SARS-CoV-2 diagnostic testing rates determine the sensitivity of genomic surveillance programs

Abstract

The first step in SARS-CoV-2 genomic surveillance is testing to identify infected people. However, global testing rates are falling as we emerge from the acute health emergency and remain low in many low- and middle-income countries (LMICs) (mean = 27 tests/100,000 people/day). We simulated COVID-19 epidemics in a prototypical LMIC 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 ~100 tests/100,000 people/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.

Fig. 1.. Global disparities in SARS-CoV-2 testing rates.

Each country is colored by the average total number of SARS-CoV-2 tests performed per 100,000 persons per day (/100K/day) (A) between 1 December 2021 and 31 March 2022 when the Omicron variant-of-concern spread around the world; (B) between 1 April 2022 and 6 May 2022 when most countries were past peak Omicron wave of infections.

Fig. 2.. Performance of current guidance on number of positive specimens to sequence for variant detection with testing rate at 27 tests per 100,000 persons per day.

First day of detection since variant introduction at 95% confidence and the corresponding circulating variant proportion using guidance from the World Health Organization (WHO)/European Centre for Disease Prevention and Control (ECDC)^,, Brito et al., and Wohl et al. (Table 1) under different genomic surveillance strategies with varying sampling coverage (i.e. all collected specimens from all healthcare facilities are sent to one facility to be sampled for sequencing (population-wide strategy); only one, 10%, 25%, 50%, or 100% of tertiary sentinel facilities would sample the specimens they collected for sequencing). Turnaround time (i.e. time from specimen collection to acquisition of sequencing data) was assumed to be negligible. 1,000 random independent simulations were performed for each guidance/surveillance strategy. We simulated epidemics for (A) Wild-type SARS-CoV-2/Alpha. (B) Delta/Omicron. Grey regions denote that we could not reliably detect the variant virus with 95% confidence using the guidance in question under the assumed genomic surveillance strategy.

Fig. 3.. Impact of SARS-CoV-2 testing rates and proportion of positive specimens to sequence on variant detection.

For each mean daily test availability (line and shading color), the expected day when the first variant specimen to be sequenced is sampled since its introduction is plotted against the proportion of positive specimens to be sampled for sequencing daily. Different genomic surveillance strategies with varying sampling coverage (i.e. all specimens collected from all healthcare facilities sent to one facility to be sampled for sequencing (population-wide strategy); only one, 10%, 25%, 50%, or 100% of tertiary sentinel facilities would sample the specimens they collected for sequencing) were simulated. (A) Wild-type SARS-CoV-2/Alpha. (B) Delta/Omicron. The expected day when the first variant specimen is sampled was computed from 1,000 random independent simulations for each surveillance strategy. The shaded region depicts the standard deviation across simulations.

Fig. 4.. Impact of SARS-CoV-2 testing rates on the capacity to monitor changes in variant prevalence based on diagnostic test availability and proportion of test-positive samples sequenced.

Different genomic surveillance strategies (i.e. all specimens collected from all healthcare facilities sent to one facility to be sampled for sequencing (population-wide strategy); only one, 10%, 25%, 50%, or 100% of tertiary sentinel facilities would sample the specimens they collected for sequencing) were simulated. (A) Maximum absolute difference between observed and circulating variant proportions. (B) Proportion of timepoints when sequencing was performed that the absolute difference between observed and circulating variant proportions is greater than 20%. All results were computed from 1,000 random independent simulations for each surveillance strategy.

Fig. 5.. Recommended approach to enhance genomic surveillance robustness.

In each plot, the operating curves of the expected day when the first Alpha variant sequence is generated are plotted for different proportion of specimens to sample for sequencing per day and turnaround times. We assumed that the Alpha variant was circulating at 1% initially with wild-type SARSCoV-2 in the background. We also assumed that positive specimens sampled within each week for sequencing are consolidated into a batch before they are referred for sequencing. Turnaround time refers to the time between collection of each weekly consolidated batch of positive specimens to the acquisition of its corresponding sequencing data. The vertical axes denote the number of days passed since the introduction of the Alpha variant (left) and its corresponding circulating proportion (right). The horizontal axes denote the proportion of positive specimens to sample for sequencing per day (bottom) and the corresponding mean number of sequences to be generated per week per 1,000,000 people over a 90-day epidemic period. (A) Specimen pools for sequencing from one tertiary sentinel facility with testing rate at 27 tests per 100,000 persons per day (tests/100k/day). (B) Specimen pools for sequencing from one tertiary sentinel facility with testing rate at 100 tests/100k/day. (C) Specimen pools for sequencing from 25% of all tertiary sentinel facilities with testing rate at 100 tests/100k/day. (D) Zoomed-in plot of (C) to highlight sequencing proportions varying between 1–25%. Sequencing 5–10% of positive specimens (blue shaded region) would ensure that we would expectedly detect Alpha in 30 days if turnaround time is kept within one week. All results were computed from 1,000 random independent simulations for each surveillance strategy. The shaded region depicts the standard deviation across simulations.