# Predictive Model of Lyme Disease Epidemic Process Using Machine Learning Approach
## 1 Introduction  
Lyme disease is a group of naturally transmitted focal infectious diseases caused by Borrelia of the _B. burgdorferi_ group and transmitted by ixodid ticks. Clinically, the disease occurs with a primary skin lesion and often involves the nervous system, musculoskeletal system, and the heart muscle, and is characterized by a tendency to a chronic course [1]. In the United States, the Centers for Disease Control and Prevention (CDC) in Atlanta registers more than 30,000 cases of borreliosis annually [2]. In European countries, Lyme disease cases are up to 8000-10,000 annually. The incidence rate of Lyme disease in France is 39.4 per 100 thousand population, while in Bulgaria it is 36.6 [1]. The name of the disease, Lyme disease or Ixodes tick-borne borreliosis, began to be used in recent years after discovering new species of Borrelia of the _B. burgdorferi_ group, which are also transmitted by ixodid ticks [3]. Prior to this, cases of this group of diseases were recorded as Lyme disease. This provision is regulated based on the short version of the International Statistical Classification of Diseases and Related Health Problems, the 10 th revision adopted by the Forty-third World Health Assembly [4]. In Ukraine, according to the Lviv Scientific Research Institute of Epidemiology and Hygiene, a variety of nosological forms of Lyme disease are associated with the circulation of three species of Borrelia pathogenic to humans: _B. burgdorferi_, _B. gariniii_, and _B. afzelli_. In the most active natural foci of Lyme disease in Ukraine, approximately 10-70% of ticks are infected with Borrelia [5]. Epidemiological and serological studies conducted by this institute showed that from 10% to 42.2% of Ukraine's population has contact with thepathogen of borreliosis from 2009 to 2014 [5]. In 2020, due to the coronavirus pandemic, the incidence of Lyme disease in Ukraine decreased by 2.1 times. In 2021, compared with the previous year, there was an increase in the incidence of 8.1%. At the same time, the number of people affected by tick bites increased by 28.7% in the Kharkiv region. The most significant number of victims was found in Kharkiv--54.1% of cases, the Izyum district--16.8%, and the Lozova district--12.4%. Due to the incomplete and untimely detection of diseases of this group, it is impossible to assess the true prevalence, transmission, and geographic distribution of the disease, which makes them epidemiologically uncontrollable and poses a significant epidemic potential [6]. In addition, the prerequisites for the occurrence of the disease cases may be an increase in the number of ticks and an increase in the infection rate of Borrelia in ticks, especially in recreational areas [7]. Due to the growth of tick populations [8], an increase in tick bites has been observed. To plan and conduct rational, preventive measures, it is necessary to understand the epidemiology of borreliosis in Ukraine and the factors associated with morbidity. The best tool for that is a mathematical simulation that builds predicted dynamics of the epidemic process and determines the factors influencing the morbidity. This work **aimed** to develop an effective model to predict the dynamics of the Lyme disease epidemic process in the given territory. This will allow for the determining of the dynamics of the development of the epidemic process of Lyme disease in the Kharkiv region (Ukraine) and will allow for the planning of effective evidence-based measures to reduce the epidemic development of the incidence. Thus, the preventive and medical institutions of Ukraine will receive an automated tool for assessing the situation with the incidence of Lyme disease. To achieve the aim of the research, the following **tasks** have been formulated:  
* Models and methods of epidemic processes simulation should be analyzed. * Models and methods of the Lyme disease epidemic process simulation should be analyzed. * Data on Lyme disease morbidity in the Kharkiv region (Ukraine) should be analyzed.