Design of a population-based longitudinal cohort study of SARS-CoV-2 incidence and prevalence among adults in the San Francisco Bay Area

Lindan, Christina; Desai, Manisha; Boothroyd, Derek B.; Judson, Timothy; Bollyky, Jennifer; Weng, Yingjie; Cheng, Yuteh; Dahlen, Alex; Hedlin, Haley; Grumbach, Kevin; Henne, Jeff; García, S.; Gonzales, Ralph; Craik, Charles S.; Maldonado, Yvonne; Rutherford, George W.

doi:10.1016/j.annepidem.2021.11.001

Cited by 7 publications

(6 citation statements)

References 62 publications

(72 reference statements)

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“…Shifts in testing strategies poses a critical problem when attempting to validate models of the total incidence of infected individuals during the early phases of a pandemic 10 . In Sweden, only persons with severe symptoms admitted to hospital were tested during the first month of the pandemic.…”

Section: Discussionmentioning

confidence: 99%

Computational models predicting the early development of the COVID-19 pandemic in Sweden: systematic review, data synthesis, and secondary validation of accuracy

Gerlee

Jöud

Spreco

et al. 2022

Sci Rep

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Computational models for predicting the early course of the COVID-19 pandemic played a central role in policy-making at regional and national levels. We performed a systematic review, data synthesis, and secondary validation of studies that reported on prediction models addressing the early stages of the COVID-19 pandemic in Sweden. A literature search in January 2021 based on the search triangle model identified 1672 peer-reviewed articles, preprints and reports. After applying inclusion criteria 52 studies remained out of which 12 passed a Risk of Bias Opinion Tool. When comparing model predictions with actual outcomes only 4 studies exhibited an acceptable forecast (mean absolute percentage error, MAPE < 20%). Models that predicted disease incidence could not be assessed due to the lack of reliable data during 2020. Drawing conclusions about the accuracy of the models with acceptable methodological quality was challenging because some models were published before the time period for the prediction, while other models were published during the prediction period or even afterwards. We conclude that the forecasting models involving Sweden developed during the early stages of the COVID-19 pandemic in 2020 had limited accuracy. The knowledge attained in this study can be used to improve the preparedness for coming pandemics.

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Section: Discussionmentioning

confidence: 99%

Computational models predicting the early development of the COVID-19 pandemic in Sweden: systematic review, data synthesis, and secondary validation of accuracy

Gerlee

Jöud

Spreco

et al. 2022

Sci Rep

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“…The longitudinal study relied on a sampling framework to randomly select residents from six Bay Area counties using census tract data. 27 We requested participants in the study to come into the clinic for a baseline visit and, subsequently, for monthly visits for up to 6 months. Evidence of infection was defined as a positive test using two types of assays measured repeatedly: (1) an rt-PCR test of a nasopharyngeal swab and (2) serologic testing of blood.…”

Section: Parent Study: the Trackcovid Studymentioning

confidence: 99%

Section: Parent Study: the Trackcovid Studymentioning

confidence: 99%

“…Importantly, to account for (1) the sampling framework, (2) the probability of selection from the household, and (3) nonresponse bias, we estimated and incorporated a combined weight variable for each participant when calculating quantities of interest (such as incidence). 27 The TrackCOVID study was designated as a public health surveillance study and not human subjects research under 45 CFR 46.102(l) by the Stanford University School of Medicine Administrative Panel on Human Subjects in Medical Research and the University of California, San Francisco Institutional Review Board.…”

Section: Parent Study: the Trackcovid Studymentioning

confidence: 99%

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Adjusting Incidence Estimates with Laboratory Test Performances: A Pragmatic Maximum Likelihood Estimation-Based Approach

Weng,

Tian,

Boothroyd

et al. 2024

Epidemiology

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Understanding the incidence of disease is often crucial for public policy decision-making, as observed during the COVID-19 pandemic. Estimating incidence is challenging, however, when the definition of incidence relies on tests that imperfectly measure disease, as in the case when assays with variable performance are used to detect the SARS-CoV-2 virus. To our knowledge there are no pragmatic methods to address the bias introduced by performance of labs in testing for the virus. In the setting of a longitudinal study, we developed a maximum likelihood estimation (MLE)-based approach to estimate laboratory performance-adjusted incidence using the expectation-maximization algorithm. We constructed confidence intervals (CIs) using both bootstrapped-based and large-sample interval estimator approaches. We evaluated our methods through extensive simulation and applied them to a real-world study (TrackCOVID), where the primary goal was to determine the incidence of and risk factors for SARS-CoV-2 infection in the San Francisco Bay Area during July 2020 to March 2021. Our simulations demonstrated that our method converged rapidly with accurate estimates under a variety of scenarios. Bootstrapped-based confidence intervals (CIs) were comparable to the large-sample estimator CIs with a reasonable number of incident cases, shown via a simulation scenario based on the real TrackCOVID study. In more extreme simulated scenarios, the coverage of large-sample interval estimation outperformed the bootstrapped-based approach. Results from application to the TrackCOVID Study suggested that assuming perfect laboratory test performance can lead to inaccurate inference of the incidence. Our flexible, pragmatic method can be extended to a variety of disease and study settings.

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“…19 A similar programme enrolling healthcare workers across the UK investigated reinfection rates by testing participants fortnightly but incorporated the results of non-programme tests via data linkage with the national notification system. 20 21 A San Francisco area longitudinal surveillance programme for COVID-19 (TrackCOVID) recruited people via mail or telephone, and asked people to go to a clinic for testing (nasopharyngeal swab for PCR and venous blood for antibody testing) once a month, 22 and a similar longitudinal study in Spain (ENE-COVID) recruited participants by telephone for point of care and antibody tests 2-4 weeks apart. 23 This paper describes a novel longitudinal testing-based sentinel surveillance programme developed to estimate the incidence of notifiable respiratory diseases (COVID-19, influenza and RSV) during a time of reduced testing, in parts of Queensland over the southern winter 2022.…”

Section: Open Accessmentioning

confidence: 99%

Estimating the incidence of COVID-19, influenza and respiratory syncytial virus infection in three regions of Queensland, Australia, winter 2022: findings from a novel longitudinal testing-based sentinel surveillance programme

May,

Ginige,

Firman

et al. 2024

BMJ Open

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ObjectiveThe 2022 Australian winter was the first time that COVID-19, influenza and respiratory syncytial virus (RSV) were circulating in the population together, after two winters of physical distancing, quarantine and borders closed to international travellers. We developed a novel surveillance system to estimate the incidence of COVID-19, influenza and RSV in three regions of Queensland, Australia.DesignWe implemented a longitudinal testing-based sentinel surveillance programme. Participants were provided with self-collection nasal swabs to be dropped off at a safe location at their workplace each week. Swabs were tested for SARS-CoV-2 by PCR. Symptomatic participants attended COVID-19 respiratory clinics to be tested by multiplex PCR for SARS-CoV-2, influenza A and B and RSV. Rapid antigen test (RAT) results reported by participants were included in the analysis.Setting and participantsBetween 4 April 2022 and 3 October 2022, 578 adults were recruited via their workplace. Due to rolling recruitment, withdrawals and completion due to positive COVID-19 results, the maximum number enrolled in any week was 423 people.ResultsA total of 4290 tests were included. Participation rates varied across the period ranging from 25.9% to 72.1% of enrolled participants. The total positivity of COVID-19 was 3.3%, with few influenza or RSV cases detected. Widespread use of RAT may have resulted in few symptomatic participants attending respiratory clinics. The weekly positivity rate of SARS-CoV-2 detected during the programme correlated with the incidence of notified cases in the corresponding communities.ConclusionThis testing-based surveillance programme could estimate disease trends and be a useful tool in settings where testing is less common or accessible. Difficulties with recruitment meant the study was underpowered. The frontline sentinel nature of workplaces meant participants were not representative of the general population but were high-risk groups providing early warning of disease.

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Design of a population-based longitudinal cohort study of SARS-CoV-2 incidence and prevalence among adults in the San Francisco Bay Area

Cited by 7 publications

References 62 publications

Computational models predicting the early development of the COVID-19 pandemic in Sweden: systematic review, data synthesis, and secondary validation of accuracy

Computational models predicting the early development of the COVID-19 pandemic in Sweden: systematic review, data synthesis, and secondary validation of accuracy

Adjusting Incidence Estimates with Laboratory Test Performances: A Pragmatic Maximum Likelihood Estimation-Based Approach

Estimating the incidence of COVID-19, influenza and respiratory syncytial virus infection in three regions of Queensland, Australia, winter 2022: findings from a novel longitudinal testing-based sentinel surveillance programme

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