Data Visualization and Descriptive Analysis for Understanding Epidemiological Characteristics of COVID-19: A Case Study of a Dataset from January 22, 2020 to March 29, 2020

Charvadeh, Yasin Khadem; Yi, Grace Y.

doi:10.6339/jds.202007_18(3).0018

Cited by 4 publications

(4 citation statements)

References 15 publications

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“…They estimated the mean incubation time to be 6.4 days with a 95% confidence interval of 5.6-7.7 days. Charvadeh and Yi [3] estimated the mean and median of the incubation period to be 5.8 and 5 days, respectively, by examining a cohort of 3397 infected cases dated from January 22, 2020 to March 29, 2020. Examining 1084 confirmed COVID-19 cases who initially showed no signs of illness at their time of departure from Wuhan city, China, Qin et al [16] conducted a forward follow-up study.…”

Section: Introductionmentioning

confidence: 99%

Is 14-Days a Sensible Quarantine Length for COVID-19? Examinations of Some Associated Issues with a Case Study of COVID-19 Incubation Times

Charvadeh

Bian

et al. 2021

Stat Biosci

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To confine the spread of an infectious disease, setting a sensible quarantine time is crucial. To this end, it is imperative to well understand the distribution of incubation times of the disease. Regarding the ongoing COVID-19 pandemic, 14-days is commonly taken as a quarantine time to curb the virus spread in balancing the impacts of COVID-19 on diverse aspects of the society, including public health, economy, and humanity perspectives, etc. However, setting a sensible quarantine time is not trivial and it depends on various underlying factors. In this article, we take an angle of examining the distribution of the COVID-19 incubation time using likelihoodbased methods. Our study is carried out on a dataset of 178 COVID-19 cases dated from January 20, 2020 to February 29, 2020, with the information of exposure periods and dates of symptom onset collected. To gain a good understanding of possible scenarios, we employ different models to describe incubation times of COVID-19. Our findings suggest that statistically, the 14-day quarantine time may not be long enough to control the probability of an early release of infected individuals to be small. While the size of the study data is not large enough to offer us a definitely acceptable quarantine time, and further in practice, the decision-makers may take account of other factors related to social and economic concerns to set up a practically acceptable quarantine time, our study demonstrates useful methods to determine a reasonable quarantine time from a statistical standpoint. Further, it reveals some associated complexity for fully understanding the COVID-19 incubation time distribution.

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Section: Introductionmentioning

confidence: 99%

Is 14-Days a Sensible Quarantine Length for COVID-19? Examinations of Some Associated Issues with a Case Study of COVID-19 Incubation Times

Charvadeh

Bian

et al. 2021

Stat Biosci

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“…Our interest is in the use of a patient’s age X to predict whether or not they will survive. More detail on this dataset can be found in [ 29 ]. The goal is to determine a cutoff age so that extra medical attention can be paid to patients beyond that age.…”

Section: Inferences For An Roc Analysismentioning

confidence: 99%

ROC Analyses Based on Measuring Evidence Using the Relative Belief Ratio

Al-Labadi

Evans

Liang

2022

Entropy

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ROC (Receiver Operating Characteristic) analyses are considered under a variety of assumptions concerning the distributions of a measurement X in two populations. These include the binormal model as well as nonparametric models where little is assumed about the form of distributions. The methodology is based on a characterization of statistical evidence which is dependent on the specification of prior distributions for the unknown population distributions as well as for the relevant prevalence w of the disease in a given population. In all cases, elicitation algorithms are provided to guide the selection of the priors. Inferences are derived for the AUC (Area Under the Curve), the cutoff c used for classification as well as the error characteristics used to assess the quality of the classification.

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“…Our interest is in the use of a patient's age X to predict whether or not they will survive. More detail on this dataset can be found in Charvadeh and Yi (2020). The goal is to determine a cutoff age so that extra medical attention can be paid to patients beyond that age.…”

Section: Nonparametric Bayes Modelmentioning

confidence: 99%

ROC Analyses Based on Measuring Evidence

Labadi,

Evans,

Liang

2021

Preprint

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ROC analyses are considered under a variety of assumptions concerning the distributions of a measurement X in two populations. These include the binormal model as well as nonparametric models where little is assumed about the form of distributions. The methodology is based on a characterization of statistical evidence which is dependent on the specification of prior distributions for the unknown population distributions as well as for the relevant prevalence w of the disease in a given population. In all cases, elicitation algorithms are provided to guide the selection of the priors. Inferences are derived for the AUC as well as the cutoff c used for classification and the associated error characteristics.

show abstract

Data Visualization and Descriptive Analysis for Understanding Epidemiological Characteristics of COVID-19: A Case Study of a Dataset from January 22, 2020 to March 29, 2020

Cited by 4 publications

References 15 publications

Is 14-Days a Sensible Quarantine Length for COVID-19? Examinations of Some Associated Issues with a Case Study of COVID-19 Incubation Times

Is 14-Days a Sensible Quarantine Length for COVID-19? Examinations of Some Associated Issues with a Case Study of COVID-19 Incubation Times

ROC Analyses Based on Measuring Evidence Using the Relative Belief Ratio

ROC Analyses Based on Measuring Evidence

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