A machine learning prediction system can analyze a dataset of users with self-reported symptoms and associated data from a wearable device to impact measure the impact of an acute health condition (such as the flu) at the population level. The machine learning prediction system can train a machine learning model to recognize individual acute health condition patterns based on differences in user activity with respect to the characteristics of determined baseline periods. For example, per-individual normalized change with respect to baseline aggregated at the population level can be used to determine individual acute health condition patterns and predict the onset of certain acute health conditions using a trained machine learning model. In response to predictions, the machine learning prediction system can take interventions to manage the impact of a predicted acute health condition on an individual.

A machine learning prediction system can analyze a dataset of users with self-reported symptoms and associated data from a wearable device to impact measure the impact of an acute health condition (such as the flu) at the population level. The machine learning prediction system can train a machine learning model to recognize individual acute health condition patterns based on differences in user activity with respect to the characteristics of determined baseline periods. For example, per-individual normalized change with respect to baseline aggregated at the population level can be used to determine individual acute health condition patterns and predict the onset of certain acute health conditions using a trained machine learning model. In response to predictions, the machine learning prediction system can take interventions to manage the impact of a predicted acute health condition on an individual.

The present disclosure provides a novel integrated entropy-based method that combines genome-wide profiling and network analyses for diagnostic and prognostic applications. The present disclosure further provides the integration of multiomics datasets, network analyses and machine learning that enable predictions on diagnosing infectious diseases and predicting the probability that they will escape treatment/the host immune system and/or become antibiotic resistant. The present disclosure provides a primary gateway towards the development of highly accurate infectious disease prognostics.

A method for monitoring disease across agricultural areas of interest is provided comprising displaying at least one virtual zone corresponding to an agricultural geographic area of interest on a map in an application on a first device, and receiving an alert message when the first device is in proximity to a virtual zone. The at least one virtual zone is defined by at least one geofence. Each virtual zone is associated with a level of risk that indicates a likelihood of an outbreak of a disease detrimental to agriculture. Each virtual zone is configured to receive access notification information from each geofence when tracked devices enter an area defined by that geofence. The access information includes the level of risk associated with other virtual zones from which the tracked devices came. The alert message indicates if the first device should enter that virtual zone.

A method for monitoring disease across agricultural areas of interest is provided comprising displaying at least one virtual zone corresponding to an agricultural geographic area of interest on a map in an application on a first device, and receiving an alert message when the first device is in proximity to a virtual zone. The at least one virtual zone is defined by at least one geofence. Each virtual zone is associated with a level of risk that indicates a likelihood of an outbreak of a disease detrimental to agriculture. Each virtual zone is configured to receive access notification information from each geofence when tracked devices enter an area defined by that geofence. The access information includes the level of risk associated with other virtual zones from which the tracked devices came. The alert message indicates if the first device should enter that virtual zone.

There is a need to accurately and dynamically evaluate an individual's risk associated with the transmission or contraction of a disease. This need can be addressed, for example, by generation of a real-time or near real-time predicted disease score for an associated user. In one example, a method includes receiving a video stream data object depicting a visual representation of a target user; processing the video stream data object to generate a protective covering indication with respect to the target user; processing the video stream data object to generate a spatial proximity determination score with respect to the target user; processing the protective covering indication and spatial proximity determination score to generate a predicted disease score associated with the target user; and providing an augmented reality video stream data object configured to depict the visual representation of the target user and the predicted disease score.

In a bio-threat alert infrastructure system and method, an analyzing processor applies statistical algorithms to the collected quantitative data to precisely estimate event data, including time and position data, associated the development of a bio-threat. An encoding processor encodes the event data into a bio-threat alert signal. A transmitting element transmits the signal for reception by a bio-threat alert device. In the bio-threat alert device, and an associated method, a receiving element receives the signal. A decoding processor decodes the signal into the event data. A presentation element presents the event data to a user of the device.

A method to track an individual wearing a battery-operated wearable and lockable tracking device. An identification number is provided on the tracking device or separately from the tracking device and remotely stored. Device location information about the tracking device is received from the location tracker. Based on the location information received, a determination is made whether the individual is potentially subject to a condition. In such case, the device location is tracked, and the individual is wirelessly alerted and tracked. Upon tracking, the individual is identified by scanning the QR code and revealing the personal information about the individual.

The present invention provides an image processing apparatus (10) including: a companion determination unit (12) that determines a companion of each of a plurality of persons detected from an image, based on the image; a distance computation unit (13) that computes a first distance being a distance to the nearest person among the persons other than a companion, for the each person; and a risk information generation unit (14) that generates, with use of the first distance, infection risk information being information related to a risk of getting an infectious disease or a safety rate of not getting an infectious disease within a target region being a region included in the image.

The present invention provides an image processing apparatus (10) including: a companion determination unit (12) that determines a companion of each of a plurality of persons detected from an image, based on the image; a distance computation unit (13) that computes a first distance being a distance to the nearest person among the persons other than a companion, for the each person; and a risk information generation unit (14) that generates, with use of the first distance, infection risk information being information related to a risk of getting an infectious disease or a safety rate of not getting an infectious disease within a target region being a region included in the image.