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[Preprint]. 2020 Sep 25:2020.09.24.20200238.
doi: 10.1101/2020.09.24.20200238.

EXTENDING THE SUSCEPTIBLE-EXPOSED-INFECTED-REMOVED(SEIR) MODEL TO HANDLE THE HIGH FALSE NEGATIVE RATE AND SYMPTOM-BASED ADMINISTRATION OF COVID-19 DIAGNOSTIC TESTS: SEIR-fansy

Ritwik Bhaduri  1 , Ritoban Kundu  2 , Soumik Purkayastha  3 , Michael Kleinsasser  3 , Lauren J Beesley  3 , Bhramar Mukherjee  4
Affiliations

EXTENDING THE SUSCEPTIBLE-EXPOSED-INFECTED-REMOVED(SEIR) MODEL TO HANDLE THE HIGH FALSE NEGATIVE RATE AND SYMPTOM-BASED ADMINISTRATION OF COVID-19 DIAGNOSTIC TESTS: SEIR-fansy

Ritwik Bhaduri et al. medRxiv. .

Update in

Abstract

The false negative rate of the diagnostic RT-PCR test for SARS-CoV-2 has been reported to be substantially high. Due to limited availability of testing, only a non-random subset of the population can get tested. Hence, the reported test counts are subject to a large degree of selection bias. We consider an extension of the Susceptible-Exposed-Infected-Removed (SEIR) model under both selection bias and misclassification. We derive closed form expression for the basic reproduction number under such data anomalies using the next generation matrix method. We conduct extensive simulation studies to quantify the effect of misclassification and selection on the resultant estimation and prediction of future case counts. Finally we apply the methods to reported case-death-recovery count data from India, a nation with more than 5 million cases reported over the last seven months. We show that correcting for misclassification and selection can lead to more accurate prediction of case-counts (and death counts) using the observed data as a beta tester. The model also provides an estimate of undetected infections and thus an under-reporting factor. For India, the estimated under-reporting factor for cases is around 21 and for deaths is around 6. We develop an R-package (SEIRfansy) for broader dissemination of the methods.

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Conflict of interest statement

Conflict of Interest

Nothing to declare.

Figures

Figure 1:
Figure 1:
Compartmental model incorporating false negative test results
Figure 2:
Figure 2:
Selection Model
Figure 3:
Figure 3:
Estimates of R0 in India across phases. The mean and 95% credible intervals (in parentheses) are provided under the Multinomial-2-parameter model
Figure 4:
Figure 4:
Reported Active Cases in India - Comparison between different models
Figure 5:
Figure 5:
COVID cases in India with number of Reported, False Negative and Untested cases. (A) Total active COVID cases in India from April 1 to August 31 including reported active cases, false negatives active and untested active cases. (B) Proportion of reported active cases among Active COVID cases in India (C) Total cumulative cases in India from April 1 to August 31 including reported cumulative cases, cumulative false negatives and untested cumulative cases. (D) Proportion of reported cases among total cumulative COVID cases in India . (E) Total deaths in India from April 1 to August 31 including reported and unreported deaths. (F) Proportion of reported deaths among total deaths in India. The dotted line in subfigures A, C and E represent the observed data.
Figure 6:
Figure 6:
Effect of misclassification on estimates for India (A) Estimates of Total Active Cases for f = 0, 0.15 and 0.3 (A) Estimates of Reported Active Cases for f = 0, 0.15 and 0.3 with the observed data
Figure 7:
Figure 7:
62 day predictions for (A) Delhi and (B) Maharashtra
Figure 8:
Figure 8:
Variation of predictions with different rates of misclassification
Figure 9:
Figure 9:
Effect of selection on (A) Total and (B) Reported Active Cases
Figure 10:
Figure 10:
Effect of test : Plot showing the number of total active cases over time for different number of tests. The numbers above the arrows indicate the multiplication factor of number of tests
See this image and copyright information in PMC

References

    1. Ai Tao, Yang Zhenlu, Hou Hongyan, Zhan Chenao, Chen Chong, Lv Wenzhi, Tao Qian, Sun Ziyong, and Xia Liming. Correlation of chest ct and rt-pcr testing in coronavirus disease 2019 (covid-19) in china: a report of 1014 cases. Radiology, page 200642, 2020. - PMC - PubMed
    1. Arevalo-Rodriguez Ingrid, Buitrago-Garcia Diana, Simancas-Racines Daniel, Zambrano-Achig Paula, Campo Rosa del, Ciapponi Agustin, Sued Omar, Martinez-Garcia Laura, Rutjes Anne, Low Nicola, et al. False-negative results of initial rt-pcr assays for covid-19: a systematic review. medRxiv, 2020. - PMC - PubMed
    1. Babu N. Percentage of people with antibodies high, shows delhi serological survey. The Hindu, 2020.
    1. Bai Yan, Yao Lingsheng, Wei Tao, Tian Fei, Jin Dong-Yan, Chen Lijuan, and Wang Meiyun. Presumed asymptomatic carrier transmission of covid-19. Jama, 323(14):1406–1407, 2020. - PMC - PubMed
    1. Beesley Lauren J and Mukherjee Bhramar. Statistical inference for association studies using electronic health records: handling both selection bias and outcome misclassification. medRxiv, 2019. doi: 10.1101/2019.12.26.19015859 URL https://www.medrxiv.org/content/early/2019/12/30/2019.12.26.19015859. - DOI - PubMed

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