# Neural Network Based Country Wise Risk Prediction of COVID-19
## 1 Introduction  
The novel coronavirus (COVID-19) hit our blue planet and became an ongoing global pandemic [1]. In a little over six months since the virus was first spotted in mainland China, it has spread to more than 180 countries, infected more than 18.4 million people, and taken more than 692,000 lives as reported in the first week of August 2020. As governments and health organizations scramble to contain the spread of coronavirus, they need all the help they can get, including from artificial intelligence (AI). Though the current AI technologies are far from replicating human intelligence, they are proving to be helpful in tracking the outbreak, diagnosing patients, disinfecting areas, and speeding up the process of finding a cure for COVID-19. Forecasting is a collection of quantitative, probabilistic statements based on historical observation. It is a process of predicting unobserved events and trends. Population health monitoring and forecasting, including epidemiological outbreaks may not have any clinical utility but can be useful tool for planning, decision making and prevention (see Figure 1).  
The early prediction of epidemics will benefit governments and health-care departments to enable a timely response to outbreaks. It will minimize the impact and ensure the use of resources in a planed manner. For many contagious diseases, location specific prediction of the trend is useful to minimize the risk of spread at community level by limiting social gatherings and imposing travel restrictions. Similarly, strategy makers utilize infectious disease forecasts towards prepare medical and economic preparedness. The forecasting of social impact [2] is also useful for long-term strategy makers.  
In the last few years, AI methods have been successfully applied to various predictive tasks such as stock value [3], sales [4] and weather [5] prediction, as well as predicting epidemic spread [6]. Prediction techniques also proved to be useful in many healthcare applications. [PERSON] et al. [7] shows how the prediction of ambulance demand provides great value to emergency service providers.  
In this paper, we have proposed an AI-guided method to predict long-term country-specific risk of coronavirus. The primary challenges of this problem are:  
Small dataset: Majority of machine learning (ML) algorithms demand a large volume of data for training. Notably, the COVID-19 dataset is less than a year-long and it is difficult to design accurate AI methods to train on such small volume of data.  
Uncertain data: The virus is quite new to the researchers and the majority of the parameters that can be used to predict the outbreak and risk factors are unknown. It is observed that the trend is also different in different countries. Hence, a generic AI tool may not be suitable for tracking all trends. It is also noted state-of-the-art deep neural networks fail because of the uncertainty in the data. This observation encourages us to design shallow and country-specific optimized neural networks.  
Data fusion: In many articles, it is claimed that the weather has a role in the outbreak of the virus. Most of the research works are shown in a country-specific manner [8; 9; 10]. A systematic analysis of worldwide different weather parameters and the outbreak status can be useful for better understanding the relation.  
Here, we have proposed to use the local data trend with a shallow Long Short-Term Memory (LSTM) based neural network combined with a fuzzy rule based system to predict long term risk of a country (Figure 1). The country-specific neural networks are optimized using Bayesian optimization.