Learning where to look for COVID-19 growth: Multivariate analysis of COVID-19 cases over time using explainable convolution–LSTM

Abstract

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. Moreover, there are education, government, morphological, health, economic, and behavioral factors contributing to the growth of COVID-19. Besides descriptive analysis, in this research, multivariate analysis is considered to provide comprehensive explanations about factors contributing to pandemic dynamics. To achieve rich explanations, visual attribution of explainable Convolution-LSTM is utilized to see high contributing factors responsible for the growth of daily COVID-19 cases. Our model consists of 1 D CNN in the first layer to capture local relationships among variables followed by LSTM layers to capture local dependencies over time. It produces the lowest prediction errors compared to the other existing models. This permits us to employ gradient-based visual attribution for generating saliency maps for each time dimension and variable. These are then used for explaining which variables throughout which period of the interval is contributing for a given time-series prediction, likewise as explaining that during that time intervals were the joint contribution of most vital variables for that prediction. The explanations are useful for stakeholders to make decisions during and post pandemics. The explainable Convolution–LSTMcode is available here: https://github.com/cbasemaster/time-series-attribution .

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