CN113823415A - System and application for improving health safety of people in environment against infectious diseases - Google Patents
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Abstract
Embodiments of the present disclosure relate to systems and applications for improving the health safety of people in an environment against infectious diseases. The present invention includes many embodiments and aspects of a novel system and framework to systematically improve the health safety of people in a physical environment against infectious diseases such as COVID-19. In a preferred embodiment, the present invention computationally models people, places, things, activities and relationships within a physical environment by connecting to an integration layer of sensors, models and devices present within the environment to create a semantic representation of the spatial environment and temporal dynamics of people, places and things within the environment. In a preferred embodiment, the present invention uses semantic representations of the environment and human activities to model pathogen transmission over time, the potential patterns and extent of spread of infection, and the risk of infection to people within the environment.
Description
Technical Field
The present invention relates generally to systems and applications for improving the health safety of a person against an infectious disease such as COVID-19 in a physical environment where there are human beings and activities such as schools and offices. Aspects of the present invention relate to monitoring and alerting unsafe conditions that may result in increased risk of infection or spread of infection to persons within a physical environment based on the presence, activity, and relationship of the person to other persons, things, and places. Other aspects of the invention relate to systems, such as contact tracking, for modeling and analyzing risk of infection spread within a physical environment and for designing and optimizing security measures for reducing risk and spread of infection within a physical environment.
Background
COVID-19 is an invisible, rapidly contagious and evolving disease. Transmission of infectious pathogens can occur through pathogen-laden droplets, which are then shed by respiratory tract actions such as sneezing, coughing, speaking, or even breathing, or by contact-based or ingestion-based transmission through direct or indirect physical contact between people. In respiratory transmission, most of the larger droplets will fall to the ground or settle on a physical surface due to gravity, while the smaller droplets may become air and stay in the air for some time. A person is infected through cumulative exposure to sufficient density of pathogens on surfaces and spaces shared with the infected person.
Preventative safety measures have been established to reduce the risk of infection exposure. They include: safety distance guidelines that maintain a minimum distance between people (typically 1-2 meters), limit people in public spaces such as churches and bars, limit occupancy (occupancy) density in buildings, or require that masks be worn in public places. Other security measures include: entry checks for infectious conditions or body temperature, physical barriers between persons to enforce distance, travel restrictions and quarantine requirements, and increasing the frequency and depth of cleaning shared public spaces.
While these security measures provide a general guide that is easy to understand and easy to adopt, a number of case studies indicate that the spread of infection is highly dependent on the nature of the physical environment and the human activity therein. For example, airflow and ventilation play a significant role in determining the extent and duration of airborne infection spread and the physical topology and domain of the environment-meat packaging plants or buffet lines or hospital wards, and the type and duration of human interaction. The risk of infection and safety measures for infectious diseases depend on the temporal and spatial characteristics of the person in the physical context (context).
The COVID-19 pandemic exposes a significant drawback in our ability to protect people from infectious diseases. The breakdown of widespread economic activity due to COVID-19 has incurred an unprecedented pain and disruption for people, communities, and businesses. It is only possible to recover normal after vaccination or immunization of most people, which is estimated to be at least one year or more. Heretofore, the world has needed to operate in a semi-normal mode that allows personnel and businesses to safely reopen and resume operation, but in a manner that ensures personnel safety and prevents recurrence of infection that could overwhelm medical capability.
Existing methods of security reopening rely on rough level security guidelines that are manually monitored and enforced, such as requiring a mask or safe distance. In a few cases, operators of physical environments have employed additional measures specific to their own domain, such as visually marking areas and requiring personnel to remain in these marked areas to force isolation, or by installing screens and partitions between adjacent workers, or by requiring occupants to wear clothing and equipment that can minimize physical proximity. The design, monitoring and enforcement of these methods is human dependent, so the risk and impact of infection and the associated costs are still high and unpredictable.
Several AI and IOT providers have begun to adjust their systems to help automate various aspects of COVID-19 risk management. Computer vision based video surveillance can now detect whether people are wearing a mask or whether people are physically separated by some minimum distance. Smart phone apps and location-based services have begun to provide proximity alert and contact tracking capabilities to help track movement and contact of people through bluetooth, GPS, or cellular positioning. Medical and approved agencies in many countries have begun to provide personnel with mobile-supported health certificates of infection risk that can be used to determine whether to allow access to a given place or service. These solutions provide piecemeal assistance that may be useful but still incoherent, require human supervision and integration, and are often based on simplistic and therefore inaccurate assumptions about the physical context and human interactions therein.
Disclosure of Invention
For physical environments with high value human presence and activities, such as schools, factories, hospitals, nursing homes, office buildings, it is possible and desirable to take a more systematic and automated approach to health safety against infectious diseases such as COVID-19. In order to fully and accurately model infection risk and spread, it is necessary to accurately model the presence, context, movement, and activity of people within a physical environment over time. On top of this model, the distribution of infection risk can then be computationally modeled based on the actions and movements of the personnel, while accounting for pathogen propagation patterns and nearby physical context and environmental dynamics, creating a more accurate infection spread and risk model that can drive more accurate monitoring, more actionable alarms and recommendations, more effective security measures, and more efficient activities and results.
The present invention includes many embodiments and aspects of a novel system and framework to systematically improve the health safety of people against infectious diseases such as COVID-19 in a physical environment. In a preferred embodiment, the present invention computationally models people, places, things, activities and relationships within a physical environment by connecting to an integration layer of sensors, models and devices present within the environment to create a semantic representation of the temporal dynamics of the spatial environment and the people, places and things therein. In a preferred embodiment, the present invention uses semantic representations of the environment and human activities to model pathogen transmission over time, the potential patterns and extent of spread of infection, and the risk of infection to people within the environment. In a preferred embodiment, the present invention uses these computational models to enable a set of applications to systematically analyze, predict, assist, and automate in real-time and over time tasks that result in a reduction in the risk of infection for personnel within an environment. In a preferred embodiment, the present invention provides a framework for computationally evaluating and optimizing the effectiveness of security measures verified against real and simulated physical environments, which can then be deployed into the physical environment and automatically monitored and enforced.
Drawings
Some embodiments of the invention are illustrated by way of example and not limitation in the figures of the accompanying drawings, in which like references may indicate similar elements, and in which:
fig. 1 shows the system architecture in a preferred embodiment of the present invention, which consists of a set of interconnected subsystems, labeled systems 1 through 12.
FIG. 2 illustrates the architecture and components for a preferred embodiment of the system 1 that automatically detects salience information from a physical environment, organized and stored in a database that describes the semantics of people, their presence, relationships and activities over time within a spatial context.
FIG. 3 illustrates the architecture and components for a preferred embodiment of the system 2 that creates and maintains an integrated health profile for personnel within the physical environment.
Fig. 4(a) shows a preferred embodiment of a computational model for modeling infection risk and spread by infection spread contours and infection risk trails associated with people in context.
FIG. 4(b) illustrates a preferred embodiment of a computational model for safe distances and safe presence within a physical environment.
FIG. 5 illustrates the architecture and components for a preferred embodiment of the system 3, which uses information about people in context and personal health profiles to construct a computational model of infection risk and spread.
FIG. 6 illustrates the architecture and components for a preferred embodiment of the system 4 that provides application development and deployment support for field applications that can deliver services and experience utilizing a computational model of environment, human presence and activity, and infection risk to improve the health safety of personnel in context.
Fig. 7 illustrates an architecture and components for a preferred embodiment of the system 5 that provides automated monitoring and alerting of unsafe events such as unsafe distances or unsafe contacts within a physical environment.
FIG. 8 illustrates the architecture and components for a preferred embodiment of the system 6, which provides analysis over time that models the spread of infection of people and areas within a physical environment.
Fig. 9 illustrates the architecture and components for a preferred embodiment of the system 8 that supports systematic assessment of the effectiveness of security measures against infection risk in a physical environment.
FIG. 10 illustrates the architecture and components for a preferred embodiment of the system 9, which provides a testing tool for computationally modeling the effectiveness of security measures based on a testbench of a physical environment.
FIG. 11 illustrates the architecture and components for a preferred embodiment of the system 10, which describes a simulation-based system to measure security measure effectiveness and infection risk impact in a virtualized physical environment.
Detailed Description
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well as the singular forms, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, elements, components, and/or groups thereof.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and the present disclosure, and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
In describing the present invention, it will be understood that a number of techniques and procedures are disclosed. Each of these has its own benefits and each can also be used in combination with one or more, or in some cases all, of the other disclosed techniques. Thus, for the sake of clarity, this description will avoid repeating the various steps or every possible combination of systems in an unnecessary fashion. However, the description and claims should be read with the understanding that such combinations are entirely within the scope of the invention and the claims.
New methods, apparatus, systems, and applications for improving the health safety of personnel against infectious diseases such as COVID-19 within a physical environment are discussed herein. In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the present invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced without these specific details.
The present disclosure is to be considered as an exemplification of the invention and is not intended to limit the invention to the specific embodiments illustrated by the following figures or description. The present invention will now be described by reference to the accompanying drawings, which illustrate preferred embodiments. Figure 1 shows an overall system diagram of the invention. FIG. 2 illustrates saliency information from a physical environment. Fig. 3 shows a health profile for an individual. Fig. 4(a) shows infection risk modeling in a physical environment. Fig. 4(b) shows infection risk modeling in a physical environment. Fig. 5 illustrates infection risk modeling in a physical environment. FIG. 6 illustrates a field application development platform. Fig. 7 shows monitoring and alarms for infection risk. Fig. 8 shows analysis of infection spread patterns over time. FIG. 9 illustrates a model of the effectiveness of security measures. FIG. 10 illustrates a test tool for safety measure effectiveness. FIG. 11 shows a simulator for the effectiveness of security measures.
FIG. 1 depicts an architecture for a preferred embodiment of a novel system and framework that systematically improves the health safety of people in a physical environment against infectious diseases such as COVID-19. The systems 1-3 create a computational model of infection risk and spread within a physical environment based on modeling the environment and the personnel dynamics within it as a whole over time. Systems 5-7 are health safety applications that utilize these computational models to deliver monitoring, prevention, and analysis services through an application-enabled layer in system 4 to proactively manage the risk of infection. The systems 8-10 enable systematic assessment and testing of the effectiveness and efficiency of security measures in a physical environment to enable rapid feedback and enhancement of security measures for different domains and contexts. The systems 11-12 enable systematic optimization of physical space, security measures, workflows and testing strategies to maximize the positive impact of security measures while minimizing their negative economic impact on personnel and enterprises.
Fig. 2 shows the architecture and components for a preferred embodiment of the system 1 that automatically detects salient information from the physical environment through sensors and models from the environment (such as cameras, wireless beacons, thermal and environmental sensors) or from occupants in the environment (such as wearable devices, tags, smartphones or smart devices of a particular individual bound to that individual). These sensor data streams flow into a data lake, which in turn drives a set of models describing the spatial structure of the physical environment, as well as personal-related and environmental dynamics over time. This information is organized as a spatio-temporal database of entities, relationships, and events that describes the semantics of people, their presence, and activities in a spatial context.
In a preferred embodiment for the system 1, the saliency information describes the spatial structure and geometric topology of the physical environment, including objects within the environment, such as furniture and fixtures. The representation of the spatial structure includes, but is not limited to, a BIM (building information model), a 3D point cloud created by a camera using SLAM (simultaneous localization and mapping), and a 3D CAD model of the environment and its contents.
In a preferred embodiment for system 1, the significance information describes environmental dynamics within the physical environment, including but not limited to airflow, temperature, barometric pressure, humidity, luminosity, acoustics, and air quality.
In a preferred embodiment for system 1, the saliency information describes the personal and interpersonal dynamics of the persons within the environment, including but not limited to their position, orientation, speed, posture, clothing, mask or gloves, whether they are coughing or sneezing, and their physical contact and interaction with persons and objects over time.
In a preferred embodiment for the system 1, the information is calculated using sensor data collected from sensors deployed within the physical environment (such as cameras, thermal sensors or IOT) and from sensors attached to the person by wearable devices, smart phones or bio-sensors.
In a preferred embodiment for system 1, the information is stored in a database indexed by time and space to support efficient querying by time and location. The database describes the dynamics across people, places, and things, as well as their activities and relationships over time within the physical environment.
FIG. 3 illustrates the architecture and components for system 2 that create and maintain integrated health profiles for personnel within a physical environment. The profile integrates data from a number of approved data sources, including the individual's medical health records, disease and infection conditions, and test history. In addition, personal health profiles from the biometric sensors and wearable devices may also be integrated into the health profile.
In a preferred embodiment for the system 2, the health profile includes infection status to a given individual collected by integration with approved medical or health organizations to provide an authoritative and timely infection status for the occupants.
In a preferred embodiment for system 2, the health profile includes a medical health record for a given individual that describes the history of health checks and medical treatments, as well as any prescribed tests or medications.
In a preferred embodiment for system 2, the health profile includes a history of tests specific to infectious diseases, such as PCR and antibody tests against COVID-19, and other tests against the individual's susceptibility to and immunity to infectious risk.
In a preferred embodiment for system 2, the health profile includes biological sensors from wearable devices on the person that record various biological and physiological metrics including, but not limited to, blood pressure, blood glucose, heart condition, and lung health.
FIG. 5 illustrates the architecture and components for the system 3, which uses the significance information of a person in context and their personal health profile to construct a computational model of infection risk and spread. Fig. 4(a) shows a set of computable infection risk models including infection spread profiles and trace models associated with individuals, and fig. 4(b) shows a computable model of safe distances and safe presence for a given individual within an environment. The modeling platform provides data processing and storage capabilities for efficient model creation, training, and reasoning. The results of the time-varying contours and trails for the person are stored in a risk model database.
In a preferred embodiment for system 3, the computational model supports a variety of query and processing modes, including real-time, on-demand, incremental, and batch-based modes.
In a preferred embodiment for the system 3, the computational model includes epidemiological profiles of infectious diseases and their transmission mechanisms, such as through physical contact, respiratory droplets, air duration, and pathogen life cycle.
In a preferred embodiment for the system 3, the risk modeling comprises a computable infection spread profile model for the individual. The infection spread profile model describes a time-varying 3D spatial region associated with an individual, wherein an elevated risk of infection exists assuming the individual is infected; the shape and duration of the outline depends on personal and environmental factors in the nearby physical context.
In a preferred embodiment for the system 3, the risk modeling comprises a computable infection spread trace model for the individual. The infection spread trace model describes a time-varying 3D surface region associated with an individual where there is an elevated risk of infection due to pathogen transmission through respiratory droplets or physical contact deposited on the physical surface region.
In a preferred embodiment for the system 3, the computational risk model may be represented by a risk gradient describing a probability distribution of infection risk, wherein the risk gradient spans a spatial region and varies over time. Alternatively, based on defined risk thresholds, the computational risk model may be represented by discrete boundaries for outline and trace regions. The risk gradient and risk threshold are based on a pathogen model that describes its life cycle and mode of transmission over time.
In a preferred embodiment for system 3, risk modeling uses counter-posed facts as the basis for causal reasoning to learn from observations, for example, to periodically update with factual evidence in an iterative fashion for what a person is likely to have spread pattern of infection in the environment, assuming that the person is infected.
In a preferred embodiment for system 3, risk modeling includes a calculable safe distance model for a group of people in a physical environment that models the aggregate infection risk of the group based on the joint distribution over time of their individual risk profiles.
In a preferred embodiment for the system 3, the risk modeling comprises a computable security presence model for the individual, which models the risk of infection when the individual is present at a given location or when in physical contact with a given surface area.
In a preferred embodiment for the system 3, the risk modeling for people and environment is stored in a database indexed in time and space, where the risk profile of each person, object and surface area is organized as a 3D space of time varying risk gradients.
FIG. 6 illustrates the architecture and components for system 4, which provides application development and deployment support for field applications. The application layer enables applications to be spatially anchored to entities or locations within the physical environment, obtaining context and infection risk awareness by accessing computational models of people and infection risks in the context and the computational resources required to run the application. Using this application platform, the present invention will deliver a set of services and experiences to monitor, alert, prevent, and optimize the risk of infection within the physical environment.
In a preferred embodiment for system 4, the platform models the live spatio-temporal context of the physical environment by a semantic representation of people, places, things, activities and relationships based on the environmental model and activity whereabouts of system 1, the health profile of system 2, and the infection risk model of system 3.
In a preferred embodiment for system 4, the platform enables an application to spatially anchor one or more entities (individuals or things or places) in a physical environment to access a live context in the vicinity of the anchored entity.
In a preferred embodiment for the system 4, the platform enables applications to be deployed and operated within the physical environment to receive and respond to real-time events and signals from the environmental dynamics and the field context of human activity, analyze, predict, assist or automate tasks within the physical environment.
Fig. 7 illustrates an architecture and components for system 5 that provide automated monitoring and alerting of unsafe events within a physical environment, such as unsafe distances or unsafe contacts. Unsafe events may be defined by programmable rules that drive an unsafe event detection module, which in turn feeds into the monitoring and alarm system to produce results and notifications to personnel through consoles and mobile endpoints.
In a preferred embodiment for system 5, elevated risk events include, but are not limited to, violations of safety distances between people as determined based on a safety distance model of system 3.
In a preferred embodiment for system 5, the elevated risk event comprises an individual entering an unsafe area or contacting an unsafe surface as determined based on the computable safe presence model of system 3.
In a preferred embodiment for use with system 5, elevated risk events include: a person who enters an area where there is a high likelihood of personal contact does not wear a mask or gauze mask or has elevated temperatures or physical symptoms.
In a preferred embodiment for the system 5, the monitoring and alerting are performed in real time.
In a preferred embodiment for system 5, the alert is delivered to personnel within the physical environment, or directly to the person at risk via their mobile phone or wearable device.
FIG. 8 illustrates an architecture and components for system 6 that provide analysis over time to model spread of infection across people and areas within a physical environment. Three models are created based on a computational model and detected unsafe events-a time-tracked representation of unsafe person-to-person contact over time necessary for contact tracking and a space-tracked representation of time-varying areas of elevated infection risk within a physical environment.
In a preferred embodiment for the system 6, the analysis includes time tracking for an infected or potentially infected individual over a period of time to identify, alert and isolate in a cascaded manner personnel that may have been infected by the individual through unsafe distances or unsafe contacts.
In a preferred embodiment for the system 6, the analysis includes spatial tracking of traces of potential infection risks left in the air and on surfaces by occupants over a period of time in the physical environment to provide guidance for cleaning, space planning, personnel navigation and occupancy planning.
System 7 in fig. 1 provides accurate contact tracking for personnel based on unsafe personnel contact in the physical environment over a period of time using representations from system 6 and integrated with other contact tracking systems such as mobile apps or OS capabilities for GPS and bluetooth tracking.
In a preferred embodiment for system 7, the contact tracking capabilities within the physical environment are integrated into other contact tracking programs and applications provided by other vendors or software developers.
FIG. 9 illustrates the architecture and components for system 8 that support computational modeling of the effectiveness of security measures to prevent spread and risk of infection in a physical environment. The effectiveness metric may use the infection risk models of system 5 and system 6 to calculate the aggregate risk with and without security measures over a period of time.
In a preferred embodiment for system 8, the security measures are programmable rules that can be used by the trigger engine to detect when a security measure is applicable, and that can be used by the violation check engine to detect whether a security measure has been executed and followed.
In a preferred embodiment for system 8, safety measures include rules regarding personnel behavior, including but not limited to wearing a mask, avoiding physical contact, practicing safe distances, and avoiding crowds.
In a preferred embodiment for system 8, the safety measures include rules regarding facility management, including but not limited to limits regarding occupancy density, safe distance, body temperature checks, and frequency of cleaning.
In a preferred embodiment for the system 8, the effectiveness of the security measures is calculated based on the rate of adoption of these measures, their effectiveness measured by the risk model, and the efforts taken by occupants and employees.
FIG. 10 shows the architecture and components for system 9, which is a test tool for the effectiveness of security measures for a physical environment based test bench. The test specifies a set of candidate security measures to run against a set of environments over a period of time. Tests are configured and deployed to test benches, then managed, and results collected and aggregated.
In a preferred embodiment for the system 9, the test bench consists of a collection of test physical environments, each of which is equipped with a system 6 that can monitor, analyze and evaluate the effectiveness of security measures within the environment.
In a preferred embodiment for system 9, the test station manages a set of tests, where each test specifies (1) a set of security guidelines to test, (2) a period of time to run the test, and (3) a set of test physical environments in which to run the test.
In a preferred embodiment for system 9, the test station evaluates the effectiveness of the test after it is complete by calculating an aggregate measure of effectiveness in all test physical environments specified in the test during the test period.
In a preferred embodiment for system 9, the test rig performs a controlled a/B experiment within a set of physical environments using random selection of occupants to form a control group and a test group, and then compares the aggregate effectiveness metrics of the control group and the test group during the test period.
FIG. 11 illustrates an architecture and components for system 10 that describes a simulation-based system to measure security measure effectiveness and infection risk impact in a virtualized physical environment. A simulated environment (such as a school) having a set of simulated people (such as students and teachers) and interacting through a set of simulated scenarios (such as class or break hours) is created and then run through a simulator to measure the impact of infection spread and risk. Each simulation run collects a set of statistics that form the basis for an evaluation of the effectiveness of the modeled security measures. Simulated models of the environment and scene may be created based on actual models and data from the physical environment.
In a preferred embodiment for the system 10, the simulator operates in a simulated environment that mimics the physical environment, such as schools, hospitals, stores and factories, in high fidelity and in 3D.
In a preferred embodiment for system 10, the simulator may be built on top of a 3D virtual gaming environment, such as Unity 3D, Blender or non Engine 4.0.
In a preferred embodiment for the system 10, the simulator supports simulated personnel within the virtual environment, including physical characteristics, movements and actions that may affect the risk of infection, such as coughing, touching or wearing a mask.
In a preferred embodiment for the system 10, the simulator supports a simulated scene that mimics human behavior and activity within a physical environment, such as a child in a classroom during class or break, a worker on a factory line, or a patron in a store.
In a preferred embodiment for system 10, the simulator uses data from real human activity in the physical environment acquired by the systems of 1, 2 and 3 to create a simulated scene.
In a preferred embodiment for system 10, the simulator supports a simulated infection risk model within the virtual environment by simulating the computational models described in system 3, including but not limited to: the disease propagation model, infection risk profile and trail for the person are represented as infection risk probability gradients across space and time.
In a preferred embodiment for the system 10, the simulator models a set of simulated safety measures that alter the behavior of simulated personnel within the virtual environment, such as safety distances or mask wear, with parameters to model the probability distribution of the likelihood of the personnel adopting the measures.
In a preferred embodiment for system 10, the effectiveness of a simulated security measure is determined by deploying a set of simulated security measures across a set of simulated scenes in a set of virtual environments and then analyzing the resulting simulated infection risk profiles and unsafe events, the simulator evaluating the effectiveness of the security measures through simulation.
The system 11 in fig. 1 enables optimization of the utility efficiency of activities in a physical environment by measuring the economic yield of activities within the environment, and then using data and models from systems 1-6 and testing techniques of systems 9-10, design and optimization across occupancy, activity, safety measures and workflows to maximize economic yield against acceptable infection risk thresholds.
In a preferred embodiment for system 11, the metric used to measure economic output from the physical environment may be domain specific, such as measuring the number of successful transactions or units of work completed, or it may be domain independent, such as measuring the number of hours a person is produced over a period of time.
In a preferred embodiment for system 11, optimization includes changing the layout and configuration of the physical environment to increase physical isolation of people in the upper and lower contexts, including but not limited to physical partitions, screens, or walls, to improve ventilation airflow.
In a preferred embodiment for the system 11, the optimization includes locking out people with high risk of infection due to high frequency and range of interaction, and frequent active testing to reduce the risk of spread.
In a preferred embodiment for the system 11, the optimization includes increasing the frequency of cleaning to reduce the physical extent and duration of infection risk.
In a preferred embodiment for system 11, optimization includes redesigning the occupancy or workflow using infection risk modeling through test benches or simulations to improve the efficiency of human activities within the risk boundaries.
By using data and models from systems 1-6, calculating infection risk profiles for individuals based on activity whereabouts and risk exposures for individuals from system 6, and then prioritizing testing and vaccines for the individuals based on the risk profiles for the individuals, system 12 in fig. 1 enables optimization of infectious disease testing and vaccines for the individuals in a physical environment, thereby maximizing the impact of limited testing and vaccine capabilities.
In a preferred embodiment for system 12, the optimization comprises: the risk of superspread infection for a given individual is modeled based on the frequency, extent, and nature of the given individual's contact and interaction with other people within the environment.
In a preferred embodiment for system 12, the optimization comprises: a predictive model for the person's hyperdiffuse infection risk is constructed that is trained based on the actual activity whereabouts from the physical environment and an infection risk model.
In a preferred embodiment for system 12, testing and vaccination of persons is prioritized using actual or predicted superspread infection risk models of persons such that those persons with the highest risk potential of infection are tested or treated earlier to maximize the risk-reducing impact of limited testing and vaccination capacity.
Claims (11)
1. A system or apparatus for automatically monitoring and/or systematically reducing the spread and risk of infection with an infectious disease such as COVID-19 in a physical environment, comprising:
a) a digitizing component that creates a digital model of the physical environment and dynamics within the physical environment including, for example, a person, a place, a thing, a state of the person, place, thing over time, an action, an interaction, and/or a relationship,
b) an infection risk modeling component that creates a spatio-temporal model of infection spread and risk for individuals, objects, and/or spaces within the physical environment,
c) an application component that monitors a physical environment for situations with elevated risk of infection and directs manual and automatic actions to reduce the risk.
2. The digitizing component of claim 1, wherein the digitizing component automatically detects significance information from a physical environment and human activity within the physical environment that can be used to accurately model the infection risk of an infectious disease, wherein the information is represented as a set of spatially anchored and temporally varying environmental models and a whereabouts of activity of a person within the physical environment.
3. The infection risk modeling component of claim 1, wherein the modeling of infection risk and spread for a person in a physical environment is based on a space-time risk gradient representation of infection risk that reflects basic epidemiology of a pathogen and propagation characteristics of the pathogen over time.
4. The application component of claim 1, wherein the application component automatically and computationally monitors and alerts for elevated risk of infection in a physical environment, including events that can lead to elevated risk of infection, such as unsafe distance or unsafe contact.
5. The application component of claim 1, wherein the application component is capable of computationally analyzing patterns of elevated spread of infection of a person in a physical environment over a period of time by creating a spatial and temporal representation that describes a history of unsafe contact and unsafe presence of the person in the physical environment.
6. The system or apparatus of claim 1, further comprising a measurement component that strictly assesses the effectiveness of security measures based on a digitization and infection risk model observed over time.
7. The measurement assembly according to claim 6, wherein said measurement of the effectiveness of safety measures uses a test tool on a test bench of a physical environment, wherein each test environment is equipped with a system according to claim 1.
8. The measurement assembly according to claim 6, wherein the measurement of security measure effectiveness uses a simulator capable of simulating a physical environment and occupancy and activity of the physical environment, wherein simulated scenarios and behaviors are based on data from a physical environment equipped with the system according to claim 1.
9. The system or apparatus of claim 1, further comprising an optimization component that supports overall optimization and trade-offs across infection risk, security measures, and economic impact.
10. The optimization component of claim 9, wherein the optimization component is capable of optimizing utility efficiency of activities in the physical environment by measuring economic outcome from activities within the environment, and then designing and optimizing across occupations, activities, security measures, and workflows to maximize the economic outcome against an acceptable infection risk threshold.
11. The optimizing component of claim 9, wherein the optimizing component is capable of optimizing infectious disease testing and vaccines against people in a physical environment by: an individual's infection risk profile is calculated based on the person's activity whereabouts and risk exposure, and then the tests and vaccines to the person are prioritized based on the person's risk profile to maximize the impact of limited test and vaccine capabilities.
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