This paper describes how information, taken from social media and public domain knowledge, such as Twitter, can be
useful in healthcare and public health management – it describes our proposed technique of: i) collecting tweets with the information
about the symptoms users suffer from; ii) filtering them; and iii) applying Dempster-Shafer theory, which deals with uncertainty, for
associating the most probable disease with the given symptoms. Additionally, location-related information taken from the tweets or user
profiles, using Twitter API, helps health care analysts and planners to identify the regions where the disease could potentially erupt as
an epidemic. When this information is superimposed on a geographical map at the local, provincial, national, or global level, to create
a heat-map, the resulting GIS tool can help public health specialists, we believe, to arrive at better pre-emptive strategies to tackle such
epidemics before they become pandemics, e.g., carry out a selective vaccination program, or a cull of the birds or animals that are the
source or vectors of the zoonotic disease, and so on.