# A Machine Learning-Based Model for Epidemic Forecasting and Faster Drug Discovery
## 2 Methods
* Neural Networks (NNs)  
Neural networks (NNs) are complex methods developed to mimic the flow and learning of information in the human brain. These methods are extremely promising because they improve on any given scheme by combining multiple algorithms at the same time. In NNs, input characteristics are fed to an input level, and predictions are generated from an output level after a number of nonlinear transformations using hidden levels. This method most often uses the reverse propagation of errors, resulting in a gradual reduction of the price difference between received and expected. If there is only one output node, then you consider the network to be a NN [26]. Back-propagation is a popular technical approach, while it is one of the most extensively used learning algorithms in epidemic prediction. Given the fact that accurate forecasts help in the informed judgment for preventive intervention, in healthcare and epidemic control, this goal can simply be achieved using appropriate techniques and methodologies. As much as predictive accuracy is imperative, model selection methods and types are vital to predict accuracy. The selection of the artificial neural network (ANN) methodology is essential for epidemic predictions with great accuracy. An ANN model is a mathematical evolutionary method of problem solving that provides solutions based on the concept of organization for transmitting signals to the human nervous system. It is a customizable processing model that receives arbitrary input and produces the result based on non-linear relationships between variables and parameters [27]. In the medical field, ANN has been applicable for clinical diagnosis, image evaluation and interpretation, and drug discovery. In epidemiology, ANN has effectively been used to examine the dynamics, growth, risk and control of infectious viruses and diseases. In epidemic forecasting, all layers and nodes of the network architecture are reconnected; it means that each neuron in one layer is connected to all neurons in the following layer. The ANN architecture for predicting an epidemic depends on the problem, based on the variables and the parameters of the problem studied. In most epidemic predictions, NN structures are in the type of multilevel network architecture that connects the perceptron promotion. The interaction of causation, disease transmission, and control aspects in epidemic expansion tends to warn of the choice of network architecture. While the intent of epidemic control is to mitigate disease development at minimal cost [28]. * Cloud Computing (CC)  
Cloud Computing (CC) is a novel architecture designed to provide resources and services stored somewhere on the internet, i.e., on cloud servers, or generally in the \"cloud\"; their users come from anywhere and from any device with internet access. Thus, CC provides many benefits for many branches of science. This tech concept refers to the use of remote server and storage facilities via the internet. The new health care and education paradigm is emerging where people learn necessary skills using cloud-based platforms. Introduction cloud computing allows businesses to save money by using shared resources to run their operations. This tech approach has become popular among tech-savvy companies, as it reduces operational costs. Running a business this way is much more practical than leasing hardware from a provider or using an app-based solution. In addition, \"bring your own device\" (BYOD) models are becoming more common in schools as well. People can easily share cloud storage with colleagues or save documents directly to a remote server via apps such as Dropbox. Enterprises and individuals alike have found great benefits in adopting cloud technology recently. In medicine, CC is regarded to be a recent and rapidly growing field of healthcare development. Thanks to its features [29], CC can be established as a new flexible infrastructure for faster response to new epidemic infectious diseases that spread with geometric progression among the global population.