Work

Improving the Accuracy of Infectious Disease Forecasts Based on Comparing Neural Network Architectures

Year: 2026

Type: article

Abstract: This paper aims to improve the accuracy of infectious disease forecasting using machine learning methods. The main results of this work are an analysis of infectious diseases spread in Ukraine during the time span from December 2016 to January 2024 and a performance comparison of different neural network architectures in the scope of time series forecasting. The following steps were taken: analysis of current forecasting methods, selection of neural network architectures, dataset preprocessing, and model testing. The developed system can be an effective tool for rational management decisions to ensure the epidemiological well-being and biosecurity of the population.

Source: Computation

DOI: 10.3390/computation14020054

Authors: Oleksandr Kovaliv, Yuriy Kondratenko, Ievgen Sidenko, Galyna Kondratenko, Dmytro Chumachenko

Institutions: Petro Mohyla Black Sea National University, Institute of Artificial Intelligence Problems, National Aerospace University – Kharkiv Aviation Institute

Language: English

Topic: Data-Driven Disease Surveillance

Subfield: Epidemiology

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $2,172