  COVID-19 continues to cause a significant impact on public health. To
minimize this impact, policy makers undertake containment measures that
however, when carried out disproportionately to the actual threat, as a result
if errorneous threat assessment, cause undesirable long-term socio-economic
complications. In addition, macro-level or national level decision making fails
to consider the localized sensitivities in small regions. Hence, the need
arises for region-wise threat assessments that provide insights on the
behaviour of COVID-19 through time, enabled through accurate forecasts. In this
study, a forecasting solution is proposed, to predict daily new cases of
COVID-19 in regions small enough where containment measures could be locally
implemented, by targeting three main shortcomings that exist in literature; the
unreliability of existing data caused by inconsistent testing patterns in
smaller regions, weak deploy-ability of forecasting models towards predicting
cases in previously unseen regions, and model training biases caused by the
imbalanced nature of data in COVID-19 epi-curves. Hence, the contributions of
this study are three-fold; an optimized smoothing technique to smoothen less
deterministic epi-curves based on epidemiological dynamics of that region, a
Long-Short-Term-Memory (LSTM) based forecasting model trained using data from
select regions to create a representative and diverse training set that
maximizes deploy-ability in regions with lack of historical data, and an
adaptive loss function whilst training to mitigate the data imbalances seen in
epi-curves. The proposed smoothing technique, the generalized training strategy
and the adaptive loss function largely increased the overall accuracy of the
forecast, which enables efficient containment measures at a more localized
micro-level.
