Gates Open Res Gates Open Research 2572-4754 F1000 Research Limited London, UK 10.12688/gatesopenres.14202.1 Research Article Articles Adaptive strategies for the deployment of rapid diagnostic tests for COVID-19: a modelling study

[version 1; peer review: 1 approved, 1 approved with reservations]

 Cilloni Lucia Conceptualization Data Curation Formal Analysis Methodology Software Visualization Writing – Original Draft Preparation Writing – Review & Editing https://orcid.org/0000-0002-5456-5723 a 1 Kendall Emily Conceptualization Investigation Methodology Project Administration Supervision Writing – Original Draft Preparation Writing – Review & Editing 1 Dowdy David Conceptualization Investigation Methodology Project Administration Supervision Writing – Original Draft Preparation Writing – Review & Editing 1 Arinaminpathy Nimalan Conceptualization Funding Acquisition Investigation Methodology Project Administration Supervision Writing – Original Draft Preparation Writing – Review & Editing 2 Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA MRC Center for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, UK lcillon1@jh.edu

No competing interests were disclosed.

 27 1 2023 2023 7 6 7 12 2022 Copyright: © 2023 Cilloni L et al. 2023 This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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 means for diagnosing coronavirus disease 2019 (COVID-19). Concentrating on urban areas in low- and middle-income countries, we examined whether ‘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 only confirmed with PCR when LFA positivity rates are below a given threshold (relative to the peak positive rate at the height of the epidemic wave), compared to confirming either 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 test 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.

 COVID-19 lateral flow assays mathematical modelling Gates Foundation INV-023013 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. Introduction

Diagnosis has been a critical component of the global coronavirus disease 2019 (COVID-19) response 1 . Although molecular tests such as reverse-transcriptase polymerase chain reaction (RT-PCR) are highly sensitive 2 , they are costly and require training to operate. These factors have limited RT-PCR use in low-and-middle-income countries (LMICs) 3 . Rapid diagnostic tests (RDTs), employing lateral flow assays, offer a more affordable approach to detecting SARS-CoV-2, and have been used widely for community-level testing, as well as for self-testing in households, including in LMICs 4– 6 . RDTs are, however, less sensitive and specific than PCR 7 .

In previous work 8 , 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 8, with initial conditions and basic reproduction number similar to those relating to the delta wave in India. 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 9, 10 . We did not model vaccination, because vaccination coverage had not yet reached substantial levels during the first wave in India 11 . Although the originally published model 8 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.

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 12 . Finally, we drew from the literature for the sensitivity and specificity of LFAs and PCR 2, 3 . See Table S1 in the Underlying data, for a full list of model parameters and values 13 . The code itself is available from GitHub and archived with Zenodo 14 .

 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. Previous analysis 8 illustrated that a major limitation of such a strategy is that it would lead to a prohibitive number of false-positive diagnoses: it would 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:

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.

A ‘medium threshold’ dynamic strategy: the same as (i), but with the LFA positivity threshold set at 50% of the peak value.

A ‘high threshold’ dynamic strategy: the same as (i), but with the LFA positivity threshold set at 90% of the peak value.

‘LFA only’: A non-dynamic strategy where PCR confirmation is never used for LFA-positive individuals, and

‘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.

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).

 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.

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.

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. In particular, 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.

We have modelled 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 15 . In future, LFAs having the capacity to report test results automatically could improve efforts to monitor the spread of infection and adapt testing strategies accordingly.

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 16 . 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 6, 17, 18 , 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. Finally, 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.

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 13 .

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 14

License: GNU General Public License v3.0

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. 33472283 10.1056/NEJMp2025173 Böger B Fachi MM Vilhena RO : Systematic review with meta-analysis of the accuracy of diagnostic tests for COVID-19. Am J Infect Control. 2021;49(1):21–29. 32659413 10.1016/j.ajic.2020.07.011 7350782 Peeling RW Olliaro PL Boeras DI : Scaling up COVID-19 rapid antigen tests: promises and challenges. Lancet Infect Dis. 2021;21(9):e290–5. 33636148 10.1016/S1473-3099(21)00048-7 7906660 Stohr JJJM Zwart VF Goderski G : 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. 34363945 10.1016/j.cmi.2021.07.039 8336990 Dinnes J Deeks JJ Berhane S : Rapid, point-of-care antigen and molecular-based tests for diagnosis of SARS-CoV-2 infection. Cochrane Database Syst Rev. 2021;3(3):CD013705. 33760236 10.1002/14651858.CD013705.pub2 807859 Lopera TJ Alzate-Ángel JC Díaz FJ : The Usefulness of Antigen Testing in Predicting Contagiousness in COVID-19. Microbiol Spectr. 2022;10(2):e0196221. 35348350 10.1128/spectrum.01962-21 9045251 Krüttgen A Cornelissen CG Dreher M : 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. 33227341 10.1016/j.jviromet.2020.114024 7678421 Baik Y Cilloni L Kendall E : Symptom-based vs asymptomatic testing for controlling SARS-CoV-2 transmission in low- and middle-income countries: A modelling analysis. Epidemics. 2022;41:100631. 36174427 10.1016/j.epidem.2022.100631 9511882 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. 32463076 10.1093/cid/ciaa654 7314132 Johansson MA Quandelacy TM Kada S : SARS-CoV-2 Transmission From People Without COVID-19 Symptoms. JAMA Netw Open. 2021;4(1):e2035057. 33410879 10.1001/jamanetworkopen.2020.35057 7791354 Choudhary OP Choudhary P Singh I : India’s COVID-19 vaccination drive: key challenges and resolutions. Lancet Infect Dis. 2021;21(11):1483–1484. 34529961 10.1016/S1473-3099(21)00567-3 8437681 Mandal S Arinaminpathy N Bhargava B : 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. 34643562 10.4103/ijmr.ijmr_1627_21 8555606 Cilloni L Kendall E Dowdy D : 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 Cilloni L : lmcilloni/covid-RDT: covid-RDT (covidRDT.v1.2). Zenodo. 2022. http://www.doi.org/10.5281/zenodo.7410262 Rader B Gertz A Iuliano AD : 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. 35358168 10.15585/mmwr.mm7113e1 8979595 Murhekar MV Bhatnagar T Selvaraju S : 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. 33515512 10.1016/S2214-109X(20)30544-1 7906675 Nordgren J Sharma S Olsson H : SARS-CoV-2 rapid antigen test: High sensitivity to detect infectious virus. J Clin Virol. 2021;140:104846. 33971580 10.1016/j.jcv.2021.104846 8105081 Kessler HH Prüller F Hardt M : Identification of contagious SARS-CoV-2 infected individuals by Roche’s Rapid Antigen Test. Clin Chem Lab Med. 2022;60(5):778–85. 35258234 10.1515/cclm-2021-1276 10.21956/gatesopenres.15508.r38537 Reviewer response for version 1 Tomassetti Flaminia 1 Referee University of Rome Tor Vergata, Rome, Italy

Competing interests: No competing interests were disclosed.

 10 1 2025 Copyright: © 2025 Tomassetti F 2025 This is an open access peer review report distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. recommendation approve-with-reservations

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

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

Is the study design appropriate and is the work technically sound?

Yes

Are the conclusions drawn adequately supported by the results?

Partly

Are sufficient details of methods and analysis provided to allow replication by others?

Yes

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.

Cilloni Lucia Johns Hopkins Bloomberg School of Public Health, USA

Competing interests: No competing interests were disclosed.

 12 5 2025

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.

 10.21956/gatesopenres.15508.r38543 Reviewer response for version 1 Papan Cihan 1 Referee University Hospital Bonn, Bonn, Germany

Competing interests: No competing interests were disclosed.

 3 1 2025 Copyright: © 2025 Papan C 2025 This is an open access peer review report distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. recommendation approve

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

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

Is the study design appropriate and is the work technically sound?

Yes

Are the conclusions drawn adequately supported by the results?

Yes

Are sufficient details of methods and analysis provided to allow replication by others?

Yes

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.

Cilloni Lucia Johns Hopkins Bloomberg School of Public Health, USA

Competing interests: No competing interests were disclosed.

 12 5 2025

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.”