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Extending the susceptible-exposed-infected-removed (SEIR) model to handle the false negative rate and symptom-based administration of COVID-19 diagnostic tests: SEIR-fansy.

ABSTRACT: False negative rates of severe acute respiratory coronavirus 2 diagnostic tests, together with selection bias due to prioritized testing can result in inaccurate modeling of COVID-19 transmission dynamics based on reported "case" counts. We propose an extension of the widely used Susceptible-Exposed-Infected-Removed (SEIR) model that accounts for misclassification error and selection bias, and derive an analytic expression for the basic reproduction number R0 as a function of false negative rates of the diagnostic tests and selection probabilities for getting tested. Analyzing data from the first two waves of the pandemic in India, we show that correcting for misclassification and selection leads to more accurate prediction in a test sample. We provide estimates of undetected infections and deaths between April 1, 2020 and August 31, 2021. At the end of the first wave in India, the estimated under-reporting factor for cases was at 11.1 (95% CI: 10.7,11.5) and for deaths at 3.58 (95% CI: 3.5,3.66) as of February 1, 2021, while they change to 19.2 (95% CI: 17.9, 19.9) and 4.55 (95% CI: 4.32, 4.68) as of July 1, 2021. Equivalently, 9.0% (95% CI: 8.7%, 9.3%) and 5.2% (95% CI: 5.0%, 5.6%) of total estimated infections were reported on these two dates, while 27.9% (95% CI: 27.3%, 28.6%) and 22% (95% CI: 21.4%, 23.1%) of estimated total deaths were reported. Extensive simulation studies demonstrate the effect of misclassification and selection on estimation of R0 and prediction of future infections. A R-package SEIRfansy is developed for broader dissemination.

SUBMITTER: Bhaduri R

PROVIDER: S-EPMC9035093 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature

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Publications

Extending the susceptible-exposed-infected-removed (SEIR) model to handle the false negative rate and symptom-based administration of COVID-19 diagnostic tests: SEIR-fansy.

Bhaduri Ritwik R Kundu Ritoban R Purkayastha Soumik S Kleinsasser Michael M Beesley Lauren J LJ Mukherjee Bhramar B Datta Jyotishka J

Statistics in medicine 20220227 13

False negative rates of severe acute respiratory coronavirus 2 diagnostic tests, together with selection bias due to prioritized testing can result in inaccurate modeling of COVID-19 transmission dynamics based on reported "case" counts. We propose an extension of the widely used Susceptible-Exposed-Infected-Removed (SEIR) model that accounts for misclassification error and selection bias, and derive an analytic expression for the basic reproduction number R0 as a function of false negative rate ...[more]

PMID: 35224743

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Project description:ObjectiveBy using a data-driven statistical approach, we isolated the net effect of multiple government interventions that were simultaneously implemented during the second wave of COVID-19 pandemic in China.Design, data sources and eligibility criteriaWe gathered epidemiological data and government interventions data of nine cities with local outbreaks during the second wave of COVID-19 pandemic in China. We employed the Susceptible-Exposed-Infectious-Recovered (SEIR) framework model to analyse the different pathways of transmission between cities with government interventions implementation and those without. We introduced new components to the standard SEIR model and investigated five themes of government interventions against COVID-19 pandemic.Data extraction and synthesisWe extracted information including study objective, design, methods, main findings and implications. These were tabulated and a narrative synthesis was undertaken given the diverse research designs, methods and implications.ResultsSupported by extensive empirical validation, our results indicated that the net effect of some specific government interventions (including masks, environmental cleaning and disinfection, tracing, tracking and 14-day centralised quarantining close contacts) had been significantly underestimated in the previous investigation. We also identified important moderators and mediators for the effect of certain government interventions, such as closure of shopping mall and restaurant in the medium-risk level areas, etc. Linking the COVID-19 epidemiological dynamics with the implementation timing of government interventions, we detected that the earlier implementation of some specific government interventions (including targeted partial lockdown, tracing, tracking and 14-day centralised quarantining close contacts) achieved the strongest and most timely effect on controlling COVID-19, especially at the early period of local outbreak.ConclusionsThese findings provide important scientific information for decisions regarding which and when government interventions should be implemented to fight against COVID-19 in China and beyond. The proposed analytical framework is useful for policy-making in future endemic and pandemic as well.

Dataset Information

Extending the susceptible-exposed-infected-removed (SEIR) model to handle the false negative rate and symptom-based administration of COVID-19 diagnostic tests: SEIR-fansy.

Publications

Extending the susceptible-exposed-infected-removed (SEIR) model to handle the false negative rate and symptom-based administration of COVID-19 diagnostic tests: SEIR-fansy.

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