# Predictive Model of Lyme Disease Epidemic Process Using Machine Learning Approach
## 2 Background
### Current Research Analysis  
A significant number of theoretically grounded models of epidemic processes in population dynamics systems have been created [23]. The beginnings of modeling epidemic processes have been known since the 19 th century. Significant improvement and use of models for predicting and investigating infectious diseases occurred in the early 20 th century with the works of [PERSON] [24], [PERSON] [25], [PERSON] and [PERSON] [26]. These works laid the mathematical foundations for modeling in epidemiology, offering a description of epidemic processes using compartmental models. Later, cellular automata, binomial chains, statistical approaches, and population multi-agent models were used to model infectious diseases. An analysis of existing approaches carried out in this research showed that existing models could be conditionally divided into four groups. Each group of approaches to epidemic processes simulation has its limitations. The results of the analysis are shown in Figure 1.  
The first group includes models using a statistical approach [27; 28; 29; 30]. These models allow us to calculate only a short-term forecast for a sufficiently large population. The second group of approaches to the modeling of epidemic processes is based on the theory of differential equations [31; 32; 33; 34; 35; 36]. These models provide an opportunity to consider the characteristics of the population and the environment but still cannot be transferred to small populations. The third group of models uses the discrete-event approach of population dynamics, which allows for the consideration of the characteristics of the population, the environment, and the factors of the epidemic process [37; 38; 39; 40]. However, this approach has the main disadvantage of the high complexity of making changes to the model, which significantly complicates the possibility to transfer models to new areas of knowledge. The fourth group of models uses the multiagent approach, which allows taking into account the features of the population, environment, and factors of the epidemic process [41; 42; 43; 44]. The effective use of the multiagent approach involves the consideration of intelligent social communications of the objects of the population and expansion to other areas, which makes such kinds of models complex and decreases the accuracy of the simulation. Novel machine learning models that use neural networks can remove the shortcomings of existing approaches and improve the accuracy of the calculated forecast. A neural network is a connected and simple interacting processor (neurons) system. As a fundamental element of a neural network, a neuron can perceive, transform, and propagate signals, in our case, time series. Furthermore, combining neurons into one network allows for solving quite complex problems. Training a neural network consists of changing the weight of connections between neurons. The neural network approach is free from model constraints. When introducing additional algorithms into models, for example, to reduce dimensions and complexity [45] or optimize models [46], you can improve the performance of neural networks. Figure 1: Results of analysis of epidemic processes simulation. result. Therefore, using an approach that uses neural network models, it is possible to eliminate all the limitations outlined in analyzing existing solutions for epidemic processes simulation. To date, some mathematical and simulation models have been developed to investigate the epidemic process of Lyme disease.