Neural Network Based Country Wise Risk Prediction of COVID-19

Abstract

The recent worldwide outbreak of the novel corona-virus (COVID-19) opened up new challenges to the research community. Artificial intelligence (AI) driven methods can be useful to predict the parameters, risks, and effects of such an epidemic. Such predictions can be helpful to control and prevent the spread of such diseases. The main challenges of applying AI is the small volume of data and the uncertain nature. Here, we propose a shallow Long short-term memory (LSTM) based neural network to predict the risk category of a country. We have used a Bayesian optimization framework to optimized and automatically design country-specific networks. We have combined the trend data and weather data together for the prediction. The results show that the proposed pipeline outperforms against state-of-the-art methods for 170 countries data and can be a useful tool for such risk categorization. The tool can be used to predict long-duration outbreak of such an epidemic such that we can take preventive steps earlier.

Artificial Intelligence (AI) and Big Data for Coronavirus (COVID-19) Pandemic: A Survey on the State-of-the-Arts

“…Applying the MAE model for the surveillance data of the confirmed Covid-19 cases in China [15], the authors also showed high forecasting capabilities of the proposed MAE model for the transmission dynamics and plateau of COVID-19 in China. The authors in [77] proposed combining medical information (e.g., susceptible and dead cases) and local weather data to predict the risk level of the country. Specifically, a shallow long short-term memory (LSTM) neural network is used to overcome the challenges of small dataset, and the risk level (high, medium, and recovering) of a country is classified by using the fuzzy rule.…”

Covid-19 Global Spread Analyzer: An ML-Based Attempt

“…The metrics they are working on include demographics ethnicity, housing, education, employment, income, climate, transit scores, healthcare system-related. To predict the country-specific risk of (COVID-19), a shallow Long Short-Term Memory (LSTM) based neural network optimized using Bayesian optimization presented in (Pal et al, 2020). Observed spread of COVID 19 found to be correlated with climatological temperatures, latitude, travel, population density and sociological trends as pointed out in (Poole, 2020).…”

Time Series Analysis and Forecast of the COVID-19 Pandemic in India using Genetic Programming

“…In order to formulate the GEP model for India, it is really very important to investigate existing models and analyse if the proposed GEP models will be significant enough or not. Various models such as Ace-Mod (Australian Census-based Epidemic Model) [27] , neural network based models [28] and others have been employed to access the situation and provide exact predictions. Though these models are a bit significant but the first AceMod model has been used for influenza prediction [27] and has little relevance to COVID-19.…”