  Data scarcity and discontinuity are common occurrences in the healthcare and
epidemiological dataset and often need help in forming an educative decision
and forecasting the upcoming scenario. Often, these data are stored as
monthly/yearly aggregate where the prevalent forecasting tools like
Autoregressive Integrated Moving Average (ARIMA), Seasonal Autoregressive
Integrated Moving Average (SARIMA), and TBATS often fail to provide
satisfactory results. Artificial data synthesis methods have been proven to be
a powerful tool for tackling these challenges. The paper aims to propose a
downscaling data algorithm based on the underlying distribution. Our findings
show that the synthesized data is in agreement with the original data in terms
of trend, seasonality, and residuals, and the synthesized data provides a
stable foothold for the forecasting tools to generate a much more accurate
forecast of the situation.
