Literature DB >> 32088335

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".

Hom Nath Dhungana1.   

Abstract

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Year:  2020        PMID: 32088335      PMCID: PMC7129612          DOI: 10.1016/j.ijid.2020.02.024

Source DB:  PubMed          Journal:  Int J Infect Dis        ISSN: 1201-9712            Impact factor:   3.623


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Dear Editor in Chief, I have read the original article "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" which is recently published in your esteemed journal "International Journal of Infectious Diseases". Firstly, I would like to congratulate the authors for a successful publication and for making some contributions. The methodology used in the paper for the estimation of the reproduction number strongly assumes that the growth rate is exponential. However, in the Results section, the growth rate and its estimate are missing. The estimation of the reproduction number is likely to change significantly if the hypothetical growth rate differs from the actual growth rate (Wallinga and Lipsitch, 2007). It is too early to say about the pattern of any specific distribution regarding growth rate, new incidence cases and the cumulative number of cases of novel Coronavirus (2019-nCoV). It would be great if authors address this as a limitation of the study and provide more details about the estimation of intrinsic growth rate. Table 1 provides the results (reproduction number estimates) for different folds of reporting rate by using three different serial intervals (SI). Since limited studies are available in detail about SI for novel coronavirus, it is obvious to use the serial interval of MERS and SARS but many studies show wide variation in their serial interval (Lipsitch et al., 2003). The estimates of serial intervals used in the paper are 7.6 ± 3.4 and 8.4 ± 3.8 (in days) respectively for MERS and SARS, hence the coefficient of variation (CV) of SI for MERS and SARS seems higher (44.73% and 45.23% respectively). Due to this variation, it is highly recommended to a perform sensitivity analysis between SI and reproduction number so that variation between SI and reproduction number for novel Coronavirus (2019-nCoV) can be obtained. There is a significant difference between the estimates of reproduction number reported by WHO and the findings of Shi Zhao et al., and this variation could be due to many reasons including the parameters used in the model (SI); this can be also understood by sensitivity analysis (Obadia et al., 2012). Conflict of interest: No conflict of interest to declare. Funding source: None. Ethical approval: Approval was not required.
  3 in total

1.  Transmission dynamics and control of severe acute respiratory syndrome.

Authors:  Marc Lipsitch; Ted Cohen; Ben Cooper; James M Robins; Stefan Ma; Lyn James; Gowri Gopalakrishna; Suok Kai Chew; Chorh Chuan Tan; Matthew H Samore; David Fisman; Megan Murray
Journal:  Science       Date:  2003-05-23       Impact factor: 47.728

2.  How generation intervals shape the relationship between growth rates and reproductive numbers.

Authors:  J Wallinga; M Lipsitch
Journal:  Proc Biol Sci       Date:  2007-02-22       Impact factor: 5.349

3.  The R0 package: a toolbox to estimate reproduction numbers for epidemic outbreaks.

Authors:  Thomas Obadia; Romana Haneef; Pierre-Yves Boëlle
Journal:  BMC Med Inform Decis Mak       Date:  2012-12-18       Impact factor: 2.796

  3 in total
  5 in total

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

Authors:  Jiawei Li; Xiao Wang; Xiaolu Huang; Fangxing Chen; Xuesong Zhang; Ying Liu; Guangzuo Luo; Xunhua Xu
Journal:  BMC Med Imaging       Date:  2020-05-24       Impact factor: 1.930

2.  Modelling of Reproduction number for COVID-19 in India and high incidence states.

Authors:  Hom Nath Dhunagna; Saroj Ghimire
Journal:  Clin Epidemiol Glob Health       Date:  2020-07-15

3.  China's practice to prevent and control COVID-19 in the context of large population movement.

Authors:  Tie-Long Xu; Mei-Ying Ao; Xu Zhou; Wei-Feng Zhu; He-Yun Nie; Jian-He Fang; Xin Sun; Bin Zheng; Xiao-Fan Chen
Journal:  Infect Dis Poverty       Date:  2020-08-19       Impact factor: 4.520

4.  Commentary: Statistical Modeling for the Prediction of Infectious Disease Dissemination With Special Reference to COVID-19 Spread.

Authors:  Hom Nath Dhungana; Saroj Ghimire
Journal:  Front Public Health       Date:  2021-10-15

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

Authors:  Dharun Kasilingam; Sakthivel Puvaneswaran Sathiya Prabhakaran; Dinesh Kumar Rajendran; Varthini Rajagopal; Thangaraj Santhosh Kumar; Ajitha Soundararaj
Journal:  Transbound Emerg Dis       Date:  2020-09-17       Impact factor: 4.521

  5 in total

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