modeling and simulating a disease outbreak by learning a contagion parameter-based model
Various advanced disease-surveillance models have been developed to provide early detection of infectious disease outbreaks and bioterrorist attacks. New methods that increase the overall detection capabilities of these systems can have a broad practical impact. This paper considers the problem of learning the Contagion Parameter (CP) in a black box model involving healthy, sick and contagious individuals. We base our study on a well-established model of contagion that is characterized by certain fixed parameters, some of which are known, while others are assumed unknown. In the modelling process, we assume that the individuals randomly move within a discretized grid, possibly infecting people or getting infected if they come in contact with healthy/sick individuals. In our study, the parameter of interest involves ? which is the probability with which an infected person will transmit the disease to a healthy person. By invoking a novel learning strategy, we show how the CP can be computed using a Training and Testing phase. The results obtained by simulations are very impressive, and are pioneering to the best of our knowledge. The policy-related implications for the contagion control and disease outbreak are also open and very challenging.
disease outbreak policies in epidemiology stochastic point location learning automata