"No test is better than a bad test": Impact of diagnostic uncertainty in mass testing on the spread of Covid-19

22nd April, 2020

Note: This is a summary of a preprint. This research is not yet peer reviewed. The full reprint can be found here

The cessation of lock-down measures will require an effective testing strategy. Much focus at the beginning of the UK's Covid-19 epidemic was directed to deficiencies in the national testing capacity. The quantity of tests may seem an important focus, but other characteristics are likely more germane. False positive tests are more probable than positive tests when the overall population has a low prevalence of the disease, even with highly accurate tests. Methods: We modify an SIR model to include quarantines states and test performance using publicly accessible estimates for the current situation. Three scenarios for cessation of lock-down measures are explored: (1) immediate end of lock-down measures, (2) continued lock-down with antibody testing based immunity passports, and (3) incremental relaxation of lock-down measures with active viral testing. Sensitivity, specifcity, prevalence and test capacity are modified for both active viral and antibody testing to determine their population level effect on the continuing epidemic. Findings: Diagnostic uncertainty can have a large effect on the epidemic dynamics of Covid-19 within the UK. The dynamics of the epidemic are more sensitive to test performance and targeting than test capacity. The quantity of tests is not a substitute for an effective strategy. Poorly targeted testing has the propensity to exacerbate the peak in infections. Interpretation: The assessment that 'no test is better than a bad test' is broadly supported by the present analysis. Antibody testing is unlikely to be a solution to the lock-down, regardless of test quality or capacity. A well designed active viral testing strategy combined with incremental relaxation of the lock-down measures is shown to be a potential strategy to restore some social activity whilst continuing to keep infections low.

The analysis does support the assertion that a bad test is worse than no tests, but a good test is onlyeffective in a carefully designed strategy. More is not necessarily better and over estimation of the test accuracy could be extremely detrimental.

This analysis is not a prediction; the numbers used in this analysis are estimates, and therefore, when such policies are devised and implemented this analysis would need to be repeated with more up-to-date numerical values. As such, the authors are not drawing firm conclusions about the absolute necessary capacity of tests. Nor do they wish to make specific statements about the necessary sensitivity or specificity of tests or the recommended rate of release from quarantine. The authors do, however, propose some conclusions that would broadly apply to the present situation, and therefore believe they should be considered by policy makers when designing strategies to tackle COVID-19.

Figure 7: Total active infections each day over the year after relaxing lock-down, under different testing intensities (columns) and various epidemiologic conditions. The per-day testing capacity is varied across the five columns of graphs. Rate, the percentage of the initial quarantined population being released each week is varied among rows. The prevalence of infections in the tested population is varied among different colours. To facilitate comparison within each column of graphs, the gray curves show the results observed for other Rates and Prevalences with the same testing intensity

  • Diagnostic uncertainty can have a large effect on the epidemic dynamics of Covid-19 within the UK. And, sensitivity, specificity, and the capacity for testing alone are not sufficient to design effective testing procedures.
  • Great caution should be exercised in the use of antibody testing. Under the assumption that the proportion of people in the UK who have had the virus is still low, it’s unlikely antibody testing at any scale will significantly support the end of lock-down measures. And, the negative consequences of un-targeted antibody screening at the population level could cause more harm than good.
  • Antibody testing, with a high specificity may be very useful on an individual basis, it certainly has scientific value, and could reduce risk for key workers. But any belief that these tests would be useful to relax lock-down measures for the majority of the population is misguided. At best it is a distraction, at worst it could be dangerous.
  • The incremental relaxation to lock-down measures, with all else equal, would significantly dampen the increase in peak infections, by 1 order of magnitude with a faster relaxation, and 2 orders of magnitude with a slower relaxation.
  • The capacity for infection screening needs to be significantly increased if it is to be used to relax quarantine measures, but only if it is well targeted, for example through effective contact tracing. Untargeted mass screening would be ineffectual and may prolong the necessary implementation of lock-down measures.
  • One interpretation of these results is that countries that had mass testing regimes early in the pandemic but had much lower case fatality rates may have been reporting a large number of false positives.
  • The results of this paper may explain what is being observed in nations such as Singapore as they continue to employ less-targeted mass testing and after a rapid cessation to their lock-down measures are now experiencing a second peak in infections.
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