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CSE 8803 EPI, Fall 2021
Data Science for Epidemiology
Lectures and Readings
08/24: Course Logistics
[SLIDES]
08/26: No lecture
08/31: Introduction
[SLIDES]
[SCRIBE-NOTES]
Readings:
Tina Rosenberg.
Stopping Pandemics Before they Start
, New York Times, June 2017.
Optional Readings:
Ed Young.
The next plauge is coming: Is America ready?
, The Atlantic, July/Aug 2018.
09/02: Models (I)
[SLIDES]
[SCRIBE-NOTES]
Readings:
H. Hethcote. Sections 1-2.4.
The mathematics of infectious diseases
, SIAM Review, 2000.
Optional Readings:
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.
09/07: Models (II)
[SLIDES]
[SCRIBE-NOTES]
Readings:
Chapter 21 from Easley and Kleinberg:
Epidemics
.
Optional Readings:
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
09/09 and 09/14: Other Contagion Models
[SLIDES]
[SCRIBE-NOTES]
Readings:
David Kempe, Jon Kleinberg, and Eva Tardos.
Maximizing the spread of influence through a social network
. SIGKDD 2003.
Optional Readings:
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.
S. Myers and J. Leskovec.
Clash of the Contagions: Cooperation and Competition in Information Diffusion.
ICDM 2012.
A. Beutel, B. A. Prakash, R. Rosenfeld and C. Faloutsos.
Interacting Viruses in Networks: Can Both Survive?
. SIGKDD 2012.
09/16: Dynamics of Models
[SLIDES]
[SCRIBE-NOTES]
Readings:
TBD
Optional Readings:
TBD
09/21: Network Construction
[SLIDES]
[SCRIBE-NOTES]
Readings:
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.
Optional Readings:
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.
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.
09/23: Reverse Engineering and Inference I
[SLIDES]
[SCRIBE-NOTES]
Readings:
Optional Readings:
09/28: Reverse Engineering and Inference II
[SLIDES]
[SCRIBE-NOTES]
Readings:
Optional Readings:
09/30: Class Canceled
10/05: Outbreak Detection I
[SLIDES]
[SCRIBE-NOTES]
Readings:
N. Christakis and J. Fowler.
Social Network Sensors for Early Detection of Contagious Outbreaks
. PLoS One 2010.
Optional Readings:
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.
10/07: Outbreak Detection II
[SLIDES]
[SCRIBE-NOTES]
Readings:
A. Krause and D. Golovin. Sections 1 and 2.
Submodular Function Maximization
. Survey, in Practical Algorithms, 2014.
Optional Readings:
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.
Daniel Neill.
Subset Scanning for Event and Pattern Detection
. Survey in Encyclopedia of Geographic Systems. 2017
10/12: Class Canceled (Fall Break)
10/14: Class Canceled
10/19: Surveillance I
[SLIDES]
[SCRIBE-NOTES]
10/21: Guest Lecture by Shihao Yang (Georgia Tech)
[SLIDES]
10/26: Surveillance II
[SLIDES]
[SCRIBE-NOTES]
10/28: Surveillance III
[SLIDES]
[SCRIBE-NOTES]
11/02: Deep Learning Approaches to Forecasting
[SLIDES]
[SCRIBE-NOTES]
11/04: Guest Lecture by Bin Yu (UC Berkeley)
[SLIDES]
11/09: COVID-19 Forecasting
[SLIDES]
[SCRIBE-NOTES]
11/11: Immunization (I)
[SLIDES]
[SCRIBE-NOTES]
11/16: Immunization (II)
[SLIDES]
[SCRIBE-NOTES]