Deep learning via LSTM models for COVID-19 infection forecasting in India

• URL: http://arxiv.org/abs/2101.11881v1
• Date: Thu, 28 Jan 2021 09:19:10 GMT
• Title: Deep learning via LSTM models for COVID-19 infection forecasting in India
• Authors: Rohitash Chandra, Ayush Jain, Divyanshu Singh Chauhan
• Abstract summary: Prominent computational and mathematical models have been unreliable due to the complexity of the spread of infections. Deep learning models such as recurrent neural networks are well suited for modelling temporal sequences. We select states with COVID-19 hotpots in terms of the rate of infections and compare with states where infections have been contained or reached their peak. Our results show that long-term forecasts are promising which motivates the application of the method in other countries or areas.
• Score: 13.163271874039191
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We have entered an era of a pandemic that has shaken the world with major impact to medical systems, economics and agriculture. Prominent computational and mathematical models have been unreliable due to the complexity of the spread of infections. Moreover, lack of data collection and reporting makes any such modelling attempts unreliable. Hence we need to re-look at the situation with the latest data sources and most comprehensive forecasting models. Deep learning models such as recurrent neural networks are well suited for modelling temporal sequences. In this paper, prominent recurrent neural networks, in particular \textit{long short term memory} (LSTMs) networks, bidirectional LSTM, and encoder-decoder LSTM models for multi-step (short-term) forecasting the spread of COVID-infections among selected states in India. We select states with COVID-19 hotpots in terms of the rate of infections and compare with states where infections have been contained or reached their peak and provide two months ahead forecast that shows that cases will slowly decline. Our results show that long-term forecasts are promising which motivates the application of the method in other countries or areas. We note that although we made some progress in forecasting, the challenges in modelling remain due to data and difficulty in capturing factors such as population density, travel logistics, and social aspects such culture and lifestyle.

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