AI helps you reading Science
AI generates interpretation videos
AI extracts and analyses the key points of the paper to generate videos automatically
AI parses the academic lineage of this thesis
AI extracts a summary of this paper
Proposes a framework called Proactive Contact Tracing which uses distributed inference of expected Covid-19 infectiousness to provide individualized, private recommendations.
Predicting Infectiousness for Proactive Contact Tracing
The COVID-19 pandemic has spread rapidly worldwide, overwhelming manual contact tracing in many countries and resulting in widespread lockdowns for emergency containment. Large-scale digital contact tracing (DCT) has emerged as a potential solution to resume economic and social activity while minimizing spread of the virus. Various DCT me...More
PPT (Upload PPT)
- Until pharmaceutical interventions such as a vaccine become available, control of the COVID-19 pandemic relies on nonpharmaceutical interventions such as lockdown and social distancing.
- While these have often been successful in limiting spread of the disease in the short term, these restrictive measures have important negative social, mental health, and economic impacts.
- Relying only on positive test results as a trigger is inefficient for a number of reasons: (i) Tests have high false negative rates (Li et al, 2020); (ii) Tests are administered late, only after symptoms appear, leaving the asymptomatic population, estimated 20%-30% of cases (Gandhi et al, 2020), likely untested; (iii) It is estimated that infectiousness is highest before symptoms appear, well before someone would get a test (Heneghan et al, 2020), allowing them to infect others before being traced, (iv) Results typically take at least 1-2 days, and (v) In many places, tests are in limited supply
- Until pharmaceutical interventions such as a vaccine become available, control of the COVID-19 pandemic relies on nonpharmaceutical interventions such as lockdown and social distancing
- Digital contact tracing (DCT), a technique to track the spread of the virus among individuals in a population using smartphones, is an attractive potential solution to help reduce growth in the number of cases and thereby allow more economic and social activities to resume while keeping the number of cases low
- We evaluate the proposed proactive contact tracing (PCT) methods, set transformer (ST)-PCT and DS-PCT, and benchmark them against: testbased binary contact tracing (BCT); a rule-based feature-based contact tracing (FCT) Heuristic method proposed by Gupta et al (2020)4; and a baseline No Tracing (NT) scenario which corresponds to recommendation level 1
- Recall R is an estimate of how many other agents an infectious agent will infects, i.e. even a numerically small improvement in R could yield an exponential improvement in the number of cases. We find that both PCT methods yield a clear improvement over BCT and the rule-based heuristic, all of which significantly improve over the no-tracing baseline
- Our results demonstrate the potential benefit of digital contact tracing approaches for saving lives while reducing mobility restrictions and preserving privacy, independently confirming previous reports (Ferretti et al, 2020; Abueg et al, 2020)
- Of all methods in our study, we find that deep learning based PCT provides the best trade-off between restrictions on mobility and reducing the spread of disease under a range of settings, making it a potentially powerful tool for saving lives in a safe deconfinement
- EXP5: In Figure 6, the authors analyze the sensitivity of the various methods to adoption rate, which measures what percent of the population actively uses the CT app.
- Adoption rate is an important parameter for DCT methods, as it directly determines the effectiveness of an app.
- The authors visualize the effect of varying the adoption rate on the reproduction number R.
- To prior work (Abueg et al, 2020), the authors find that all DCT methods improve over the no-tracing baseline even at low adoptions and PCT methods dominate at all levels of adoption.
- In a model in which 15% of the population participated, the authors found that digital exposure notification systems could reduce infections and deaths by approximately 8% and 6%, effectively complementing traditional contact tracing.
- The authors' results demonstrate the potential benefit of digital contact tracing approaches for saving lives while reducing mobility restrictions and preserving privacy, independently confirming previous reports (Ferretti et al, 2020; Abueg et al, 2020).
- Of all methods in the study, the authors find that deep learning based PCT provides the best trade-off between restrictions on mobility and reducing the spread of disease under a range of settings, making it a potentially powerful tool for saving lives in a safe deconfinement
- This area of research holds many interesting avenues of future work in machine learning, including: (1) The comparison of methods for fitting parameters of the epidemiological simulator data using spatial information, (2) Using reinforcement learning to learn the mapping from estimated infectiousness and individual-level features, and thereby target desirable outcomes for heterogeneous populations.
- The authors hope this work can play a role in fostering this necessary collaboration
- Table1: Adoption Rate vs Uptake: The left column show the total percentage of the population with the app, while the right column shows the proportion of smartphone users with the app
- Table2: Daily contacts per location type: This table shows for each location l the pre-confinement mean number of daily contacts Cl and the reduction in number of contacts αl based on data collected in the Region of Montréal
- Table3: Recommendation level and corresponding effective contacts: Recommendation levels and corresponding expected number of daily effective contacts for a location type l
- In a model in which 15% of the population participated, we found that digital exposure notification systems could reduce infections and deaths by approximately 8% and 6%, effectively complementing traditional contact tracing
- Matthew Abueg, Robert Hinch, Neo Wu, Luyang Liu, William J M Probert, Austin Wu, Paul Eastham, Yusef Shafi, Matt Rosencrantz, Michael Dikovsky, Zhao Cheng, Anel Nurtay, Lucie Abeler-Dörner, David G Bonsall, Michael V McConnell, Shawn O’Banion, and Christophe Fraser. Modeling the combined effect of digital exposure notification and non-pharmaceutical interventions on the covid-19 epidemic in washington state. medRxiv, 2020. doi: 10.1101/2020. 08.29.20184135. URL https://www.medrxiv.org/content/early/2020/09/02/2020.08.29.20184135.
- Alberto Aleta, David Martin-Corral, Ana Pastore y Piontti, Marco Ajelli, Maria Litvinova, Matteo Chinazzi, Natalie E Dean, M Elizabeth Halloran, Ira M Longini Jr, Stefano Merler, et al. Modeling the impact of social distancing, testing, contact tracing and household quarantine on second-wave scenarios of the covid-19 epidemic. medRxiv, 2020.
- Hannah Alsdurf, Edmond Belliveau, Yoshua Bengio, Tristan Deleu, Prateek Gupta, Daphne Ippolito, Richard Janda, Max Jarvie, Tyler Kolody, Sekoul Krastev, Tegan Maharaj, Robert Obryk, Dan Pilat, Valerie Pisano, Benjamin Prud’homme, Meng Qu, Nasim Rahaman, Irina Rish, Jean-Francois Rousseau, Abhinav Sharma, Brooke Struck, Jian Tang, Martin Weiss, and Yun William Yu. Covi white paper. arXiv preprint arXiv:2005.08502, 2020.
- Antoine Baker, Indaco Biazzo, Alfredo Braunstein, Giovanni Catania, Luca Dall’Asta, Alessandro Ingrosso, Florent Krzakala, Fabio Mazza, Marc Mézard, Anna Paola Muntoni, Maria Refinetti, Stefano Sarao Mannelli, and Lenka Zdeborová. Epidemic mitigation by statistical inference from contact tracing data, 2020.
- James Bergstra and Yoshua Bengio. Random search for hyper-parameter optimization. Journal of Machine Learning Research, 13(10):281–305, 2012. URL http://jmlr.org/papers/v13/bergstra12a.html.
- Mark Briers, Marcos Charalambides, and Chris Holmes. Risk scoring calculation for the current nhsx contact tracing app. arXiv preprint arXiv:2005.11057, 2020.
- Marc Brisson and et al. Épidémiologie et modélisation de l’évolution de la covid-19 au québec. INSPQ, 2020. URL https://www.inspq.qc.ca/covid-19/.
- Jacob Buckman, Aurko Roy, Colin Raffel, and Ian Goodfellow. Thermometer encoding: One hot way to resist adversarial examples. In International Conference on Learning Representations, 201URL https://openreview.net/forum?id=S18Su--CW.
- Yevgen Chebotar, Ankur Handa, Viktor Makoviychuk, Miles Macklin, Jan Issac, Nathan Ratliff, and Dieter Fox. Closing the sim-to-real loop: Adapting simulation randomization with real world experience, 2018.
- Khaled El Emam, Elizabeth Jonker, Luk Arbuckle, and Bradley Malin. A systematic review of re-identification attacks on health data. PloS one, 6(12):e28071, 2011.
- Kai Fan, Chunyuan Li, and Katherine Heller. A unifying variational inference framework for hierarchical graph-coupled hmm with an application to influenza infection. In Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, AAAI’16, pp. 3828–3834. AAAI Press, 2016.
- Luca Ferretti, Chris Wymant, Michelle Kendall, Lele Zhao, Anel Nurtay, Lucie Abeler-Dörner, Michael Parker, David Bonsall, and Christophe Fraser. Quantifying sars-cov-2 transmission suggests epidemic control with digital contact tracing. Science, 368(6491), 2020. URL https://doi.org/10.1126/science.abb6936.
- Monica Gandhi, Deborah S. Yokoe, and Diane V. Havlir. Asymptomatic transmission, the achilles’ heel of current strategies to control COVID-19. New England Journal of Medicine, 382(22): 2158–2160, 2020. URL https://doi.org/10.1056/NEJMe2009758.
- Kyra H Grantz, Elizabeth C Lee, Lucy D’Agostino McGowan, Kyu Han Lee, C Jessica E Metcalf, Emily S Gurley, and Justin Lessler. Maximizing and evaluating the impact of test-trace-isolate programs. medRxiv, 2020.
- Prateek Gupta, Tegan Maharaj, Martin Weiss, Nasim Rahaman, Hannah Alsdurf, Abhinav Sharma, Nanor Minoyan, Soren Harnois-Leblanc, Victor Schmidt, Pierre-Luc St. Charles, Tristan Deleu, Andrew Williams, Akshay Patel, Meng Qu, Olexa Bilaniuk, Gáetan Marceau Caron, Pierre-Luc Carrier, Satya Ortiz-Gagne, Marc-Andre Rousseau, David Buckeridge, Joumana Ghosn, Yang Zhang, Bernhard Schölkopf, Jian Tang, Irina Rish, Christopher Pal, Joanna Merckx, Eilif B. Muller, and Yoshua Bengio. COVI-agentsim: an agent-based model for evaluating methods of digital contact tracing, 2020. URL https://arxiv.org/abs/2010.16004.
- https://www.canada.ca/en/publichealth/services/diseases/2019-novel-coronavirus-infection/health-professionals/interimguidance-cases-contacts.html, April 2020.
- Joel Hellewell, Sam Abbott, Amy Gimma, Nikos I Bosse, Christopher I Jarvis, Timothy W Russell, James D Munday, Adam J Kucharski, W John Edmunds, Fiona Sun, et al. Feasibility of controlling covid-19 outbreaks by isolation of cases and contacts. The Lancet Global Health, 2020.
- Carl Heneghan, Jon Brassey, and Tom Jefferson. Sars-cov-2 viral load and the severity of COVID-19, 2020.
- URL https://www.cebm.net/covid-19/
- Accessed: 2020-06-02.
- Ralf Herbrich, Rajeev Rastogi, and Roland Vollgraf. Crisp: A probabilistic model for individual-level covid-19 infection risk estimation based on contact data, 2020.
- Robert Hinch, W Probert, A Nurtay, M Kendall, C Wymant, Matthew Hall, and C Fraser. Effective configurations of a digital contact tracing app: A report to nhsx. en. In:(Apr. 2020). Available here. url: https://github.com/BDI-pathogens/covid-19_instant_tracing/blob/master/Report, 2020.
- Grefenstette JJ, Brown ST, Rosenfeld R, Depasse J, Stone NT, Cooley PC, Wheaton WD, Fyshe A, Galloway DD, Sriram A, Guclu H, Abraham T, and Burke DS. Fred (a framework for reconstructing epidemic dynamics): An open-source software system for modeling infectious diseases and control strategies using census-based populations. BMC Public Health, 2013.
- David Krueger, Tegan Maharaj, and Jan Leike. Hidden incentives for auto-induced distributional shift. arXiv preprint arXiv:2009.09153, 2020.
- Juho Lee, Yoonho Lee, Jungtaek Kim, Adam R. Kosiorek, Seungjin Choi, and Yee Whye Teh. Set transformer: A framework for attention-based permutation-invariant neural networks, 2018. URL https://arxiv.org/abs/1810.00825.
- Sergey Levine, Aviral Kumar, George Tucker, and Justin Fu. Offline reinforcement learning: Tutorial, review, and perspectives on open problems. arXiv preprint arXiv:2005.01643, 2020.
- Dasheng Li, Dawei Wang, Jianping Dong, Nana Wang, He Huang, Haiwang Xu, and Chen Xia. False-negative results of real-time reverse-transcriptase polymerase chain reaction for severe acute respiratory syndrome coronavirus 2: role of deep-learning-based ct diagnosis and insights from two cases. Korean journal of radiology, 21(4):505–508, 2020.
- Andrey Y Lokhov, Marc Mézard, Hiroki Ohta, and Lenka Zdeborová. Inferring the origin of an epidemic with a dynamic message-passing algorithm. Physical Review E, 90(1), 2014. URL https://doi.org/10.1103/PhysRevE.90.012801.
- Lars Lorch, Heiner Kremer, William Trouleau, Stratis Tsirtsis, Aron Szanto, Bernhard Schölkopf, and Manuel Gomez-Rodriguez. Quantifying the effects of contact tracing, testing, and containment, 2020.
- Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng. Nerf: Representing scenes as neural radiance fields for view synthesis. arXiv preprint arXiv:2003.08934, 2020.
- Thomas P. Minka. Expectation propagation for approximate bayesian inference. In Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence, UAI’01, pp. 362–369, San Francisco, CA, USA, 2001. Morgan Kaufmann Publishers Inc. ISBN 1558608001.
- Kevin Murphy. Bayesian contact tracing. "Personal Correspondance.", 2020.
- Kevin P Murphy, Yair Weiss, and Michael I Jordan. Loopy belief propagation for approximate inference: an empirical study. In Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence, pp. 467–475, 1999.
- OpenAI, Marcin Andrychowicz, Bowen Baker, Maciek Chociej, Rafal Jozefowicz, Bob McGrew, Jakub Pachocki, Arthur Petron, Matthias Plappert, Glenn Powell, Alex Ray, Jonas Schneider, Szymon Sidor, Josh Tobin, Peter Welinder, Lilian Weng, and Wojciech Zaremba. Learning dexterous in-hand manipulation, 2018.
- K. Prem, A. Cook, and M. Jit. Projecting social contact matrices in 152 countries using contact surveys and demographic data. PLoS Computational Biology, 13, 2017.
- Nasim Rahaman, Aristide Baratin, Devansh Arpit, Felix Draxler, Min Lin, Fred Hamprecht, Yoshua Bengio, and Aaron Courville. On the spectral bias of neural networks. In International Conference on Machine Learning, pp. 5301–5310. PMLR, 2019.
- A Russell. The rise of coronavirus hate crimes. The New Yorker, 2020.
- Fereshteh Sadeghi and Sergey Levine. Cad2rl: Real single-image flight without a single real image. arXiv preprint arXiv:1611.04201, 2016.
- Victor Garcia Satorras and Max Welling. Neural enhanced belief propagation on factor graphs, 2020.
- Harry Stevens. Why outbreaks like coronavirus spread exponentially, and how to "flatten the curve", 2020. URL https://www.washingtonpost.com/graphics/2020/world/corona-simulator/.
- Latanya Sweeney. K-anonymity: A model for protecting privacy. Int. J. Uncertain. Fuzziness Knowl.Based Syst., 10(5):557–570, October 2002. ISSN 0218-4885. doi: 10.1142/S0218488502001648. URL https://doi.org/10.1142/S0218488502001648.
- Josh Tobin, Rachel Fong, Alex Ray, Jonas Schneider, Wojciech Zaremba, and Pieter Abbeel. Domain randomization for transferring deep neural networks from simulation to the real world, 2017.
- Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need, 2017.
- Robert Verity, Lucy C Okell, Ilaria Dorigatti, Peter Winskill, Charles Whittaker, Natsuko Imai, Gina Cuomo-Dannenburg, Hayley Thompson, Patrick GT Walker, Han Fu, et al. Estimates of the severity of coronavirus disease 2019: a model-based analysis. The Lancet infectious diseases, 2020.
- Alessandro Vespignani, Matteo Chinazzi, Jessica T. Davis, Kunpeng Mu, Ana Pastore y Piontti, Nicole Samay, Xinyue Xiongand Corrado Gioannini, Maria Litvinova, Paolo Milano, Daniela Paolotti, Marco Quaggiotto, Luca Rossi, Michele Tizzoni, and Ivan Vismara. Gleam project. https://www.gleamproject.org/, 2020. Accessed:2020-06-11.
- John Winn and Christopher M Bishop. Variational message passing. Journal of Machine Learning Research, 6(Apr):661–694, 2005.
- Frank Wood, Andrew Warrington, Saeid Naderiparizi, Christian Weilbach, Vaden Masrani, William Harvey, Adam Scibior, Boyan Beronov, and Ali Nasseri. Planning as inference in epidemiological models. arXiv preprint arXiv:2003.13221, 2020.
- BCKlMuniEhzvpQ-ha0jhdksvqdINUAUHA8J9LSr_Dc/edit#gid=0, 2020. Accessed: 2020-06-11.
- Jonathan S. Yedidia, William T. Freeman, and Yair Weiss. Generalized belief propagation. In Proceedings of the 13th International Conference on Neural Information Processing Systems, pp. 668–674, 2000. URL https://dl.acm.org/doi/10.5555/3008751.3008848.
- Under review as a conference paper at ICLR 2021 Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabas Poczos, Russ R Salakhutdinov, and Alexander J Smola. Deep sets. In Advances in neural information processing systems, pp. 3391–3401, 2017.