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

OBJECTIVES: This 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. SETTING: We 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 MEASURES: Simulation 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. CONCLUSIONS: Reducing 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.

A Time-Phased Machine Learning Model for Real-Time Prediction of Sepsis in Critical Care.

OBJECTIVES: As a life-threatening condition, sepsis is one of the major public health issues worldwide. Early prediction can improve sepsis outcomes with appropriate interventions. With the PhysioNet/Computing in Cardiology Challenge 2019, we aimed to develop and validate a machine learning algorithm with high prediction performance and clinical interpretability for prediction of sepsis onset during critical care in real-time. DESIGN: Retrospective observational cohort study. SETTING: The dataset was collected from three ICUs in three different U.S. hospitals. Two of them were publicly available for model development (offline) and one was used for testing (online). PATIENTS: Forty-thousand three-hundred thirty-six ICU patients from the two model development databases and 24,819 from the test database. There are up to 40 hourly-recorded clinical variables for each ICU stay. The Sepsis-3 criteria were used to confirm sepsis onset. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Three-hundred twelve features were constructed hourly as the input of our proposed Time-phAsed machine learning model for Sepsis Prediction. Time-phAsed machine learning model for Sepsis Prediction first estimates the likelihood of sepsis onset for each hour of an ICU stay in the following 6 hours, and then makes a binary prediction with three time-phased cutoff values. On the internal validation set, the utility score (official challenge measurement) achieved by Time-phAsed machine learning model for Sepsis Prediction was 0.430. On the test set, the utility score reached was 0.354. Furthermore, Time-phAsed machine learning model for Sepsis Prediction provides an intuitive way to illustrate the impact of the input features on the outcome prediction, which makes it clinically interpretable. CONCLUSIONS: The proposed Time-phAsed machine learning model for Sepsis Prediction model is accurate and interpretable for real-time prediction of sepsis onset in critical care, which holds great potential for further evaluation in prospective studies.

A Clinically Practical and Interpretable Deep Model for ICU Mortality Prediction with External Validation.

Deep learning models are increasingly studied in the field of critical care. However, due to the lack of external validation and interpretability, it is difficult to generalize deep learning models in critical care senarios. Few works have validated the performance of the deep learning models with external datasets. To address this, we propose a clinically practical and interpretable deep model for intensive care unit (ICU) mortality prediction with external validation. We use the newly published dataset Philips eICU to train a recurrent neural network model with two-level attention mechanism, and use the MIMIC III dataset as the external validation set to verify the model performance. This model achieves a high accuracy (AUC = 0.855 on the external validation set) and have good interpretability. Based on this model, we develop a system to support clinical decision-making in ICUs.

A Multi-directional Approach for Missing Value Estimation in Multivariate Time Series Clinical Data.

Missing values are common in clinical datasets which bring obstacles for clinical data analysis. Correctly estimating the missing parts plays a critical role in utilizing these analysis approaches. However, only limited works focus on the missing value estimation of multivariate time series (MTS) clinical data, which is one of the most challenge data types in this area. We attempt to develop a methodology (MD-MTS) with high accuracy for the missing value estimation in MTS clinical data. In MD-MTS, temporal and cross-variable information are constructed as multi-directional features for an efficient gradient boosting decision tree (LightGBM). For each patient, temporal information represents the sequential relations among the values of one variable in different time-stamps, and cross-variable information refers to the correlations among the values of different variables in a fixed time-stamp. We evaluated the estimation method performance based on the gap between the true values and the estimated values on the randomly masked parts. MD-MTS outperformed three baseline methods (3D-MICE, Amelia II and BRITS) on the ICHI challenge 2019 datasets that containing 13 time series variables. The root-mean-square error of MD-MTS, 3D-MICE, Amelia II and BRITS on offline-test dataset are 0.1717, 0.2247, 0.1900, and 0.1862, respectively. On online-test dataset, the performance for the former three methods is 0.1720, 0.2235, and 0.1927, respectively. Furthermore, MD-MTS got the first in ICHI challenge 2019 among dozens of competition models. MD-MTS provides an accurate and robust approach for estimating the missing values in MTS clinical data, which can be easily used as a preprocessing step for the downstream clinical data analysis.