Effect of changing case definitions for COVID-19 on the epidemic curve and transmission parameters in mainland China: a modelling study

Tsang, Tim K.; Wu, Peng; Lin, Yun; Lau, Eric H. Y.; Leung, Gabriel M.; Cowling, Benjamin J.

doi:10.1016/s2468-2667(20)30089-x

Cited by 220 publications

(253 citation statements)

References 16 publications

(19 reference statements)

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“…Biomarkers and demographic data can be used as a proxy for disease status, potentially eliminating the need for extensive testing, which has exhausted healthcare resources, particularly for COVID-19 worldwide. Also, ML models can be robust tools for COVID-19 case de nitions, and therefore, may help avoid inaccurate mapping of epidemic trajectories through public health surveillance activities [40]. It worth noting that while our ML model identi ed community type as an important feature (Fig.…”

Section: Discussionmentioning

confidence: 95%

Using Machine Learning to Unveil Demographic and Clinical Features of COVID-19 Symptomatic and Asymptomatic Patients

Youha¹,

Alkhamis

Mazeedi³

et al. 2020

Preprint

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Background: Demographic and clinical features of COVID-19 patients are critical components in shaping their symptomatic status. However, the relationship between patients' symptomatic status and their features are typically complicated and nonlinear.Methods: We explored important features that drive the symptomatic status of COVID-19 patients and reveal their interactions with other relevant factors. We used an extensive multi-algorithm machine learning (ML) pipeline and 68 demographic and clinical features to fit a predictive model to 3,995 patients in the State of Kuwait between February and June 2020. Our ML pipeline comprised five algorithms, including logistic regression (LR), random forest (RF), support vector machine (SVM), gradient boosting (GBM), and extreme gradient boosting (XGM).Results: SVM outperformed all algorithms (AUC = 0.77 and accuracy = 70.01%), while logistic regression had the lowest predictive power (AUC = 0.65 and accuracy = 66.14%). Our ML model identified C-reactive, respiratory rate, transmission dynamics, and other demographics as the most important predictors of COVID-19 symptomatic patients. While, only demographic features were important predictors for asymptomatic patients. However, our ML model further revealed that the non-linear relationships between impaired renal function, other clinical biomarkers and demographic features were critical in shaping the risk of being symptomatic patient. Conclusions: We demonstrated remarkable predictive performance of our ML model over traditional statistical methods in identifying important clinical and demographic features of symptomatic vs. asymptomatic. Further application of our ML pipeline in the COVID-19 case definition and guiding pharmaceutical and none-pharmaceutical interventions will help reduce the public health and economic implications of this devastating virus on local and global scales.

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Section: Discussionmentioning

confidence: 95%

Using Machine Learning to Unveil Demographic and Clinical Features of COVID-19 Symptomatic and Asymptomatic Patients

Youha¹,

Alkhamis

Mazeedi³

et al. 2020

Preprint

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“…Moreover, since a data-driven approach was used to parameterize the models, it might underestimate the severity of the pandemic because of the existence of undocumented infections due to the limitation of diagnostic capacity 33 . Other factors not included in the model, such as seasonality in transmission of coronaviruses 14 , existence of asymptotic infections 34 , and change of case definition 35…”

Section: Discussionmentioning

confidence: 99%

Cooperative virus propagation in COVID-19 transmission

Dai

Locasale

2020

Preprint

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The global pandemic due to the emergence of a novel coronavirus is a threat to the future health of humanity. There remains an urgent need to understand its transmission characteristics and design effective interventions to mitigate its spread. In this study, we define a non-linear (known in biochemistry models as allosteric or cooperative) relationship between viral shedding, viral dose and COVID-19 infection propagation. We develop a mathematical model of the dynamics of COVID-19 to link quantitative features of viral shedding, human exposure and transmission in nine countries impacted by the ongoing COVID-19 pandemic. The model was then used to evaluate the efficacy of interventions against virus transmission. We found that cooperativity was important to capture country-specific transmission dynamics and leads to resistance to mitigating transmission in mild or moderate interventions. The behaviors of the model emphasize that strict interventions greatly limiting both virus shedding and human exposure are indispensable to achieving effective containment of COVID-19.

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“…Second, the spread and transmission of COVID-19 were not considered mathematically and globally. Changes in case definitions affected inferences on the transmission dynamics of COVID-19 allow detection of more cases as knowledge increased in China [35].…”

Section: Strengths and Weaknesses Of The Studymentioning

confidence: 99%

A panel analysis on preventing and controlling efficiency of COVID-19 pandemic

Guan¹

2020

Preprint

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BACKGROUND: Currently, the globe is making efforts to curb the spreading of coronavirus disease 2019 (COVID-19). However, only a few studies have examined the associations among spreading growth, treatment efficiency, and death increase. This study aimed to examine the associations of COVID-2019 dead cases, healed cases, and recovered cases.METHODS: Data from China Data Lab in Harvard Dataverse with the China (January 22, 2020 to March 18, 2020), United States of America (USA, March 17, 2020 to April 12, 2020), and the world (January 22, 2020 to April 4, 2020) were analyzed. The main variables included in the analysis were the numbers of COVID-2019 total confirmed cases (TC), total dead cases (TD), newly dead cases (ND), newly confirmed cases (NC), newly healed cases (NH), total healed cases (TH), and newly recovered cases (NR). Pooled, dynamic, and event study analyses were conducted to reflect the associations among them.RESULTS: Descriptive analyses showed that NH/NC ratio in China are bigger than NR/NC in USA and the world. Pooled analysis showed that various roles of regions in NH and NR in a specific sample. Dynamic analysis showed significant roles of lags and NC in NH and NR. Panel event study showed that key events influence ND and NH in China significantly and NR in the world rather than NR in USA.CONCLUSION: The findings in this study indirectly confirmed the relationship between spreading growth, treatment efficiency, and death increase. China’s control strategies of COVID-19 pandemic are worth of learning by the globe.

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Effect of changing case definitions for COVID-19 on the epidemic curve and transmission parameters in mainland China: a modelling study

Cited by 220 publications

References 16 publications

Using Machine Learning to Unveil Demographic and Clinical Features of COVID-19 Symptomatic and Asymptomatic Patients

Using Machine Learning to Unveil Demographic and Clinical Features of COVID-19 Symptomatic and Asymptomatic Patients

Cooperative virus propagation in COVID-19 transmission

A panel analysis on preventing and controlling efficiency of COVID-19 pandemic

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