The present disclosure relates to information processing methods, apparatus, and systems. In one example method, a first network element receives data analytics information from a first data analytics network element, where the data analytics information includes one or more of an identifier of a first terminal, activity degree information of the first terminal, moving trajectory information of the first terminal, and first risk information of the first terminal. The first network element determines an association relationship between the first terminal and an emergency event based on the data analytics information.

Epidemiological modeling and simulation includes generating a virtual representation of a predetermined geospatial region by communicating in real time via a data communications network with a plurality of sensor-endowed computing nodes that capture and convey sensor-generated data in real time to the networked computer. A causal network is derived for mapping biodata onto inferences regarding pathogenic vector dynamics of a known pathogen based on stochastic vector probabilities. The biodata can be culled from historical data associated with the predetermined geospatial region. A visualization of selected characteristics of the predetermined geospatial region is generated and a corresponding epidemiological model created based on the historical data. Sensor-generated data and historical data are correlated. An expected effect of the known pathogen on a population of the geospatial region is predicted using a model simulation based on correlating the sensor-generated data and historical data.

Epidemiological modeling and simulation includes generating a virtual representation of a predetermined geospatial region by communicating in real time via a data communications network with a plurality of sensor-endowed computing nodes that capture and convey sensor-generated data in real time to the networked computer. A causal network is derived for mapping biodata onto inferences regarding pathogenic vector dynamics of a known pathogen based on stochastic vector probabilities. The biodata can be culled from historical data associated with the predetermined geospatial region. A visualization of selected characteristics of the predetermined geospatial region is generated and a corresponding epidemiological model created based on the historical data. Sensor-generated data and historical data are correlated. An expected effect of the known pathogen on a population of the geospatial region is predicted using a model simulation based on correlating the sensor-generated data and historical data.

The present invention comprises the capture and display of arthropod, human and arthropod-based metadata, which is capable of tracking and displaying the metadata, which is time and location-based, in order to show migration paths of arthropods and/or the diseases they have the potential to carry. This real-time view can help predict future arthropod and disease based on various scenarios such as, but not limited to: increased exposure based on the following: a user's geo-location, date and/or time of year, carrier type, etc. These variables can then assist with the education, awareness and potential prevention of disease.

A risk evaluation system is provided with an imager and a controller. The imager takes images at different times of equipment having zones that do not overlap each other, and outputs the images that are taken. The controller detects a contact count of a number of times a living body contacts each of the zones in accordance with the images, decides evaluation information about a contact infection risk in each of the zones in accordance with the contact count, and outputs the evaluation information.

Embodiments facilitate interoperability and secure patient selection for clinical trials and drug/device deployments. An entity may obtain a first set of health parameters and collective demographic information associated with one or more population segments and receive Electronic Health Record (EHR) sub-blocks with patient profile information and corresponding patient medical histories for patients. The entity may determine a subset of eligible candidate patients for a treatment based on information in the EHR sub-blocks and eligibility criteria for the treatment, which may be based the first set of health parameters, and/or the collective demographic information. The entity may transmit sub-blocks comprising eligible candidate patient profiles and medical information associated with the at least one treatment, and, in response to a received transaction block with a transaction confirmation, the entity may augment a multi-dimensional blockchain by linking: the transaction block, with a drug device information block and an EHR block.

A method for performing contact tracing. An analysis system performing the method receives geo-temporal data comprising location data points for various wireless terminals, including the wireless terminal being used by a person diagnosed as having a specified disease and the wireless terminals of people who possibly have come in contact with the infected person. Based on filtering the geo-temporal data, the analysis system generates relatively-condensed mobility profiles that are representative of each person's locations and movements, and analyzes the mobility profiles. Through careful selections of various parameters based on the disease that is being analyzed, the mobility profiles are used instead of the relatively large amounts of geo-temporal data, to represent users of wireless terminals and to determine their interactions in regard to disease transmission.

A method for performing contact tracing. An analysis system performing the method receives geo-temporal data comprising location data points for various wireless terminals, including the wireless terminal being used by a person diagnosed as having a specified disease and the wireless terminals of people who possibly have come in contact with the infected person. Based on filtering the geo-temporal data, the analysis system generates relatively-condensed mobility profiles that are representative of each person's locations and movements, and analyzes the mobility profiles. Through careful selections of various parameters based on the disease that is being analyzed, the mobility profiles are used instead of the relatively large amounts of geo-temporal data, to represent users of wireless terminals and to determine their interactions in regard to disease transmission.

Diagnostic and prognostic assays and a portable point of care system is provided that performs such assays using an automated, artificial intelligence (AI) based molecular analyses of a subject sample. The system provides for rapid, cost-efficient multiplexable assessment of biomarker panels in a cell sample and may be easily used for global and remote applications.

Proposed are concepts for generating recommendations of disease-related resources in different geographical regions. In particular, it is proposed to leverage information about the popularity (e.g. frequency, rate or regularity of use) of keywords associated with diseases across different geographic regions. Using such information, a relevancy value (i.e. geography weighting score score) for a disease-related resource with respect to a specific geographic region may be calculated. These relevance values may then be used to generate recommendations for the disease-related resources for the specific geographic region.