Data Visualization of the Spread of a Virus on a Campus
--Thiebaut 15:55, 26 January 2015 (EST)
Lujun Jian's Independent Study Page
- 1 Lujun Jian's Independent Study Page
- 2 Abstract
- 3 Poster
- 4 Coding and the Logic of the Code
- 5 Log of Activities
- 6 Plan of Study for This Semester
- 7 Discoveries from Reading Literature
- 8 References
In this research we study how a disease such as Ebola, or the Measles, would spread on a closed campus such as Smith College, with its 2400 students. We have picked the Measles, as it spreads more easily than Ebola. The main engine for this project is a discrete-even simulation program that runs several hundreds of times, always starting with a random student being infected by a virus, and starting taking classes at the beginning of the semester. We have generated random, synthetic weekly schedules for all the students at Smith. A random schedule consists of the meeting times of 5 randomly picked courses and lab(s), meeting in different academic buildings on campus. We do not attempt to limit the enrollment in any given class. The schedules for all students are kept in a mySQL database. When an infected student enters a classroom, every other student in the room gets infected with a probability p extracted from the susceptible, infected, recovered (SIR) model for the given virus (typically ~1%). A student changing state in the SIR model, for example going from susceptible to infected generates new events in the discrete-event queue. The discrete event-simulations run until the queue is empty, or until the whole population is infected, or until the end of the semester (14 weeks). The statistics for each run is kept in the database, and allows the generation of a heat map of the average spread of the virus in campus dorms as a function of time, or a plot of the growth of the infected population of students as a function of time, for different parameters relating to the virus.
This poster was presented at CCSC-NE 2015, at the College of the Holly Cross, Worcester, MA.
Coding and the Logic of the Code
Go to this page to explore the code of this special study.
Log of Activities
- Define plan for this semester
- Phase 1: research, state of the art.
- Phase 2: review what particular questions need to be answered, and generate plan of attack
- Phase 3: coding + data visualization
- Define schedule (can be refined as semester progresses)
- Identify faculty resources
- R. Dorit
- J. Carris
- Come up with list of questions for faculty resources
- Organize and maintain this wiki throughout the process.
2/5/15 Meeting with Prof. Dorit and Jon Caris
- Disease we want to focus : Ebola/Measles
- Parameters of disease should be concerned
- Basic reproduction rate - (R0): "the number of secondary infections produced by a typical case of an infection in a population" R0 of Ebola 1.7; R0 of Measles 18 (http://www.healthknowledge.org.uk/public-health-textbook/research-methods/1a-epidemiology/epidemic-theory)
- the individual probability of people getting infections (different age, different health condition...)
- incubation period : "the time from the moment of exposure to an infectious agent until signs and symptoms of the disease appear"(http://www.medicinenet.com/script/main/art.asp?articlekey=18956)
- percentage of vaccination in the population
- Can talk to Dr. Jaffe in the health center about more insight of disease
- Map tools
- one card data
- Google API - heat map
- Valuable info we should keep and generate - path of individual spread track -- network about infected individuals
2/17/15 Meeting with Prof. Baumer
- Figure out what Plot would be most useful plot or graph of disease spread
- KML - generating heat map
- Density Plot of number of stimulations versus percentage of infected students
- Look into SIR model
Plan of Study for This Semester
--Ljian 10:33, 27 January 2015 (EST)
- Look in to how Ebola or any virus spread in the campus
- Timeline for research: Identify problem --> Identify 1 or 2 problems to solve --> Coding --> Simulation
- search published researches (get help from librarians)
- talk to human resources (faculty)
- Prof. Robert Dorit
- Identify parameters
- how long contact between 2 people for passing virus
- distance minimal for passing virus (e.g. sitting in a same classroom for an hour, walking to each other, being roommates)
- Identify parameters
- Jon Caris (GIS Specialist)
- tools for mapping dynamically area on a map (heat map- growth of area over map, GIS info for building (GPS)
- Apps for generating traces? (Google?)
- Format of the traces?
- data representation(DPS position + time)
- D.T. (programming)
- mobile app. (QT5 - iphone)
- data visualization
- data base SQL
- simulate students(synthetic traces)
- All houses --> GPS position of all
- All students distributions/houses
- catalog --> information about location of the classrooms / class schedule
- model--> pick a student in a given houses. At random, pick 4 classes from catalog that do not intersect in time. Generate a trace from house - class 1 - class 2 - lunch at random dinning hall/ the one closed to class 2 - class 3 - class 4 - dinner - random evening destination
- Prof. Robert Dorit
Discoveries from Reading Literature
Gallery of Interesting Charts/Data Visualization
Go to this page to see a gallery of interesting charts discovered in the literature.
Papers describing research close to our project
- Visualization and analytics tools for infectious disease epidemiology: A system review, Carroll, et. al, J. of Biomedical Informatics, Vol. 51, pp. 287-298, 2014.
- Towards a Hybrid Agent-based Model for Mosquito Borne Disease, Mniszewski, et. al,2014 Summer Simulation MulticonferenceArticle No. 10, 2014.
- Mobile Agent-Based Approach for Modeling the Epidemics of Communicable Diseases, Saravanan, et. al, Paper of IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 2013.
- Agent-Based Stochastic Simulations of Shipboard Disease Outbreaks, Yu, et. al, SpringSim '10 Proceedings of the 2010 Spring Simulation Multiconference Article No. 123, 2010.
- Integration and visualization of host–pathogen data related to infectious diseases, Driscoll, et. al, Original Paper on Bioinformatics, Vol.27, no.16, pp. 2279-2287, 2011.
- Visualization of Infectious Disease Outbreaks in Routine Practice, Karlsson, et. al, Article of MEDINFO 2013, Vo.192, pp. 697-701, 2013.
Papers about Measles
- Likelihood-based estimation of continuous-time epidemic models from time-series data: application to measles transmission in London, Cauchemez, et. al, J. of The Royal Society: Interface, Vol. 5, pp. 885-897, 2008.
- KEY PARAMETERS OF MEASLES VIRUS PRODUCTION FOR ONCOLYTIC VIROTHERAPY, Weiss, et. al, American Journal of Biochemistry and Biotechnology, Vol. 8(@), pp. 81-98, 2012.
Papers about Ebola
- Transmission of Ebola Viruses: What We Know and What We Do Not Know, Osterholm, et. al, J. of American Society For Microbiology, Vol. 6, no. 2 e00137-15, 2015.
- Exponential Growth in Ebola Outbreak Since May 14, 2014, Hunt, et. al, Article of Complexity, Vol. 20, Issue 2, p.8-11, 2014.