Modeling Behavioural Evolution as Social Predictor for the Coronavirus Contagion and Immunization in Nigeria

  • Arnold Adimabua Ojugo Federal University of Petroleum Resources Effurun (NG)
  • Andrew Okonji Eboka Department of Network Computing, Coventry University, United Kingdom (GB)
Keywords: Coronavirus, COVID-19, graph-model, SIS, SIR, epidemiology, pandemic

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Abstract

Since the outbreak of the novel coronavirus (covid-19) pandemic from China in 2019, it has left the world leaders in great confusing due to its fast-paced propagation and spread that has left infected a world population of over Eleven Million persons with over five hundred and thirty four thousand deaths and counting with the United States of America, Brazil, Russia, India and Peru in the lead on these death toll. The pandemic whose increased mortality rate is targeted at ‘aged’ citizens, patients with low immunology as well as patients with chronic diseases and underlying health conditions. Study models covid-19 pandemic via a susceptible-infect-remove actor-based graph, with covid-19 virus as the innovation diffused within the social graph. We measure the rich connective patterns of the actor-based graph, and explore personal feats as they influence other nodes to adopt or reject an innovation. Results shows current triggers (lifting of inter-intra state migration bans) and shocks (exposure to covid-19 by migrants) will lead to late widespread majority adoption of 23.8-percent. At this, the death toll will climb from between 4.43-to-5.61-percent to over 12%.



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References

World Health Organization. Coronavirus Disease (COVID-2019) Situation Reports. Available online: https://www.who.int/emergencies/diseases/novel-coronavirus-2019/situation-reports (accessed on 2March 2020).

A.A. Ojugo., O.D. Otakore., (2020). Forging an optimized Bayesian network model with selected parameter for detection of the Coronavirus in Delta State of Nigeria, J. of Applied Sci., Engr., Tech. & Edu., 3(1): pp37–45, 2020, doi: 10.35877/454RI.asci2163

Adegboye, O.; Adekunle, A.; Pak, A.; Gayawan, E.; Leung, D.; Rojas, D.; Elfaki, F.; McBryde, E.; Eisen, D. Change in outbreak and impact on importation risks of COVID-19 progression: modelling study. medRxiv 2020. [CrossRef]

Martinez-Alvarez,M.; Jarde, A.; Usuf, E.; Brotherton,H.; Bittaye,M.; Samateh, A.L.; Antonio,M.; Vives-Tomas, J.; D’Alessandro, U.; Roca, A. COVID-19 pandemic in west Africa. Lancet Glob. Health 2020. [CrossRef]

Gilbert, M.; Pullano, G.; Pinotti, F.; Valdano, E.; Poletto, C.; Boëlle, P.-Y.; et al., Preparedness and vulnerability of African countries against importations of COVID-19: A modelling study. Lancet 2020, 395, 871–877. [CrossRef]

Ojugo, A.A., O.D. Otakore., Forging an optimized Bayesian network model with selected parameters for detection of corona-virus in Delta State Nigeria, J. Applied Sci., Eng, Tech & Edu. 3(2): pp128-138

Ojugo, A.A., D.A. Oyemade., Predicting potential spread of corona-virus in Nigeria through ties threshold for dynamic SIR graph model, submitted to Nigerian Journal of Research and Technology. 2020.

Adegboye, O., Adekunle, A, Gayawan, E., Early transmission dynamics of novel COVID-19, Int. J. Environmental Research and Public Health, medRxiv 2020. [CrossRef]

J. Hellewell, S. Abbott, A. Gimma, N.I. Bosse, C.I. Jarvis, T.W. Russell et al. Feasibility of controlling COVID-19 outbreaks by isolation of cases and contacts, 2013, doi: 10.1371/journal.pone.0072168

McKibbin, W. & Fernando, R. (2020). The Global Macroeconomic Impacts of COVID-19: Seven Scenarios. Retrieved from https://researchgate.net

Worldometers (2020).Global Statistics on COVID-19 Pandemic Outbreak. Retrieved from, https://worldometers.info/coronavirus

Addi, R. A., Benksim, A., Amine, M., &Cherkaoui, M. (2020). Asymptomatic COVID-19 infection management: The key to stopping COVID-19. Journal of Clinical and Experimental Investigations, 11(3), 1-2

Kim, K. H. (2020). COVID-19. International Neurourol Journal, 24(1), 1-1.doi.org/10.5213/inj.2020edi.001

Nigeria Centre for Disease Control [NCDC] (2020). COVID-19 Pandemic Outbreak. Retrieved from, https://covid19.ncdc.gov.ng

Bai, Y., Yao, L., Wei, T., Tian, F., Jin, D, Y., Chen, L. & Wang, M. (2020). Presumed asymptomatic carrier transmission of COVID-19. JAMA.

Nigeria Centre for Disease Control Twitter Handle[NCDC] (2020). COVID-19 Pandemic Outbreak of Statistics. Retrieved from, https://twitter.com/ncdc.gov.ng

Tashanova, D., Sekerbay, A., Chen, D., Luo, Y., Zhao, S. & Zhang, Q. (2020). Investment opportunities an, d strategies in an era of coronavirus pandemic. Retrieved from https://ssrn.com/abstract=3567445

Kim, J., Kim, J., Lee, S. K., & Tang, L. R. (2020). Effects of epidemic disease outbreaks on the financial performance of restaurants: Event study method approach. Journal of Hospitality and Tourism Management, 43, 32–41.

Jung, H., Park, M., Hong, K & Hyun, E. (2016). The impact of an epidemic outbreak on consumer expenditures: An empirical assessment for MERS Korea. Sustainability, 8(454), 1-15. DOI:10.3390/su8050454

A.A. Ojugo., F. Aghware., R. Yoro., M. Yerokun., A. Eboka., C. Anujeonye., F. Efozia., Predict behavioral evolution on graph model, Advances in Networks, 3(2): pp8-21, 2015

Aggarwal CC, Reddy CK. Data Clustering: Algorithms and Applications. Chapman and Hall, 2013.

M. Azarafza, M. Azarafza, H. Akgunc, Clustering method of spread pattern analysis of corona-virus (covid-19) infection in Iran, J. Applied Science, Engineering Tech. & Education, 3(1) pp1-6, doi: 10.35877/454ARI.asci31109, 2020.

David, P.C., (2007). Path dependence – a foundational concept for historical social sciences, Climetrica – Journal of Historical Economics and Econometric History, 1(2).

Granovetter, M. (1978). Threshold Models of Collective Behavior. American J. Sociology, 83(6), p1420–1443. doi:10.1086/226707, (http://dx.doi.org/10.1086%2F226707). JSTOR 2778111

Sala, A., Cao, N., Wilson, C., Zablit, R., Zheng, H and Zhao, B.Y., (2010). Measurement calibrated graph models for social network experiments, IW3C2, ACM 978-1-60558-799-8/10/04.

Gilbert, E and Karahalois, K., (2009). Predicting tie strengths with social media, Journal of computer and Human Interface, 15, p76-97, ACM 978-1-60558-246-7/09/04.

Golbeck, J. (2013). Analyzing the Social Web, Morgan Kaufmann, ISBN: 0-12-405856-6.

Granovetter, M. (1985). Economic action and social structure: The problem of embeddedness, American J. Sociology, 91(3), p481–510, doi:10.1086/228311 (http://dx.doi.org/10.1086%2F228311).

Handcock, M.S and Gile, K.J., (2009). Modeling social networks from sampled data*, Annals of Applied Statistics, arXiv: math.PR/00000.

Schnettler, S., (2009). A small world on feet of clay? A comparison of empirical small-world studies against best-practice criteria, Social Networks, 31(3), p179-189, doi:10.1016/j.socnet.2008.12.005.

Scott, J.P., (2000). Social network analysis: A Handbook (2nd Ed). Thousand Oaks, CA: Sage Publications.

Smith T.S and Stevens, G.T., (1999). The architecture of small networks: strong interaction and dynamic organization in small social systems, American Sociological Review, 64, p403–20

Toivonen, R., Kovanena, L., Kiveläa, M., Onnela, J.K., Saramäkia, J and Kaskia, K., (2009). A comparative study of social networks models: network evolution and nodal attributes models, Social Networks, 31, p240-254, doi:10.1016/j.socnet.2009.06.004.

Valente, T.W., (1996). Social network thresholds in the diffusion of innovation, Social Networks, 18, p69-89, SSDI 0378-8733(95)00256

Wilson, C., Boe, B., Sala, A., Puttaswamy, K.P and Zhao, B.Y., (2009). User interactions in social networks and their implications. In Proc. of EuroSys (April 2009).

Ojugo, A.A., A. Eboka., E. Okonta., et al, Genetic algorithm rule-based intrusion detection system, J. of Emerging Trends in Computing Information Sys., 3(8): pp1182-1194, 2012

Published
2020-07-18
Section
Articles
How to Cite
[1]
A. A. Ojugo and A. O. Eboka, “Modeling Behavioural Evolution as Social Predictor for the Coronavirus Contagion and Immunization in Nigeria”, J. Appl. Sci. Eng. Technol. Educ., vol. 3, no. 2, pp. 135-144, Jul. 2020.