Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In the Susceptible–Infectious–Recovered (SIR) model of disease spreading, the time to extinction of the epidemics happens at an intermediate value of the per-contact transmission probability. Too contagious infections burn out fast in the population. Infections that are not contagious enough die out before they spread to a large fraction of people.  characterize how the maximal extinction time in SIR simulations on networks depend on the network structure. For example  find that the average distances in isolated components, ighted by the component size, is a good predictor of the maximal time to extinction. Furthermore, the transmission probability giving the longest outbreaks is larger than, but otherwise seemingly independent of, the epidemic threshold.
['Petter Holme']
I am writing a research paper on a idea Extinction Times of Epidemic Outbreaks in Networks
[{"from": "human", "value": "I am writing a research paper on a idea Extinction Times of Epidemic Outbreaks in Networks"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In the Susceptible\u2013Infectious\u2013Recovered (SIR) model of disease spreading, the time to extinction of the epidemics happens at an intermediate value of the per-contact transmission probability. Too contagious infections burn out fast in the population. Infections that are not contagious enough die out before they spread to a large fraction of people.  characterize how the maximal extinction time in SIR simulations on networks depend on the network structure. For example  find that the average distances in isolated components, ighted by the component size, is a good predictor of the maximal time to extinction. Furthermore, the transmission probability giving the longest outbreaks is larger than, but otherwise seemingly independent of, the epidemic threshold."}]