e-ISSN: 2617-7668     print ISSN: 2522-9176
Graph modelling for tracking the COVID-19 pandemic spread
##common.pageHeaderLogo.altText## EURASIAN JOURNAL OF CLINICAL SCIENCES

Abstract

The modelling is widely used in determining the best strategies for the mitigation of the impact of infectious diseases. Currently, the modelling of a complex system such as the spread of COVID-19 infection is among the topical issues. The aim of this article is graph-based modelling of the COVID-19 infection spread. The article investigates the studies related to the modelling of COVID-19 pandemic and analyses the factors affecting the spread of the disease and its main characteristics. We propose a conceptual model of COVID-19 epidemic by considering the social distance, the duration of contact with an infected person and their location-based demographic characteristics. Based on the hypothetical scenario of the spread of the virus, a graph model of the process are developed starting from the first confirmed infection case to human-to-human transmission of the virus and visualized by considering the epidemiological characteristics of COVID-19. The application of graph for the pandemic modelling allows for considering multiple factors affecting the epidemiological process and conducting numerical experiments. The advantage of this approach is justified with the fact that it enables the reverse analysis the spread as a result of the dynamic record of detected cases of the infection in the model. This approach allows for to determining undetected cases of infection based on the social distance and duration of contact and eliminating the uncertainty significantly. Note that social, economic, demographic factors, the population density, mental values and etc. affect the increase in number of cases of infection and hence, the research was not able to consider all factors. In future research will analyze multiple factors impacting the number of infections and their use in the models will be considered.

References

Apple Google partner on COVID 19, Apple & Google partner on COVID-19, Apple and Google partner on COVID-19 contact tracing technology, apple.com/newsroom/2020/04/apple-and-google-partner-on-covid-19-. Google Scholar Chan et al., 2020

J.F.-W. Chan, S. Yuan, K.-H. Kok, K.K.-W. To, H. Chu, J. Yang, et al.A familial cluster of pneumonia associated with the 2019 novel coronavirus indicating person-to-person transmission: A study of a family cluster Lancet, 395 (2020), pp. 514-523

https://www.thelancet.com/action/showPdf?pii=S0140-6736%2820%2930154-9 ArticleDownload PDFView Record in ScopusGoogle Scholar Chen et al., 2020

T.-M. Chen, J. Rui, Q.-P. Wang, Z.-Y. Zhao, et al.A mathematical model for simulating the phase-based transmissibility of a novel coronavirus Infectious Diseases of Poverty, 9 (2020), 10.1186/s40249-020-00640-3 Google Scholar Connell, 2015

C. ConnellWhat’s the difference between measuring location by UWB, Wi-Fi, and Bluetooth?

www.electronicdesign.com/technologies/communications/article/21800581/whats-the-difference-between-measuring-location-by-uwb-wifi-and-bluetooth (2015) Google Scholar

COVID Community Alert, COVID Community Alert, https://coronavirus-outbreak-control.github.io/web/. Google Scholar Currie et al., 2020

C.S.M. Currie, J.W. Fowler, K. Kotiadis, T. Monks, et al.How simulation modelling can help reduce the impact of COVID-19 Journal of Simulation (2020), 10.1080/17477778.2020.1751570 Google Scholar

ECDC Technical Report, 2020 ECDC Technical ReportContact tracing: Public health management of persons, including healthcare workers, having had contact with COVID-19 cases in the European Union Technical report

https://www.ecdc.europa.eu/sites/default/files/documents/covid-19-public-health-management-contact-novel-coronavirus-cases-EU.pdf (2020) Google Scholar

eRouška, eRouška https://erouska.cz

Flourish, FlourishData visualization & storytelling https://flourish.studio/ Google Scholar Giordano et al., 2020

G. Giordano, F. Blanchini, R. Bruno, P. ColaneriModelling the COVID-19 epidemic and implementation of population-wide interventions in Italy Nature Medicine (2020), 10.1038/s41591-020-0883-7 Google Scholar Gleick, 2020

P.H. GleickNo COVID-19 models are perfect, but some are useful https://time.com/5838335/covid-19-prediction-models/ (2020) Google Scholar

HaMagen https://govextra.gov.il/ministry-of-health/hamagen-app/download-en/ Holmdahl and Buckee, 2020

Holmdahl, C. BuckeeWrong but useful – what Covid-19 epidemiologic models can and Cannot Tell Us https://www.nejm.org/doi/full/10.1056/NEJMp2016822 (2020) Google Scholar

E.C. Holmes, A. Rambaut, K.G. AndersenPandemics: Spend on surveillance, not prediction Nature, 558 (7709) (2018), pp. 180-182, 10.1038/d41586-018-05373-w CrossRefView Record in ScopusGoogle Scholar

Imai et al., 2020

N. Imai, A. Cori, I. Dorigatti, M. Baguelin, et al.Report 3: Transmissibility of 2019-nCoV, reference source

https://www.imperial.ac.uk/mrc-global-infectious-disease-analysis/news--wuhan-coronavirus/ (2020) Google Scholar

B. Ivorra, M.R. Ferrández, M. Vela-Pérez, A.M. RamosMathematical modeling of the spread of the coronavirus disease 2019 (COVID-19) considering its particular characteristics

The case of China (2020), 10.13140/RG.2.2.21543.29604 Preprint Google Scholar

M.D.V. Kerkhove, N.M. FergusonEpidemic and intervention modelling–a scientific rationale for policy decisions? Lessons from the 2009 influenza pandemic

Bulletin of the World Health Organization, 90 (4) (2012), pp. 306-310, 10.2471/BLT.11.097949 Google Scholar M. KetchellLack of data makes predicting COVID-19’s spread difficult but models are still vital (2020)

https://theconversation.com/lack-of-data-makes-predicting-covid-19s-spread-difficult-but-models-are-still-vital-135797 Google Scholar

M.A. Khan, A. AtanganaModeling the dynamics of novel coronavirus (2019-nCov) with fractional derivative Alexandria Engineering Journal (2020), 10.1016/j.aej.2020.02.033 (in press) Google Scholar

S.M. Kissler, C. Tedijanto, E. Goldstein, Y.G. Grad, M. LipsitchProjecting the transmission dynamics of SARS-CoV-2 through the postpandemic period Science, 368 (6493) (2020), pp. 860-868

I.S. Kristiansen, E.A. Burger, B.F. De BlasioCovid-19: Simulation models for epidemics (2020) https://tidsskriftet.no/en/2020/03/kronikk/covid-19-simulation-models-epidemics Google Scholar

A.J. Kucharski, T.W. Russell, Ch. Diamond, et al.Early dynamics of transmission and control of COVID-19: A mathematical modelling study The Lancet Infectious Diseases, 20 (2020), pp. 553-558, 10.1016/S1473-3099(20)30144-4

L. Li, Z. Yang, Z. Dang, et al.Propagation analysis and prediction of the COVID-19 Infectious Disease Modelling, 5 (2020), pp. 282-292

Q. Lin, S. Zhao, D. Gao, Y. Lou, et al.A conceptual model for the coronavirus disease 2019 (COVID-19) outbreak in Wuhan, China with individual reaction and governmental action International Journal of Infectious Diseases, 93 (2020), pp. 211-216

J. Michaud, J. Kates, L. LevittCOVID-19 models: Can they tell us what we want to know? (2020) https://www.kff.org/coronavirus-policy-watch/covid-19-models/ Google Scholar

MorrowFrance to test controversial Covid-19 tracking app during lockdown exit (2020) www.rfi.fr/en/science-and-technology Google Scholar

M. PanzarinoApple and Google are launching a joint COVID-19 tracing tool for iOS and Android TechCrunch

https://techcrunch.com/2020/04/10/apple-and-google-are-launching-a-joint-covid-19-tracing-tool/ (2020) Google Scholar

B.M. PavlyshenkoRegression approach for modeling COVID-19 spread and its impact on stock market (Preprint) (2020)

https://arxiv.org/pdf/2004.01489 Google Scholar

QR health code, Q R health code, Expats in China hail QR health code, globaltimes.cn/content/1181828.shtml. Google Scholar

J. RigginsThe challenges to building a predictive COVID-19 model (2020)

https://thenewstack.io/the-challenges-to-building-a-predictive-covid-19-model/ Google Scholar

K. Roosa, Y. Lee, R. Luo, A. Kirpich, R. Rothenberg, et al.Real-time forecasts of the COVID-19

epidemic in China from february 5th to february 24th Infectious Disease Modelling, 5 (2020), pp. 256-263, 10.1016/j.idm.2020.02.002

Sahin, A. Erdogan, P.M. Agaoglu, Y. Dineri, A. Cakirci, M. Senel, et al.2019 novel coronavirus (COVID-19) outbreak: A review of the current literature Eurasian J. Med. Oncol., 4 (2020), pp. 1-7

R. SameniMathematical modeling of epidemic diseases; A case study of the COVID-19 coronavirus (2020) https://arxiv.org/pdf/2003.11371 Google Scholar

F.M. Shearer, R. Moss, J. McVernon, J.V. Ross, J.M. McCawInfectious disease pandemic planning and response: Incorporating decision analysis PLoS Medicine, 17 (1) (2020), 10.1371/journal.pmed.1003018 Google Scholar

M. SlamichBluetooth vs ultra-wideband: Which indoor location system? https://blog.pointr.tech/bluetooth-vs-ultra-wideband-which-technology-to-use-for-indoor-location (2020) Google Scholar

Stopp Corona APP https://www.roteskreuz.at/site/meet-the-stopp-corona-app/

TraceTogether www.tracetogether.gov.sg

L. Wang, Y. Wang, Y. Chen, Q. QinUnique epidemiological and clinical features of the emerging 2019 novel coronavirus pneumonia (COVID-19) implicate special control measures Journal of Medical Virology (2020), 10.1002/jmv.25748

WHO IHRStatement on the meeting of the international health regulations (2005) emergency committee regarding the outbreak of novel coronavirus (2019-nCoV) (2020)

https://www.who.int/news-room/detail/23-01-2020-statement-on-the-meeting-of-the-international-health-regulations-(2005)-emergency-committee-regarding-the-outbreak-of-novel-coronavirus-(2019-ncov Google Scholar

World Health Organization (WHO)WHO coronavirus disease (COVID-19) dashboard (28 October 2020) https://covid19.who.int Google Scholar

Ch. Yang, J. WangA mathematical model for the novel coronavirus epidemic in Wuhan, China Mathematical Biosciences and Engineering, 17 (3) (2020), pp. 2708-2724

D. Zhao, L. Li, H. Peng, Y. YangMultiple routes transmitted epidemics on multiplex networks Physics Letters A, 378 (10) (2014), pp. 770-776

S. Zhao, S.S. Musa, Q. Lin, J. Ran, G. Yan, 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 Journal of Clinical Medicine, 9 (2) (2020), p. 388, 10.3390/jcm902038810.3390/jcm9020388

Ch. Zhou, F. Su, T. Pei, A. Zhang, et al.COVID-19: Challenges to GIS with big data Geography and Sustainability, 1 (1) (2020), pp. 77-87

PDF
PDF

Keywords

COVID-19
pandemic
modelling
epidemiological characteristics
graph visualization COVID-19
пандемия
моделирование
эпидемиологические характеристики
визуализация графика COVID-19
pandemiya
modelləşdirmə
epidemioloji xüsusiyyətləri
qrafik vizuallaşdırma
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.