Who and When to Screen: Multi-Round Active Screening for Recurrent Infectious Diseases Under Uncertainty

Controlling recurrent infectious diseases is a vital yet complicated problem. In this paper, we propose a novel active screening model (ACTS) and algorithms to facilitate active screening for recurrent diseases (no permanent immunity) under infection uncertainty. Our contributions are: (1) A new approach to modeling multi-round network-based screening/contact tracing under uncertainty, which is a common real-life practice in a variety of diseases; (2) Two novel algorithms, Full- and Fast-REMEDY. Full-REMEDY considers the effect of future actions and finds a policy that provides high solution quality, where Fast-REMEDY scales linearly in the size of the network; (3) We evaluate Full- and Fast-REMEDY on several real-world datasets which emulate human contact and find that they control diseases better than the baselines. To the best of our knowledge, this is the first work on multi-round active screening with uncertainty for diseases with no permanent immunity.

[1]  Matt J Keeling,et al.  Contact tracing and disease control , 2003, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[2]  Michalis Faloutsos,et al.  Threshold conditions for arbitrary cascade models on arbitrary networks , 2011, 2011 IEEE 11th International Conference on Data Mining.

[3]  Maria A. Kazandjieva,et al.  A high-resolution human contact network for infectious disease transmission , 2010, Proceedings of the National Academy of Sciences.

[4]  C. M. Saad-Roy,et al.  A mathematical model of syphilis transmission in an MSM population. , 2016, Mathematical biosciences.

[5]  Yao Zhang,et al.  Data-Aware Vaccine Allocation Over Large Networks , 2015, ACM Trans. Knowl. Discov. Data.

[6]  Christos Faloutsos,et al.  Epidemic spreading in real networks: an eigenvalue viewpoint , 2003, 22nd International Symposium on Reliable Distributed Systems, 2003. Proceedings..

[7]  E. Namukose,et al.  Active Case Finding for Improved Ebola Virus Disease Case Detection in Nimba County, Liberia, 2014/2015: Lessons Learned , 2018, Advances in Public Health.

[8]  R. May,et al.  Infectious Diseases of Humans: Dynamics and Control , 1991, Annals of Internal Medicine.

[9]  C. Lanczos.  An iteration method for the solution of the eigenvalue problem of linear differential and integral operators , 1950 .

[10]  A. Barrat,et al.  Estimating Potential Infection Transmission Routes in Hospital Wards Using Wearable Proximity Sensors , 2013, PloS one.

[11]  L. Chambers,et al.  Assessing the effectiveness of community screening programs. , 1984, JAMA.

[12]  A. Srinivasan,et al.  Modeling and analysis of massive social networks , 2005 .

[13]  Sudip Saha,et al.  Approximation Algorithms for Reducing the Spectral Radius to Control Epidemic Spread , 2015, SDM.

[14]  Éva Tardos,et al.  Maximizing the Spread of Influence through a Social Network , 2015, Theory Comput..

[15]  Philip D O'Neill,et al.  Stochastic epidemic models featuring contact tracing with delays. , 2015, Mathematical biosciences.

[16]  Alexander Grey,et al.  The Mathematical Theory of Infectious Diseases and Its Applications , 1977 .

[17]  Donald F. Towsley,et al.  The effect of network topology on the spread of epidemics , 2005, Proceedings IEEE 24th Annual Joint Conference of the IEEE Computer and Communications Societies..

[18]  Ciro Cattuto,et al.  What's in a crowd? Analysis of face-to-face behavioral networks , 2010, Journal of theoretical biology.

[19]  Arun G. Chandrasekhar,et al.  The Diffusion of Microfinance , 2012, Science.

[20]  Elizabeth A Torrone,et al.  Sexually transmitted disease surveillance 2017 , 2018 .

[21]  H. Hethcote,et al.  An age-structured model for pertussis transmission. , 1997, Mathematical biosciences.

[22]  N. Ling.  The Mathematical Theory of Infectious Diseases and its applications , 1978 .

[23]  Alex Pentland,et al.  Modeling Infection with Multi-agent Dynamics , 2012, SBP.

[24]  Luis E C Rocha,et al.  Information dynamics shape the sexual networks of Internet-mediated prostitution , 2010, Proceedings of the National Academy of Sciences.

[25]  Philip Wolfe,et al.  An algorithm for quadratic programming , 1956 .

[26]  Kaare Græsbøll,et al.  Methods for estimating disease transmission rates: Evaluating the precision of Poisson regression and two novel methods , 2017, Scientific Reports.

[27]  C. Schofield.  Sexually transmitted disease surveillance. , 1982, British medical journal.

[28]  Y. Hsieh,et al.  Global analysis of an SEIR model with varying population size and vaccination , 2010 .

[29]  Michalis Faloutsos,et al.  Gelling, and melting, large graphs by edge manipulation , 2012, CIKM.

[30]  Kathleen M. Carley,et al.  Patterns and dynamics of users' behavior and interaction: Network analysis of an online community , 2009, J. Assoc. Inf. Sci. Technol..

[31]  Wei Chen,et al.  Efficient influence maximization in social networks , 2009, KDD.