Epidemic Modeling and Flattening the Infection Curve in Social Networks | ||
| مدل سازی در مهندسی | ||
| Volume 22, Issue 76, May 2024, Pages 155-165 PDF (828.48 K) | ||
| Document Type: Research Paper | ||
| DOI: 10.22075/jme.2023.30259.2425 | ||
| Authors | ||
| Mohammadreza Doostmohammadian* 1; Soraya Doustmohamadian2; Najmeh Doostmohammadian2; Azam Doustmohammadian3; Houman Zarrabi4; Hamid R. Rabiee5 | ||
| 1Assistant Professor, Faculty of Mechanical Engineering, Semnan University, Semnan, Iran | ||
| 2Assistant Professor, Semnan University of Medical Sciences, Semnan, Iran | ||
| 3Assistant Professor, Gastrointestinal and Liver Diseases Research Center, Iran University of Medical Sciences, Tehran, Iran | ||
| 4Assistant Professor, Iran Telecom Research Center (ITRC), Tehran, Iran | ||
| 5Professor, Faculty of Computer Engineering, Sharif University of Technology, Tehran, iran | ||
| Receive Date: 26 March 2023, Revise Date: 22 July 2023, Accept Date: 01 October 2023 | ||
| Abstract | ||
| The main goal of this paper is to model the epidemic and flattening the infection curve of the social networks. Flattening the infection curve implies slowing down the spread of the disease and reducing the infection rate via social-distancing, isolation (quarantine) and vaccination. The nan-pharmaceutical methods are a much simpler and efficient way to control the spread of epidemic and infection rate. By specifying a target group with high centrality for isolation and quarantine one can reach a much flatter infection curve (related to Corona for example) without adding extra costs to health services. The aim of this research is, first, modeling the epidemic and, then, giving strategies and structural algorithms for targeted vaccination or targeted non-pharmaceutical methods for reducing the peak of the viral disease and flattening the infection curve. These methods are more efficient for nan-pharmaceutical interventions as finding the target quarantine group flattens the infection curve much easier. For this purpose, a few number of particular nodes with high centrality are isolated and the infection curve is analyzed. Our research shows meaningful results for flattening the infection curve only by isolating a few number of targeted nodes in the social network. The proposed methods are independent of the type of the disease and are effective for any viral disease, e.g., Covid-19. . | ||
| Keywords | ||
| Epidemic; Flattening the infection curve; Social networks; Graph theory | ||
| References | ||
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[1] Block. P. M Hoffman. I. J Raabe. J. B Dowd. C Rahal. R Kashyap. and M. C Mills. “Social network-based distancing strategies to flatten the COVID-19 curve in a post-lockdown world”. Nature Human Behavior. 4 (2020): 588–596. [2] Reyna-Lara. A. D Soriano-Paños. S Gómez. C Granell. J. T Matamalas. B Steinegger. A Arenas. and J Gómez-Gardeñes. “Virus spread versus contact tracing: Two competing contagion processes”. Physical Review Research. 3. no. 1 (2021): 013163. [3] Doostmohammadian. M. H.R Rabiee. U.A Khan. “Centrality based Epidemic Control in Complex Social Networks”. Social Network Analysis and Mining. 10. no. 32 (2020): 1-11. [4] Nowzari. C. V.M Preciado. and G. J Pappas, “Analysis and control of epidemics: A survey of spreading processes on complex networks”. IEEE Control Systems Magazine. 36. no. 1 (2016): 26-46. [5] Chang. S. E Pierson. P.W Koh. J Gerardin. B Redbird. D Grusky. and J Leskovec. “Mobility network models of COVID-19 explain inequities and inform reopening”. Nature. 589. no 7840 (2021): 82-87. [6] Karaivanov. A. “A social network model of COVID-19”. Plos one. 15. no. 10 (2020): e0240878. [7] Brandon. W.P. “Flattening Epidemic Curves and COVID-19: Policy Rationales. Inequality. and Racism.” Journal of Health Care for the Poor and Underserved. 33 (2022): 1700-1714. [8] Brauer. F. “Compartmental models in epidemiology”. Mathematical epidemiology. (2008): 19-79. [9] Brauer. F. C Castillo-Chavez. Z Feng. F Brauer. C Castillo-Chavez. and Z Feng. “Simple compartmental models for disease transmission”. Mathematical Models in Epidemiology. (2019): 21-61. [10] Fosu. G.O. J.M Opong. and J.K Appati. “Construction of compartmental models for COVID-19 with quarantine”. lockdown and vaccine interventions. (2020). [11] Ferguson. N. M. et al. “Strategies for mitigating an influenza pandemic”. Nature. 442 (2006): 448–452. [12] Glass. R.J. L.M Glass. W.E Beyeler. and H.J Min. “Targeted social distancing designs for pandemic influenza”. Emerging infectious diseases. 12. no. 11 (2006): 1671. [13] Siettos. C.I. and L Russo. “Mathematical modeling of infectious disease dynamics”. Virulence. 4(2013): 295–306. [14] World Health Organization Writing Group et al. “Non-pharmaceutical interventions for pandemic influenza, national and community measures”. Emerging Infectious Disease, 12(2012): 88–94. [15] Klepac. P. S Kissler. and J Gog, “Contagion! The BBC Four Pandemic–the model behind the documentary”. Epidemics. 24 (2018): 49-59. [16] Firth. J. A. J Hellewell. P Klepac. S Kissler. A.J Kucharski. and L.G Spurgin. “Using a real-world network to model localized COVID-19 control strategies”. Nature medicine. 26, no. 10 (2020): 1616-1622. [17] Toivonen. R. J. P Onnela. J Saramäki. J Hyvönen. and K Kaski. “A model for social networks”. Physica A: Statistical Mechanics and its Applications. 371. no. 2 (2006): 851-860. [18] Barabási. A. L. and R Albert. “Emergence of scaling in random networks”. Science. 286. no. 5439 (1999): 509-512. [19] Holme. P. and B. J Kim. “Growing scale-free networks with tunable clustering”. Physical review E. 65. no. 2 (2002): 026107. [20] Doostmohammadian. M. and U. A Khan. “On the controllability of clustered Scale-Free networks”. Journal of Complex Networks. 8. no. 1 (2020): 1-13. [21] Wasserman. S. and K Faust. " Social network analysis: Methods and applications". Cambridge University Press. Cambridge. UK. 1994. [22] Luce. R.D. and A.D Perry. “A method of matrix analysis of group structure”. Psychometrika. 14. no. 1. (1949): 95-116. [23] Lü. L. D Chen. X.L Ren. Q.M Zhang. Y.C Zhang. and T Zhou. “Vital nodes identification in complex networks”. Physics reports. 650 (2016): 1-63. [24] Bonacich. P. and P Lloyd. “Eigenvector-like measures of centrality for asymmetric relations”. Social networks. 23. no. 3 (2001): 191-201. [25] Katz. L. “A new status index derived from sociometric analysis”. Psychometrika. 18. no. 1 (1953): 39-43. [26] Arasu. A. J Novak. A Tomkins. and J Tomlin. “PageRank computation and the structure of the web: Experiments and algorithms”. 11th International World Wide Web Conference. 2002. p. 107-117. [27] Lawyer. G. “Understanding the influence of all nodes in a network”. Scientific reports. 5. no. 1 (2015): 8665. [28] Doostmohammadian. M. S Pourazarm. and U.A Khan. “Distributed algorithm for shortest path problem via randomized strategy”. 11th IEEE International Conference on Networking. Sensing and Control. Miami. FL. 2014. p. 463-46. | ||
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