Agent-based investigation of sexually transmitted infection

Dmytro Chumachenko, Tetyana Chumachenko

Abstract


ObjectiveTo develop agent-based model of sexually transmitted infectionsspreading by example of Syphilis and its analysis.IntroductionEvery year nearly 12 million new cases of syphilis in the world areregistered. Currently, in many countries of the world the stabilizationor even reduction of the incidence of syphilis is marked, but this doesnot apply to Ukraine. The current stage of development of the syphilisproblem in Ukraine is characterized by not only high morbidity, butalso the fact that in the overwhelming number of cases, we are talkingabout the latent forms and atypical manifestations of the disease andresistance to therapy [1].Preventive and prophylactic measures are important in maintainingthe public health. Predicting the dynamics of disease spreading allowsdeveloping appropriate countermeasures and ensuring rational useof human and material resources. Qualitative forecast of syphilisspreading is possible to implement by means of mathematicalmodeling.MethodsDeterministic analytical models that are most common inepidemiological studies do not take into account the dynamic andstochastic nature of epidemics. Agent-based simulation approachto modeling allows fixing these shortcomings. It allows conveyingthe social structure of simulated system by the most natural and easyway. Each agent has individual state variables and rules of behaviorthat allows detailing the model very deeply. Therefore there is noneed to describe the complex system of mathematical formulasand probability of the dynamics of the epidemic process is definedparametrically. The NetLogo software has been used for the programrealization of the developed model.ResultsThe model of morbidity by syphilis spreading has been developedby the tradition SIR model expansion. Thus, agents can be in followingstates: S (Susceptible) for health people, IP(Infected Primary) forinfected people who stay in primary stage and can transmit theinfection by direct sexual contact with susceptible person, IS(InfectedSecondary) for infected people who stay in secondary stage and havealso infectious skin lesions, IL(Infected Latent) for infected peoplewho stay in latent stage and change its contagious rate from earlylatent syphilis to late latent syphilis, IT(Infected Tertiary) for infectedpeople who stay in tertiary stage and transmit the infection partially,and R (Recovered) for people who are recovered from the infection.Infecting of agents in the model depends on the number and stateof agents and the stage of infected agent’s disease. Also, in orderto correctly determine the intensity of contacts with other agentsdifferent age groups of agents have been highlighted in the model.Screen form of developed agent-based model of syphilis spreadingis shown in Figure.The transmission between agent’s states are defined by probabilisticway and depends on features of particular states as well as differentfactors, such as coupling tendency, condom use, commitment, testfrequency etc.The analysis of experiments under developed model has shownthat the most influencing factor in the reduction in the percentage ofpatients is frequency of checks on the disease and isolation of patients,the second most important factor is constancy of sexual partners, thethird is the use of condoms, and finally, the fourth is the number ofexchangeable partners.ConclusionsThe agent-based model of syphilis spreading has been developed.The model allows forecasting the morbidity by infection andanalyzing the disease by changing the initial data. All data has beenchecked by the factual statistics on the syphilis incidence in Kharkivregion (Ukraine) from 1975 to 2015 years. The simulation resultsallow us determining the direction of prevention of syphilis treatmentand the main factors in reducing morbidity. As is evident from thesimulation results, social factors take precedence over the healthcare that gives grounds for advocacy in health policy among thepopulation, especially the youth. Developed model can be configuredfor other sexually transmitted infections by changing the diseasetransition rules.Figure. The main panel of simulation management and graphic visualization.

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DOI: https://doi.org/10.5210/ojphi.v9i1.7638



Online Journal of Public Health Informatics * ISSN 1947-2579 * http://ojphi.org