{"type":"ScholarlyArticle","title":"Scalable and Memory-Efficient Algorithms for Controlling Networked Epidemic Processes Using Multiplicative Weights Update Method","authors":[{"name":"Prathyush Sambaturu","slug":"prathyush-sambaturu","given":"Prathyush","family":"Sambaturu","affiliation":"University of Virginia , Charlottesville , VA , USA"},{"name":"Marco Minutoli","slug":"marco-minutoli","given":"Marco","family":"Minutoli","affiliation":"Pacific Northwest National Laboratory , Richland , WA , USA"},{"name":"Mahantesh Halappanavar","slug":"mahantesh-halappanavar","given":"Mahantesh","family":"Halappanavar","affiliation":"Pacific Northwest National Laboratory , Richland , WA , USA; Washington State University , Pullman , WA , USA"},{"name":"Ananth Kalyanaraman","slug":"ananth-kalyanaraman","given":"Ananth","family":"Kalyanaraman","affiliation":"Washington State University , Pullman , WA , USA; Pacific Northwest National Laboratory , Richland , WA , USA"},{"name":"Anil Vullikanti","slug":"anil-vullikanti","given":"Anil","family":"Vullikanti","affiliation":"University of Virginia , Charlottesville , VA , USA"}],"abstract":"We study the problem of designing scalable algorithms to find effective intervention strategies for controlling stochastic epidemic processes on networks. This is a common problem arising in agent based models for epidemic spread.\r\nPrevious approaches to this problem focus on either heuristics with no guarantees or approximation algorithms that scale only to networks corresponding to county-sized populations, typically, with less than a million nodes. In particular, the mathematical-programming based approaches need to solve the Linear Program (LP) relaxation of the problem using an LP solver, which restricts the scalability of this approach. In this work, we overcome this restriction by designing an algorithm that adapts the multiplicative weights update (MWU) framework, along with the sample average approximation (SAA) technique, to approximately solve the linear program (LP) relaxation for the problem. To scale this approach further, we provide a memory-efficient algorithm that enables scaling to large networks, corresponding to country-size populations, with over 300 million nodes and 30  billion edges. Furthermore, we show that this approach provides near-optimal solutions to the LP in practice.","doi":"10.24963/ijcai.2022/717","url":"https://ijcai.org/proceedings/2022/717","pdf":"https://ijcai.org/proceedings/2022/0717.pdf","markdown":"https://ijcai.org/proceedings/2022/717.md","bibtex":"https://ijcai.org/proceedings/2022/bibtex/717","volume":{"slug":"2022","title":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2022","url":"https://ijcai.org/proceedings/2022/","year":2022,"location":"Vienna","dates":"23-29 July 2022","editors":"Luc De Raedt","isbn":"978-1-956792-00-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2022"},"section":"Research Papers","track":"AI for Good","number":717,"pages":{"first":5164,"last":5170},"keywords":["Multidisciplinary Topics and Applications: Health and Medicine","Agent-based and Multi-agent Systems: Resource Allocation","AI Ethics, Trust, Fairness: Fairness & Diversity","Data Mining: Networks"],"videos":[{"label":"Video #1","url":"https://slideslive.com/38987143","length_sec":81,"provider":"slideslive"},{"label":"Video #2","url":"https://slideslive.com/38985286","length_sec":694,"provider":"slideslive"}],"references":[{"position":1,"raw":"[Anderson and May, 1991] R.M. Anderson and R.M. May. Infectious Diseases of Humans. Oxford University Press, Oxford, 1991.","title":"Infectious Diseases of Humans","authors":"R M Anderson, R M May","year":"1991","venue":null,"doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Arora et al., 2012] S. Arora, E. Hazan, and S. Kale. The multiplicative weights update method: a meta-algorithm and applications. Theory of Computing, 8(1):121-164, 2012.","title":"The multiplicative weights update method: a meta-algorithm and applications","authors":"S Arora, E Hazan, S Kale","year":"2012","venue":"Theory of Computing","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Babay et al., 2022] Amy Babay, Mike Dinitz, Aravind Srinivasan, Leonidas Tsepenekas, and Anil Vullikanti. Controlling epidemic spread using probabilistic diffusion models on networks. In Proc. AISTATS, 2022.","title":"Controlling epidemic spread using probabilistic diffusion models on networks","authors":"Amy Babay, Mike Dinitz, Aravind Srinivasan, Leonidas Tsepenekas, Anil Vullikanti","year":"2022","venue":"Proc. AISTATS","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Barabási and Albert, 1999] A. Barabási and R. Albert. Emergence of scaling in random networks. Science, 286(5439):509-512, 1999.","title":"Emergence of scaling in random networks","authors":"A Barabási, R Albert","year":"1999","venue":"Science","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Barocas et al., 2019] Solon Barocas, Moritz Hardt, and Arvind Narayanan. Fairness and Machine Learning. fairmlbook.org, 2019. http://www.fairmlbook.org.","title":"Fairness and Machine Learning. fairmlbook","authors":"Solon Barocas, Moritz Hardt, Arvind Narayanan","year":"2019","venue":null,"doi":null,"url":"http://www.fairmlbook.org","internal_url":null},{"position":6,"raw":"[Chen et al., 2021] J. Chen, S. Hoops, et al. Prioritizing al- location of covid-19 vaccines based on social contacts in- creases vaccination effectiveness. medRxiv, 2021.","title":"Prioritizing allocation of covid-19 vaccines based on social contacts increases vaccination effectiveness","authors":"J Chen, S Hoops","year":"2021","venue":"medRxiv","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Cohen et al., 2003] R. Cohen, S. Havlin, and D. ben Avra- ham. Efficient immunization strategies for computer net- works and populations. Phys. Rev. Lett., 91:247901, Dec 2003.","title":"Efficient immunization strategies for computer networks and populations","authors":"R Cohen, S Havlin, D Ben Avraham","year":"2003","venue":"Phys. Rev. Lett","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Dinitz et al., 2022] Mike Dinitz, Aravind Srinivasan, Leonidas Tsepenekas, and Anil Vullikanti. Fair disaster containment via graph-cut problems. In Proc. AISTATS, 2022.","title":"Fair disaster containment via graph-cut problems","authors":"Mike Dinitz, Aravind Srinivasan, Leonidas Tsepenekas, Anil Vullikanti","year":"2022","venue":"Proc. AISTATS","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Eubank et al., 2004] S. Eubank, H. Guclu, V. S. Anil Ku- mar, M. Marathe, A. Srinivasan, Z. Toroczkai, and N. Wang. Modelling disease outbreaks in realistic urban social networks. Nature, 429:180-184, 2004.","title":"Modelling disease outbreaks in realistic urban social networks","authors":"S Eubank, H Guclu, V S Kumar, M Marathe, A Srinivasan, Z Toroczkai, N Wang","year":"2004","venue":"Nature","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Eubank et al., 2006] S. Eubank, V. S. Anil Kumar, M. V. Marathe, A. Srinivasan, and N. Wang. Structure of So- cial Contact Networks and Their Impact on Epidemics. In Discrete Methods in Epidemiology, volume 70, pages 179-200. American Math. Soc., Providence, RI, 2006.","title":"Structure of Social Contact Networks and Their Impact on Epidemics","authors":"S Eubank, V S Kumar, M V Marathe, A Srinivasan, N Wang","year":"2006","venue":"Discrete Methods in Epidemiology","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Fleischer, 2000] L. K. Fleischer. Approximating fractional multicommodity flow independent of the number of com- modities. SIAM J. Discret. Math., 13(4):505-520, October 2000.","title":"Approximating fractional multicommodity flow independent of the number of commodities","authors":"L K Fleischer","year":"2000","venue":"SIAM J. Discret. Math","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Germann et al., 2006] T.C. Germann, K. Kadau, I.M. Longini Jr, and C.A. Macken. Mitigation strategies for pandemic influenza in the united states. PNAS, 103(15):5935-5940, 2006.","title":"Mitigation strategies for pandemic influenza in the united states","authors":"T C Germann, K Kadau, I M Longini, C A Macken","year":"2006","venue":"PNAS","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Halloran et al., 2008] M. E. Halloran, N. M. Ferguson, et al. Modeling targeted layered containment of an influenza pandemic in the United States. In PNAS, pages 4639- 4644, March 10 2008.","title":"Modeling targeted layered containment of an influenza pandemic in the United States","authors":"M E Halloran, N M Ferguson","year":"2008","venue":"PNAS","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Hayrapetyan et al., 2005] A. Hayrapetyan, D. Kempe, M. Pál, and Z. Svitkina. Unbalanced graph cuts. In ESA 2005, ESA'05, page 191-202, Berlin, Heidelberg, 2005. Springer-Verlag.","title":"Unbalanced graph cuts","authors":"A Hayrapetyan, D Kempe, M Pál, Z Svitkina","year":"2005","venue":"ESA 2005, ESA'05","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Kleywegt et al., 2002] A. J Kleywegt, A. Shapiro, and T. Homem-de Mello. The sample average approximation method for stochastic discrete optimization. SIAM Journal on Optimization, 12(2):479-502, 2002.","title":"The sample average approximation method for stochastic discrete optimization","authors":"A Kleywegt, A Shapiro, T Homem-De Mello","year":"2002","venue":"SIAM Journal on Optimization","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Leskovec et al., 2007] J. Leskovec, J. Kleinberg, and C. Faloutsos. Graph evolution: Densification and shrink- ing diameters. ACM TKDD, 1(1), March 2007.","title":"Graph evolution: Densification and shrinking diameters","authors":"J Leskovec, J Kleinberg, C Faloutsos","year":"2007","venue":"ACM TKDD","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Lofgren et al., 2014] E. Lofgren, M. E. Halloran, et al. Opinion: Mathematical models: A key tool for outbreak response. PNAS, pages 18095-18096, 2014.","title":"Opinion: Mathematical models: A key tool for outbreak response","authors":"E Lofgren, M E Halloran","year":"2014","venue":"PNAS","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Marathe and Vullikanti, 2013] M. Marathe and A. Vul- likanti. Computational epidemiology. Communications of the ACM, 56(7):88-96, 2013.","title":"Computational epidemiology","authors":"M Marathe, A Vullikanti","year":"2013","venue":"Communications of the ACM","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Medlock and Galvani, 2009] J. Medlock and A. P. Gal- vani. Optimizing influenza vaccine distribution. Science, 325(5948):1705-1708, 2009.","title":"Optimizing influenza vaccine distribution","authors":"J Medlock, A P Galvani","year":"2009","venue":"Science","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Miller and Hyman, 2007] Joel C. Miller and James M. Hy- man. Effective vaccination strategies for realistic social networks. Physica A: Statistical Mechanics and its Appli- cations, 386(2):780-785, 2007. Disorder and Complexity. [Minutoli et al., 2020]","title":"Effective vaccination strategies for realistic social networks","authors":"Joel C Miller, James M Hyman","year":"2007","venue":"Disorder and Complexity","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"M. Minutoli, P. Sambaturu, M. Ha- lappanavar, A. Tumeo, A. Kalyananaraman, and A. Vul- likanti. Preempt: Scalable epidemic interventions using submodular optimization on multi-gpu systems. In SC20, pages 1-15, 2020.","title":"Preempt: Scalable epidemic interventions using submodular optimization on multi-gpu systems","authors":"M Minutoli, P Sambaturu, M Halappanavar, A Tumeo, A Kalyananaraman, A Vullikanti","year":"2020","venue":"SC","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Saha et al., 2015] S. Saha, A. Adiga, B. A. Prakash, and A. Vullikanti. Approximation algorithms for reducing the spectral radius to control epidemic spread. In SIAM SDM, 2015.","title":"Approximation algorithms for reducing the spectral radius to control epidemic spread","authors":"S Saha, A Adiga, B A Prakash, A Vullikanti","year":"2015","venue":"SIAM SDM","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Sambaturu et al., 2020] Prathyush Sambaturu, Bijaya Ad- hikari, B. Aditya Prakash, Srinivasan Venkatramanan, and Anil Vullikanti. Designing effective and practical inter- ventions to contain epidemics. In AAMAS, AAMAS '20, page 1187-1195, 2020.","title":"Designing effective and practical interventions to contain epidemics","authors":"Sambaturu","year":"2020","venue":"AAMAS, AAMAS '20","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Truelove et al., 2021] S. Truelove, C. P. Smith, et al. Pro- jected resurgence of covid-19 in the united states in july-december 2021 resulting from the increased trans- missibility of the delta variant and faltering vaccination. medRxiv, 2021.","title":"Projected resurgence of covid-19 in the united states in july-december 2021 resulting from the increased transmissibility of the delta variant and faltering vaccination","authors":"S Truelove, C P Smith","year":"2021","venue":null,"doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}