"Forecasting and Surveillance of Covid-19 Pandemic: A Case Study Using Artificial Neural Network "

Abstract

Outbreak of Covid-19 and related its progression across the extensive landscape around the globe affecting a humongous extent of population at various geographical locales have posed a state of pandemic, hard to quantify of its extensiveness, temporal progression, and spatial proliferation. Psychological stress and panic syndrome caused by the virility of Corona virus have posed a query on the uncertainty aspects of its spatiotemporal spreading in human society. Assessing the associated etiology, diagnostic schemes, therapeutic regimens, and prevention methods etc. is explicitly vital and obviously needs quantifiable trends or forecasting techniques. It goes without saying that such schemes have great importance in present situation and as such, conceived and elaborated in this study is a strategy towards forecasting the temporal trend of the disease-progression on ex post basis using the observed ex ante details word wide from the onset of pandemic outbreak of Covid-19. The associated stochastic modelling of the proposed forecast method is based on artificial neural network (ANN) with feasible extensions of deep-learning heuristics. This paper outlines the challenges involved thereof in performing forecasting of widely spread outbreaks specified in vagaries of timeslots and across ensembles of incidence plus mortalities. The underlying state of evolution is modelled using ergodic profiles of pandemic state of the disease in question; in all, the scope of this study is to forecast temporal progression of Covid-19 pandemic in two chosen countries via a fast-Forecasting …