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MRSA in the Community: A Serious New Drug Resistant Bacteria. Supercomputing Challenge Kickoff New Mexico Tech October 12, 2013. Beginning NetLogo 1 strand Irene Lee, Santa Fe Institute Maureen Psaila - Dombrowski , NM- CSforAll w ith Diane Lauderdale, University of Chicago.
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MRSA in the Community: A Serious New Drug Resistant Bacteria Supercomputing Challenge Kickoff New Mexico Tech October 12, 2013 Beginning NetLogo 1 strand Irene Lee, Santa Fe Institute Maureen Psaila-Dombrowski, NM-CSforAll • with Diane Lauderdale, University of Chicago
Today’s Workshop • Slide show introduction to MRSA • Hands-on activity (Toss Up) to learn about how infectious diseases spread • View and deconstruct a NetLogo model for disease spread • Run experiments on NetLogo version of Toss Up • --- SUNDAY --- • Construct a simple contagion model in NetLogo • Run experiments, collect data, look for patterns. • Your role: • Listen, learn about and modify code, run experiments, and give us feedback.
The History of MRSA 1880Staphylococcus aureusfirst identified in Scotland 1959Methicillin licensed in England to treat S. aureus infections 1961S. aureus infections acquire resistance to Methicillin. 1961-1990s MRSA infections in hospitals increased Before 1990s, almost all MRSA cases were among sick patients in the healthcare setting.
New Community-Associated MRSA (CA-MRSA) 1990sScattered reports of MRSA cases and outbreaks among persons w/o healthcare risk factors Outbreaks in sports teams, daycare centers, army bases and other groups 2000s MRSA becomes the most common type of skin infection in the USA CA-MRSA strains are genetically different from the older healthcare strains, affect healthy people and are more likely to cause skin infections.
Typical Skin Infections Often appear as pustules or boils that are red, swollen, painful, and have pus. They may look like spider bites at first.
How does CA-MRSA spread? • Individuals may be colonized with MRSA on their skin or in their nose. • People have no idea whether or not they are colonized, and most colonized people will not develop an infection. Colonization may last a few days or months. • Direct physical contact (such as hugging, holding hands, child care or contact sports) with a colonized or infected person can spread MRSA. • Uncovered skin infections are more likely than colonizations to spread to another person. • In some cases, a skin infection develops where there was an obvious cut or bruise, but not always. • We believe that colonization always precedes infection (although the colonization phase before infection may be quick). • MRSA can also linger on surfaces and spread from person to person if they touch the same item, such as a towel.
How are CA-MRSA Infections Treated? • MRSA-like skin infections should be seen by a health professional • The infection is usually drained, cleaned and covered • Patients are told how to reduce risk of transmission to others (keep it covered and don’t share personal items) • May be treated with an appropriate antibiotic depending on several factors • Without medical care, would in almost all cases still recover, but would take longer and be more likely to infect others
Can you get a CA-MRSA infection more than once? • Some diseases, like measles, give you lifelong immunity so you only can get them once. • Individuals develop resistance • CA-MRSA does not give lifelong immunity and repeat infections are possible. • Individuals remain susceptible
MRSA Transmission INFECTED SUSCEPTIBLE (HEALTHY) COLONIZED
How do we study MRSA? • Lab studies of the bacteria • Determine strain and genetic features • Determine nature of antibiotic resistance • Studies of People • Clinical Trials are experiments that assign people to prevention measures or treatments • Epidemiologic Studies collect data to learn about the distribution and risk factors for disease
Types of Epidemiologic Studies • Compare individuals who become colonized or infected to those who do not • To determine risk factors for MRSA • Track an outbreak • Figure out what happened • Characterize the “natural history” of colonization or infection • How long do individuals remain colonized/infected? • What types of infections? • Risk factors and frequency of repeat infections.
Computer Models to Study MRSA • Carry out experiments that are not practical • Can estimate population-wide impact of changes in risk factors, behaviors or treatments
Next, Toss Up paper based game • We will look at a simple model of contagion. • First, we will consider a model in which infection leads to lifelong immunity. • This is called an SIR model • susceptible-infected-recovered RECOVERED SUSCEPTIBLE (HEALTHY) INFECTED
Let’s look at some code • Show interface of NetLogo Toss Up SIR. • Hand out code • Take a few minutes to decipher the code thinking back on the Toss Up Game and then we will share out. • Then we will run experiments with the NetLogo SIR model.
SIR -> SIS • Moving from SIR to SIS • What do you remember about SIS? What’s an example? • What would need to change in code? • How is that change implemented? • Make a prediction – what is the dynamics of an SIS disease transmission? • Next, we will run experiments with the NetLogo SIS model.
Wrap Up • Diseases become resistant to antibiotics. • Antibiotic resistant bacteria pose a global threat. • Direct analogy between Participatory Simulations and Computer Simulations • Modeling and Simulation can be used to study dynamics of disease spread.
Modeling and Computational Science • A model is a representation of the interaction of real-world objects in a complex system. • The goal is to gain an understanding of how the model’s results relate to real-world phenomena. • Random factors built into the model and variables changed by the user cause different results to be generated when the model is run repeatedly.
Agent-based modeling in NetLogo • The “Observer”–sets up and runs the world • The “Turtles”– the agents in the world • The “Patches” – the places in the world
Agent based modeling phases • Setup– setting up the world • Go / Runtime Loop– the agents put into motion. • Exit
Agent-based modeling Abstractions • Agents with rules • Environment or space in which they exist • Time