CSE 8803 EPI, Fall 2022
Data Science for Epidemiology
Data Science for Epidemiology
Lectures and Readings
- Tina Rosenberg. Stopping Pandemics Before they Start, New York Times, June 2017.
- Ed Young. The next plauge is coming: Is America ready?, The Atlantic, July/Aug 2018.
- H. Hethcote. Sections 1-2.4. The mathematics of infectious diseases, SIAM Review, 2000.
- Ina Holmdahl and Caroline Buckee. Wrong but Useful — What Covid-19 Epidemiologic Models Can and Cannot Tell Us, The New England Journal of Medicine, July 2020.
- Simon Frost, Epidemiological Models Interactive Example (try it out!): SIR in Python using scipy, Accessed Aug 2020.
- Another Interactive COVID-19 SEIR model (try it out!): Modeling COVID-19 Spread vs Healthcare Capacity, Accessed Aug 2020.
- (Details about Bernoulli's model) M. Glomski and E. Ohanian. Eradicating a Disease: Lessons from Mathematical Epidemiology. College Mathematics Journal. 2012.
- Chapter 21 from Easley and Kleinberg: Epidemics.
- Stephen Eubank, Hasan Guclu, V.S. Anil Kumar, Madhav Marathe, Aravind Srinivasan, Zoltan Toroczkai and Nan Wang. Modeling disease outbreaks in realistic urban social networks. Nature. 2004.
- D. Balcan, V. Colizza, B. Goncalves, H. Hu, J. Ramasco and A. Vespignani. Multiscale mobility networks and the spatial spreading of infectious diseases. PNAS 2009.
- L. Pellis, F. Ball, S. Bansal, K. Eames, T. House, V. Isham and P. Trapman. Eight challenges for network epidemic models. Epidemics 2015
- C. T. Kelley. Iterative methods for Optimization. SIAM 1999.
- David Kempe, Jon Kleinberg, and Eva Tardos. Maximizing the spread of influence through a social network. SIGKDD 2003.
- L. Pelley. Why COVID-19 and flu could be in a 'tug of war' in the years ahead. CBC 2022.
- Joao Gama Oliveira and Albert-Lazlo Barabasi. Darwin and Einstein correspondence patterns . Nature, 2005.
- P. S. Dodds and D. J. Watts. Universal behavior in a generalized model of contagion. PRL 2004.
- Y. Matsubara, Y. Sakurai, B. A. Prakash, L. Li, C. Faloutsos. Rise and Fall Patterns of Information Diffusion: Model and Implications. SIGKDD, 2012.
- D. Romero, B. Meeder, J. Kleinberg. Differences in the Mechanics of Information Diffusion Across Topics: Idioms, Political Hashtags, and Complex Contagion on Twitter. WWW, 2011.
- The Lotka-Volterra Model. Wikipedia.
- A. Beutel, B. A. Prakash, R. Rosenfeld and C. Faloutsos. Interacting Viruses in Networks: Can Both Survive?. SIGKDD 2012.
- S. Nickbakhsh et al. Virus–virus interactions impact the population dynamics of influenza and the common cold. PNAS 2019.
- R. Baker et al. The impact of COVID-19 nonpharmaceutical interventions on the future dynamics of endemic infections. PNAS 2020.
- B. A. Prakash et al. Threshold Conditions for Arbitrary Cascade Models on Arbitrary Networks. ICDM 2011.
- R. Pastor-Santorras and A. Vespignani. Epidemic spreading in scale-free networks. Physical Review Letters 86, 14, 2001.
- A. Ganesh et al. The effect of network topology on the spread of epidemics , INFOCOM 2005.
- B. Grenfell et al. Travelling waves and spatial hierarchies in measles epidemics. Nature 2001.
- N. C. Grassly et al. Host immunity and synchronized epidemics of syphilis across the United States Nature 2005
- B. A. Prakash et al. Winner-takes-all: Competing Viruses on fair-play networks. WWW 2012
- Chapter 21 from Easley and Kleinberg: Epidemics. (See Sec 21.5)
- Madhav Marathe and Anil Vullikanti. Computational Epidemiology, CACM 2013
- N. Dimitrov and L. Meyers. Section 5. Mathematical Approaches to Infectious Disease Prediction and Control, INFORMS 2010.
- V. Das Swain, J. Xie et al. WiFi mobility models for COVID-19 enable less burdensome and more localized interventions for university campuses. MedArxiv 2021.
- S. Pai et al. Spatiotemporal clustering of in-hospital Clostridioides difficile infection. Infection Control and Hospital Epi. 2020
- A. Aleta et al. Modelling the impact of testing, contact tracing and household quarantine on second waves of COVID-19. Nature Human Behavior 2020.
- K. Eames, S. Bansal, S. Frost and S. Riley. Six challenges in measuring contact networks for use in modelling. Epidemics, 2015.
- P. S. Bearman, J. Moody, and K. Stoval. Chains of affection: The structure of adolescent romantic and sexual networks AJS 2004.
- P. Sapiezynski, A. Stopczynski, D. D. Lassen, and S. Lehmann Interaction data from the copenhagen networks study. Nature Scientific Data, 2019.
- A. Hess, K. A. Hummel, W. N. Gansterer, G. Haring. Data-driven Human Mobility Modeling: A Survey and Engineering Guidance for Mobile Networking. ACM Computing Surveys, 2016.
- B. A. Prakash, J. Vreeken and C. Faloutsos. Spotting Culprits in Epidemics: How many and Which ones? IEEE ICDM 2012
- D. Shah and T. Zaman. Rumors in a Network: Who’s the Culprit?. IEEE ToIT 2011.
- P. Rozhenshtein et al. Reconstructing an Epidemic over Time. ACM SIGKDD 2016
- N. Christakis and J. Fowler. Social Network Sensors for Early Detection of Contagious Outbreaks. PLoS One 2010.
- H. Shao et al. Forecasting the Flu: Designing Social Network Sensors for Epidemics. SIGKDD epiDAMIK 2018.
- J. Leskovec, A. Krause, C. Guestrin, C. Faloutsos, J. VanBriesen, N. Glance. Cost-effective Outbreak Detection in Networks. SIGKDD, 2007.
- A. Krause and D. Golovin. Sections 1 and 2. Submodular Function Maximization. Survey, in Practical Algorithms, 2014.
- Bijaya Adhikari, B. Lewis, A. Vullikanti, J. M. Jimenez, and B. A. Prakash. Fast and Near-Optimal Monitoring for Healthcare Acquired Infection Outbreaks. PLoS Computational Biology 2019.
- H. Bastani et al. Efficient and Targeted COVID-19 Border Testing via Reinforcement Learning. Nature 2021.
- Daniel Neill. Subset Scanning for Event and Pattern Detection. Survey in Encyclopedia of Geographic Systems. 2017
- J. Pandit et al. Smartphone apps in the COVID-19 pandemic. Nature Biotechnology 2022.
- A. Rodriguez et al. See Section 2. Data-Centric Epidemic Forecasting: A Survey. Arxiv 2022.
- S. Bavadekar et al. Google COVID-19 search trends symptoms dataset: Anonymization process description (version 1.0). Arxiv 2020.
- C. M. Astley et al. Global monitoring of the impact of the COVID-19 pandemic through online surveys sampled from the Facebook user base.. PNAS 2022.
- J. Peccia et al. Measurement of SARS-CoV-2 RNA in wastewater tracks community infection dynamics. Nature Biotechnology 2020.
- J. Ginsberg et al. Detecting influenza epidemics using search engine query data. Nature 2009.
- D. Lazer et al. The Parable of Google Flu: Traps in Big Data Analysis. Science 2013
- A. Sadelik et al. Machine-learned epidemiology: real-time detection of foodborne illness at scale. Nature Digital Medicine 2018.
- L. Chen et al. Flu Gone Viral: Syndromic Surveillance of Flu on Twitter using Temporal Topic Models. IEEE ICDM 2014.
- V. Lampos et al. Tracking COVID-19 using online search. Nature Digital Medicine 2021.
- A. Rodriguez et al. See Section 3. Data-Centric Epidemic Forecasting: A Survey. Arxiv 2022.
- S. Yang et al. Accurate estimation of influenza epidemics using google search data via argo. PNAS 2015.
- Lecture notes from GT CS 4803/7643 course. See Lectures W7 and W8 for an intro to RNNs and LSTMs. Here .
- A. Rodriguez et al. See Section 6. Data-Centric Epidemic Forecasting: A Survey. Arxiv 2022.
- H. Kamarthi et al. When in Doubt: Neural Non-Parametric Uncertainty Quantification for Epidemic Forecasting NeurIPS 2021.
- L. Brooks et al. Nonmechanistic forecasts of seasonal influenza with iterative one-week-ahead distributions. PLoS Comp Bio. 2018.
- B. Adhikari et al. Epideep: Exploiting embeddings for epidemic forecasting. SIGKDD 2019.
- A. Rodriguez et al. See Section 7. Data-Centric Epidemic Forecasting: A Survey. Arxiv 2022.
- H. Kamarthi et al. Back2Future: Leveraging Backfill Dynamics for Improving Real-time Predictions in Future. ICLR 2022
- S. Arik et al. Interpretable Sequence Learning for COVID-19 Forecasting. NeurIPS 2020.
- J. Shaman and A. Karspeck. Forecasting seasonal outbreaks of influenza. PNAS 2012
- D. Osthus and K. R. Moran. Multiscale influenza forecasting Nature Communications 2021
- A. Rodriguez et al. See Section 8-9. Data-Centric Epidemic Forecasting: A Survey. Arxiv 2022.
- N. Reich et al. A collaborative multiyear, multimodel assessment of seasonal influenza forecasting in the United States.. PNAS 2019.
- E. Y. Cramer et al. Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States PNAS 2022
- A. Rodriguez et al. Deepcovid: An operational deep learning-driven framework for explainable real-time covid-19 forecasting. AAAI 2021
- J. Medlock and A. Galvani. Optimizing Influenza Vaccine Distribution . Science 2009.
- K. Bubar et al. Model-informed COVID-19 vaccine prioritization strategies by age and serostatus.. Science 2021.
- S. M. Moghadas et al. The implications of silent transmission for the control of COVID-19 outbreaks . PNAS 2020.
- J. Chen et al. Effective Social Network-based Allocation of COVID-19 Vaccines. SIGKDD 2022.
- M. P. Kain et al. Chopping the tail: How preventing superspreading can help to maintain COVID-19 control. Epidemics 2021.
- H. Tong et al. Gelling, and Melting, Large Graphs through Edge Manipulation. CIKM 2012.
- S. Saha et al. Approximation Algorithms for Reducing the Spectral Radius to control Epidemic Spread. SDM 2015.
- P. Sambaturu et al Designing Near-Optimal Temporal Interventions to Contain Epidemics. AAMAS 2020.
- A. Mate et al Field Study in Deploying Restless Multi-Armed Bandits: Assisting Non-profits in Improving Maternal and Child Health . AAAI 2022.
- A. Biswas et al. Learn to Intervene: An Adaptive Learning Policy for Restless Bandits in Application to Preventive Healthcare. AAAI 2021.
- J. Aspnes et al. Inoculation strategies for victims of viruses and the sum-of-squares partition problem SODA 2005
- V. Mhasawade et al. Machine learning and algorithmic fairness in public and population health Nature Machine Intelligence 2021
- S. Barocas, M. Hardt, A. Narayanan. Fairness and Machine Learning.
- T. C. Tsai et al. Algorithmic fairness in pandemic forecasting: lessons from COVID-19. Nature Digital Medicine 2022