Artificial Intelligence (AI)

With the increase in the amount of public health data along with the growth of public health informatics, it is important for epidemiologists to understand the current trends in technology and the impact they may have in the field. Because it is unfeasible for public health professionals to be an expert in every emerging technology, this presentation seeks to provide them with a better understanding of how emerging technologies may impact the field and the level of expertise required to realize benefits from the new technologies. Furthermore, understanding the capabilities provided by emerging technologies may guide future training and continuing education for public health professionals.

Objective: The objective of this presentation is to explore emerging technologies and how they will impact the public health field. New technologies such as blockchain, artificial intelligence (AI), and the Internet of Things (IoT) will likely be incorporated into epidemiological methods and processes. This presentation will provide an overview of these technologies and focus on how they may impact public health surveillance in the future.

Climate warming, globalization, social and economic crises lead to the activation of natural foci of vector-borne infections, among which a special place belongs to Lyme disease (Ixodic tick borreliosis – ITB), the vectors of which are the Ixodes ticks. More than 5,000 cases are registered in the United States every year. In European countries, the number of cases may reach up to 8,000-10,000 per year. Incidence rate for ITB in France is 39.4 per 100,000 population, in Bulgaria – 36.6. In Ukraine, among all ticks, 10-70% are infected with Borrelia; from 10% to 42.2% of Ukrainian population had contact with the causative agent of ITB. Mathematical modeling as an element of monitoring of natural focal infections makes it possible to assess the epidemiological potential of foci in the region and in individual territories, to forecast the trends of the epidemic process and to determine the main priorities and directions in the prevention of ITB. The most modern and effective method of simulation is multi-agent simulation, which is associated with the concept of an intelligent agent, as some robot, purposefully interacting with other similar elements and the external environment under given conditions. An intelligent agent is an imitation model of an active element, the state and behavior of which in various situations of achieving the goal vary depending on the state and behavior of other agents and the environment, in analogy with the intellectual behavior of a live organism (including a human) under similar conditions. As the epidemic process of Lyme disease is characterized by vector transmission, heterogeneous tick population, variable pathogen infectivity, heterogeneous environment, and seasonal changes in tick activity, the use of classical statistical methods for predicting the dynamics of morbidity cannot show high accuracy. The multiagent approach to simulation of the epidemic process of Lyme disease allows considering all of the above features, and since the dynamics of the modeled system is formed from the behavior of local objects (humans and ticks), we expect that a model constructed using a multiagent approach will yield a higher accuracy of prognosis morbidity. The multiagent model will allow not only to calculate the forecast, but also to reveal the factors influencing increase of the incidence of Lyme disease the most.

Objective: The objective of this research is to develop the model for calculating the forecast of the Lyme disease dynamics what will help to take effective preventive and control measures using the intelligent multi-agent approach.