# Neural Network Based Country Wise Risk Prediction of COVID-19
## 2 Related Works  
We note three communities of the related work: (A) AI in epidemic research, (B) research works on COVID-19, and (C) multivariate regression in AI. These are discussed below:  
(A) AI based epidemic researches: Real-time epidemic-forecasting attracts several researchers due to the emerging applicability of the method. [PERSON] et al. [11] proposed a neural network for predicting the outbreak of hand-foot-mouth diseases. [PERSON] et al. [12] used ML algorithms to predict spatio-temporal epidemic spread of pathological diseases. AI tools for predicting outbreak in cardiovascular diseases [13; 14], influenza [15], and epidemic diarrhea [16] is also proposed. A review of the application of AI for such a prediction is reported in [17]. A collective learning based approach [18] is proposed to identify individual risk. In the last few years, machine learning analysis was used to predict epidemiological characteristics of the Ebola virus (EBOV) outbreak in West Africa [19] and the risk of Nipah virus [20]. [PERSON] et al. [21] proposed a surveillance method to monitor Nipah virus in India. Recently, [PERSON] et al. [22] proposed a method for predicting future Rift Valley fever virus outbreaks. The majority of the algorithms use a combined decision-making application using statistical and machine learning methods to predict future growth based on past incident data. Figure 1: Artificial intelligence (AI) method for trend prediction and applications of such prediction. (B) Researches on COVID-19: The recent COVID-19 outbreak has motivated many researchers to help and find a way to recover from the pandemic. [PERSON] et al. [23] proposed methods to detect COVID-19 patients using a mobile phone. [PERSON] et al. [24] built a predictive model to identify early detection of high-risk patients before their health status is transformed from mild to critically ill. In recent times, numerous research articles have been published on epidemic prediction of the coronavirus pandemic [25; 26; 27; 28; 29; 30; 31; 32; 33; 34]. Researchers designed new paradigms of AI-driven tools [35; 36] that combine ML algorithms and different modalities of data. An improved adaptive neuro-fuzzy inference system (ANFIS) methodology is proposed in [37]. The algorithm is based on an enhanced flower pollination algorithm (FPA) by using the salp swarm algorithm (SSA) to estimate confirmed cases in the next 10 days. [PERSON] et al. [38] developed a regression model to calculate the exponential growth of COVID-19 infection based on the total number of daily diagnoses cases outside China. Analysts in [39] obtained projections from 10 familiar machine learning and statistical ecological niche models against the large-scale climatology variation. (C) Multivariate Regression in AI: The key point in time series study [40] is forecasting. Time series analysis for business prediction helps to forecast the probable future values of a practical field in the industry [41; 42; 43; 44]. The method is also applicable in the health domain to predict the health condition of a person on the last diagnosis data [45]. The method uses a feature attention mechanism to predict future health risks. [PERSON] et al. [46] use a combination of convolutional neural network (CNN) and LSTM for automated diagnosis of arrhythmia. The input electrocardiogram signal is processed using CNN and processed using LSTM to handle variable length signal. A multiple regression predictive model [47] was used to predict patient volume in the hospital emergency departments. The authors used Google trend for forecasting. Other health areas such as antibiotic resistance outbreaks [48] and influenza outbreaks [49; 50] utilized multivariate regression models. Different algorithms such as deep neural network [51; 52], long short-term memory model (LSTM) [53] and gated recurrent unit (GRU)-based model [54] have been successfully applied in various forecasts.