Artificial neural networks (ANNs) ability to learn, correct errors, and transform a large amount
of raw data into beneficial medical decisions for treatment and care has increased in …

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

This paper presents a critical analysis of the literature and perspective research ideas for
modeling the epidemics caused by the SARS-CoV-2 virus. It goes beyond deterministic …

Abstract Machine intelligence is at great height these days and has been evident with its
effective provenance in almost all domains of science and technology. This work will focus …

An early warning model for infectious diseases is a crucial tool for timely monitoring,
prevention, and control of disease outbreaks. The integration of diverse multi-source data …

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 …

Epidemics of acute respiratory infections, such as influenza and COVID-19, pose a serious
threat to public health. To control the spread of infections, statistical methods and …

Background The continuous emergence of novel SARS-CoV-2 variants with markedly
increased transmissibility presents major challenges to the zero-COVID policy in China. It is …

A dynamics informed neural networks (DINNs) incorporating the susceptible-exposed-
infectious-recovered-vaccinated (SEIRV) model was developed to enhance the …

Accurate epidemic forecasting is a critical task in controlling epidemic spread. Many deep
learning-based models focus only on static or dynamic graphs when dealing with spatial …