The first step in SARS-CoV-2 genomic surveillance is testing to identify people who are infected. However, global testing rates are falling as we emerge from the acute health emergency and remain low in many low- and middle-income countries (mean = 27 tests per 100,000 people per day). We simulated COVID-19 epidemics in a prototypical low- and middle-income country to investigate how testing rates, sampling strategies and sequencing proportions jointly impact surveillance outcomes, and showed that low testing rates and spatiotemporal biases delay time to detection of new variants by weeks to months and can lead to unreliable estimates of variant prevalence, even when the proportion of samples sequenced is increased. Accordingly, investments in wider access to diagnostics to support testing rates of approximately 100 tests per 100,000 people per day could enable more timely detection of new variants and reliable estimates of variant prevalence. The performance of global SARS-CoV-2 genomic surveillance programs is fundamentally limited by access to diagnostic testing.

Background Increasing the availability of antigen rapid diagnostic tests (Ag-RDTs) in low- and middle-income countries (LMICs) is key to alleviating global SARS-CoV-2 testing inequity (median testing rate in December 2021-March 2022 when the Omicron variant was spreading in multiple countries; high-income countries = 600 tests/100,000 people/day; LMICs = 14 tests/100,000 people/day). However, target testing levels and effectiveness of asymptomatic community screening to impact SARS-CoV-2 transmission in LMICs are unclear. Methods We used PATAT, an LMIC-focused agent-based model to simulate COVID-19 epidemics, varying the amount of Ag-RDTs available for symptomatic testing at healthcare facilities and asymptomatic community testing in different social settings. We assumed that testing was a function of access to healthcare facilities and availability of Ag-RDTs. We explicitly modelled symptomatic testing demand from non-SARS-CoV-2 infected individuals and measured impact based on the number of infections averted due to test-and-isolate. Results Testing symptomatic individuals yields greater benefits than any asymptomatic community testing strategy until most symptomatic individuals who sought testing have been tested. Meeting symptomatic testing demand likely requires at least 200-400 tests/100,000 people/day on average as symptomatic testing demand is highly influenced by non-SARS-CoV-2 infected individuals. After symptomatic testing demand is satisfied, excess tests to proactively screen for asymptomatic infections among household members yields the largest additional infections averted. Conclusions Testing strategies aimed at reducing transmission should prioritize symptomatic testing and incentivizing test-positive individuals to adhere to isolation to maximize effectiveness.

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Background Genomic surveillance is essential for monitoring the emergence and spread of SARS-CoV-2 variants. SARS-CoV-2 diagnostic testing is the starting point for SARS-CoV-2 genomic sequencing. However, testing rates in many low- and middle-income countries (LMICs) are low (mean = 27 tests/100,000 people/day) and global testing rates are falling in the post-crisis phase of the pandemic, leading to spatiotemporal biases in sample collection. Various public health agencies and academic groups have produced recommendations on sample sizes and sequencing strategies for effective genomic surveillance. However, these recommendations assume very high volumes of diagnostic testing that are currently well beyond reach in most LMICs. Methods To investigate how testing rates, sequencing strategies and the degree of spatiotemporal bias in sample collection impact variant detection and monitoring outcomes, we used an individual-based model to simulate COVID-19 epidemics in a prototypical LMIC. Within the model, we simulated a range of testing rates, accounted for likely testing demand and applied various genomic surveillance strategies, including sentinel surveillance. Findings Diagnostic testing rates play a substantially larger role in monitoring the prevalence and emergence of new variants than the proportion of samples sequenced. To enable timely detection and monitoring of emerging variants, programs should achieve average testing rates of at least 100 tests/100,000 people/day and sequence 5-10% of test-positive specimens, which may be accomplished through sentinel or other routine surveillance systems. Under realistic assumptions, this averages to ~10 samples for sequencing/1,000,000 people/week. Interpretation For countries where testing capacities are low and sample collection is spatiotemporally biased, surveillance programs should prioritize investments in wider access to diagnostic testing to enable more representative sampling, ahead of simply increasing quantities of sequenced samples. Funding European Research Council, the Rockefeller Foundation, and the Governments of Germany, Canada, UK, Australia, Norway, Saudi Arabia, Kuwait, Netherlands and Portugal.

Diagnostic assays for various infectious diseases, including COVID-19, have been challenged for their utility as standalone point-of-care diagnostic tests due to suboptimal accuracy, complexity, high cost or long turnaround times for results. It is therefore critical to optimise their use to meet the needs of users. We used a simulation approach to estimate diagnostic outcomes, number of tests required and average turnaround time of using two-test algorithms compared with singular testing; the two tests were reverse transcription polymerase chain reaction (RT-PCR) and an antigen-based rapid diagnostic test (Ag-RDT). A web-based application of the model was developed to visualise and compare diagnostic outcomes for different disease prevalence and test performance characteristics (sensitivity and specificity). We tested the model using hypothetical prevalence data for COVID-19, representing low- and high-prevalence contexts and performance characteristics of RT-PCR and Ag-RDTs. The two-test algorithm when RT-PCR was applied to samples negative by Ag-RDT predicted gains in sensitivity of 27% and 7%, respectively, compared with Ag-RDT and RT-PCR alone. Similarly, when RT-PCR was applied to samples positive by Ag-RDT, specificity gains of 2.9% and 1.9%, respectively, were predicted. The algorithm using Ag-RDT followed by RT-PCR as a confirmatory test for positive patients limited the requirement of RT-PCR testing resources to 16,400 and 3,034 tests when testing a population of 100,000 with an infection prevalence of 20% and 0.05%, respectively. A two-test algorithm comprising a rapid screening test followed by confirmatory laboratory testing can reduce false positive rate, produce rapid results and conserve laboratory resources, but can lead to large number of missed cases in high prevalence setting. The web application of the model can identify the best testing strategies, tailored to specific use cases and we also present some examples how it was used as part of the Access to Covid-19 Tools (ACT) Accelerator Diagnostics Pillar.

Critical challenges for North American epidemiologists include social determinants of disease distribution and the underlying inequalities in access to and benefit from preventive services and healthcare, particularly in the USA. The gains in life expectancy also underscore the need for research on health promotion and prevention of disease and disability in older adults. The diversity in epidemiological subspecialties poses new challenges in training and accreditation and has occurred in parallel with a decrease in research funding.

SummaryBackgroundIncreasing the availability of antigen rapid diagnostic tests (Ag-RDTs) in low- and middle-income countries (LMICs) is key to alleviating global SARS-CoV-2 testing inequity (median testing rate in December 2021-March 2022 when the Omicron variant was spreading in multiple countries; high-income countries=600 tests/100,000 people/day; LMICs=14 tests/ 100,000 people/day). However, target testing levels and effectiveness of asymptomatic community screening to impact SARS-CoV-2 transmission in LMICs are unclear.MethodsWe used PATAT, an LMIC-focused agent-based model to simulate COVID-19 epidemics, varying the amount of Ag-RDTs available for symptomatic testing at healthcare facilities and asymptomatic community testing in different social settings. We assumed that testing was a function of access to healthcare facilities and availability of Ag-RDTs. We explicitly modelled symptomatic testing demand from non-SARS-CoV-2 infected individuals and measured impact based on the number of infections averted due to test-and-isolate.FindingsTesting symptomatic individuals yields greater benefits than any asymptomatic community testing strategy until most symptomatic individuals who sought testing have been tested. Meeting symptomatic testing demand likely requires ∼200-400 tests/100,000 people/day on average as symptomatic testing demand is highly influenced by non-SARS-CoV-2 infected individuals. After symptomatic testing demand is satisfied, excess tests to proactively screen for asymptomatic infections among household members yields the largest additional infections averted.InterpretationTesting strategies aimed at reducing transmission should prioritize symptomatic testing and incentivizing test-positive individuals to adhere to isolation to maximize effectiveness.FundingEuropean Research Council, the Rockefeller Foundation, and the Governments of Germany, Canada, UK, Australia, Norway, Saudi Arabia, Kuwait, Netherlands and Portugal.Research in contextEvidence before this studySARS-CoV-2 transmission can be reduced by test-trace-and-isolate strategies. However, large gaps in global COVID-19 testing equity exist: only ∼20% of tests administered globally as of May 2022 (https://www.finddx.org/covid-19/test-tracker/) were performed in low- and middle-income countries (LMICs) where half the world’s population reside. To narrow the equity gap, expanded access to SARS-CoV-2 antigen rapid diagnostic tests (Ag-RDTs) in low-resource settings has been prioritized, with the diagnostics pillar of the global Access to COVID-19 Tools (ACT)-Accelerator setting a target testing rate of 100 tests per 100,000 persons per day (/100k/day). Community testing to detect asymptomatic infected individuals has also been proposed to be a key component in bridging the equity gap. We searched PubMed and Google Scholar using combinations of search terms (i.e. “SARS-CoV-2”, “COVID-19”, “diagnostic”, “testing”, “rapid diagnostic tests”, “lateral flow tests”, “Ag-RDT”, “LMIC”) and critically reviewed publications and preprints on how scaling up testing availability using Ag-RDTs and implementing community testing programs alongside healthcare facility-based symptomatic testing would impact community transmissions in LMICs. There is currently no robust quantitative evidence on the strategies or amount of testing needed to reduce community transmission of SARS-CoV-2.Added value of this studyTo our knowledge, this is the first study that critically assessed the minimum COVID-19 testing targets set by the ACT-Accelerator in LMICs. This is also the first study that estimated the reduction in secondary SARS-CoV-2 transmissions in LMICs given varying levels of Ag-RDT availability and testing strategies, including the implementation of asymptomatic community testing across different social settings (e.g. households, schools, formal workplaces and regular mass gatherings such as religious gatherings). In doing so, this is the first robust evidence-base on the utility of using Ag-RDTs to scale-up testing-for-mitigation strategies in LMICs.Implications of all the available evidenceOur model showed that testing of symptomatic individuals yields greater reduction in transmissions than any asymptomatic community testing program. Asymptomatic community testing will only support reduced infections after prioritizing available tests for symptomatic individuals who sought testing. The current minimum COVID-19 testing rate target of 100 tests/100k/day can modestly reduce transmissions in LMICs but substantially larger volumes of tests are needed to saturate symptomatic testing demand or effectively implement community testing.

Background Countries around the world have implemented restrictions on mobility, especially cross-border travel to reduce or prevent SARS-CoV-2 community transmission. Rapid antigen testing (Ag-RDT), with on-site administration and rapid turnaround time may provide a valuable screening measure to ease cross-border travel while minimizing risk of local transmission. To maximize impact, we developed an optimal Ag-RDT screening algorithm for cross-border entry. Methods Using a previously developed mathematical model, we determined the daily number of imported COVID-19 cases that would generate no more than a relative 1% increase in cases over one month for different effective reproductive numbers (Rt) of the recipient country. We then developed an algorithm- for differing levels of Rt, arrivals per day, mode of travel, and SARS-CoV-2 prevalence amongst travelers- to determine the minimum proportion of people that would need Ag-RDT testing at border crossings to ensure no greater than the relative 1% community spread increase. Findings When daily international arrivals and/or COVID-19 prevalence amongst arrivals increases, the proportion of arrivals required to test using Ag-RDT increases. At very high numbers of international arrivals/COVID-19 prevalence, Ag-RDT testing is not sufficient to prevent increased community spread, especially for lower levels of Rt. In these cases, Ag-RDT screening would need to be supplemented with other measures to prevent an increase in community transmission. Interpretation An efficient Ag-RDT algorithm for SARS-CoV-2 testing depends strongly on Rt, volume of travel, proportion of land and air arrivals, test sensitivity, and COVID-19 prevalence among travelers. Funding USAID, Government of the Netherlands