Disease data can be analyzed in context of spatio-temporal data as it includes both spatial and temporal information. A unique quality of ST data that differentiates it from other data studied in classical data-mining literature is the presence of dependencies among measurements induced by the spatial and temporal dimensions. For example, many of the widely used data-mining methods are founded on the assumption that data instances are independent and identically distributed (i.i.d.). However, this assumption is violated when dealing with ST data, where instances are structurally related to each other in the context of space and time and show varying properties in different spatial regions and time periods. Ignoring these dependencies during data analysis can lead to poor accuracy and interpretability of results.  This research project aims at exploring predictive capabilities of multiple forecasting models using positive corona cases in multiple districts of Punjab, Pakistan.  Better forecasting models directly impact public health policies, lockdown rules, vaccination policies and social lives in general. The evaluations are useful to understand the behavioral aspects  influencing the frequency, peak and duration of disease outbreaks, that will allow better intervention strategies in order to prevent them or minimize the disease impact.