Learning Where to Look for COVID-19 Growth: Multivariate Analysis of COVID-19 Cases Over Time using Explainable Convolution-LSTM

Yudistira, N.; Sumitro, S. B.; Nahas, A. C.; Riama, N. F.

Determinant factors which contribute to the prediction should take into account multivariate analysis for capturing coarse-to-fine contextual information. From the preliminary descriptive analysis, it shows that environmental factor such as UV (ultraviolet) is one of the essential factors that should be considered to observe the COVID-19 epidemic drivers, During summer, UV can inactivate viruses that live in the air and on the surface of the objects especially at noon in tropical or subtropical countries. However, it may not be significant in closed spaces like workspace and areas with the intensive human-to-human transmission, especially in densely populated areas. Different COVID-19 pandemic growth patterns in northern subtropical, southern subtropical and tropical countries occur over time. Moreover, there are education, government, morphological, health, economic, and behavioral factors contributing to the growth of COVID-19. Multivariate analysis via visual attribution of explainable Convolution- LSTM is utilized to see high contributing factors responsible for the growth of daily COVID-19 cases. For future works, data should be more detail in terms of region sample and more time. For future works, data to be analyzed should be more detailed in terms of the region and the period where the time-series sample is acquired. The explainable Convolution-LSTM code is available here: https://github.com/cbasemaster/time-series-attribution