A new extended Chen distribution for modelling COVID-19 data.

In this paper, we propose a new flexible statistical distribution, the Topp-Leone Exponentiated Chen distribution, to model real-world data effectively, with a particular focus on COVID-19 data. The motivation behind this study is the need for a more flexible distribution that can capture various hazard rate shapes (e.g.

Bernoulli polynomials for a new subclass of Te-univalent functions.

This paper introduces a novel subclass, denoted as , of Te-univalent functions utilizing Bernoulli polynomials. The study investigates this subclass, establishing initial coefficient bounds for , , and the Fekete-Szegö inequality, namely , are derived for this class. Additionally, several corollaries are provided to further elucidate the implications of the findings.

A comprehensive analysis of the artificial neural networks model for predicting monkeypox outbreaks.

Monkeypox is a viral disease that causes outbreaks in various countries, significantly impacting public health and healthcare systems. Effective preparedness and response efforts require accurately predicting the severity of these outbreaks. Currently, there are no publicly released studies for nations like Chile and Mexico on monkeypox, leading to this study's creation.

Machine learning in epidemiology: Neural networks forecasting of monkeypox cases.

This study integrates advanced machine learning techniques, namely Artificial Neural Networks, Long Short-Term Memory, and Gated Recurrent Unit models, to forecast monkeypox outbreaks in Canada, Spain, the USA, and Portugal. The research focuses on the effectiveness of these models in predicting the spread and severity of cases using data from June 3 to December 31, 2022, and evaluates them against test data from January 1 to February 7, 2023. The study highlights the potential of neural networks in epidemiology, especially concerning recent monkeypox outbreaks.