A health data management system includes a health data measurement apparatus and a health management server communicable therewith. The apparatus includes medical and health equipment that measures health data of a user and first circuitry that collects the health data. The management server includes a first memory that stores the health data from the apparatus and second circuitry. Based on the stored health data, the second circuitry generates health management data representing a health state of the user, and analyzes at least one of an item indicating a worsening health state of the user, an item with insufficient measurement data, or an item lacking measurement data as a required check item. When determined that the health management data includes the required check item, the second circuitry transmits to the apparatus a message prompting the user to take a measurement of the required check item, and requests the health data.

Disclosed are a method and device for calculating the probability of being infected with or having a disease and a method and device for outputting a subject to be tested for a disease. The method for calculating the probability of being infected with a disease according to one embodiment of the present invention, is a method carried out on a computing device provided with one or more processors, and memory storing one or more programs executed by the one or more processors, the method comprising the steps of: receiving location information of each of a plurality of persons; and calculating the probability of being infected with a disease for each of the plurality of persons, on the basis of infected person information including identification information thereof and disease information thereof, and location information of each of the plurality of persons.

In one embodiment, a method includes: accepting input data for a design including arrangement of spaces of a structure, operating parameters of a ventilation system, and locations and relative positions of an uninfected individual and one or more infected individuals in the structure with respect to air flowing in the structure and influenced by the ventilation system; calculating an inverse protection factor for the structure using a lumped element model, the inverse protection factor being an inverse of a protection factor which is a ratio of contaminant which the one or more infected individuals exhale in the structure and contaminant which the uninfected individual inhales in the structure; comparing the calculated inverse protection factor to a preset criterion; and if the calculated inverse protection factor fails to meet the preset criterion, changing the design, and repeating the calculating, comparing, and changing until the calculated inverse protection factor meets the preset criterion.

In one embodiment, a method includes: accepting input data for a design including arrangement of spaces of a structure, operating parameters of a ventilation system, and locations and relative positions of an uninfected individual and one or more infected individuals in the structure with respect to air flowing in the structure and influenced by the ventilation system; calculating an inverse protection factor for the structure using a lumped element model, the inverse protection factor being an inverse of a protection factor which is a ratio of contaminant which the one or more infected individuals exhale in the structure and contaminant which the uninfected individual inhales in the structure; comparing the calculated inverse protection factor to a preset criterion; and if the calculated inverse protection factor fails to meet the preset criterion, changing the design, and repeating the calculating, comparing, and changing until the calculated inverse protection factor meets the preset criterion.

In an approach, a processor receives device identification information corresponding to at least one device local to a location of a transaction. A processor receives notification of an infected user. A processor determines that the infected user is associated with the transaction. A processor identifies a second user from the device identification information. A processor sends a notification to the second user.

In an approach, a processor receives device identification information corresponding to at least one device local to a location of a transaction. A processor receives notification of an infected user. A processor determines that the infected user is associated with the transaction. A processor identifies a second user from the device identification information. A processor sends a notification to the second user.

In an approach for building a machine learning model with a flexible prediction horizon, a processor gathers statistical data related to a disease from one or more regional sources. A processor clusters the statistical data according to a plurality of localized regional source similarity criteria and a plurality of region criteria. A processor builds a plurality of training models based on the clustered statistical data. A processor builds a plurality of feature vectors based on the plurality of localized regional source similarity criteria and the plurality of region criteria. A processor trains the plurality of training models separately against the plurality of feature vectors. A processor selects a best performing training model for each of the plurality of localized regional source similarity criteria and the plurality of region criteria based on a performance criterion. A processor tests the best performing training model to predict one or more future outcomes.

In an approach for building a machine learning model with a flexible prediction horizon, a processor gathers statistical data related to a disease from one or more regional sources. A processor clusters the statistical data according to a plurality of localized regional source similarity criteria and a plurality of region criteria. A processor builds a plurality of training models based on the clustered statistical data. A processor builds a plurality of feature vectors based on the plurality of localized regional source similarity criteria and the plurality of region criteria. A processor trains the plurality of training models separately against the plurality of feature vectors. A processor selects a best performing training model for each of the plurality of localized regional source similarity criteria and the plurality of region criteria based on a performance criterion. A processor tests the best performing training model to predict one or more future outcomes.

The disclosed technology provides solutions for protecting the health of ride-sharing passengers by detecting passenger illnesses, and taking precautions to safely address potentially exposed vehicles. A process of the disclosed technology can include steps for: collecting sensor-data corresponding with one or more AV passengers, determining a likelihood that at least one of the AV passengers is suffering from a physical illness, and transmitting a wellness notification to a fleet management system if the likelihood exceeds a predetermined threshold. Systems and machine-readable media are also provided.

A security portal has a body having sidewalls and a top, a floor, and an entrance passageway, a remotely operable entrance barrier at the entrance passageway, a remotely operable exit barrier at the exit passageway, input apparatus in the security portal, an optical temperature sensor positioned and remotely operable to sense the subject's skin temperature, and a computerized base station remote from the security portal. The operative opens the entrance barrier to admit the subject, uses the input apparatus to solicit information, performs an evaluation process involving identity and skin temperature, and, depending on result of the evaluation process, the operative opens the entrance barrier and asks the subject to leave through the entrance passageway, or opens the exit barrier and asks the subject to leave through the exit passageway into the secured area.