Dataset Information

Model training periods impact estimation of COVID-19 incidence from wastewater viral loads.

ABSTRACT: Wastewater-based epidemiology (WBE) has been deployed broadly as an early warning tool for emerging COVID-19 outbreaks. WBE can inform targeted interventions and identify communities with high transmission, enabling quick and effective responses. As the wastewater (WW) becomes an increasingly important indicator for COVID-19 transmission, more robust methods and metrics are needed to guide public health decision-making. This research aimed to develop and implement a mathematical framework to infer incident cases of COVID-19 from SARS-CoV-2 levels measured in WW. We propose a classification scheme to assess the adequacy of model training periods based on clinical testing rates and assess the sensitivity of model predictions to training periods. A testing period is classified as adequate when the rate of change in testing is greater than the rate of change in cases. We present a Bayesian deconvolution and linear regression model to estimate COVID-19 cases from WW data. The effective reproductive number is estimated from reconstructed cases using WW. The proposed modeling framework was applied to three Northern California communities served by distinct WW treatment plants. The results showed that training periods with adequate testing are essential to provide accurate projections of COVID-19 incidence.

SUBMITTER: Daza-Torres ML

PROVIDER: S-EPMC9597566 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature

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Model training periods impact estimation of COVID-19 incidence from wastewater viral loads.

Daza-Torres Maria L ML Montesinos-López J Cricelio JC Kim Minji M Olson Rachel R Bess C Winston CW Rueda Lezlie L Susa Mirjana M Tucker Linnea L García Yury E YE Schmidt Alec J AJ Naughton Colleen C CC Pollock Brad H BH Shapiro Karen K Nuño Miriam M Bischel Heather N HN

The Science of the total environment 20221026 Pt 1

Wastewater-based epidemiology (WBE) has been deployed broadly as an early warning tool for emerging COVID-19 outbreaks. WBE can inform targeted interventions and identify communities with high transmission, enabling quick and effective responses. As the wastewater (WW) becomes an increasingly important indicator for COVID-19 transmission, more robust methods and metrics are needed to guide public health decision-making. This research aimed to develop and implement a mathematical framework to inf ...[more]

PMID: 36306854

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Project description:Background: The impact of the coronavirus disease (COVID-19) pandemic extends beyond the realms of patient care and healthcare resource use to include medical education; however, the repercussions of COVID-19 on the quality of training and trainee perceptions have yet to be explored. Objective: The purpose of this study was to determine the degree of interventional pulmonology (IP) fellows' involvement in the care of COVID-19 and its impact on fellows' clinical education, procedure skills, and postgraduation employment search. Methods: An internet-based survey was validated and distributed among IP fellows in North American fellowship training programs. Results: Of 40 eligible fellows, 38 (95%) completed the survey. A majority of fellows (76%) reported involvement in the care of patients with COVID-19. Fellows training in the Northeast United States reported involvement in the care of a higher number of patients with COVID-19 than in other regions (median, 30 [interquartile range, 20-50] vs. 10 [5-13], respectively; P < 0.01). Fifty-two percent of fellows reported redeployment outside IP during COVID-19, mostly into intensive care units. IP procedure volume decreased by 21% during COVID-19 compared with pre-COVID-19 volume. This decrease was mainly accounted for by a reduction in bronchoscopies. A majority of fellows (82%) reported retainment of outpatient clinics during COVID-19 with the transition from face-to-face to telehealth-predominant format. Continuation of academic and research activities during COVID-19 was reported by 86% and 82% of fellows, respectively. After graduation, all fellows reported having secured employment positions. Conclusion: Although IP fellows were extensively involved in the care of patients with COVID-19, most IP programs retained educational activities through the COVID-19 outbreak. The impact of the decrease in procedure volume on trainee competency would be best addressed individually within each training program. These data may assist in focusing efforts regarding the education of medical trainees during the current and future healthcare crises.

Project description:BackgroundThe COVID-19 pandemic has revealed significant challenges in disease forecasting and in developing a public health response, emphasizing the need to manage missing data from various sources in making accurate forecasts.ObjectiveWe aimed to show how handling missing data can affect estimates of the COVID-19 incidence rate (CIR) in different pandemic situations.MethodsThis study used data from the COVID-19/SARS-CoV-2 surveillance system at the National Institute of Hygiene and Epidemiology, Vietnam. We separated the available data set into 3 distinct periods: zero COVID-19, transition, and new normal. We randomly removed 5% to 30% of data that were missing completely at random, with a break of 5% at each time point in the variable daily caseload of COVID-19. We selected 7 analytical methods to assess the effects of handling missing data and calculated statistical and epidemiological indices to measure the effectiveness of each method.ResultsOur study examined missing data imputation performance across 3 study time periods: zero COVID-19 (n=3149), transition (n=1290), and new normal (n=9288). Imputation analyses showed that K-nearest neighbor (KNN) had the lowest mean absolute percentage change (APC) in CIR across the range (5% to 30%) of missing data. For instance, with 15% missing data, KNN resulted in 10.6%, 10.6%, and 9.7% average bias across the zero COVID-19, transition, and new normal periods, compared to 39.9%, 51.9%, and 289.7% with the maximum likelihood method. The autoregressive integrated moving average model showed the greatest mean APC in the mean number of confirmed cases of COVID-19 during each COVID-19 containment cycle (CCC) when we imputed the missing data in the zero COVID-19 period, rising from 226.3% at the 5% missing level to 6955.7% at the 30% missing level. Imputing missing data with median imputation methods had the lowest bias in the average number of confirmed cases in each CCC at all levels of missing data. In detail, in the 20% missing scenario, while median imputation had an average bias of 16.3% for confirmed cases in each CCC, which was lower than the KNN figure, maximum likelihood imputation showed a bias on average of 92.4% for confirmed cases in each CCC, which was the highest figure. During the new normal period in the 25% and 30% missing data scenarios, KNN imputation had average biases for CIR and confirmed cases in each CCC ranging from 21% to 32% for both, while maximum likelihood and moving average imputation showed biases on average above 250% for both CIR and confirmed cases in each CCC.ConclusionsOur study emphasizes the importance of understanding that the specific imputation method used by investigators should be tailored to the specific epidemiological context and data collection environment to ensure reliable estimates of the CIR.

Dataset Information

Model training periods impact estimation of COVID-19 incidence from wastewater viral loads.

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Model training periods impact estimation of COVID-19 incidence from wastewater viral loads.

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