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Symptom-Based Testing in a Compartmental Model of Covid-19

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Analysis of Infectious Disease Problems (Covid-19) and Their Global Impact

Part of the book series: Infosys Science Foundation Series ((ISFM))

Abstract

Testing and isolation of cases is an important component of our strategies to fight SARS-CoV-2. In this work, we consider a compartmental model for Covid-19 including a nonlinear term representing symptom-based testing. We analyze how the considered clinical spectrum of symptoms and the testing rate affect the outcome and the severity of the outbreak.

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References

  1. WHO. Novel Coronavirus (2019-nCoV): situation reports. World Health Organization 2020. https://www.who.int/emergencies/diseases/novel-coronavirus-2019/situation-reports

  2. Vetter, P., Vu Diem, L., L’Huillier, A.G., Schibler, M., Kaiser, L., Jacquerioz, F., et al.: Clinical features of COVID-19. BMJ 2020, 369: 1470. https://doi.org/10.1136/bmj.m1470

  3. He, X., et al.: Temporal dynamics in viral shedding and transmissibility of COVID-19. Nat. Med. 26, 672–675 (2020). https://doi.org/10.1038/s41591-020-0869-5

  4. Ashcroft, P., Huisman, J.S., Lehtinen, S., Bouman, J.A., Althaus, C.L., Regoes, R.R., Bonhoeffer, S.: COVID-19 infectivity profile correction. Swiss Med. Wkly, 150:w20336 (2020) https://doi.org/10.4414/smw.2020.20336

  5. He, X., et al.: Author Correction: Temporal dynamics in viral shedding and transmissibility of COVID-19. Nat Med. 26, 1491–1493 (2020). https://doi.org/10.1038/s41591-020-1016-z

  6. R. Mao et al. Manifestations and prognosis of gastrointestinal and liver involvement in patients with COVID-19: a systematic review and meta-analysis. The Lancet 5(7), 667–678 (2020). https://doi.org/10.1016/S2468-1253(20)30126-6

  7. Docherty, A.B., et al.: Features of 16,749 hospitalised UK patients with COVID-19 using the ISARIC WHO Clinical Characterisation Protocol. medR\(\chi \) iv 2020.04.28. https://doi.org/10.1101/2020.04.23.20076042

  8. ECDC. Clinical characteristics of COVID-19. European Centre for Disease Prevention and Control (2020). https://www.ecdc.europa.eu/en/covid-19/latest-evidence/clinical

  9. Guan, W., et al.: Clinical Characteristics of Coronavirus Disease 2019 in China. N. Engl. J. Med. 382, 1708–1720 (2020). https://doi.org/10.1056/NEJMoa2002032

  10. Menni, C., et al.: Real-time tracking of self-reported symptoms to predict potential COVID-19. Nat. Med. 26, 1037–1040 (2020). https://doi.org/10.1038/s41591-020-0916-2

  11. CDC. Real-Time RT-PCR Panel for Detection 2019-nCoV. Centers for Disease Control and Prevention 2020.01.29.

    Google Scholar 

  12. Lia, M.Y., Graef, J.R., Wang, L., Karsai, J.: Global dynamics of a SEIR model with varying total population size. Math. Biosci. 160(2), 191–213 (1990). https://doi.org/10.1016/S0025-5564(99)00030-9

  13. Feng, Z.: Applications of epidemiological models to public health policymaking: the role of heterogeneity in model predictions. World Scientific (2014)

    Google Scholar 

  14. Péni, T., Csutak, B., Szederkényi, G., Röst, G.: Nonlinear model predictive control for COVID-19 management. Nonlinear Dyn. in press

    Google Scholar 

  15. Yang, C., Wang, Y.: A mathematical model for the novel coronavirus epidemic in Wuhan, China. Math. Biosci. Eng. 17(3), 2708–2724 (2020). https://doi.org/10.3934/mbe.2020148

  16. Boldog, P., Tekeli, T., Vizi, Zs., Dénes, A., Bartha, F.A., Röst, G.: Risk Assessment of Novel Coronavirus COVID–19 Outbreaks Outside China. J. Clin. Med. 9(2), 571. https://doi.org/10.3390/jcm9020571

  17. Berger, D.W., Herkenhoff, K.F., Mongey, S.: An SEIR infectious disease model with testing and conditional quarantine. NBER Working Paper No. 26901 (2020). https://doi.org/10.3386/w26901

  18. Weitz, J.S.: COVID-19 Epidemic Risk Assessment for Georgia. Github 2020.03.24. https://github.com/jsweitz/covid-19-ga-summer-2020

  19. Röst, G., et al.: Early phase of the COVID-19 outbreak in Hungary and post-lockdown scenarios. Viruses 12(7), 708 (2020). https://www.mdpi.com/1999-4915/12/7/708

  20. Lofgren, E., Fefferman, N.H., Naumov, Y.N., Gorski, J., Naumova, E.N.: Influenza seasonality: underlying causes and modeling theories. J. Virol. 81(11), 5429–5436 (2007). https://doi.org/10.1128/JVI.01680-06

  21. Olson, K.L., Mandl, K.D.: Seasonal patterns of gastrointestinal illness. Adv. Dis. Surveill. 4, 262. http://faculty.washington.edu/lober/www.isdsjournal.org/htdocs/articles/2188.pdf

  22. Giordano, G., et al.: Modelling the COVID-19 epidemic and implementation of population-wide interventions in Italy. Nat. Med. 26, 855–860 (2020). https://doi.org/10.1038/s41591-020-0883-7

  23. Barbarossa, M.V., et al.: Modeling the spread of COVID-19 in Germany: Early assessment and possible scenarios. PLOS ONE 15(9), e0238559 (2020). https://doi.org/10.1371/journal.pone.0238559

  24. Lauer, S.A., et al.: The incubation period of coronavirus disease 2019 (COVID-19) from publicly reported confirmed cases: estimation and application. Ann. Intern. Med. 172(9), 577–582 (2020). https://doi.org/10.7326/M20-0504

  25. Diekmann, O., Heesterbeek, J.A.P., Roberts, M.G.: The construction of next-generation matrices for compartmental epidemic models. J. R. Soc. Interface 7(47), 873–885 (2020). https://doi.org/10.1098/rsif.2009.0386

  26. Chowell, G., Fenimore, P., Castillo-Garsow, M., Castillo-Chavez, C.: SARS outbreaks in Ontario, Hong Kong and Singapore: the role of diagnosis and isolation as a control mechanism. J. Theor. Biol. 224(1). https://doi.org/10.1098/rsif.2007.1036

  27. Cori, A., Ferguson, N.M., Fraser, C., Cauchemez, S.: A new framework and software to estimate time-varying reproduction numbers during epidemics. Am. J. Epidemiol. 178(9), 1505–1512 (2020). https://doi.org/10.1093/aje/kwt133

  28. Wallinga, J., Lipsitch. M.: How generation intervals shape the relationship between growth rates and reproductive numbers. Proc. R. Soc. B: Biol. Sci. 274(1609), 599–604 (2020). https://royalsocietypublishing.org/doi/full/10.1098/rspb.2006.3754

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Acknowledgements

This work was done in the framework of the Hungarian National Development, Research, and Innovation (NKFIH) Fund 2020-2.1.1-ED-2020-00003 and of the grants TUDFO/47138-1/2019-ITM and EFOP-3.6.2-16-2017-00015. Some authors were also supported by NKFIH KKP 129877 (J.K.), NKFIH FK 124016 (T.T.), János Bolyai Research Scholarship of the Hungarian Academy of Sciences (F.B.).

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Authors and Affiliations

  1. Bolyai Institute, University of Szeged, Szeged, Hungary

    Ferenc A. Bartha, János Karsai, Tamás Tekeli & Gergely Röst

Authors
  1. Ferenc A. Bartha
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  2. János Karsai
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  3. Tamás Tekeli
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  4. Gergely Röst
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Corresponding author

Correspondence to Ferenc A. Bartha .

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Editors and Affiliations

  1. Anand International College of Engineering, Jaipur, Rajasthan, India

    Praveen Agarwal

  2. Institute of Mathematics, University of Santiago de Compostela, Santiago, Spain

    Juan J. Nieto

  3. Mathematical Sciences, Queen Mary University of London, London, UK

    Michael Ruzhansky

  4. Department of Mathematics, University of Aveiro, Aveiro, Portugal

    Delfim F. M. Torres

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© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Bartha, F.A., Karsai, J., Tekeli, T., Röst, G. (2021). Symptom-Based Testing in a Compartmental Model of Covid-19. In: Agarwal, P., Nieto, J.J., Ruzhansky, M., Torres, D.F.M. (eds) Analysis of Infectious Disease Problems (Covid-19) and Their Global Impact. Infosys Science Foundation Series(). Springer, Singapore. https://doi.org/10.1007/978-981-16-2450-6_16

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