Networks and Dynamics Flashcards
Quiz 3
Dynamics
how we describe infectious disease’ movements, patterns and behaviors over time and geography
System
any group of interacting things that form a whole.
Complicated system
has many interacting parts that need to work correctly in a sequence to reach the desired result. However, each step of the process is completely predictable based on the step before it.
Complex system
the results brought about by the interacting parts of a complex system are not directly predictable
Emergent Properties
behaviors or outputs of a system that arise from 2 or more interacting components that cannot be explained by either of them on their own
Epidemic curve
a graph of the number of cases or incidence rate of cases versus time.
Propagate
travel
Human ecology
how humans interact with and are impacted by their surrounding environment and each other
Node
an individual entity in the network, like a person or a hospital
Edge
What connects two nodes
Random network
a lot of people/things randomly connected to each other with no rhyme or reason
Scale-free network
where some nodes are more highly connected than others
Hubs
nodes that are more connected than others
Synchronous spread
the timing of an epidemic overlapping in multiple locations
Concurrency
meaning individuals having multiple partners at the same time
Compartmental
we are essentially coming up with a fake scenario in which people flow from one “compartment” or stage to another
Deterministic
the model does not worry about random effects, like individual differences in susceptibility. Everyone follows the same rules of probability about what compartment they are in at any given time.
SIR Model
“susceptible, infectious, and recovered.” Each person is either in the S, I or R compartment at any given time. The simplest version of the SIR model doesn’t take into account birth and death.
Stochastic model
A model that takes individuality into account. They are very commonly used and based off of the same mathematical principles as deterministic models. They just use more complicated probability formulas describing whether someone will move into the next compartment, and are not deterministic.
Agent-based models
helpful to study diseases that are very difficult to trace through a network to a particular infecting person (like respiratory diseases). They use computer simulations to estimate the behaviors of a real-world city, region or country. The simulations can help predict what emergent properties might occur as an infectious disease travels.
Ro
the number of infected individuals one person is expected to infect while they are infectious
herd-immunity threshold
the proportion of people that need to immunity in order to eradicate a disease, or prevent an outbreak
hysterisis
A change in status. ex. a new emerging disease can cause an epidemic, and if not eliminated, reach a new stable equilibrium where it is now endemic.
Rt
real-time or effective reproduction. Our goal when trying to prevent infectious diseases is to push Rt below 1, so that the epidemic will die out. In essence, you want to “remove” susceptible or infectious individuals from the population faster than the disease spreads. Vaccines do this very effectively.
