Forecasting COVID-19 cases: A comparative analysis between Recurrent and Convolutional Neural Networks
Open Access
- 30 November 2020
- preprint content
- research article
- Published by Cold Spring Harbor Laboratory in medRxiv
Abstract
When the entire world is waiting restlessly for a safe and effective COVID-19 vaccine that could soon become a reality, numerous countries around the globe are grappling with unprecedented surges of new COVID-19 cases. As the number of new cases is skyrocketing, pandemic fatigue and public apathy towards different intervention strategies are posing new challenges to the government officials to combat the pandemic. Henceforth, it is indispensable for the government officials to understand the future dynamics of COVID-19 flawlessly in order to develop strategic preparedness and resilient response planning. In light of the above circumstances, probable future outbreak scenarios in Bangladesh, Brazil, India, Russia and the United kingdom have been sketched in this study with the help of four deep learning models: long short term memory (LSTM), gated recurrent unit (GRU), convolutional neural network (CNN) and multivariate convolutional neural network (MCNN). In our analysis, CNN algorithm has outperformed other deep learning models in terms of validation accuracy and forecasting consistency. Importantly, CNN model showed clear indications of imminent second wave of COVID-19 in the above-mentioned countries. It has been unearthed in our study that CNN can provide robust long term forecasting results in time series analysis due to its capability of essential features learning, distortion invariance and temporal dependence learning. However, the prediction accuracy of LSTM algorithm has been found to be poor as it tries to discover seasonality and periodic intervals from any time series dataset, which were absent in our studied countries. Our results enlighten some surprising results that the studied countries are going to witness dreadful consequences of second wave of COVID-19 in near future. Quick responses from government officials and public health experts are required in order to mitigate the future burden of the pandemic in the above-mentioned countries.Keywords
This publication has 20 references indexed in Scilit:
- Projections and fractional dynamics of COVID-19 with optimal control analysisPublished by Cold Spring Harbor Laboratory ,2020
- Forecasting of COVID-19 pandemic: From integer derivatives to fractional derivativesChaos, Solitons, and Fractals, 2020
- Lockdown-type measures look effective against covid-19BMJ, 2020
- Forecasting COVID-19 pandemic: A data-driven analysisChaos, Solitons, and Fractals, 2020
- Effectiveness of isolation, testing, contact tracing, and physical distancing on reducing transmission of SARS-CoV-2 in different settings: a mathematical modelling studyThe Lancet Infectious Diseases, 2020
- Mathematical assessment of the impact of non-pharmaceutical interventions on curtailing the 2019 novel CoronavirusMathematical Biosciences, 2020
- Can mathematical modelling solve the current Covid-19 crisis?BMC Public Health, 2020
- To mask or not to mask: Modeling the potential for face mask use by the general public to curtail the COVID-19 pandemicInfectious Disease Modelling, 2020
- A conceptual model for the coronavirus disease 2019 (COVID-19) outbreak in Wuhan, China with individual reaction and governmental actionInternational Journal of Infectious Diseases, 2020
- An updated estimation of the risk of transmission of the novel coronavirus (2019-nCov)Infectious Disease Modelling, 2020