Impact of insufficient detection in COVID-19 outbreaks

Yue Deng; Siming Xing; Meixia Zhu; Jinzhi Lei; Yue Deng; Siming Xing; Meixia Zhu; Jinzhi Lei

doi:10.3934/mbe.2021476

Mathematical Biosciences and Engineering

2021, Volume 18, Issue 6: 9727-9742. doi: 10.3934/mbe.2021476

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Research article Special Issues

Impact of insufficient detection in COVID-19 outbreaks

1.
School of Computer Science and Technology, Tiangong University, Tianjin, 300387, China
2.
School of Mathematical Sciences, Tiangong University, Tianjin, 300387, China
3.
School of Software, Tiangong University, Tianjin, 300387, China
4.
Center for Applied Mathematics, Tiangong University, Tianjin, 300387, China

Academic Editor: Amina Eladdad

Received: 19 July 2021 Accepted: 28 October 2021 Published: 04 November 2021

The COVID-19 (novel coronavirus disease 2019) pandemic has tremendously impacted global health and economics. Early detection of COVID-19 infections is important for patient treatment and for controlling the epidemic. However, many countries/regions suffer from a shortage of nucleic acid testing (NAT) due to either resource limitations or epidemic control measures. The exact number of infective cases is mostly unknown in counties/regions with insufficient NAT, which has been a major issue in predicting and controlling the epidemic. In this paper, we propose a mathematical model to quantitatively identify the influences of insufficient detection on the COVID-19 epidemic. We extend the classical SEIR (susceptible-exposed-infections-recovered) model to include random detections which are described by Poisson processes. We apply the model to the epidemic in Guam, Texas, the Virgin Islands, and Wyoming in the United States and determine the detection probabilities by fitting model simulations with the reported number of infected, recovered, and dead cases. We further study the effects of varying the detection probabilities and show that low level-detection probabilities significantly affect the epidemic; increasing the detection probability of asymptomatic infections can effectively reduce the the scale of the epidemic. This study suggests that early detection is important for the control of the COVID-19 epidemic.
- COVID-19,
- nucleic acid testing,
- detection probability,
- epidemic scale
Citation: Yue Deng, Siming Xing, Meixia Zhu, Jinzhi Lei. Impact of insufficient detection in COVID-19 outbreaks[J]. Mathematical Biosciences and Engineering, 2021, 18(6): 9727-9742. doi: 10.3934/mbe.2021476

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Abstract

The COVID-19 (novel coronavirus disease 2019) pandemic has tremendously impacted global health and economics. Early detection of COVID-19 infections is important for patient treatment and for controlling the epidemic. However, many countries/regions suffer from a shortage of nucleic acid testing (NAT) due to either resource limitations or epidemic control measures. The exact number of infective cases is mostly unknown in counties/regions with insufficient NAT, which has been a major issue in predicting and controlling the epidemic. In this paper, we propose a mathematical model to quantitatively identify the influences of insufficient detection on the COVID-19 epidemic. We extend the classical SEIR (susceptible-exposed-infections-recovered) model to include random detections which are described by Poisson processes. We apply the model to the epidemic in Guam, Texas, the Virgin Islands, and Wyoming in the United States and determine the detection probabilities by fitting model simulations with the reported number of infected, recovered, and dead cases. We further study the effects of varying the detection probabilities and show that low level-detection probabilities significantly affect the epidemic; increasing the detection probability of asymptomatic infections can effectively reduce the the scale of the epidemic. This study suggests that early detection is important for the control of the COVID-19 epidemic.

References

[1]	World Health Organization, WHO Coronavirus Disease (COVID-19) Dashboard, available from: https://covid19.who.int/table.
[2]	S. Flaxman, S. Mishra, A. Gandy, H. J. T. Unwin, T. A. Mellan, H. Coupland, et al., Estimating the effects of non-pharmaceutical interventions on COVID-19 in Europe, Nature, 584 (2020), 257–261. doi:10.1038/s41586-020-2405-7. doi: 10.1038/s41586-020-2405-7
[3]	N. Ferguson, D. Laydon, G. Nedjati Gilani, N. Imai, K. Ainslie, M. Baguelin, et al., Report 9: Impact of non-pharmaceutical interventions (NPIs) to reduce COVID-19 mortality and healthcare demand, 2020. doi: 10.25561/77482.
[4]	S. Lai, N. W. Ruktanonchai, L. Zhou, O. Prosper, W. Luo, J. R. Floyd, et al., Effect of non-pharmaceutical interventions to contain COVID-19 in China, Nature, 585 (2020), 410–413. doi:10.1038/s41586-020-2293-x. doi: 10.1038/s41586-020-2293-x
[5]	Y. Huang, L. Yang, H. Dai, F. Tian, K. Chen, Epidemic situation and forecasting of COVID-19 in and outside China, Bull World Health Organization, 4 (2020). doi: 10.2471/BLT.20.255158.
[6]	J. Labadin, B. H. Hong, Transmission dynamics of 2019-nCOV in Malaysia proior to the movement Control Order, 2020. medRxiv 2020.02.07.20021188; doi: https://doi.org/10.1101/2020.02.07.20021188
[7]	B. Tang, X. Wang, Q. Li, N. L. Bragazzi, S. Y. Tang, Y. N. Xiao, et al., Estimation of the transmission risk of 2019-nCov and its implication for public health interventions, J. Clin. Med., 9 (2020), 462. doi: 10.3390/jcm9020462
[8]	Y. Chen, J. Cheng, Y. Jiang, K. Liu, A time delay dynamical model for outbreak of 2019-nCOV and the parameter identification, J. Inv. Ill-Posed Problem, 28 (2020), 243–250. doi: 10.1515/jiip-2020-0010
[9]	Y. Yan, Y. Chen, K. Liu, X. Luo, B. Xu, Y. Jiang, et al., Modeling and prediction for the trend of outbreak of NCP based on a time-delay dynamic system, Sci. Sinica Math., 50 (2020), 385. doi:10.1360/SSM-2020-0026. doi: 10.1360/SSM-2020-0026
[10]	X. M. Rong, L. Yang, H. D. Chu, M. Fan, Effect of delay in diagnosis on transmission of COVID-19, Math. Biosci. Eng., 17 (2020), 2725–2740. doi: 10.3934/mbe.2020149
[11]	V. Naveen, C. Aasish, M. Kavya, M. Vidhyalakshmi, Forecasting the number of infections of novel coronavirus with deep learning, Int. J. Comput. Appl., 176 (2020), 21–24.
[12]	H. Abbasimehr, R. Paki, Prediction of COVID-19 confirmed cases combining deep learning methods and Bayesian optimization, Chaos Soliton Fract., 142 (2021), 110511. doi: 10.1016/j.chaos.2020.110511
[13]	L. Qin, Q. Sun, Y. D. Wang, K. F. Wu, M. Chen, B. C. Shia, et al., Prediction of number of cases of 2019 novel coronavirus (COVID-19) using social media search index, Int. J. Environ. Res. Pub. Health, 17 (2020), 2365. doi: 10.3390/ijerph17072365
[14]	A. R. Akhmetzhanov, K. Mizumoto, S. M. Jung, N. M. Linton, R. Omori, H. Nishiura, et al., Estimation of the actual incidence of coronavirus disease (COVID-19) in emergent hotspots: The example of Hokkaido Japan during February-March 2020, Clin. Med., 10 (2021), 2392.
[15]	G. Pullano, L. D. Domenico, C. E. Sabbatini, E. Valdano, C. Turbelin, M. Debin, et al, Under detection of COVID-19 cases in France threatens epidemic control, Nature, 590 (2020), 134–139.
[16]	C. Xu, Y. Z. Pei, S. Q. Liu, J. Z. Lei, Effectiveness of non-pharmaceutical intervention against local transmission of COVID-19: An individual-based modelling study, Infect. Dis. Model., 6 (2021), 848–858.
[17]	T. Alamo, D. G. Reina, P. M. Gata, V. M. Preciado, G. Giordano, Data-driven methods for present and future pandemics: monitoring, modeling and managing, Annu Rev Control, (2021), arXiv preprint arXiv: 2102.13130. doi: 10.1016/j.arcontrol.2021.05.003.
[18]	S. Venkatramanan, B. Lewis, J. Chen, D. Higdon, A. Vullikanti, M. Marathe, Using data-driven agent-based models for forecasting emerging infectious diseases, Epidemics, 22 (2018), 43–49. doi: 10.1016/j.epidem.2017.02.010
[19]	D. Bertsimas, L. Boussioux, R. Cory-Wright, A. Delarue, V. Digalakis, A. Jacquillat, et al., From predictions to prescriptions: A data-driven response to COVID-19, Health Care Manag. Sci., 2 (2021), 1–20.
[20]	S. Y. Tang, B. Tang, N. L. Bragazzi, et al, Data mining of covid-19 epidemic and analysis of discrete random propagation dynamic model, Sci. China, 50 (2020), 1–16 (in Chinese).
[21]	L. Böttcher, M. R. D'Orsogna, T. Chou, Using excess deaths and testing statistics to determine COVID-19 mortalities, Eur. J. Epidemiol. 36 (2021), 545–558. doi: 10.1007/s10654-021-00748-2
[22]	J. S. Faust, Z. Lin, C. Del Rio, Comparison of estimated excess deaths in New York City during the COVID-19 and 1918 influenza pandemics, JAMA Netw. Open, 3 (2020), e2017527. doi: 10.1001/jamanetworkopen.2020.17527
[23]	A. L. Bertozzi, E. Franco, G. Mohler, M. B. Short, D. Sledge, The challenges of modeling and forecasting the spread of COVID-19, Proc. Natl. Acad. Sci. USA, 117 (2020), 16732–16738. doi: 10.1073/pnas.1914072117
[24]	COVID-19 Map: Johns Hopkins Coronavirus Resource Center, https://coronavirus.jhu.edu/map.html.
[25]	C. Kuhbandner, S. Homburg, Commentary: Estimating the effects of non-pharmaceutical interventions on COVID-19 in Europe, Front Med., 7 (2020), 580361. doi: 10.3389/fmed.2020.580361
[26]	C. Cakmakli, Y. Simsek, Bridging the COVID-19 data and the epidemiological model using time varying parameter SIRD model, (2020), arXiv preprint arXiv: 2007.02726. doi: abs/2007.02726.
[27]	J. Rocklv, H. Sjdin, A. Wilder-Smith, COVID-19 outbreak on the Diamond Princess cruise ship: estimating the epidemic potential and effectiveness of public health countermeasures, J. Travel Med., 27 (2020), taaa030. doi:10.1093/jtm/taaa030. doi: 10.1093/jtm/taaa030
[28]	S.Bentout, A. Chekroun, T. Kuniya, Parameter estimation and prediction for coronavirus disease outbreak 2019 (COVID-19) in Algeria, AIMS Public Health, 7 (2020), 306–318. doi: 10.3934/publichealth.2020026
[29]	K. Chatterjee, K. Chatterjee, A. Kumar, S. Shankar, Healthcare impact of COVID-19 epidemic in India: A stochastic mathematical model, Med. J. Armed. Forces India, 76 (2020), 147–155. doi: 10.1016/j.mjafi.2020.03.022
[30]	T. Kuniya, Prediction of the epidemic peak of coronavirus disease in Japan, 2020, Clin. Med., 9 (2020), 789.
[31]	Q. Li, Y. N. Xiao, J. H. Wu, COVID-19 epidemic time lag model construction and confirmed case-driven tracking and isolation measures analysis, Acta Math. Appl. Sinica, 43 (2020), 96–108(in Chinese).
[32]	M. Gatto, E. Bertuzzo, L. Mari, S. Miccoli, L. Cararo, R. Casagrandi, et al., Spread and dynamics of the COVID-19 epidemic in Italy: Effects of emergency containment measures, Proc. Natl. Acad. Sci. USA, 117 (2020), 202004978.
[33]	S. Y. Tang, B. Tang, N. L. Bragazzi, F. Xia, T. Li, S. He, et al., Analysis of COVID-19 epidemic traced data and stochastic discrete transmission dynamic model, Sci. Sinica Math., 50 (2020), 1–16(in Chinese).
[34]	K. Prem, Y. liu, T. W. Russell, A. J. Kucharski, R. M. Eggo, N. Davies, et al., The effect of control strategies to reduce social mixing on outcomes of the COVID-19 epidemic in Wuhan, China: a modelling study, Lancet Public Health, 5 (2020), e261–e270. doi: 10.1016/S2468-2667(20)30073-6
[35]	B. Tang, F. Xia, S. Y. Tang, The effectiveness of quarantine and isolation determine the trend of the COVID-19 epidemic in the final phase of the current outbreak in China, Int. J. Infect. Dis., 95 (2020), 288–293. doi: 10.1016/j.ijid.2020.03.018
[36]	Population Ranking of American State Governments (2020), available from: http://blog.sina.com.cn/s/blog\_5ce1af980102z91y.html.
[37]	J. E. Forde, S. M. Ciupe, Quantification of the tradeoff between test sensitivity and test frequency in a COVID-19 epidemic–A multi-scale modeling approach, Viruses, 13 (2021), 457. doi: 10.3390/v13030457
[38]	M. T. Xia, L. Bőttcher, T. Chou, Controlling epidemics through optimal allocation of test kits and vaccine doses across networks, (2021), arXiv preprint arXiv: 2107.13709. doi: abs/2107.13709.

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