Comments on "Preliminary estimation of the basic reproduction number of novel Coronavirus (2019-nCoV) in China, from 2019 to 2020: A data-driven Analysis in the early phase of the outbreak"

Dhungana, Hom Nath

doi:10.1016/j.ijid.2020.02.024

Cited by 11 publications

(5 citation statements)

References 3 publications

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“…This research identifies countries that have been successful or have shown signs of containing COVID‐19 since infection using the exponential growth modelling in stage I of the research. Various studies have used statistical models like exponential growth, decay and exponential adjustments, computational modelling, and stochastic simulations to model the reproductive number of the COVID‐19 infections (Dhungana, 2020; Liu, Gayle, Wilder‐Smith, & Rocklöv, 2020; Wu, Leung, & Leung, 2020). However, the estimations have varied from 1.50 to 6.47 for the same data set.…”

Section: Discussionmentioning

confidence: 99%

Exploring the growth of COVID‐19 cases using exponential modelling across 42 countries and predicting signs of early containment using machine learning

Kasilingam¹,

Prabhakaran

Rajendran

et al. 2020

Transbound Emerg Dis

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COVID‐19 pandemic disease spread by the SARS‐COV‐2 single‐strand structure RNA virus, belongs to the 7 th generation of the coronavirus family. Following an unusual replication mechanism, it’s extreme ease of transmissivity has put many counties under lockdown. With uncertainty of developing a cure/vaccine for the infection in the near future, the onus currently lies on healthcare infrastructure, policies, government activities, and behaviour of the people to contain the virus. This research uses exponential growth modelling studies to understand the spreading patterns of the COVID‐19 virus and identifies countries that have shown early signs of containment until 26 th March 2020. Predictive supervised machine learning models are built using infrastructure, environment, policies, and infection‐related independent variables to predict early containment. COVID‐19 infection data across 42 countries are used. Logistic regression results show a positive significant relationship between healthcare infrastructure and lockdown policies, and signs of early containment. Machine learning models based on logistic regression, decision tree, random forest, and support vector machines are developed and show accuracies between 76.2% to 92.9% to predict early signs of infection containment. Other policies and the decisions taken by countries to contain the infection are also discussed.

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Section: Discussionmentioning

confidence: 99%

Exploring the growth of COVID‐19 cases using exponential modelling across 42 countries and predicting signs of early containment using machine learning

Kasilingam¹,

Prabhakaran

Rajendran

et al. 2020

Transbound Emerg Dis

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“…Coronavirus disease 2019 (COVID-19) is a highly infectious disease caused by the new coronavirus [1,2]. Timely and effective intervention can improve the success rate of treatment of critically ill patients [3].…”

Section: Introductionmentioning

confidence: 99%

Application of CareDose 4D combined with Karl 3D technology in the low dose computed tomography for the follow-up of COVID-19

Wang

Huang

et al. 2020

BMC Med Imaging

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Background: Coronavirus disease 2019 (COVID-19) is a highly infectious disease caused by the new coronavirus. Previous studies have shown that the chest CT examination plays an important role in the diagnosis and monitoring of COVID-19. However, some patients with COVID-19 had low white blood cell counts and reduced lymphocyte ratios. Multiple CT examinations may cause radiation damages as well as increase the apoptosis of peripheral blood lymphocytes. A new low-dose CT method should be developed because the regular CT may aggravate the disease. Method: Sixty cases were randomly divided into the study group (n = 30) and control group (n = 30). The lung window was reconstructed by Karl 3D iterative technique in the study group. The image quality was subjectively evaluated by two senior chest group diagnostic physicians using a 5-point double-blind method. The value of CT measurement and its standard deviation (SD) was used as an objective evaluation criteria. The volume of CT dose index (CTDI vol), dose length product (DLP) and effective dose (ED) from the two groups were compared and analyzed statistically. Result: There was no significant difference in the occurrence rates of ground glass opacities, consolidation, crazypaving pattern, fiber cable shadow and axial interstitial thickening between the study group and control group (p > 0.05). In addition, no significant difference was found for the subjective score of overall image quality and image noise level (SD) between the two groups (p > 0.05). However, significant differences was found in CTDI vol , DLP, and ED between the study group and the control group (p < 0.05). The effective dose of the study group was reduced by 76% compared to the control group.

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“…12 In addition, the incubation period of the virus is between 3 and 14 days. 13,14 Therefore, the exact time of virus transmission to the patient, the onset time of symptoms, and the sampling time affect the results of the RT-PCR tests. Wo ¨lfel et al 12 investigated various specimens from patients who had close contact with patients diagnosed with COVID-19.…”

Section: Discussionmentioning

confidence: 99%

Analysis of Factors Causing False-Negative Real-Time Polymerase Chain Reaction Results in Oropharyngeal and Nasopharyngeal Swabs of Patients With COVID-19

İşlek¹,

Balci

2021

Ear Nose Throat J

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Objectives: To analyze factors regarding patient characteristics, sampling techniques, and coronavirus disease 2019 (COVID-19) specific manifestations that may cause false-negative reverse-transcription polymerase chain reaction (RT-PCR). Materials and Methods: A cross-sectional study was conducted. For the diagnosis of COVID-19, patients with RT-PCR test positive in the first nasopharyngeal and oropharyngeal swabs were accepted as true positive, and patients with negative 3 consecutive swab results were considered true negative. Those who had a negative initial swab were considered false negatives if they subsequently tested positive on the second or third swab. Demographic data of the patients, the onset of the disease, presence of nasal septal deviation, presence of epistaxis, the clinician (otolaryngologist/other physicians [OP]) who collected the samples, and medical treatments for laryngopharyngeal reflux, allergic respiratory diseases, allergic rhinitis, which include proton pump inhibitors and nasal steroids (NS), were documented. The analysis of dependent variables was performed with the chi-square test. Binary logistic regression was performed for significant variables. Results: A total of 399 patients were included in the study, and 357 (89.5%) patients were detected as positive after 2 or 3 consecutive RT-PCR tests. The presence of ageusia, anosmia, and collecting the samples within 7 days following the onset of symptoms were determined as significant factors for positive RT-PCR results ( P = <.001; odds ratio [OR] = 6.2, 5.8, 11.6, respectively). The profession of the clinician (OP), NS use, and the presence of epistaxis were detected as significant factors for the false-negative RT-PCR results ( P < .001; OR = 2.3, 3.1, 8.7, respectively). Conclusions: Patient- and/or sample-related factors can affect RT-PCR results of possible COVID-19 cases. The presence of these factors can easily be determined in cases with high clinical suspicion and negative RT-PCR results. The presence of ageusia, anosmia, early sampling (<7days), and appropriate collection of swabs decrease false-negative RT-PCR results.

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Comments on "Preliminary estimation of the basic reproduction number of novel Coronavirus (2019-nCoV) in China, from 2019 to 2020: A data-driven Analysis in the early phase of the outbreak"

Cited by 11 publications

References 3 publications

Exploring the growth of COVID‐19 cases using exponential modelling across 42 countries and predicting signs of early containment using machine learning

Exploring the growth of COVID‐19 cases using exponential modelling across 42 countries and predicting signs of early containment using machine learning

Application of CareDose 4D combined with Karl 3D technology in the low dose computed tomography for the follow-up of COVID-19

Analysis of Factors Causing False-Negative Real-Time Polymerase Chain Reaction Results in Oropharyngeal and Nasopharyngeal Swabs of Patients With COVID-19

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