A generalized forecasting solution to enable future insights of COVID-19 at sub-national level resolutions

• Published in: arXiv.org
• Main Authors: Umar Marikkar, Weligampola, Harshana, Perera, Rumali, Hassan, Jameel, Sritharan, Suren, Jayatilaka, Gihan, Godaliyadda, Roshan, Herath, Vijitha, Ekanayake, Parakrama, Ekanayake, Janaka, Rathnayake, Anuruddhika, Dharmaratne, Samath
• Format: Paper
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 21.08.2021
• Subjects: Containment; Coronaviruses; COVID-19; Decision making; Forecasting; Mathematical models; Public health; Smoothing; Threat evaluation; Training
• ISSN: 2331-8422

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.