Using Audio Processing To Determine Disease Spread

During epidemics and at other times, it is important for public health officials, individuals, and other entities to make health-related decisions based on sound epidemiological statistics. Such statistics are today generated only when individuals voluntarily seek medical treatment. Diseases that are contagious but initially asymptomatic can spread rapidly through vulnerable populations with little warning. Due to the initial mildness of symptoms, individuals may not realize that they have a dangerous infection and may fail to approach medical professionals for treatment or diagnosis. This disclosure describes techniques that can leverage sensors, networks, and consumer electronic devices to determine a fine-grained estimate of epidemiological statistics. With user permission, ambient sounds can be analyzed to detect audio symptoms of disease, e.g., cough, changes in voice, etc.; to classify the symptom by disease; and to geographically aggregate disease detections to automatically construct real-time epidemiological maps. Such epidemiological maps can enable accurate public-health decisions and efficient healthcare delivery

A Hybrid System For Pandemic Evolution Prediction

The areas of data science and data engineering have experienced strong advances in recent years. This has had a particular impact in areas such as healthcare, where, as a result of the pandemic caused by the COVID-19 virus, technological development has accelerated. This has led to a need to produce solutions that enable the collection, integration and efficient use of information for decision making scenarios. This is evidenced by the proliferation of monitoring, data collection, analysis, and prediction systems aimed at controlling the pandemic. This article proposes a hybrid model that combines the dynamics of epidemiological processes with the predictive capabilities of artificial neural networks to go beyond the prediction of the first ones. In addition, the system allows for the introduction of additional information through an expert system, thus allowing the incorporation of additional hypotheses on the adoption of containment measures./n/n/n/n/n/

Effects of Environment and Socioeconomics on Salmonella Infections

Objectives:Salmonella is a major public health concern particularly in areas of low socioeconomic status (SES) and high temperature. In this chapter, we examined several socioeconomic and environmental factors that may increase the spread of Salmonella in the southern states of the USA

Learning the Probability of Activation in the Presence of Latent Spreaders

When an infection spreads in a community, an individual's probability of becoming infected depends on both her susceptibility and exposure to the contagion through contact with others. While one often has knowledge regarding an individual's susceptibility, in many cases, whether or not an individual's contacts are contagious is unknown. We study the problem of predicting if an individual will adopt a contagion in the presence of multiple modes of infection (exposure/susceptibility) and latent neighbor influence. We present a generative probabilistic model and a variational inference method to learn the parameters of our model. Through a series of experiments on synthetic data, we measure the ability of the proposed model to identify latent spreaders, and predict the risk of infection. Applied to a real dataset of 20,000 hospital patients, we demonstrate the utility of our model in predicting the onset of a healthcare associated infection using patient room-sharing and nurse-sharing networks. Our model outperforms existing benchmarks and provides actionable insights for the design and implementation of targeted interventions to curb the spread of infection.Comment: To appear in AAA1-1