AIMC Topic: Disease Outbreaks

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A machine learning-driven early warning system for cryptocaryoniasis in marine aquaculture.

Parasites & vectors
BACKGROUND: Disease outbreaks, particularly cryptocaryoniasis caused by the ciliate Cryptocaryon irritans, pose significant barriers to sustainable marine fish aquaculture, undermining productivity, profitability, and biosecurity. Despite its impact,...

Improving outbreak forecasts through model augmentation.

Proceedings of the National Academy of Sciences of the United States of America
Accurate forecasts of disease outbreaks are critical for effective public health responses, management of healthcare surge capacity, and communication of public risk. There are a growing number of powerful forecasting methods that fall into two broad...

Investigating environmental determinants and spatiotemporal dynamics of highly pathogenic avian influenza H5N1 outbreaks in India through machine learning.

Scientific reports
Avian Influenza (AI), caused by highly pathogenic strains of influenza viruses, poses a significant threat to poultry populations and public health worldwide. This study offers a comprehensive evaluation of the spatial and temporal dynamics of HPAI o...

A comparative evaluation of multiple machine learning approaches for forecasting dengue outbreaks in Bangladesh.

Scientific reports
This study aims to forecast dengue incidence in Bangladesh by applying and comparing machine learning techniques. Dengue surveillance data from January 1, 2022, to December 1, 2023, for five divisions of Bangladesh was obtained from the Directorate G...

Understanding cholera dynamics in African countries with persistent outbreaks: a mathematical modeling approach.

BMC public health
BACKGROUND: Cholera, caused by Vibrio cholerae, is a global health challenge, spreading through water in areas lacking clean water and sanitation. Since 2021, the reemergence of cholera cases has increased significantly in endemic regions in Africa. ...

Gaussian process modelling of infectious diseases using the Greta software package and GPUs.

Journal of theoretical biology
Gaussian process are a widely-used statistical tool for conducting non-parametric inference in applied sciences, with many computational packages available to fit to data and predict future observations. We study the use of the Greta software for Bay...

Machine learning-based evaluation of risk factors for carbapenem-resistant dissemination in neonatal units.

mSystems
Healthcare-associated infections (HAIs), particularly in neonatal intensive care units (NICUs), pose significant challenges due to neonates' vulnerability and the rapid infection spread. However, risk factors facilitating pathogen persistence and dis...

Bridging the predictive divide: A hybrid early warning system for scalable and real-time dengue surveillance in LMICs.

Acta tropica
The global resurgence of dengue presents an ongoing challenge for public health systems, particularly in low- and middle-income countries (LMICs) where conventional early warning systems (EWS) often suffer from reporting delays and under-detection. W...

Public concerns about human metapneumovirus: insights from Google search trends, X social networks, and web news mining to enhance public health communication.

BMC public health
The respiratory virus known as human metapneumovirus (hMPV) is linked to seasonal outbreaks and primarily affects elderly people and young children. Infodemiology, which uses digital data sources, including social media, online news, and search trend...

Oropouche fever outbreak in Brazil: Key factors behind the largest epidemic in history.

PloS one
Oropouche virus (OROV) is an arthropod-borne virus responsible for outbreaks of Oropouche fever (ORO) in Central and South America since the 1950s. Herein, we investigated the climatic and socioenvironmental factors contributing to the reemergence of...