Understanding the Contact Rate in SIS Epidemic Modelling

  • Thread starter rickywaldron
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In summary, the formula for the probability of the number of infective's increasing in one time step is ∆tβi(N-i)/N, where β is the contact rate, ∆t is the time step, i is the number of infectives, and N is the total number of susceptible's and infective's. The contact rate is a measure of the likelihood of encounters, and the division by N may be related to understanding the concept of probability.
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rickywaldron
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For the probability of the number of infective's increasing in one time step, I found the answer is:
∆tβi(N-i)/N
where β is the contact rate, ∆t is the time step, i is number of infectives,N is total number of susceptible's and infective's

I can't quite see where this is coming from. β is the contact rate, so it makes sense to multiply by i since for more i, more chances of encounters. Then multiplying by N-i makes sense since more susceptible also means more chance of encounters.

But then why the division by N? I think my problem may be in understanding what the "contact rate" actually means
 
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  • #2
rickywaldron said:
But then why the division by N? I think my problem may be in understanding what the "contact rate" actually means
I think it actually has to do with understanding what a probability is. :-p

What is a probability? What does it tell you?

Answering those questions should tell you the answer to your question.
 

Related to Understanding the Contact Rate in SIS Epidemic Modelling

What is SIS Epidemic Modelling?

SIS Epidemic Modelling is a type of mathematical modeling used to study the spread of infectious diseases within a population. It focuses on the susceptible-infected-susceptible (SIS) model, where individuals can become infected and then recover, but are not immune to future infections.

How is SIS Epidemic Modelling used?

SIS Epidemic Modelling is used to understand and predict the spread of infectious diseases in a population. It can help identify potential outbreaks, evaluate the effectiveness of different control measures, and inform public health policies.

What are the key assumptions of SIS Epidemic Modelling?

The key assumptions of SIS Epidemic Modelling include a well-mixed population, homogeneous mixing, and a constant population size. It also assumes that individuals can become infected multiple times and that there is no significant time delay between infection and recovery.

What are the limitations of SIS Epidemic Modelling?

SIS Epidemic Modelling has several limitations, including the assumption of a well-mixed population, which may not accurately reflect real-world populations. It also does not consider demographic factors, such as age and gender, and may not account for behavioral changes in response to an outbreak.

How can SIS Epidemic Modelling be improved?

SIS Epidemic Modelling can be improved by incorporating more complex models, such as SIR (susceptible-infected-recovered) or SEIR (susceptible-exposed-infected-recovered) models, which account for factors like immunity and incubation periods. It can also be improved by using more accurate data and considering the impact of social and behavioral factors on disease spread.

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