Effect of delay in diagnosis on transmission of COVID-19

Xinmiao Rong; Liu Yang; Huidi Chu; Meng Fan; Xinmiao Rong; Liu Yang; Huidi Chu; Meng Fan

doi:10.3934/mbe.2020149

Mathematical Biosciences and Engineering

2020, Volume 17, Issue 3: 2725-2740. doi: 10.3934/mbe.2020149

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Effect of delay in diagnosis on transmission of COVID-19

1.
Center for Mathematical Biosciences, School of Mathematics and Statistics, Northeast Normal University, 5268 Renmin Street, Changchun, Jilin, 130024, China
2.
National Center for Applied Mathematics (Jilin), Northeast Normal University, 5268 Renmin Street, Changchun, Jilin, 130024, China

Received: 25 February 2020 Accepted: 08 March 2020 Published: 11 March 2020

Abstract
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The outbreak of COVID-19 caused by SARS-CoV-2 in Wuhan and other cities of China is a growing global concern. Delay in diagnosis and limited hospital resources lead to a rapid spread of COVID-19. In this study, we investigate the effect of delay in diagnosis on the disease transmission with a new formulated dynamic model. Sensitivity analyses and numerical simulations reveal that, improving the proportion of timely diagnosis and shortening the waiting time for diagnosis can not eliminate COVID-19 but can effectively decrease the basic reproduction number, significantly reduce the transmission risk, and effectively prevent the endemic of COVID-19, e.g., shorten the peak time and reduce the peak value of new confirmed cases and new infection, decrease the cumulative number of confirmed cases and total infection. More rigorous prevention measures and better treatment of patients are needed to control its further spread, e.g., increasing available hospital beds, shortening the period from symptom onset to isolation of patients, quarantining and isolating the suspected cases as well as all confirmed patients.
- COVID-19,
- delay in diagnosis,
- dynamic model,
- hospital resources
Citation: Xinmiao Rong, Liu Yang, Huidi Chu, Meng Fan. Effect of delay in diagnosis on transmission of COVID-19[J]. Mathematical Biosciences and Engineering, 2020, 17(3): 2725-2740. doi: 10.3934/mbe.2020149

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Abstract

The outbreak of COVID-19 caused by SARS-CoV-2 in Wuhan and other cities of China is a growing global concern. Delay in diagnosis and limited hospital resources lead to a rapid spread of COVID-19. In this study, we investigate the effect of delay in diagnosis on the disease transmission with a new formulated dynamic model. Sensitivity analyses and numerical simulations reveal that, improving the proportion of timely diagnosis and shortening the waiting time for diagnosis can not eliminate COVID-19 but can effectively decrease the basic reproduction number, significantly reduce the transmission risk, and effectively prevent the endemic of COVID-19, e.g., shorten the peak time and reduce the peak value of new confirmed cases and new infection, decrease the cumulative number of confirmed cases and total infection. More rigorous prevention measures and better treatment of patients are needed to control its further spread, e.g., increasing available hospital beds, shortening the period from symptom onset to isolation of patients, quarantining and isolating the suspected cases as well as all confirmed patients.

References

[1]	World Health Organization, Pneumonia of Unknown Cause-China, 2020. Available from: https://www.who.int/csr/don/05-january-2020-pneumonia-of-unkown-cause-china/en/.
[2]	World Health Organization, Clinical Management of Severe Acute Respiratory Infection When Infection is Suspected, 2020. Available from: https://www.who.int/publications-detail/clinical-management-of-severe-acute-respiratory-infection-when-novel-/coronavirus-(ncov)-infection-is-suspected.
[3]	National Health Commission of the People's Republic of China, Situation Report of the Pneumonia Cases Caused by the Novel Coronavirus, 2020. Available from: http://www.nhc.gov.cn/xcs/yqfkdt/202002/3db09278e3034f289841300ed09bd0e1.shtml.
[4]	Health Commission of Hubei Province, Prevention Measures of Wuhan, 2020. Available from: http://wjw.hubei.gov.cn/fbjd/dtyw/202001/t20200123_2014421.shtml.
[5]	National Health Commission of the People's Republic of China, Data of Confirmed Cases on COVID-19, 2020. Available from: http://www.nhc.gov.cn/xcs/xxgzbd/gzbd_index. shtml.
[6]	The State Council, the People's Republic of China, Annunciate of Prevention and Control Headquarters for 2019-nCoV Pneumonia, 2020. Available from: http://www.gov.cn/xinwen/2020-01/23/content_5471751.htm.
[7]	National Health Commission of the People's Republic of China, Prevention and Control of Novel Coronavirus Pneumonia (4th edition), 2020. Available from: http://www.nhc.gov.cn/xcs/zhengcwj/202002/573340613ab243b3a7f61df260551dd4.shtml.
[8]	D. S. Hui, E. I. Azhar, T. A. Madani, F. Ntoumi, R. Kock, O. Dar, et al., The continuing 2019-nCoV epidemic threat of novel coronaviruses to global health-The latest 2019 novel coronavirus outbreak in Wuhan, China, Int. J. Infect. Dis., 91 (2020), 264-266.
[9]	C. Huang, Y. Wang, X. Li, L. Ren, J. Zhao, Y. Hu, et al., Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China, Lancet, 395 (2020), 497-506.
[10]	Q. Li, X. Guan, P. Wu, X. Wang, L. Zhou, Y. Tong, et al., Early transmission dynamics in Wuhan, China, of novel coronavirus-infected pneumonia, N. Engl. J. Med., (2020).
[11]	T. E. Carpenter, J. M. O'Brien, A. D. Hagerman, B. A. McCarl, Epidemic and economic impacts of delayed detection of foot-and-mouth disease: a case study of a simulated outbreak in California, J. Vet. Diagn. Invest., 23 (2010), 26-33.
[12]	P. W. Uys, R. Warren, P. D. van Helden, M. Murray, T. C. Victor, Potential of rapid diagnosis for controlling drug-susceptible and drug-resistant tuberculosis in communities where Mycobacterium tuberculosis infections are highly prevalent, J. Clin. Mircrobiol., 47 (2009), 1484-1490.
[13]	I. N. Okeke, R. S. Manning, T. Pfeiffer, Diagnostic schemes for reducing epidemic size of African viral hemorrhagic fever outbreaks, J. Infect. Dev. Countr., 8 (2014), 1148-1159.
[14]	P. K. Drain, E. P. Hyle, F. Noubary, K. A. Freedberg, D. Wilson, W. R. Bishai, et al., Diagnostic point-of-care tests in resource-limited settings, Lancet. Infect. Dis., 14 (2014), 239-249.
[15]	D. Chowell, C. Castillo-Chavez, S. Krishna, X. Qiu, K. S. Anderson, Modelling the effect of early detection of Ebola, Lancet. Infect. Dis., 15 (2015), 148-149.
[16]	P. Nouvellet, T. Garske, H. L. Mills, G. Nedjati-Gilani, W. Hinsley, I. M. Blake, et al., The role of rapid diagnostics in managing Ebola epidemics, Nature, 528 (2015), S109.
[17]	B. Tang, X. Wang, Q. Li, N. L. Bragazzi, S. Tang, Y. Xiao, et al., Estimation of the transmission risk of the 2019-nCoV and its implication for public health interventions, J. Clin. Med., 9 (2020), 462.
[18]	N. Imai, I. Dorigatti, A. Cori, S. Riley, N. M. Ferguson, Estimating the potential total number of novel Coronavirus (2019-nCoV) cases in Wuhan City, China, Imperial College London, 2020. Available from: https://www.imperial.ac.uk/mrc-global-infectious-disease-analysis/news--wuhan-coronavirus/.
[19]	B. Tang, F. Xia, S. Tang, N. L. Bragazzi, Q. Li, X. Sun, et al., The evolution of quarantined and suspected cases determines the final trend of the 2019-nCoV epidemics based on multi-source data analyses, 2020. Available from: https://ssrn.com/abstract=3537099.
[20]	J. T. Wu, K. Leung, G. M. Leung, Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan, China: a modelling study, Lancet, 395 (2020), 689-697.
[21]	J. M. Read, J. R. Bridgen, D. A. Cummings, C. P. Jewell, Novel coronavirus 2019-nCoV: early estimation of epidemiological parameters and epidemic predictions, medRxiv, 2020.
[22]	Y. Bai, X. Nie, C. Wen, Epidemic prediction of 2019-nCoV in Hubei Province and comparison with SARS in Guangdong Province. Available at SSRN 3531427 (2020).
[23]	S. W. Park, B. M. D. Bolker, D. J. D. Champredon, M. Earn, J. Li, S. Weitz, et al., Reconciling early-outbreak estimates of the basic reproductive number and its uncertainty: framework and applications to the novel coronavirus (2019-nCoV) outbreak, medRxiv, 2020.
[24]	S. Zhao, S. S. Musa, Q. Lin, J. Ran, G. Yang, W. Wang, et al., Estimating the unreported number of novel coronavirus (2019-nCoV) cases in China in the first half of January 2020: a data-driven modelling analysis of the early outbreak, J. Clin. Med., 9 (2020), 388.
[25]	I. I. Bogoch, A. Watts, A. Thomas-Bachli, C. Huber, M. U. G. Kraemer, K. Kha, Pneumonia of unknown etiology in Wuhan, China: potential for international spread via commercial air travel, J. Travel Med., 2020.
[26]	S. Lai, I. Bogoch, N. Ruktanonchai, A. Watts, Y. Li, J. Yu, et al., Assessing spread risk of Wuhan novel coronavirus within and beyond China, January-April 2020: a travel network-based modelling study, medRxiv, 2020.
[27]	H. Tian, Y. Liu, Y. Li, M. U. G. Kraemer, B. Chen, C. H. Wu, et al., Early evaluation of the Wuhan City travel restrictions in response to the 2019 novel coronavirus outbreak, medRxiv, 2020.
[28]	S. Ai, G. Zhu, F. Tian, H. Li, Y. Gao, Y. Wu, et al., Population movement, city closure and spatial transmission of the 2019-nCoV infection in China, medRxiv, 2020.
[29]	T. Liu, J. X. Hu, M. Kang, L. Lin, H. Zhong, J. Xiao, et al., Transmission dynamics of 2019 novel coronavirus (2019-nCoV), bioRxiv, 2020.
[30]	WUHAN, CHINA, Population, The information of population in Wuhan, 2020. Available from: http://english.wh.gov.cn/whgk_3581/dqrk/.
[31]	P. Van den Driessche, J. Watmough, Reproduction numbers and subthreshold endemic equilibria for compartmental models of disease transmission, Math. Biosci., 180 (2002), 29-48.
[32]	S. Marino, I. B. Hogue, C. J. Ray, D. E. Kirschner, A methodology for performing global uncertainty and sensitivity analysis in systems biology, J. Theor. Biol., 254 (2008), 178-196.
[33]	Italy blasts virus panic as it eyes new testing criteria, 2020. Available from: https://world.huanqiu.com/article/3xDoSjd87PM.
[34]	Diagnostic criteria of COVID-19 in Japan, 2020. Available from: https://www.mhlw.go.jp/stf/seisakunitsuite/newpage_00006.html.

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