Development of a Machine Learning Modelling Tool for Predicting HIV Incidence Using Public Health Data from a County in the Southern United States.

BACKGROUND: Recent advancements in Machine Learning (ML) have significantly improved the accuracy of models predicting HIV incidence. These models typically utilize electronic medical records and patient registries. This study aims to broaden the application of these tools by utilizing de-identified public health datasets for notifiable sexually transmitted infections (STIs) from a southern U.S. County known for high HIV incidence rates. The goal is to assess the feasibility and accuracy of ML in predicting HIV incidence, which could potentially inform and enhance public health interventions. METHODS: We analyzed two de-identified public health datasets, spanning January 2010 to December 2021, focusing on notifiable STIs. Our process involved data processing and feature extraction, including sociodemographic factors, STI cases, and social vulnerability index (SVI) metrics. Various ML algorithms were trained and evaluated for predicting HIV incidence, using metrics such as accuracy, precision, recall, and F1 score. RESULTS: The study included 85,224 individuals, with 2,027 (2.37%) newly diagnosed with HIV during the study period. The ML models demonstrated high performance in predicting HIV incidence among males and females. Influential predictive features for males included age at STI diagnosis, previous STI information, provider type, and SVI. For females, they included age, ethnicity, previous STIs information, overall SVI, and race. CONCLUSIONS: The high accuracy of our ML models in predicting HIV incidence highlights the potential of using public health datasets for public health interventions such as tailored HIV testing and prevention. While these findings are promising, further research is needed to translate these models into practical public health applications.

The African Crane Database (1978-2014): Records of three threatened crane species (Family: Gruidae) from southern and eastern Africa.

BACKGROUND: The International Crane Foundation (ICF) / Endangered Wildlife Trust's (EWT) African Crane Conservation Programme has recorded 26 403 crane sightings in its database from 1978 to 2014. This sightings collection is currently ongoing and records are continuously added to the database by the EWT field staff, ICF/EWT Partnership staff, various partner organizations and private individuals. The dataset has two peak collection periods: 1994-1996 and 2008-2012. The dataset collection spans five African countries: Kenya, Rwanda, South Africa, Uganda and Zambia; 98% of the data were collected in South Africa. Georeferencing of the dataset was verified before publication of the data. The dataset contains data on three African crane species: Blue Crane Anthropoides paradiseus, Grey Crowned Crane Balearica regulorum and Wattled Crane Bugeranus carunculatus. The Blue and Wattled Cranes are classified by the IUCN Red List of Threatened Species as Vulnerable and the Grey Crowned Crane as Endangered. NEW INFORMATION: This is the single most comprehensive dataset published on African Crane species that adds new information about the distribution of these three threatened species. We hope this will further aid conservation authorities to monitor and protect these species. The dataset continues to grow and especially to expand in geographic coverage into new countries in Africa and new sites within countries. The dataset can be freely accessed through the Global Biodiversity Information Facility data portal.