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Previous studies have ignored this characteristic of information epidemics.
Although there are a lot of studies on preventing the spread of disease and computer viruses in human and computer networks through optimal control [23, 24], information epidemics have attracted less attention (see for example [25]).
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A major difference between an information epidemic and a biological epidemic is that in the case of a biological epidemic, the infection rate, and recovery rate are constant throughout the season.
Existing regulations allow the organisation to take information about epidemics only from official government notifications.
With advances in telecommunication, newer systems such as the telegraph were used to transmit medical information about epidemics and war casualties.
Persons in Biratnagar were given information regarding epidemics and ways to contain them.
Internet surveillance networks provided information on epidemics in real time, alerting clinicians in the industrialized world to the spread of CHIKV and enabling them to more easily diagnose infection among travelers with fevers (7 ).
Information dissemination epidemic models have been developed for different network topologies, e.g., wireless networks [18], social networks [8] and multiple social networks [3], and generalized networks [1].
Also, in [16], there is an implicit introduction (i.e., via the time-varying state of the system) of time-varying behavior on the parameters of the information diffusion epidemic model.
It is however unclear how much co-presence data can inform us on actual face-to-face contacts, of particular interest to study the structure of a population in social groups or for use in data-driven models of information or epidemic spreading processes.
This model provides the strength of information for epidemic alert and captures the uncertainty inherent in prediction.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com