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. 2020 Oct 1;49(5):1419-1426.
doi: 10.1093/ije/dyaa116.

Accounting for incomplete testing in the estimation of epidemic parameters

Rebecca A Betensky  1 , Yang Feng  1
Affiliations

Accounting for incomplete testing in the estimation of epidemic parameters

Rebecca A Betensky et al. Int J Epidemiol. .
No abstract available

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Figures

Figure 1
Figure 1
Doubling times with sensitivity to testing for the 12 states with the most cases as of 7 April 2020 (‘day 0’). The black circle indicates the estimate under the assumption that the probability of testing infected individuals has not changed over the past 6–12 days relative to the current date of 7 April 2020 for the states depicted. We linearly interpolated the discrete-time estimates as described in the Methods. The 95% bootstrap percentile confidence interval based on the crude estimate is included. We calculated doubling times under the assumption that the probability of testing of infected individuals was constant in the past, subsequently changed on a single day in the past, and sustained that change through the current time; i.e. if c denotes that number of days prior to time t on which the change in probability of testing occurred, then R(t, u) = 1 for u ≤ c and R(t, u) = 1 + ε for u > c. We considered values of ε of −0.2, −0.09, 0.11, 0.33, corresponding to decreases in testing of 25% (two-dashed) and 10% (dashed) and to increases of 10% (long-dashed) and 25% (dotted), respectively.
Figure 2
Figure 2
Doubling time on each day from 7 April 7 (‘day 0’) back to 19 March 2020 (‘day-19’). In this figure, we used a testing model in which we assumed that there was a single change in testing probability and it occurred on the day for which the doubling time is calculated. The solid curves depict stable probability of testing over the 20 days considered, the two-dashed curves (ε = −0.2) depict a decrease of 25% in probability of testing infected individuals on each current day versus past days, the dashed curves (ε = −0.09) depict a decrease of 10%, the long-dashed (ε = 0.11) curves depict an increase of 10% and the dotted curves (ε = 0.33) depict an increase of 25%. We have included 95% bootstrap percentile confidence intervals around the solid line for the unadjusted doubling time.
Figure 3
Figure 3
Cumulative epidemic curves from 30 March 2020 (day-8) through 7 April 2020 (day 0), standardized by the number of cases identified on 29 March 2020 (day-9). The solid curve assumes that testing of infected individuals has not changed in this timeframe. The dashed and dotted curves include the adjustment for differential testing and depict the scenarios in which the probability of testing of infected individuals changed on 30 March 2020 and remained constant thereafter. In particular, we considered values of ε of −0.2, −0.09, 0.11, 0.33, corresponding to decreases in testing of 25% (two-dashed) and 10% (dashed) and to increases of 10% (long-dashed) and 25% (dotted), respectively.
Figure 4
Figure 4
Estimates of R(t, u) (ratio of probabilities of testing of infected individuals at time t−u relative to time t) for New York City, for u ranging from 25 March 2020 through 7 April 2020 and for lag time, l, equal to 18 days. The solid curve that obtains the minimum value at u = 11 is that for t = 25 March 2020, and the seven curves that lie above it at u = 11 are those for the subsequent dates, in chronological order (see legend). Beyond 1 April 2020, all curves are very close to each other.
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References

    1. Wilson EB, Burke MH. The epidemic curve. Proc Natl Acad Sci U S A 1942;28:361–7. - PMC - PubMed
    1. Muniz-Rodriguez K, Chowell G, Cheung C-H et al. Doubling time of the COVID-19 epidemic by province, China. Emerg Infect Dis 2020. doi:10.3201/eid2608.200219. - DOI - PMC - PubMed
    1. Nunes-Vaz R. Visualising the doubling time of COVID-19 allows comparison of the success of containment measures. Glob Biosecur 2020. doi:http://doi.org/10.31646/gbio.61.
    1. Baud D, Qi X, Nielsen-Saines K, Musso D, Pomar L, Favre G. Real estimates of mortality following COVID-19 infection. Lancet Infect Dis 2020. doi:10.1016/S1473-3099(20)30195-X. - DOI - PMC - PubMed
    1. Dhenain M. Estimation of COVID-19 cases in France and in different countries: homogeneisation based on mortality 2020. medRxiv, doi:10.1101/2020.04.07.20055913, 13 May 2020, preprint: not peer reviewed.

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