Quantitative Analysis of the Effectiveness of Antigen- and Polymerase Chain Reaction-Based Combination Strategies for Containing COVID-19 Transmission in a Simulated Community

Huang, Qiangru; Sun, Yanxia; Jia, Mengmeng; Zhang, Ting; Chen, Fangyuan; Jiang, Mingyue; Wang, Qing; Feng, Luzhao; Yang, Weizhong

doi:10.1016/j.eng.2023.01.004

Cited by 2 publications

(1 citation statement)

References 26 publications

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“…This characteristic of interpretability not only further validates our model, but also provides meaningful information in the clinical setting, quantifying the effect that appropriate action on each of these parameters would have in preventing HAI. It is well known that interpretability of the decision support model is vital to the acceptance of such a predictor in the clinical setting ( 33 ).…”

Section: Discussionmentioning

confidence: 99%

Machine learning-based clinical decision support for infection risk prediction

Feng,

Noren,

Kulkarni

et al. 2023

Front. Med.

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BackgroundHealthcare-associated infection (HAI) remains a significant risk for hospitalized patients and a challenging burden for the healthcare system. This study presents a clinical decision support tool that can be used in clinical workflows to proactively engage secondary assessments of pre-symptomatic and at-risk infection patients, thereby enabling earlier diagnosis and treatment.MethodsThis study applies machine learning, specifically ensemble-based boosted decision trees, on large retrospective hospital datasets to develop an infection risk score that predicts infection before obvious symptoms present. We extracted a stratified machine learning dataset of 36,782 healthcare-associated infection patients. The model leveraged vital signs, laboratory measurements and demographics to predict HAI before clinical suspicion, defined as the order of a microbiology test or administration of antibiotics.ResultsOur best performing infection risk model achieves a cross-validated AUC of 0.88 at 1 h before clinical suspicion and maintains an AUC >0.85 for 48 h before suspicion by aggregating information across demographics and a set of 163 vital signs and laboratory measurements. A second model trained on a reduced feature space comprising demographics and the 36 most frequently measured vital signs and laboratory measurements can still achieve an AUC of 0.86 at 1 h before clinical suspicion. These results compare favorably against using temperature alone and clinical rules such as the quick sequential organ failure assessment (qSOFA) score. Along with the performance results, we also provide an analysis of model interpretability via feature importance rankings.ConclusionThe predictive model aggregates information from multiple physiological parameters such as vital signs and laboratory measurements to provide a continuous risk score of infection that can be deployed in hospitals to provide advance warning of patient deterioration.

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Section: Discussionmentioning

confidence: 99%

Machine learning-based clinical decision support for infection risk prediction

Feng,

Noren,

Kulkarni

et al. 2023

Front. Med.

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Lessons drawn from Shanghai for controlling highly transmissible SARS-CoV-2 variants: insights from a modelling study

Wang

Gao

et al. 2023

BMC Infect Dis

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Background The continuous emergence of novel SARS-CoV-2 variants with markedly increased transmissibility presents major challenges to the zero-COVID policy in China. It is critical to adjust aspects of the policy about non-pharmaceutical interventions (NPIs) by searching for and implementing more effective ways. We use a mathematical model to mimic the epidemic pattern of the Omicron variant in Shanghai to quantitatively show the control challenges and investigate the feasibility of different control patterns in avoiding other epidemic waves. Methods We initially construct a dynamic model with a core step-by-step release strategy to reveal its role in controlling the spread of COVID-19, including the city-based pattern and the district-based pattern. We used the least squares method and real reported case data to fit the model for Shanghai and its 16 districts, respectively. Optimal control theory was utilized to explore the quantitative and optimal solutions of the time-varying control strength (i.e., contact rate) to suppress the highly transmissible SARS-CoV-2 variants. Results The necessary period for reaching the zero-COVID goal can be nearly 4 months, and the final epidemic size was 629,625 (95%CI: [608,049, 651,201]). By adopting the city-based pattern, 7 out of 16 strategies released the NPIs more or earlier than the baseline and ensured a zero-resurgence risk at the average cost of 10 to 129 more cases in June. By adopting the district-based pattern, a regional linked release can allow resumption of social activity to ~ 100% in the boundary-region group about 14 days earlier and allow people to flow between different districts without causing infection resurgence. Optimal solutions of the contact rate were obtained with various testing intensities, and higher diagnosis rate correlated with higher optimal contact rate while the number of daily reported cases remained almost unchanged. Conclusions Shanghai could have been bolder and more flexible in unleashing social activity than they did. The boundary-region group should be relaxed earlier and more attention should be paid to the centre-region group. With a more intensive testing strategy, people could return to normal life as much as possible but still ensure the epidemic was maintained at a relatively low level.

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Quantitative Analysis of the Effectiveness of Antigen- and Polymerase Chain Reaction-Based Combination Strategies for Containing COVID-19 Transmission in a Simulated Community

Cited by 2 publications

References 26 publications

Machine learning-based clinical decision support for infection risk prediction

Machine learning-based clinical decision support for infection risk prediction

Lessons drawn from Shanghai for controlling highly transmissible SARS-CoV-2 variants: insights from a modelling study

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