Explaining Causal Influence of External Factors on Incidence Rate of Covid-19

Classical susceptible-infected-removed model with constant transmission rate and removal rate may not capture real world dynamics of epidemic due to complex influence of multiple external factors on the spread and spatio-temporal variation of transmission rate. Also, explainability of a model is of prime necessity to understand the influence of multiple factors on transmission rate. Thus, we modified discrete global susceptible-infected-removed model with time-varying transmission rate, recovery rate and multiple spatially local models. We have derived the criteria for disease-free equilibrium within a specific time period. A convolutional LSTM model is created and trained to map multiple spatiotemporal features to transmission rate. The model achieved 8.39% mean absolute percent error in terms of cumulative infection cases in each locality in a region in USA for a 10-day prediction period. Comparison with current state of the art methods reveals performance superiority of the proposed method. A perturbation-based spatio-temporal model interpretation method is proposed which generates spatio-temporal local interpretations. Global interpretations are generated by statistically accumulating the local interpretations. Global interpretations of transmission rate for a region in USA shows consistent positive influence of population density, whereas, temperature and humidity have very minor influence. An experiment with what-if scenario reveals locality specific quick identification of positive cases, rapid isolation and improving healthcare facilities are keys to rapid convergence to disease-free equilibrium. A long-term forecasting of 160 days predicts new infection cases in a region in USA with a median error of 455 cases.