Current approaches to modeling of epidemic process of non-polio Enterovirus infections

Aim . To study mathematical models for predicting the incidence of non-polio enterovirus infections (NPEVI) in the Kemerovo Region. Materials and Methods . Here we conducted a retrospective epidemiological study of NPEVI incidence in the Kemerovo region from 2006 to 2021 (n = 2152 cases). Epidemic process was studied using autocorrelation analysis, Fourier analysis, and neural networks using STATISTICA Automated Neural Networks (SANN) tool and StatTech v. 3.0.5. Results . The incidence rates of NPEVI were 9,39 per 100,000 population (2009), 15,78 per 100,000 population (2015) and 8,41 per 100,000 population (2019), exceeding the average median value (2006- 2021) by a factor of 2.4, 4.1, and 2.2, respectively. NPEVI incidence was largely determined by enteroviral meningitis. The majority of cases (89.94%) were registered in children. Notably, standard mathematical models failed to provide an objective analysis of the incidence trend. Autocorrelation analysis found the summer-autumn seasonality (August-October) by evaluating the ratio of actual data to 12-month rolling averages. Modeling of the epidemic process of NPEVI using neural networks highly likely predicted its incidence up to 52 months. Conclusion . The epidemic process of NPEVI in Kemerovo region has been characterized by a low intensity and summer-autumn seasonality. Neural networks are suggested as a promising tool to forecast the incidence of NPEVI.