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[Preprint]. 2020 Apr 11:2020.04.08.20058313.
doi: 10.1101/2020.04.08.20058313.

Accounting for incomplete testing in the estimation of epidemic parameters

R A Betensky  1 , Y Feng  1
Affiliations

Accounting for incomplete testing in the estimation of epidemic parameters

R A Betensky et al. medRxiv. .

Update in

Abstract

As the COVID-19 pandemic spreads across the world, it is important to understand its features and responses to public health interventions in real-time. The field of infectious diseases epidemiology has highly advanced modeling strategies that yield relevant estimates. These include the doubling time of the epidemic and various other representations of the numbers of cases identified over time. Crude estimates of these quantities suffer from dependence on the underlying testing strategies within communities. We clarify the functional relationship between testing and the epidemic parameters, and thereby derive sensitivity analyses that explore the range of possible truths under various testing dynamics. We derive the required adjustment to the estimates of interest for New York City. We demonstrate that crude estimates that assume stable testing or complete testing can be biased.

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Conflict of interest statement

Competing interests: The authors declare no competing interests.

Figures

Fig. 1.
Fig. 1.
Doubling times with sensitivity to testing for the 12 states with the most cases as of April 7, 2020 (“day 0”). The red “x” is the doubling time estimate on April 7, 2020. The curves depict the current doubling time under the scenario that the probability of testing of infecteds was constant in the past, subsequently decreased on a single day in the past, and sustained that decrease through the current time. The x-axis represents the day on which the probability of testing decreased. The decreases depicted are 10% (green), 20% (blue) and 25% (purple).
Fig. 2.
Fig. 2.
Doubling time on each day from April 7, 2020 back to March 17, 2020. The red curves assume stable probability of testing over the 20 days considered, the green curves assume a decrease of 10% in probability of testing infected individuals on each current day versus past days, the blue curves assume a decrease of 20% and the purple curves assume a decrease of 25%.
Fig. 3.
Fig. 3.
Cumulative incidence curves, standardized by the number of cases identified 9 days ago on March 29, 2020. The red curve assumes that testing of infected individuals has not changed in this timeframe. The green curves assume a decrease of 10% in probability of testing infected individuals on each current day versus March 29, 2020, the blue curves assume a decrease of 20% and the purple curves assume a decrease of 25%.
Fig. 4.
Fig. 4.
Estimates of R(t,d) for New York City, for t ranging from March 25, 2020 through April 7, 2020 and for lag time, l, equal to 18 days.
See this image and copyright information in PMC

References

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