gates Open Res 2025, 7 :6 ( https://doi.org/10.21956/gatesopenres.17750.r39709 ) The direct URL for this report is: https://gatesopenresearch.org/articles/7-6/v2#referee-response-39709 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Views 0 Cite How to cite this report: Tomassetti F. Reviewer Report For: Adaptive strategies for the deployment of rapid diagnostic tests for COVID-19: a modelling study [version 2; peer review: 2 approved, 1 approved with reservations] . Gates Open Res 2025, 7 :6 ( https://doi.org/10.21956/gatesopenres.17750.r39567 ) The direct URL for this report is: https://gatesopenresearch.org/articles/7-6/v2#referee-response-39567 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 12 Jun 2025 Flaminia Tomassetti , University of Rome Tor Vergata, Rome, Italy Approved with Reservations VIEWS 0 https://doi.org/10.21956/gatesopenres.17750.r39567 I appreciate the Authors' effort to response partly to my comments; however, ... Continue reading READ ALL I appreciate the Authors' effort to response partly to my comments; however, I have one last concern. Where is Figure 1? The Discussion still lacks criticism. Competing Interests: No competing interests were disclosed. Reviewer Expertise: Clinical Pathology, COVID-19, Clinical Biochemistry, Immunology I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Tomassetti F. Reviewer Report For: Adaptive strategies for the deployment of rapid diagnostic tests for COVID-19: a modelling study [version 2; peer review: 2 approved, 1 approved with reservations] . Gates Open Res 2025, 7 :6 ( https://doi.org/10.21956/gatesopenres.17750.r39567 ) The direct URL for this report is: https://gatesopenresearch.org/articles/7-6/v2#referee-response-39567 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Version 1 VERSION 1 PUBLISHED 27 Jan 2023 Views 0 Cite How to cite this report: Tomassetti F. Reviewer Report For: Adaptive strategies for the deployment of rapid diagnostic tests for COVID-19: a modelling study [version 2; peer review: 2 approved, 1 approved with reservations] . Gates Open Res 2025, 7 :6 ( https://doi.org/10.21956/gatesopenres.15508.r38537 ) The direct URL for this report is: https://gatesopenresearch.org/articles/7-6/v1#referee-response-38537 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 10 Jan 2025 Flaminia Tomassetti , University of Rome Tor Vergata, Rome, Italy Approved with Reservations VIEWS 0 https://doi.org/10.21956/gatesopenres.15508.r38537 The manuscript titled “Adaptive strategies for the deployment of rapid diagnostic tests for COVID-19: a modelling study” has the potential to add to managing SARS-CoV-2 spread and could be more interesting if the strategy proposed could fit also for another ... Continue reading READ ALL The manuscript titled “Adaptive strategies for the deployment of rapid diagnostic tests for COVID-19: a modelling study” has the potential to add to managing SARS-CoV-2 spread and could be more interesting if the strategy proposed could fit also for another health emergency. However, some vital information is missing in the manuscript as follows: Major revision: Abstract The aim is not totally clear. Include the threshold value in the Methods Introduction The introduction is poor, and the references are a little outdated. The Authors should amplify the first paragraph of this section. The Authors should include something about managing the virus spread, the anti-contagion rules and the safety protocols for virus detection in megalopolis, such as New Delhi. Methods Please include the years of collecting data (2020-21) for the model. Figure 1 is not clear: should it summaries the current and well-known infection and detection of SARS-CoV-2? I think that the Figure is redundant and is not adding anything vital to the text. Results The Results are well written and the figures/tables support consistently the data. Discussion The Discussion should be more contextualized to the results obtained in this study. How do the Authors explain the increase of false positive test results? How do the Authors justify their model for the other pandemic wave (Delta/Omicron)? The Authors should also discuss about the benefits/costs of their model. Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Partly Competing Interests: No competing interests were disclosed. Reviewer Expertise: Clinical Pathology, COVID-19, Clinical Biochemistry, Immunology I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Tomassetti F. Reviewer Report For: Adaptive strategies for the deployment of rapid diagnostic tests for COVID-19: a modelling study [version 2; peer review: 2 approved, 1 approved with reservations] . Gates Open Res 2025, 7 :6 ( https://doi.org/10.21956/gatesopenres.15508.r38537 ) The direct URL for this report is: https://gatesopenresearch.org/articles/7-6/v1#referee-response-38537 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Author Response 03 Jul 2025 Lucia Cilloni , Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, USA 03 Jul 2025 Author Response General comments response: We thank the reviewer for their feedback. While the strategy discussed in this paper is specific to the COVID-19 pandemic, we believe the ideas could be generalizable ... Continue reading General comments response: We thank the reviewer for their feedback. While the strategy discussed in this paper is specific to the COVID-19 pandemic, we believe the ideas could be generalizable to other infectious diseases as well. We have therefore added the following brief paragraph to the Discussion: “Importantly, while we have evaluated the role of LFAs in testing for SARS-CoV-2, the principle of this analysis – evaluating tradeoffs between rapid isolation and reduction of false-positives through confirmatory testing – applies to epidemics of other infectious diseases as well. Often, LFAs aim for speed and high sensitivity but may have insufficient specificity to take definitive action without confirmatory testing. As such, analyses such as this one, in which tradeoffs between speed of testing and the cost of false-positive results, will be relevant as novel LFAs are developed for other infectious diseases.” Abstract comments response: We have amended the Abstract to include the following text: “Concentrating on urban areas in low- and middle-income countries, the aim of this analysis was to estimate the degree to which ‘dynamic’ screening algorithms, that adjust the use of confirmatory polymerase chain reaction (PCR) testing based on epidemiological conditions, could reduce cost without substantially reducing the impact of testing.” And: “We considered dynamic testing strategies where LFA positive cases are confirmed with PCR when LFA positivity rates are below a given threshold (10%, 50% and 90% of the peak positivity rate at the height of the epidemic wave),…” Introduction comments response: In response to the comment about the outdated "Introduction" references, similar to the comment from Reviewer 1, we performed a detailed literature review of LFAs for SARS-CoV-2. However, we found that the most-cited references were all from 2022 or earlier. We have added additional references, though noting that these are still from 2021 and before. We have added a paragraph to the Introduction about COVID-19 in India, and in New Delhi, at the time of the second wave. “In New Delhi, India, during the first wave of the pandemic, the state government implemented a task force in charge of testing, tracing and tracking new infections, with a combination of reverse-transcriptase polymerase chain reaction (RT-PCR) tests, point-of-care molecular tests and rapid antibody tests, in an effort to detect and isolate incident cases of COVID-19 quickly and reduce transmission…” We have also added to the Discussion to highlight this point as a limitation of our study: that we did not address the potential impact of other interventions, such as social distancing and vaccination (both of which played a key role in the COVID response in India). Please see p.10, “Concentrating on the impact of testing strategies, we do not model the potential impact of other measures such as social distancing and lockdowns or indeed of vaccination. In reality, in the wake of the devastating ‘delta wave’ in India, the rapid rollout of the world’s largest COVID-19 vaccination programme had substantial impact on disease burden.” Methods comments response: We have added the years of collecting data to the Methods. Regarding Figure 1, this figure aims to outline the compartments that make up the model used for the analysis. It highlights both the natural history processes of SARS-CoV-2 that are accounted for in the modeling, but also the compartments and processes that are involved in the proposed interventions, with the blue arrows and squares highlighting what processes are being changed to assess the different scenarios being considered. While these data are in the text, we feel that the Figure will help readers visualize the model more effectively and would thus argue for its retention. Discussion comments response: Performing tests in a sequential manner increases the overall testing algorithm’s specificity considerably. Therefore when it is possible to utilize a confirmatory test, such as PCR, this will ensure that true-negatives are appropriately identified, if they get through the first test as false-positives. The reason the model focused on the Delta wave in India was because at the time of the analyses the omicron wave was still new and developing. We have added a justification in the Discussion: “The model was calibrated to reflect transmission dynamics representative of the delta wave, rather than the more recent omicron wave. At the time of the analysis, the omicron wave was still new and developing. Since the omicron variant was found to be more contagious than the previous ones, we believe we believe our results would show higher epidemiological impact from utilizing LFAs without PCR confirmation in order to isolate infected individuals as quickly as possible.” An important benefit of this updated model is its ability to utilize a dynamic LFA positivity threshold to decide whether PCR confirmation is or isn’t necessary. The implications of this are outlined in the Discussion: “In particular, our analysis found that strategies that impose a threshold for LFA positivity, above which PCR confirmation of positive LFA results is no longer necessary, can offer a compromise between the large number of PCR tests required when confirming all LFA positives with PCR, and the large number of unnecessary isolations when using LFA alone.” We discuss model limitations in the Discussion paragraph, “As with any modelling analysis, our work has some limitations to note…”, noting limitations around model assumptions and on the possible combinations of tools used for diagnosis. General comments response: We thank the reviewer for their feedback. While the strategy discussed in this paper is specific to the COVID-19 pandemic, we believe the ideas could be generalizable to other infectious diseases as well. We have therefore added the following brief paragraph to the Discussion: “Importantly, while we have evaluated the role of LFAs in testing for SARS-CoV-2, the principle of this analysis – evaluating tradeoffs between rapid isolation and reduction of false-positives through confirmatory testing – applies to epidemics of other infectious diseases as well. Often, LFAs aim for speed and high sensitivity but may have insufficient specificity to take definitive action without confirmatory testing. As such, analyses such as this one, in which tradeoffs between speed of testing and the cost of false-positive results, will be relevant as novel LFAs are developed for other infectious diseases.” Abstract comments response: We have amended the Abstract to include the following text: “Concentrating on urban areas in low- and middle-income countries, the aim of this analysis was to estimate the degree to which ‘dynamic’ screening algorithms, that adjust the use of confirmatory polymerase chain reaction (PCR) testing based on epidemiological conditions, could reduce cost without substantially reducing the impact of testing.” And: “We considered dynamic testing strategies where LFA positive cases are confirmed with PCR when LFA positivity rates are below a given threshold (10%, 50% and 90% of the peak positivity rate at the height of the epidemic wave),…” Introduction comments response: In response to the comment about the outdated "Introduction" references, similar to the comment from Reviewer 1, we performed a detailed literature review of LFAs for SARS-CoV-2. However, we found that the most-cited references were all from 2022 or earlier. We have added additional references, though noting that these are still from 2021 and before. We have added a paragraph to the Introduction about COVID-19 in India, and in New Delhi, at the time of the second wave. “In New Delhi, India, during the first wave of the pandemic, the state government implemented a task force in charge of testing, tracing and tracking new infections, with a combination of reverse-transcriptase polymerase chain reaction (RT-PCR) tests, point-of-care molecular tests and rapid antibody tests, in an effort to detect and isolate incident cases of COVID-19 quickly and reduce transmission…” We have also added to the Discussion to highlight this point as a limitation of our study: that we did not address the potential impact of other interventions, such as social distancing and vaccination (both of which played a key role in the COVID response in India). Please see p.10, “Concentrating on the impact of testing strategies, we do not model the potential impact of other measures such as social distancing and lockdowns or indeed of vaccination. In reality, in the wake of the devastating ‘delta wave’ in India, the rapid rollout of the world’s largest COVID-19 vaccination programme had substantial impact on disease burden.” Methods comments response: We have added the years of collecting data to the Methods. Regarding Figure 1, this figure aims to outline the compartments that make up the model used for the analysis. It highlights both the natural history processes of SARS-CoV-2 that are accounted for in the modeling, but also the compartments and processes that are involved in the proposed interventions, with the blue arrows and squares highlighting what processes are being changed to assess the different scenarios being considered. While these data are in the text, we feel that the Figure will help readers visualize the model more effectively and would thus argue for its retention. Discussion comments response: Performing tests in a sequential manner increases the overall testing algorithm’s specificity considerably. Therefore when it is possible to utilize a confirmatory test, such as PCR, this will ensure that true-negatives are appropriately identified, if they get through the first test as false-positives. The reason the model focused on the Delta wave in India was because at the time of the analyses the omicron wave was still new and developing. We have added a justification in the Discussion: “The model was calibrated to reflect transmission dynamics representative of the delta wave, rather than the more recent omicron wave. At the time of the analysis, the omicron wave was still new and developing. Since the omicron variant was found to be more contagious than the previous ones, we believe we believe our results would show higher epidemiological impact from utilizing LFAs without PCR confirmation in order to isolate infected individuals as quickly as possible.” An important benefit of this updated model is its ability to utilize a dynamic LFA positivity threshold to decide whether PCR confirmation is or isn’t necessary. The implications of this are outlined in the Discussion: “In particular, our analysis found that strategies that impose a threshold for LFA positivity, above which PCR confirmation of positive LFA results is no longer necessary, can offer a compromise between the large number of PCR tests required when confirming all LFA positives with PCR, and the large number of unnecessary isolations when using LFA alone.” We discuss model limitations in the Discussion paragraph, “As with any modelling analysis, our work has some limitations to note…”, noting limitations around model assumptions and on the possible combinations of tools used for diagnosis. Competing Interests: No competing interests were disclosed. Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 03 Jul 2025 Lucia Cilloni , Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, USA 03 Jul 2025 Author Response General comments response: We thank the reviewer for their feedback. While the strategy discussed in this paper is specific to the COVID-19 pandemic, we believe the ideas could be generalizable ... Continue reading General comments response: We thank the reviewer for their feedback. While the strategy discussed in this paper is specific to the COVID-19 pandemic, we believe the ideas could be generalizable to other infectious diseases as well. We have therefore added the following brief paragraph to the Discussion: “Importantly, while we have evaluated the role of LFAs in testing for SARS-CoV-2, the principle of this analysis – evaluating tradeoffs between rapid isolation and reduction of false-positives through confirmatory testing – applies to epidemics of other infectious diseases as well. Often, LFAs aim for speed and high sensitivity but may have insufficient specificity to take definitive action without confirmatory testing. As such, analyses such as this one, in which tradeoffs between speed of testing and the cost of false-positive results, will be relevant as novel LFAs are developed for other infectious diseases.” Abstract comments response: We have amended the Abstract to include the following text: “Concentrating on urban areas in low- and middle-income countries, the aim of this analysis was to estimate the degree to which ‘dynamic’ screening algorithms, that adjust the use of confirmatory polymerase chain reaction (PCR) testing based on epidemiological conditions, could reduce cost without substantially reducing the impact of testing.” And: “We considered dynamic testing strategies where LFA positive cases are confirmed with PCR when LFA positivity rates are below a given threshold (10%, 50% and 90% of the peak positivity rate at the height of the epidemic wave),…” Introduction comments response: In response to the comment about the outdated "Introduction" references, similar to the comment from Reviewer 1, we performed a detailed literature review of LFAs for SARS-CoV-2. However, we found that the most-cited references were all from 2022 or earlier. We have added additional references, though noting that these are still from 2021 and before. We have added a paragraph to the Introduction about COVID-19 in India, and in New Delhi, at the time of the second wave. “In New Delhi, India, during the first wave of the pandemic, the state government implemented a task force in charge of testing, tracing and tracking new infections, with a combination of reverse-transcriptase polymerase chain reaction (RT-PCR) tests, point-of-care molecular tests and rapid antibody tests, in an effort to detect and isolate incident cases of COVID-19 quickly and reduce transmission…” We have also added to the Discussion to highlight this point as a limitation of our study: that we did not address the potential impact of other interventions, such as social distancing and vaccination (both of which played a key role in the COVID response in India). Please see p.10, “Concentrating on the impact of testing strategies, we do not model the potential impact of other measures such as social distancing and lockdowns or indeed of vaccination. In reality, in the wake of the devastating ‘delta wave’ in India, the rapid rollout of the world’s largest COVID-19 vaccination programme had substantial impact on disease burden.” Methods comments response: We have added the years of collecting data to the Methods. Regarding Figure 1, this figure aims to outline the compartments that make up the model used for the analysis. It highlights both the natural history processes of SARS-CoV-2 that are accounted for in the modeling, but also the compartments and processes that are involved in the proposed interventions, with the blue arrows and squares highlighting what processes are being changed to assess the different scenarios being considered. While these data are in the text, we feel that the Figure will help readers visualize the model more effectively and would thus argue for its retention. Discussion comments response: Performing tests in a sequential manner increases the overall testing algorithm’s specificity considerably. Therefore when it is possible to utilize a confirmatory test, such as PCR, this will ensure that true-negatives are appropriately identified, if they get through the first test as false-positives. The reason the model focused on the Delta wave in India was because at the time of the analyses the omicron wave was still new and developing. We have added a justification in the Discussion: “The model was calibrated to reflect transmission dynamics representative of the delta wave, rather than the more recent omicron wave. At the time of the analysis, the omicron wave was still new and developing. Since the omicron variant was found to be more contagious than the previous ones, we believe we believe our results would show higher epidemiological impact from utilizing LFAs without PCR confirmation in order to isolate infected individuals as quickly as possible.” An important benefit of this updated model is its ability to utilize a dynamic LFA positivity threshold to decide whether PCR confirmation is or isn’t necessary. The implications of this are outlined in the Discussion: “In particular, our analysis found that strategies that impose a threshold for LFA positivity, above which PCR confirmation of positive LFA results is no longer necessary, can offer a compromise between the large number of PCR tests required when confirming all LFA positives with PCR, and the large number of unnecessary isolations when using LFA alone.” We discuss model limitations in the Discussion paragraph, “As with any modelling analysis, our work has some limitations to note…”, noting limitations around model assumptions and on the possible combinations of tools used for diagnosis. General comments response: We thank the reviewer for their feedback. While the strategy discussed in this paper is specific to the COVID-19 pandemic, we believe the ideas could be generalizable to other infectious diseases as well. We have therefore added the following brief paragraph to the Discussion: “Importantly, while we have evaluated the role of LFAs in testing for SARS-CoV-2, the principle of this analysis – evaluating tradeoffs between rapid isolation and reduction of false-positives through confirmatory testing – applies to epidemics of other infectious diseases as well. Often, LFAs aim for speed and high sensitivity but may have insufficient specificity to take definitive action without confirmatory testing. As such, analyses such as this one, in which tradeoffs between speed of testing and the cost of false-positive results, will be relevant as novel LFAs are developed for other infectious diseases.” Abstract comments response: We have amended the Abstract to include the following text: “Concentrating on urban areas in low- and middle-income countries, the aim of this analysis was to estimate the degree to which ‘dynamic’ screening algorithms, that adjust the use of confirmatory polymerase chain reaction (PCR) testing based on epidemiological conditions, could reduce cost without substantially reducing the impact of testing.” And: “We considered dynamic testing strategies where LFA positive cases are confirmed with PCR when LFA positivity rates are below a given threshold (10%, 50% and 90% of the peak positivity rate at the height of the epidemic wave),…” Introduction comments response: In response to the comment about the outdated "Introduction" references, similar to the comment from Reviewer 1, we performed a detailed literature review of LFAs for SARS-CoV-2. However, we found that the most-cited references were all from 2022 or earlier. We have added additional references, though noting that these are still from 2021 and before. We have added a paragraph to the Introduction about COVID-19 in India, and in New Delhi, at the time of the second wave. “In New Delhi, India, during the first wave of the pandemic, the state government implemented a task force in charge of testing, tracing and tracking new infections, with a combination of reverse-transcriptase polymerase chain reaction (RT-PCR) tests, point-of-care molecular tests and rapid antibody tests, in an effort to detect and isolate incident cases of COVID-19 quickly and reduce transmission…” We have also added to the Discussion to highlight this point as a limitation of our study: that we did not address the potential impact of other interventions, such as social distancing and vaccination (both of which played a key role in the COVID response in India). Please see p.10, “Concentrating on the impact of testing strategies, we do not model the potential impact of other measures such as social distancing and lockdowns or indeed of vaccination. In reality, in the wake of the devastating ‘delta wave’ in India, the rapid rollout of the world’s largest COVID-19 vaccination programme had substantial impact on disease burden.” Methods comments response: We have added the years of collecting data to the Methods. Regarding Figure 1, this figure aims to outline the compartments that make up the model used for the analysis. It highlights both the natural history processes of SARS-CoV-2 that are accounted for in the modeling, but also the compartments and processes that are involved in the proposed interventions, with the blue arrows and squares highlighting what processes are being changed to assess the different scenarios being considered. While these data are in the text, we feel that the Figure will help readers visualize the model more effectively and would thus argue for its retention. Discussion comments response: Performing tests in a sequential manner increases the overall testing algorithm’s specificity considerably. Therefore when it is possible to utilize a confirmatory test, such as PCR, this will ensure that true-negatives are appropriately identified, if they get through the first test as false-positives. The reason the model focused on the Delta wave in India was because at the time of the analyses the omicron wave was still new and developing. We have added a justification in the Discussion: “The model was calibrated to reflect transmission dynamics representative of the delta wave, rather than the more recent omicron wave. At the time of the analysis, the omicron wave was still new and developing. Since the omicron variant was found to be more contagious than the previous ones, we believe we believe our results would show higher epidemiological impact from utilizing LFAs without PCR confirmation in order to isolate infected individuals as quickly as possible.” An important benefit of this updated model is its ability to utilize a dynamic LFA positivity threshold to decide whether PCR confirmation is or isn’t necessary. The implications of this are outlined in the Discussion: “In particular, our analysis found that strategies that impose a threshold for LFA positivity, above which PCR confirmation of positive LFA results is no longer necessary, can offer a compromise between the large number of PCR tests required when confirming all LFA positives with PCR, and the large number of unnecessary isolations when using LFA alone.” We discuss model limitations in the Discussion paragraph, “As with any modelling analysis, our work has some limitations to note…”, noting limitations around model assumptions and on the possible combinations of tools used for diagnosis. Competing Interests: No competing interests were disclosed. Close Report a concern COMMENT ON THIS REPORT Views 0 Cite How to cite this report: Papan C. Reviewer Report For: Adaptive strategies for the deployment of rapid diagnostic tests for COVID-19: a modelling study [version 2; peer review: 2 approved, 1 approved with reservations] . Gates Open Res 2025, 7 :6 ( https://doi.org/10.21956/gatesopenres.15508.r38543 ) The direct URL for this report is: https://gatesopenresearch.org/articles/7-6/v1#referee-response-38543 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 03 Jan 2025 Cihan Papan , University Hospital Bonn, Bonn, Germany Approved VIEWS 0 https://doi.org/10.21956/gatesopenres.15508.r38543 This is modelling study that investigated different testing strategies with regard to SARS-CoV-2 in India, ranging from lateral flow assay tests based on distinct thresholds of positivity to confirmation by polymerase chain reaction of all positive lateral flow assay tests. ... Continue reading READ ALL This is modelling study that investigated different testing strategies with regard to SARS-CoV-2 in India, ranging from lateral flow assay tests based on distinct thresholds of positivity to confirmation by polymerase chain reaction of all positive lateral flow assay tests. The manuscript is well written, and the methodology is sound. The assumptions are explained with enough details and reference. Some of the references seem to be older, owing to the fact that manuscript was originally written in 2022/23. Hence, the authors could make an effort to find some more recent references. I have one additional question: I may have misunderstood this, but I am unsure how a LFA only strategy would lead to the highest rate of averted symptomatic cases (you would have the most false positives here) as opposed to the scenario where all positives are PCR confirmed. Maybe an explanation could help. Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes Competing Interests: No competing interests were disclosed. Reviewer Expertise: infectious diseases epidemiology, diagnostics I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Papan C. Reviewer Report For: Adaptive strategies for the deployment of rapid diagnostic tests for COVID-19: a modelling study [version 2; peer review: 2 approved, 1 approved with reservations] . Gates Open Res 2025, 7 :6 ( https://doi.org/10.21956/gatesopenres.15508.r38543 ) The direct URL for this report is: https://gatesopenresearch.org/articles/7-6/v1#referee-response-38543 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Author Response 03 Jun 2025 Lucia Cilloni , Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, USA 03 Jun 2025 Author Response We thank the reviewer for their feedback and comments. In response to the comment about outdated references, we performed a revised review of references – but found that the highest-quality, ... Continue reading We thank the reviewer for their feedback and comments. In response to the comment about outdated references, we performed a revised review of references – but found that the highest-quality, most-cited references were all from 2022 or earlier, likely owing to the reduction in interest in testing for SARS-CoV-2 after the stabilization of the COVID-19 pandemic. We have added a more recent reference to our introduction. Regarding the question about why the LFA strategy averts more cases than the scenario where all LFA-positives also receive PCR, while it is true that this strategy results in a large number of false-positives, it also results in the highest number of true-positives (i.e., loss of specificity in favor of sensitivity). When all individuals who are LFA-positive need to be confirmed with PCR, those individuals experience a delay in isolation while awaiting PCR confirmation. We now clarify in the Methods, under “Scenarios modelled”: “While using LFAs alone would lead to rapid isolation of people with SARS-CoV-2 (and thus greater reduction in transmission), previous analysis illustrated that a major limitation of such a strategy is that it would lead to a prohibitive number of false-positive diagnoses. It would therefore be critical to implement confirmatory testing, for example using PCR, following any LFA positive test results.” We thank the reviewer for their feedback and comments. In response to the comment about outdated references, we performed a revised review of references – but found that the highest-quality, most-cited references were all from 2022 or earlier, likely owing to the reduction in interest in testing for SARS-CoV-2 after the stabilization of the COVID-19 pandemic. We have added a more recent reference to our introduction. Regarding the question about why the LFA strategy averts more cases than the scenario where all LFA-positives also receive PCR, while it is true that this strategy results in a large number of false-positives, it also results in the highest number of true-positives (i.e., loss of specificity in favor of sensitivity). When all individuals who are LFA-positive need to be confirmed with PCR, those individuals experience a delay in isolation while awaiting PCR confirmation. We now clarify in the Methods, under “Scenarios modelled”: “While using LFAs alone would lead to rapid isolation of people with SARS-CoV-2 (and thus greater reduction in transmission), previous analysis illustrated that a major limitation of such a strategy is that it would lead to a prohibitive number of false-positive diagnoses. It would therefore be critical to implement confirmatory testing, for example using PCR, following any LFA positive test results.” Competing Interests: No competing interests were disclosed. Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 03 Jun 2025 Lucia Cilloni , Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, USA 03 Jun 2025 Author Response We thank the reviewer for their feedback and comments. In response to the comment about outdated references, we performed a revised review of references – but found that the highest-quality, ... Continue reading We thank the reviewer for their feedback and comments. In response to the comment about outdated references, we performed a revised review of references – but found that the highest-quality, most-cited references were all from 2022 or earlier, likely owing to the reduction in interest in testing for SARS-CoV-2 after the stabilization of the COVID-19 pandemic. We have added a more recent reference to our introduction. Regarding the question about why the LFA strategy averts more cases than the scenario where all LFA-positives also receive PCR, while it is true that this strategy results in a large number of false-positives, it also results in the highest number of true-positives (i.e., loss of specificity in favor of sensitivity). When all individuals who are LFA-positive need to be confirmed with PCR, those individuals experience a delay in isolation while awaiting PCR confirmation. We now clarify in the Methods, under “Scenarios modelled”: “While using LFAs alone would lead to rapid isolation of people with SARS-CoV-2 (and thus greater reduction in transmission), previous analysis illustrated that a major limitation of such a strategy is that it would lead to a prohibitive number of false-positive diagnoses. It would therefore be critical to implement confirmatory testing, for example using PCR, following any LFA positive test results.” We thank the reviewer for their feedback and comments. In response to the comment about outdated references, we performed a revised review of references – but found that the highest-quality, most-cited references were all from 2022 or earlier, likely owing to the reduction in interest in testing for SARS-CoV-2 after the stabilization of the COVID-19 pandemic. We have added a more recent reference to our introduction. Regarding the question about why the LFA strategy averts more cases than the scenario where all LFA-positives also receive PCR, while it is true that this strategy results in a large number of false-positives, it also results in the highest number of true-positives (i.e., loss of specificity in favor of sensitivity). When all individuals who are LFA-positive need to be confirmed with PCR, those individuals experience a delay in isolation while awaiting PCR confirmation. We now clarify in the Methods, under “Scenarios modelled”: “While using LFAs alone would lead to rapid isolation of people with SARS-CoV-2 (and thus greater reduction in transmission), previous analysis illustrated that a major limitation of such a strategy is that it would lead to a prohibitive number of false-positive diagnoses. It would therefore be critical to implement confirmatory testing, for example using PCR, following any LFA positive test results.” Competing Interests: No competing interests were disclosed. Close Report a concern COMMENT ON THIS REPORT Comments on this article Comments (0) Version 2 VERSION 2 PUBLISHED 27 Jan 2023 ADD YOUR COMMENT Comment keyboard_arrow_left keyboard_arrow_right Open Peer Review Reviewer Status info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Reviewer Reports Invited Reviewers 1 2 3 Version 2 (revision) 27 May 25 read read Version 1 27 Jan 23 read read Cihan Papan , University Hospital Bonn, Bonn, Germany Flaminia Tomassetti , University of Rome Tor Vergata, Rome, Italy Naim Che Kamaruddin , Universiti Malaya, Kuala Lumpur, Malaysia; Sunway Medical Centre (Ringgold ID: 538534), Bandar Sunway, Malaysia Comments on this article All Comments (0) Add a comment Sign up for content alerts Sign Up You are now signed up to receive this alert keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Che Kamaruddin N. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 18 Jul 2025 | for Version 2 Naim Che Kamaruddin , Universiti Malaya, Kuala Lumpur, Federal Territory of Kuala Lumpur, Malaysia; Clinical Research Centre, Sunway Medical Centre (Ringgold ID: 538534), Bandar Sunway, Selangor, Malaysia 0 Views copyright © 2025 Che Kamaruddin N. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Dear Authors, Congratulations on the completion of your study and thank you for sharing your work in this manuscript. I agree the earlier comments, particularly those made by Dr. Lucia, who has outlined several important revisions. I would like to offer the following additional suggestions to further strengthen the manuscript: 1. To specify the study aim. While the manuscript is generally understandable and the results are adequately presented, the overall readability would benefit from improved sentence structure. I recommend clearly stating the study aim in a concise sentence to support your hypothesis. For example: “To examine and model COVID-19 testing algorithms/strategies to reduce testing costs and minimise unnecessary quarantine or isolation.” 2. To clarify the vaccination Coverage. In the sentence, “Despite the introduction of the COVID-19 vaccine in January of 2021, the coverage by March was not high enough to effectively curb the delta wave surge,” the term “not high enough” is vague. Please consider specifying the vaccination coverage during the study period. Additionally, clarify whether the delta wave was primarily due to insufficient vaccination coverage, or due to the variant’s high transmissibility or a combination of both. 3. To define abbreviations. Please define “LFA” (lateral flow assay) upon first use in the introduction (paragraph 3). Please double check for the whole manuscript. 4. To justify the false positive implications. The discussion around false positives is currently underdeveloped. You mention that the main implication is unnecessary isolation. However, from a public health standpoint, false negatives may have more serious consequences as they could allow for transmission. Please expand your justification regarding the trade-offs between false positives and false negatives, and the broader implications for public health decision-making. 5. To clarify on the rationale against universal PCR testing. An important question arises, which is why not recommend universal PCR testing for exposed or suspected cases, followed by a short quarantine or isolation for the incubation period (e.g., 3–5 days), and release upon a negative PCR and absence of symptoms? This approach could also be cost-effective and reduce transmission. How does your proposed strategy in this manuscript compare in terms of cost-effectiveness and public health benefit? 6. To add information on the LFA test. Please include the sensitivity and specificity of the LFA tests used in your model, as these values are critical for interpreting the results. 7. To rephrase sentences for clarity. Consider rephrasing this sentence for clarity and connections with your content, “In practice, strategies that can adapt during the course of a pandemic wave – for example, switching to more simplified, rapid algorithms as prevalence increases – may provide an approach for maximising the benefit of LFAs.” 8. To clarify feferenced model. In the sentence “We built on a deterministic, compartmental model of a hypothetical second wave of SARS-CoV-2 transmission in New Delhi, originally developed in 16,” please clarify what reference “16” refers to. Is this your previous work or an external study? 9. To define the sample size. How many total individuals were included in the model? How were populations such as uninfected, exposed, asymptomatic, and presymptomatic individuals defined and tracked? I suggest to summarizing key details currently in the supplementary material into the main methodology section for transparency. 10. To clarify figure/table interpretation. The interpretation of Table 3 and the associated figures is somewhat confusing. You state that LFA+PCR results in the highest PCR test volume and the fewest unnecessary isolations. However, wouldn’t the identification of unnecessary isolations require a confirmatory PCR in all cases? If so, how is unnecessary isolation measured in the LFA-only scenario without PCR confirmation? Please clarify this comparison and the underlying assumptions. 11. To refine concluding remark. The current conclusion is too general and does not fully reflect the study’s specific findings. I recommend rephrasing it to synthesise your results more directly. For example: Our modelling suggests that strategic implementation of LFA-based algorithms particularly when combined with PCR confirmation can reduce transmission and testing costs during high-prevalence phases, while minimising unnecessary isolation. These findings support the importance of adaptable, evidence-based testing policies for future pandemic preparedness.” Thank you again for the opportunity to review this interesting manuscript. I hope these comments help strengthen your final submission. Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Partly Are sufficient details of methods and analysis provided to allow replication by others? No If applicable, is the statistical analysis and its interpretation appropriate? Partly Are all the source data underlying the results available to ensure full reproducibility? Partly Are the conclusions drawn adequately supported by the results? Partly Competing Interests No competing interests were disclosed. Reviewer Expertise Epidemiology; Infectious Disease Immunology; Tropical Medicine; Respiratory Infections I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. reply Respond to this report Responses (0) Che Kamaruddin N. Peer Review Report For: Adaptive strategies for the deployment of rapid diagnostic tests for COVID-19: a modelling study [version 2; peer review: 2 approved, 1 approved with reservations] . Gates Open Res 2025, 7 :6 ( https://doi.org/10.21956/gatesopenres.17750.r39709) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://gatesopenresearch.org/articles/7-6/v2#referee-response-39709 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Tomassetti F. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 12 Jun 2025 | for Version 2 Flaminia Tomassetti , University of Rome Tor Vergata, Rome, Italy 0 Views copyright © 2025 Tomassetti F. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions I appreciate the Authors' effort to response partly to my comments; however, I have one last concern. Where is Figure 1? The Discussion still lacks criticism. Competing Interests No competing interests were disclosed. Reviewer Expertise Clinical Pathology, COVID-19, Clinical Biochemistry, Immunology I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (0) Tomassetti F. Peer Review Report For: Adaptive strategies for the deployment of rapid diagnostic tests for COVID-19: a modelling study [version 2; peer review: 2 approved, 1 approved with reservations] . Gates Open Res 2025, 7 :6 ( https://doi.org/10.21956/gatesopenres.17750.r39567) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://gatesopenresearch.org/articles/7-6/v2#referee-response-39567 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Tomassetti F. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 10 Jan 2025 | for Version 1 Flaminia Tomassetti , University of Rome Tor Vergata, Rome, Italy 0 Views copyright © 2025 Tomassetti F. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (1) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions The manuscript titled “Adaptive strategies for the deployment of rapid diagnostic tests for COVID-19: a modelling study” has the potential to add to managing SARS-CoV-2 spread and could be more interesting if the strategy proposed could fit also for another health emergency. However, some vital information is missing in the manuscript as follows: Major revision: Abstract The aim is not totally clear. Include the threshold value in the Methods Introduction The introduction is poor, and the references are a little outdated. The Authors should amplify the first paragraph of this section. The Authors should include something about managing the virus spread, the anti-contagion rules and the safety protocols for virus detection in megalopolis, such as New Delhi. Methods Please include the years of collecting data (2020-21) for the model. Figure 1 is not clear: should it summaries the current and well-known infection and detection of SARS-CoV-2? I think that the Figure is redundant and is not adding anything vital to the text. Results The Results are well written and the figures/tables support consistently the data. Discussion The Discussion should be more contextualized to the results obtained in this study. How do the Authors explain the increase of false positive test results? How do the Authors justify their model for the other pandemic wave (Delta/Omicron)? The Authors should also discuss about the benefits/costs of their model. Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Partly Competing Interests No competing interests were disclosed. Reviewer Expertise Clinical Pathology, COVID-19, Clinical Biochemistry, Immunology I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (1) Author Response 03 Jul 2025 Lucia Cilloni, Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, USA General comments response: We thank the reviewer for their feedback. While the strategy discussed in this paper is specific to the COVID-19 pandemic, we believe the ideas could be generalizable to other infectious diseases as well. We have therefore added the following brief paragraph to the Discussion: “Importantly, while we have evaluated the role of LFAs in testing for SARS-CoV-2, the principle of this analysis – evaluating tradeoffs between rapid isolation and reduction of false-positives through confirmatory testing – applies to epidemics of other infectious diseases as well. Often, LFAs aim for speed and high sensitivity but may have insufficient specificity to take definitive action without confirmatory testing. As such, analyses such as this one, in which tradeoffs between speed of testing and the cost of false-positive results, will be relevant as novel LFAs are developed for other infectious diseases.” Abstract comments response: We have amended the Abstract to include the following text: “Concentrating on urban areas in low- and middle-income countries, the aim of this analysis was to estimate the degree to which ‘dynamic’ screening algorithms, that adjust the use of confirmatory polymerase chain reaction (PCR) testing based on epidemiological conditions, could reduce cost without substantially reducing the impact of testing.” And: “We considered dynamic testing strategies where LFA positive cases are confirmed with PCR when LFA positivity rates are below a given threshold (10%, 50% and 90% of the peak positivity rate at the height of the epidemic wave),…” Introduction comments response: In response to the comment about the outdated "Introduction" references, similar to the comment from Reviewer 1, we performed a detailed literature review of LFAs for SARS-CoV-2. However, we found that the most-cited references were all from 2022 or earlier. We have added additional references, though noting that these are still from 2021 and before. We have added a paragraph to the Introduction about COVID-19 in India, and in New Delhi, at the time of the second wave. “In New Delhi, India, during the first wave of the pandemic, the state government implemented a task force in charge of testing, tracing and tracking new infections, with a combination of reverse-transcriptase polymerase chain reaction (RT-PCR) tests, point-of-care molecular tests and rapid antibody tests, in an effort to detect and isolate incident cases of COVID-19 quickly and reduce transmission…” We have also added to the Discussion to highlight this point as a limitation of our study: that we did not address the potential impact of other interventions, such as social distancing and vaccination (both of which played a key role in the COVID response in India). Please see p.10, “Concentrating on the impact of testing strategies, we do not model the potential impact of other measures such as social distancing and lockdowns or indeed of vaccination. In reality, in the wake of the devastating ‘delta wave’ in India, the rapid rollout of the world’s largest COVID-19 vaccination programme had substantial impact on disease burden.” Methods comments response: We have added the years of collecting data to the Methods. Regarding Figure 1, this figure aims to outline the compartments that make up the model used for the analysis. It highlights both the natural history processes of SARS-CoV-2 that are accounted for in the modeling, but also the compartments and processes that are involved in the proposed interventions, with the blue arrows and squares highlighting what processes are being changed to assess the different scenarios being considered. While these data are in the text, we feel that the Figure will help readers visualize the model more effectively and would thus argue for its retention. Discussion comments response: Performing tests in a sequential manner increases the overall testing algorithm’s specificity considerably. Therefore when it is possible to utilize a confirmatory test, such as PCR, this will ensure that true-negatives are appropriately identified, if they get through the first test as false-positives. The reason the model focused on the Delta wave in India was because at the time of the analyses the omicron wave was still new and developing. We have added a justification in the Discussion: “The model was calibrated to reflect transmission dynamics representative of the delta wave, rather than the more recent omicron wave. At the time of the analysis, the omicron wave was still new and developing. Since the omicron variant was found to be more contagious than the previous ones, we believe we believe our results would show higher epidemiological impact from utilizing LFAs without PCR confirmation in order to isolate infected individuals as quickly as possible.” An important benefit of this updated model is its ability to utilize a dynamic LFA positivity threshold to decide whether PCR confirmation is or isn’t necessary. The implications of this are outlined in the Discussion: “In particular, our analysis found that strategies that impose a threshold for LFA positivity, above which PCR confirmation of positive LFA results is no longer necessary, can offer a compromise between the large number of PCR tests required when confirming all LFA positives with PCR, and the large number of unnecessary isolations when using LFA alone.” We discuss model limitations in the Discussion paragraph, “As with any modelling analysis, our work has some limitations to note…”, noting limitations around model assumptions and on the possible combinations of tools used for diagnosis. View more View less Competing Interests No competing interests were disclosed. reply Respond Report a concern Tomassetti F. Peer Review Report For: Adaptive strategies for the deployment of rapid diagnostic tests for COVID-19: a modelling study [version 2; peer review: 2 approved, 1 approved with reservations] . Gates Open Res 2025, 7 :6 ( https://doi.org/10.21956/gatesopenres.15508.r38537) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://gatesopenresearch.org/articles/7-6/v1#referee-response-38537 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Papan C. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 03 Jan 2025 | for Version 1 Cihan Papan , University Hospital Bonn, Bonn, Germany 0 Views copyright © 2025 Papan C. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (1) Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions This is modelling study that investigated different testing strategies with regard to SARS-CoV-2 in India, ranging from lateral flow assay tests based on distinct thresholds of positivity to confirmation by polymerase chain reaction of all positive lateral flow assay tests. The manuscript is well written, and the methodology is sound. The assumptions are explained with enough details and reference. Some of the references seem to be older, owing to the fact that manuscript was originally written in 2022/23. Hence, the authors could make an effort to find some more recent references. I have one additional question: I may have misunderstood this, but I am unsure how a LFA only strategy would lead to the highest rate of averted symptomatic cases (you would have the most false positives here) as opposed to the scenario where all positives are PCR confirmed. Maybe an explanation could help. Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes Competing Interests No competing interests were disclosed. Reviewer Expertise infectious diseases epidemiology, diagnostics I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. reply Respond to this report Responses (1) Author Response 03 Jun 2025 Lucia Cilloni, Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, USA We thank the reviewer for their feedback and comments. In response to the comment about outdated references, we performed a revised review of references – but found that the highest-quality, most-cited references were all from 2022 or earlier, likely owing to the reduction in interest in testing for SARS-CoV-2 after the stabilization of the COVID-19 pandemic. We have added a more recent reference to our introduction. Regarding the question about why the LFA strategy averts more cases than the scenario where all LFA-positives also receive PCR, while it is true that this strategy results in a large number of false-positives, it also results in the highest number of true-positives (i.e., loss of specificity in favor of sensitivity). When all individuals who are LFA-positive need to be confirmed with PCR, those individuals experience a delay in isolation while awaiting PCR confirmation. We now clarify in the Methods, under “Scenarios modelled”: “While using LFAs alone would lead to rapid isolation of people with SARS-CoV-2 (and thus greater reduction in transmission), previous analysis illustrated that a major limitation of such a strategy is that it would lead to a prohibitive number of false-positive diagnoses. It would therefore be critical to implement confirmatory testing, for example using PCR, following any LFA positive test results.” View more View less Competing Interests No competing interests were disclosed. reply Respond Report a concern Papan C. Peer Review Report For: Adaptive strategies for the deployment of rapid diagnostic tests for COVID-19: a modelling study [version 2; peer review: 2 approved, 1 approved with reservations] . Gates Open Res 2025, 7 :6 ( https://doi.org/10.21956/gatesopenres.15508.r38543) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. 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