Integrating prior epidemiological knowledge embedded within mechanistic models with the
data-mining capabilities of artificial intelligence (AI) offers transformative potential for …

Background The interaction between modelers and policymakers is becoming more
common due to the increase in computing speed seen in recent decades. The recent …

In this paper, we replace the standard numerical approach of estimating parameters in a
mathematical model using numerical solvers for differential equations with a physics …

Data preprocessing is a fundamental stage in deep learning modeling and serves as the
cornerstone of reliable data analytics. These deep learning models require significant …

Infectious disease epidemics are challenging for medical and public health practitioners.
They require prompt treatment, but it is challenging to recognize and define epidemics in …

Compartmental models provide simple and efficient tools to analyze the relevant
transmission processes during an outbreak, to produce short-term forecasts or transmission …

In this study, we provide a fractional-order mathematical model that considers the effect of
vaccination on COVID-19 spread dynamics. The model accounts for the latent period of …

Artificial neural networks have changed many fields by giving scientists a strong way to
model complex phenomena. They are also becoming increasingly useful for solving various …

This paper presents classification and analysis of the mathematical models of the spread of
COVID-19 in different groups of population such as family, school, office (3–100 people) …

The neural network of COVID-19 5 days forecasting in Russian Federation region based on
epidemic and social data from 2020 to 2023 is constructed and analyzed. The structure of …