Role of efficient testing and contact tracing in mitigating the COVID-19 pandemic: a network modelling study

ObjectivesThis study quantified how the efficiency of testing and contact tracing impacts the spread of COVID-19. The average time interval between infection and quarantine, whether asymptomatic cases are tested or not, and initial delays to beginning a testing and tracing programme were investigated.SettingWe developed a novel individual-level network model, called CoTECT (Testing Efficiency and Contact Tracing model for COVID-19), using key parameters from recent studies to quantify the impacts of testing and tracing efficiency. The model distinguishes infection from confirmation by integrating a ‘T’ compartment, which represents infections confirmed by testing and quarantine. The compartments of presymptomatic (E), asymptomatic (I), symptomatic (Is), and death with (F) or without (f) test confirmation were also included in the model. Three scenarios were evaluated in a closed population of 3000 individuals to mimic community-level dynamics. Real-world data from four Nordic countries were also analysed.Primary and secondary outcome measuresSimulation result: total/peak daily infections and confirmed cases, total deaths (confirmed/unconfirmed by testing), fatalities and the case fatality rate. Real-world analysis: confirmed cases and deaths per million people.Results(1) Shortening the duration between Is and T from 12 to 4 days reduces infections by 85.2% and deaths by 88.8%. (2) Testing and tracing regardless of symptoms reduce infections by 35.7% and deaths by 46.2% compared with testing only symptomatic cases. (3) Reducing the delay to implementing a testing and tracing programme from 50 to 10 days reduces infections by 35.2% and deaths by 44.6%. These results were robust to sensitivity analysis. An analysis of real-world data showed that tests per case early in the pandemic are critical for reducing confirmed cases and the fatality rate.ConclusionsReducing testing delays will help to contain outbreaks. These results provide policymakers with quantitative evidence of efficiency as a critical value in developing testing and contact tracing strategies.

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Analyzing treatment patterns and time to the next treatment in chronic lymphocytic leukemia real-world data using automated temporal phenotyping.

Journal of Clinical Oncology ◽

10.1200/jco.2021.39.15_suppl.e19512 ◽

2021 ◽

Vol 39 (15_suppl) ◽

pp. e19512-e19512

Author(s):

Kyeryoung Lee ◽

Zongzhi Liu ◽

Meng Ma ◽

Yun Mai ◽

Christopher Gilman ◽

...

Keyword(s):

Chronic Lymphocytic Leukemia ◽

Real World ◽

Clinical Decision Making ◽

Single Agent ◽

Treatment Patterns ◽

Lymphocytic Leukemia ◽

Time Interval ◽

Real World Data ◽

World Data ◽

Therapeutic Class

e19512 Background: Targeted therapy is an important treatment for chronic lymphocytic leukemia (CLL). However, optimal strategies for deploying small molecule inhibitors or antibody therapies in the real world are not well understood, largely due to a lack of outcomes data. We implemented a novel temporal phenotyping algorithm pipeline to derive lines of therapy (LOT) and disease progression in CLL patients. Here, the CLL treatment pattern and time to the next treatment (TTNT) were analyzed in real-world data (RWD) using patient electronic health records. Methods: We identified a CLL cohort with LOT from the Mount Sinai Data Warehouse (2003-2020). Each LOT consisted of either a single agent or combinations defined by NCCN CLL guidelines. We developed a natural language processing (NLP)-based temporal phenotyping approach to automatically identify the number of lines and therapeutic regimens. The sequence of treatment and time interval for each patient were derived from the systematic treatment data. Time to event analysis and multivariate (i.e., age, gender, race, other treatment patterns) Cox proportional hazard (CoxPH) models were used to analyze the patterns and predictors of TTNT. Results: Four hundred eleven CLL patients received 1 to 7 LOTs. Ibrutinib was the predominant 1st LOT (40.8% of patients) followed by anti-CD20-based antibody therapies and chemotherapy in 30.6 and 19.2% of patients, respectively, followed by Acalabrutinib, Venetoclax, and Idelalisib in 3.4, 2.7, and 0.7% of patients, respectively (Table 1). The 2nd to 5th LOT showed the same or similar trends. We next analyzed the TTNT in the 1st line of each therapeutic class. Acalabrutinib resulted in a longer median TTNT than Ibrutinib. Both Acalabrutinib and Ibrutinib showed longer TTNT compared to Venetoclax (median TTNTs were 742 and 598 vs. 373 days: HR = 0.23, p=0.015 and HR = 0.48, p=0.03, respectively). In addition, patients with age equal to or older than 65 showed longer TNNT (HR=0.16, p=0.016). Conclusions: Our result shows the potential of RWD usage in clinical decision making as real-world evidence reported here is consistent with results derived from clinical trial data. Linking this study to genetic data and other covariates affecting treatment outcomes may provide additional insights into the optimal sequences of the targeted therapies in CLL. Table 1: Therapeutic class and patient numbers (%) in each line.[Table: see text]

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Looking in the medicine cabinet: methods for using real-world data to assess the impact of measles, mumps and rubella (MMR) and recombinant adjuvanted varicella-zoster vaccines on coronavirus disease 2019 (COVID-19) prevention and case fatality

Gates Open Research ◽

10.12688/gatesopenres.13329.1 ◽

2021 ◽

Vol 5 ◽

pp. 115

Author(s):

Douglas McNair ◽

Hao Hu ◽

Casey Selwyn

Keyword(s):

Real World ◽

Case Fatality ◽

Data Interpretation ◽

The United States ◽

Zoster Vaccine ◽

Real World Data ◽

World Data ◽

Varicella Zoster ◽

Viral Vaccines ◽

The Impact

Background: Analysis of real-world data can be used to identify promising leads and dead ends among products being repurposed for clinical practice for coronavirus disease 2019 (COVID-19). This paper uses real-world data from Cerner Labs collected from 90 source institutions in the United States to assess the potential impact of live viral vaccines on COVID-19 case fatality rates. Methods: We identified 373,032 polymerase chase reaction (PCR)-positive COVID-19 cases in the Cerner Labs database between 01-MAR-2020 and 31-DEC-2020 and identified patients that had received measles, mumps and rubella (MMR) or a recombinant adjuvanted varicella-zoster vaccine within the previous 5 years. We calculated heterogeneity scores to support interpretation of results across institutions, and used stepwise forward variable selection to construct covariable-based propensity scores. These scores were used to match cases and control for biasing and confounding issues inherent in observational data. Results: Neither the recombinant adjuvanted varicella-zoster vaccine nor MMR showed significant efficacy in prevention of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. We could not derive clinically significant results on the impact of MMR for case fatality rates due to persistently high rates of heterogeneity between institutions. However, we were able to achieve acceptable levels of heterogeneity for the analysis of the recombinant adjuvanted varicella-zoster vaccine, and found a clinically meaningful benefit of reduced case fatality rate, with an odds ratio of 0.43 (95% confidence interval [CI]: 0.38 – 0.48). Conclusions: Using propensity score matching and heterogeneity statistics can help guide our interpretation of real-world data, and rigorous statistical methods are needed to reduce bias or disparities in data interpretation. Applying these methods to the impact of live viral vaccines on COVID-19 case fatalities yields actionable findings for further analysis.

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