The main reason which makes epidemics so dangerous and difficult to contain is their highly infectious nature. In the case of Covid-19 also, data shows that its contagious nature is increasing along with its various mutant strains. One of the primary methods adopted to fight the pandemic has been to break the infection chain and thus reduce the rate of persons getting infected every day, through lockdowns, self-isolation, social distancing, and other measures. But although there are already many existing epidemic models, to predict and track the spread of the disease, it is evident from the difference in the rates of infection and fatalities in different countries, that a uniform set of parameters is not sufficient to accurately predict the curves. In this paper, we have suggested some additional benchmarks that could be considered and at a higher granularity for more accurate predictions at more local levels. We also propose an IoT-based framework for the collection of such types of data through smartphones for more consolidated information to be made available to the authorities, for the effective management of epidemics. The framework also issues warnings to other users through smartphones if the app detects the presence of a potentially infected person within close range.