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

• URL: http://arxiv.org/abs/2108.09556v1
• Date: Sat, 21 Aug 2021 17:47:52 GMT
• Title: A generalized forecasting solution to enable future insights of COVID-19 at sub-national level resolutions
• Authors: Umar Marikkar, Harshana Weligampola, Rumali Perera, Jameel Hassan, Suren Sritharan, Gihan Jayatilaka, Roshan Godaliyadda, Vijitha Herath, Parakrama Ekanayake, Janaka Ekanayake, Anuruddhika Rathnayake, Samath Dharmaratne
• Abstract summary: This study aims to predict daily new cases of COVID-19 in regions small enough where containment measures could be locally implemented. 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, and an adaptive loss function whilst training to mitigate the data imbalances seen in epi-curves.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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