# A Machine Learning-Based Model for Epidemic Forecasting and Faster Drug Discovery
## 2 Methods
AI practices have been used not only for the detection but also early warning of infectious diseases, the calculation of trends and the modeling and response of health. Such large-scale public health supervision and response work presents distinct technical challenges. Conventional health supervision depends heavily on statistics. In recent years, there has been a remarkable development of methodologies with the possibilities of AI that complement statistical approaches [20]. AI unlocks the access to use it with an array of innovative or unexplored sources for public supervision objectives, particularly those not initially or deliberately intended to counter epidemiological subjects. For example, with the speedy improvement of the internet and IoT applications, pervasive social and device sensing potentials are becoming realistic, appearing with noteworthy surveillance capabilities. A diversity of open data, external to old-fashioned health supervision schemes, can be prolifically utilized to enrich surveillance capabilities. Health supervision is concerned with the early and effective evaluation of the threat of an epidemic, spotting irregular behavior in the spatiotemporal condition of the epidemic, so as to make available timely notifications and estimates concerning the trend of the epidemic. Furthermore, AI delivers modeling frameworks for simulating data and multiple scenarios of disease spread and health reactions. The evolution of epidemics over time and in humans has a high degree of difficulty and uncertainty. The spread of the infection is non-linear in nature, with vague features. Furthermore, AI delivers framework models for simulating complex requirements and scenarios for the spread of diseases in public health responses. The progress of epidemics presents to a great extent the difficulty and uncertainty. The spread of infection is non-linear in nature, with vague characteristics and predictability. Therefore, the models are based on aggregate statistics and linear interactions. An AI subdomain, multi-factor systems, provides modeling frameworks that make available the analysis of epidemic development under different conditions [20]. * Machine Learning (ML) Machine learning is a field of exploration devoted to understanding and developing styles of \"learning\", a system of using data to ameliorate the performance of a range of tasks. It is considered part of artificial intelligence. Machine learning algorithms make models grounded on sample data (called training data) to make prognostications or opinions without being explicitly programmed. Machine learning algorithms are used in various functions like those used in drug discovery, speech recognition, and computer vision. In cases that are sensitive or intractable, traditional algorithms are used to perform the necessary tasks. A subset of machine learning is closely related to computer statistics, which focuses on using computers to make prognostications, but not all machine learning is statistical literacy. The study of fine optimization provides proposition, and operation areas for machine learning. Data mining is an affiliated field of study that focuses on exploratory data analysis through unsupervised learning. Some executions of machine learning use data and neural networks in ways that mimic the way biological brains work. In its cross-business operations, machine learning is also known as predictive analytics. In computer science, machine learning (ML) is considered as a subfield of AI. An ML algorithm defines any computational process where the output from previous decisions or actions is exploited to enhance the final decisions or predictions. Today, ML approaches are widely used in research, since they offer an automation in multi-scale biological data analysis [21]. ML can be separated into three categories: supervised learning, unsupervised learning, and sequential learning. The goal in supervised learning is to label new observations. The unsupervised learning identifies underlying relationships or patterns.