# Predictive Model of Lyme Disease Epidemic Process Using Machine Learning Approach
## 4 Results
As can be seen from the results, the machine learning approach has shown the most accurate results. A software package has been developed that makes it possible to calculate the predicted incidence rate of Lyme disease based on machine learning, namely neural networks. The adequacy of the constructed forecast was tested on actual statistical data on Lyme disease incidence. The calculated forecast shows the persistence of an unstable epidemiological situation for Lyme disease, which requires preventive measures at both the population level and individual protection, with the purpose to minimize the risk of people contacting ticks and reducing the incidence of Lyme disease. The software product developed based on the model can be used for any territory and population group. The software product will increase the efficiency of decision-making regarding the implementation of preventive measures on the incidence of Lyme disease, which will reduce the incidence in the study area. Calculating the number of cases and the disease's trajectory helps determine what interventions should be implemented and their volume. This will allow for the assessing of the financial and human resources to combat the epidemic dynamics effectively. A virtual test of the effectiveness of such measures will be the next stage of our study. Taking into account the fact that epidemiological surveillance does not allow assessing the actual state of morbidity (slow introduction of laboratory diagnosis of Lyme disease, errors in clinical diagnosis, etc. ), predicting the dynamics of morbidity, the epidemic process forecast model makes it possible to assess the epidemic situation, with the revealed dynamics of the increase in morbidity in resource-limited settings, the most effective non-specific prevention methods can be strengthened, which include, at the population level, the control of tick vectors in endemic foci; at the individual level--individual protection of a person from tick attacks, as well as sanitary and educational work among the population. Tick control in an endemic area includes: \begin{table}
\begin{tabular}{c c} \hline
**Approach** & **Average Absolute Prediction Error** \\ \hline Differential equations & 21.4\% \\ Binomial chains & 18.3\% \\ Cellular automata & 17.2\% \\ Moving average method & 14.6\% \\ Exponential smoothing & 11.2\% \\ Brown polynomial model & 10.8\% \\ Adaptive Holt model & 10.2\% \\ Multiagent approach & 5.0\% \\ Neural network approach & 3.8\% \\ \hline \end{tabular}
\end{table} 
Table 1: Accuracy of the Lyme disease epidemic process models. 1. Creation of unfavorable conditions for the habitat of carriers of Lyme disease--clearing and landscaping of forest areas (clearing debris, removing deadwood, brushwood, undersized shrubs, moving grass). 2. Carrying out extermination measures (disinfestation, the use of a chemical method of combating ticks, deratization, the destruction of hosts of tick larvae and nymphs). Extermination measures are carried out only according to epidemiological indications in limited volumes in the locations of health facilities for children and adults, in places of permanent residence of professionally threatened contingents, recreation and tourism centers, campsites, motels, gardening cooperatives, as well as in forest areas, the most frequently visited by the population for domestic and other purposes, where the infection of Lyme disease most often occurs. Individual protection of a person from the attack of ticks should include:  
* systematic self- and mutual examination of clothing and body, which is extremely important for preventing ticks from being sucked. They are held every two hours of being in a natural focus without taking off their clothes;
* timely and correct removal of sucked ticks, if possible, in a medical facility;
* wearing protective clothing while staying in dangerous areas of the natural focus;
* impregnation of clothing with repellents or insect repellents.