Solving Markov Chain Problem: Probability of Infection

In summary, this conversation discusses a simple epidemic model where a population of size N consists of infected and susceptible individuals. The probability of transmission is determined by the number of pairs in contact with each other, and a specified susceptible person has a probability of becoming infected based on the number of infected individuals in the population. The solution involves calculating the probability of coming in contact with infected individuals and using a transition probability matrix to determine the number of new infections in the next time period.
  • #1
daneault23
32
0

Homework Statement



Consider the following (simple) epidemic model: A population of size N consists of infected and susceptible individuals. During each time period, each of the N choose 2 possible pairs in the population will come in contact with probability p. If a pair is in contact and one person in the pair is infected and the other susceptible, then the disease will be transmitted to the infected person. Nobody is ever cured of the disease.

If there are k (k < N) infected individuals at time t in the population, what is
the probability that a specified susceptible person will become infected in the
period t->t + 1?

Homework Equations



Don't see any not posted in problem description.

The Attempt at a Solution



Maybe I am making this too hard, but it seems like the answer should be just p. Say there are two people, one infected and one susceptible to the infection. The infected person will always stay infected with probability 1 and the person who is susceptible will become infected with probability p since the probability that they will come in contact with an infected person is just that, p.

A markov chain would look like this I presume, with I=infected, S=susceptible, but not infected

I S
I | 1 0
S | p 1-p
 
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  • #2
Nevermind guys. I think I got it. For every susceptible individual, there will be n-1 individuals, with the chance of coming in contact with any of them is p. If k of these individuals are infected, the probability of the susceptible individuals coming in contact with none of them is (1-p)^k. The probability that the susceptible individual will come in contact with at least 1 of the infected people from time t to time t+1 (and therefore become infected) is 1-(1-p)^k.
 
Last edited:
  • #3
daneault23 said:

Homework Statement



Consider the following (simple) epidemic model: A population of size N consists of infected and susceptible individuals. During each time period, each of the N choose 2 possible pairs in the population will come in contact with probability p. If a pair is in contact and one person in the pair is infected and the other susceptible, then the disease will be transmitted to the infected person. Nobody is ever cured of the disease.

If there are k (k < N) infected individuals at time t in the population, what is
the probability that a specified susceptible person will become infected in the
period t->t + 1?

Homework Equations



Don't see any not posted in problem description.

The Attempt at a Solution



Maybe I am making this too hard, but it seems like the answer should be just p. Say there are two people, one infected and one susceptible to the infection. The infected person will always stay infected with probability 1 and the person who is susceptible will become infected with probability p since the probability that they will come in contact with an infected person is just that, p.

A markov chain would look like this I presume, with I=infected, S=susceptible, but not infected

I S
I | 1 0
S | p 1-p

Start again: your transition probability matrix does not look anything like what the problem description dictates.

You need to worry about the following: if I = N-S are the # infected, how many of the C(N,2) pairs contain no infected individuals? How many contain exactly one infected? How many contain two infected? So, in the next time period, what is the probability distribution of the number of new infections?
 

1. What is a Markov chain?

A Markov chain is a mathematical model used to describe a sequence of events where the probability of each event depends only on the state of the previous event. It is often used to model real-world scenarios involving probability, such as the spread of diseases.

2. How is a Markov chain used to solve the probability of infection?

A Markov chain can be used to calculate the probability of a person becoming infected by a disease by considering the states of being infected or not infected, and the probabilities of transitioning between these states over time.

3. What factors are included in a Markov chain model for infection?

A Markov chain model for infection includes factors such as the probability of exposure to the disease, the probability of transmission, and the probability of recovery or death.

4. How accurate is a Markov chain in predicting the spread of disease?

The accuracy of a Markov chain in predicting the spread of disease depends on the accuracy of the input data and assumptions used in the model. It is important to regularly update and adjust the model as new information becomes available.

5. Can a Markov chain be used to predict the probability of infection in a population?

Yes, a Markov chain can be used to predict the probability of infection in a population by considering the probabilities of transmission and recovery for each individual in the population and how these probabilities change over time.

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