WO2023079312A1

WO2023079312A1 - Method of factoring the context of an encounter into determination of disease infection risk

Info

Publication number: WO2023079312A1
Application number: PCT/GB2022/052812
Authority: WO
Inventors: Peter WHAWELL; Wael ELRIFAI
Original assignee: Proxximos Limited
Priority date: 2021-11-08
Filing date: 2022-11-07
Publication date: 2023-05-11

Abstract

Disclosed is a method of collecting information for providing to a risk calculation application which calculates the risk of a person contracting an infectious disease, comprising the steps of: collecting first information from a mobile device which has been carried by a first person who has entered a particular physical location, the information including: first proximity data indicating a distance between the first person and at least one other person who has also entered the particular physical location and carried a mobile device; second proximity data indicating a distance between the first person and a hub located at the particular physical location; collecting second information from the hub, said second information indicating a specific context of the particular physical location; and providing the collected first and second information to the risk calculation application which performs the further step of calculating a risk of a person contracting an infectious disease, wherein the first and second information is used by the risk calculation application in modifying the risk associated with a given proximity,

Description

METHOD OF FACTORING THE CONTEXT OF AN ENCOUNTER INTO DETERMINATION OF DISEASE INFECTION RISK

Field of the Invention

The present invention relates to providing a technical solution assist in reducing the transmission of an infectious disease from one person to another or from an object or place to a person, and more specifically to a method of technically tracking the risk of transmission of an infectious disease to an individual enabling preventative measures to be taken.

Background of the Invention

Infectious diseases, such as COVID-19, can rapidly spread from one person to another, as we have seen during the COVID-19 pandemic. Being in close contact with an infected person or spending a significant amount of time in a poorly ventilated room where an infected person has been, or poor cleanliness and hygiene of a physical location are the key means of transmission of infectious disease that can be address via the invention.

In response to the pandemic various authorities have at times introduced policies or preventative measures aimed at reducing the spread of infection. A problem with general policies is that they may not accurately reflect the underlying risk. This can enable more risky activity to be permitted and over time lead to decreased compliance as users perceive a disconnect between measures and actual risk.

A goal of the present invention is to provide an improved technical measure to reduce the risk of onward transmission of an infectious disease by tracking the risk of transmission during an interaction between two people, or infections occurring at a common location in the case of poor cleanliness or hygiene. This can lead to improved scientific modelling and decision making. Outputs can include improved data models, improved correlation detection and improved direction to mitigate risk.

A technical issue that must be solved for such a system to work effectively is for smart, body borne devices (wearables or phones) to understand the fixed context in which an encounter with another user took place. There are several fixed aspects of an encounter that are relevant. In particular, the following aspects of the fixed context in which an encounter took place should preferably be addressed:

1. If the transmission is related to poor hygiene enabling transmission by fomites (e.g. contaminated surfaces) or by food hygiene (diarrhoeal diseases) then it is important to know when infected people have visited a common location, determined by their proximity to that location.

2. If the transmission is via human-to-human (e.g. communicable) transmission then it is important to understand the type of venue where the encounter took place. An encounter indoors carries more risk than the same encounter outdoors. And certain indoor environments carry more risk, for example gyms, than other locations, such as libraries. 3. The presence of other interventions for example protective measures (Personal Protective Equipment (PPE) or plastic/acrylic screens) also effects the risk of transmission from human- to-human and so a system designed to track the risk of transmission needs to also understand this context.

4. The propagation characteristics of Bluetooth radio signals. The apparent strength of a Bluetooth signal between two devices will be significantly stronger is some environments than others. Without correction, a system would judge the distance between devices inaccurately. The Bluetooth signal can appear much better in unobstructed open space for example, than inside the metal box of a bus with lots of metal seats, where there is a lot of multi-path noise. Knowing how to correct for this factor is important to enable accurate calculation of risk.

A technical issue that must be solved for such a system to work more effectively is for smart, body borne devices (wearables or phones) to understand the variable context in which an encounter with another user took place. There are several aspects of an encounter that are relevant and which vary over time. In particular, the following aspects of the variable context in which an encounter took place should preferably be addressed:

1. If the transmission is by an airborne mechanism, then an estimate of how much virus has accumulated in the air would be very helpful. The more time an infected person spends in an enclosed space the more airborne virus particles will accumulate in the air. Ventilation removes these particles. The concentration of virus in the air is therefore affected by the volume of the space and effectiveness of ventilation. Ventilation is variable depending on whether windows are open, the wind conditions, or the power of a mechanical ventilation system.

2. Other variable factors. For example, it may be that ambient sound in a venue directly effects the transmission risk via the droplet (face-to-face speaking) mechanism. The louder the ambient noise then the more forcefully people speak which generates more droplets that can carry to the other individual. And individuals lean in towards each other to improve hearing in a noisy space. This is not fully understood currently, but by measuring such factors a digital contact tracing system can discover these factors.

Without correction, a system would judge the risk of cross infection inaccurately. Knowing how to correct for the variable context is important to enable accurate calculation of risk.

A further set of adjustments that the system could factor into risk calculations relates to the individual carrying the device. Characteristics of the individual will alter their level of infectiousness or infectious, and their susceptibility of catching the virus if they meet someone infectious. These factors could include whether or not they are vaccinated, the type and date of vaccination, their age, and possibility other personal characteristics.

A technical issue that must be solved for such a system to work more effectively is for body borne devices (wearables or phones) to understand that they have become separated from the person who would normally be carrying the phone or wearing the wearable (e.g, smart watch or badge).

The purpose of the invention is to avoid taking into account, in calculating the transmission risk of an infectious disease, a detected proximity between devices, in a situation where the devices (which are detected as being in proximity to each other) have become separated from the person who normally carries the device.

Another purpose is to manage battery consumption, enabling some functions to close-down for periods when such separation has occured.

As examples, the following circumstances are instances when users may be separated from the devices that they normally carry or wear:

1. The device might be left behind accidentally. For example, a user may enter a shop but leave their mobile phone in their car.

2. Some work environments have a no-mobile phone policy. For example, in prisons the staff do not take phones into the spaces occupied by prisoners. Mobile phones are stowed in racks next to each other. These phones need to recognise that they are separated from their users so that the system can disregard the apparent close proximity in calculating the risk of transmission of an infectious disease.

3. In some contexts, phones are removed and collected. For example, when entering a sensitive building, or at a school where phones are collected during registration. Again, these phones need to avoid recording false encounters between the users. That is, if such separation of phone from user is not taken into account, and two phones are detecting each other as being in close proximity to each other, but the two people who normally carry those phones are not in proximity to each other, so this should be accounted for in order to give a more accurate determination of the risk of contamination. This is very important to avoid widespread disruption through false detection of proximity.

4. When a user is asleep and not carrying their device. Detecting the separation creates the opportunity to preserve battery usage.

5. In the case of wearables, it could be the case that several of the wearable devices may be charged together in a community charging station. The devices may be active in these circumstances and would detect that they are in close proximity to each other. However, the person who normally wears the wearable device is not in close proximity to the person who wears the other wearable device that is also charging in the community charging station. Summary of the Invention

The present invention provides a method of collecting information for providing to a risk calculation application which calculates the risk of a person contracting an infectious disease, comprising the steps of: collecting first information from a mobile device which has been carried by a first person who has entered a particular physical location, the information including:

(a) first proximity data indicating a distance between the first person and at least one other person who has also entered the particular physical location and carried a mobile device;

(b) second proximity data indicating a distance between the first person and a hub located at the particular physical location; collecting second information from the hub, said second information indicating a specific context of the particular physical location; and providing the collected first and second information to the risk calculation application which performs the further step of calculating a risk of a person contracting an infectious disease, wherein the first and second information is used by the risk calculation application in modifying the risk associated with a given proximity.

By the use of the present invention, a risk calculation application can be provided with technical information which allows the application to provide a much more accurate calculation of the risk of a person contracting an infectious disease, as compared to the risk calculation application using only the proximity of mobile devices to each other.

This technical information relates to the fixed context of an encounter , where a user carrying a mobile phone or wearing a smart wearable, enters a location where the location is one in which other such users are currently present, or have been present recently. By taking this fixed context information into account, the risk calculation performed by the risk calculation application provides much more accuracy and can be based on much more factual information about the location where the encounter takes place.

Preferably, the hub is a Bluetooth receiver/transmitter.

Preferably, the mobile device is a mobile phone or smart phone.

Preferably, the mobile device is a smart watch or wearable computer.

Preferably, the first information also includes information specific to the first person.

This allows the risk calculation to be improved further, based on specifics of the first person. Preferably, the information specific to the first person includes a vaccination status of the first person, an age of the first person or historical data regarding the first person's infection with the infectious disease.

This further allows the risk calculation to be improved, based on the further specifics of the first person.

Preferably, the specific context of the particular physical location is an indication of a type of venue of the particular physical location.

Different types of venues have different risks of infection, so this feature allows better calculation of the risk of infection by taking into account the specifics of the type of location (for example, a gym/fitness centre may have a higher risk than a library).

Preferably, the specific context of the particular physical location is an indication of whether a physical protective device is present at the particular physical location.

This also helps to increase the accuracy of the risk calculation, because the presence of such a protective device helps reduce the risk, and if the risk calculation application is made aware of such presence, the presence of the same can be taken into account.

Preferably, the physical protective device is a screen for separating people at the particular physical location.

Preferably, the method further comprising the step of the mobile device providing information to the application regarding protective measures the person carrying the mobile device has in place while visiting the particular physical location.

This further helps to increase the accuracy of the risk calculation because, for example, if the person is wearing protective clothing, the person’s risk reduces significantly, and the application would not know this unless such information was provided thereto.

Preferably, the second information also includes proximity data indicating the distance between the first person and the hub, and wherein an average is taken of: a) the proximity data included in the first information, indicating the distance between the first person and the hub, and b) the proximity data included in the second information, indicating the distance between the first person and the hub.

This calculation of an average helps to increase the accuracy of the proximity data determination, thus helping to increase the overall accuracy of the risk calculation assessment.

Preferably, the hub registers its particular physical location with the application. This enables the application to make its risk assessment based on the specific location in which the hub is located.

Preferably, ultra wide band, UWB, radio signals are used to determine the first and second proximity data.

UWB signals have been found by the Applicant to provide for an increased accuracy in the proximity data determination.

Preferably, the hub uses ultra wide band, UWB, radio signals to determine the proximity data indicating the distance between the first person and the hub.

Preferably, a plurality of hubs are present at the particular physical location, and the plurality of hubs are used as a communications relay to communicate with the application.

This allows the plurality of hubs to work together to relay information, in a mesh configuration, thus compensating, for example, for a temporary loss of internet connectivity by one of the plurality of hubs.

Preferably, the hub transmits a rotating temporary identifier.

This helps to improve the privacy/security of the information.

Preferably, the hub records a signal strength of the mobile device.

This helps to solve the problem of Bluetooth signals being noisy and thus can sometimes be found to have lower accuracy in determining proximity data. Having the hub record the signal strength, increases such accuracy.

Preferably, the risk calculation application provides a data model of interactions and associated risk associated with an individual or a group of individuals.

The invention also provides a system comprising means adapted for carrying out all the steps of the method according to the above described method.

The invention also provides a computer program comprising instructions for carrying out all the steps of the method according to the above described method, when said computer program is executed on a computer system.

The invention provides a method of collecting information for providing to a risk calculation application which calculates the risk of a person contracting an infectious disease, comprising the steps of: collecting first information from a mobile device which has been carried by a first person who has entered a particular physical location, the information including: (c) first proximity data indicating a distance between the first person and at least one other person who has also entered the particular physical location and carried a mobile device;

(d) second proximity data indicating a distance between the first person and a hub located at the particular physical location; collecting second information from the hub, said second information comprising data received from one or more sensors that sense the variable context of the particular physical location in which the one or more sensors are placed; and providing the collected first and second information to the risk calculation application which performs the further step of calculating a risk of a person contracting an infectious disease, wherein the first and second information is used by the risk calculation application to vary the risk associated with a given proximity.

This technical information relates to the variable context of an encounter, where a user carrying a mobile phone or wearing a smart wearable, enters a location where the location is one in which other such users are currently present, or have been present recently. By taking this variable context information into account, the risk calculation performed by the risk calculation application provides much more accuracy and can be based on much more factual information about the location where the encounter takes place.

Preferably, the hub is a Bluetooth receiver/transmitter.

Preferably, the mobile device is a mobile phone or smart phone.

Preferably, the mobile device is a smart watch or wearable computer.

This allows the risk calculation to be improved further, based on specifics of the first person.

Preferably, the information specific to the first person includes a vaccination status of the first person, an age of the first person or historical data regarding the first person's infection with the infectious disease.

Preferably, the one or more sensors include a carbon dioxide, CO2, meter.

Prefererably, the one or more sensors include a sensor for ambient sound.

Preferably, the one or more sensors include a sensor for temperature. Preferably, the one or more sensors include a sensor for humidity.

Preferably, the second information also includes proximity data indicating the distance between the first person and the hub, and wherein an average is taken of: c) the proximity data included in the first information, indicating the distance between the first person and the hub, and d) the proximity data included in the second information, indicating the distance between the first person and the hub.

Preferably, the hub registers details of the sensors with the application.

UWB signals have been found by the Applicant to provide for an increased accuracy in the proximity data determination

This allows the plurality of hubs to work together to relay information, in a mesh configuration, thus compensating, for example, for a temporary loss of internet connectivity by one of the plurality of hubs

Preferably, the hub transmits a rotating temporary identifier.

This helps to improve the privacy/security of the information.

Preferably, the hub records a signal strength of the mobile device.

This helps to solve the problem of Bluetooth signals being noisy and thus can sometimes be found to have lower accuracy in determining proximity data. Having the hub record the signal strength, increases such accuracy

Preferably, the hub records information regarding the location where the hub is located.

Preferably, the information regarding the location where the hub is located includes the volume of space associated with the location. Preferably, the second proximity data also includes a period of time that the first person spends in proximity to the hub.

Preferably, the risk calculation application provides a data model of interactions and associated risk associated with an individual or a group of individuals

The invention also provides a system comprising means adapted for carrying out all the steps of the method according to any preceding method claim.

The invention also provides a computer program comprising instructions for carrying out all the steps of the method according to any preceding method claim, when said computer program is executed on a computer system.

The invention provides a method of collecting information for providing to a risk calculation application which calculates the risk of a person contracting an infectious disease, where the method involves the collection of information from a mobile device which has been carried by a first person who has entered a particular physical location, the information including proximity data indicating a distance between the first person and at least one other person who has also entered the particular physical location and carried a mobile device, the method comprising the steps of: detecting that the first person has become separated from the mobile device; and providing the collected information to the risk calculation application which, upon receiving the collected information, performs the further step of calculating a risk of a person contracting an infectious disease, where the application disregards the collected information corresponding to the detection that the first person has become separated from the mobile device.

By the use of the present invention, a risk calculation application can be provided with technical information which allows the application to provide a much more accurate calculation of the risk of a person contracting an infectious disease, as compared to the risk calculation application not disregarding the detected separation.

Preferably, the mobile device is a mobile phone or smart phone.

Preferably, the mobile device is a smart watch or wearable computer.

Preferably, the step of detecting that the first person has become separated from the mobile device includes detecting that the mobile device has remained stationary for a time period which is longer than a threshold time period.

Preferably, a machine learning algorithm is used to determine a measure of likelihood that the person has become separated from the mobile device based on location history. Preferably, the risk calculation application provides a data model of interactions and associated risk.

Preferably, the method includes a step of providing a measure of risk associated with an individual or a group of individuals.

Preferably, the method includes a step of providing an identification of a location associated with a risk above a threshold.

The invention also provides a computer program comprising instructions for carrying out all the steps of the above described method, when said computer program is executed on a computer system.

Brief Description of the Figures

Figure 1 is a block diagram of the overall system, according to a preferred embodiment of the present invention, specifically, the overall technical components required to modify transmission risk according to the fixed context of an encounter, or to identify repeated infection results from poor cleanliness or hygiene at the location;

Figure 2 is a block diagram showing the details of a hub device, according to a preferred embodiment;

Figure 3 is a flow chart showing the steps carried out by the process, according to a preferred embodiment of the present invention;

Figure 4 is a block diagram of the overall system, according to a preferred embodiment of the present invention, specifically, the overall technical components required to modify transmission risk according to the variable context of an encounter;

Figure 5 is a block diagram showing the details of a hub device , according to a preferred embodiment of the present invention;

Figure 6 is a flow chart showing the steps carried out by the process, according to a preferred embodiment of the present invention;

Figure 7 is a block diagram of the overall system, according to a preferred embodiment of the present invention; and

Figure 8 is a flow chart showing the steps carried out by the process, according to a preferred embodiment of the present invention. Detailed Description of the Preferred Embodiments

Figure 1. Overall system

Figure 1 is a block diagram of the overall system which is used to obtain information regarding the context of an encounter between two people or between one person and a particular location, and to feed that information regarding such context into an application for assessing the risk of infection of a disease.

100. Hub Device . This is a device (such as a Bluetooth hub) present at a specific location that contains a unique reference number forthat location. The purpose of this hub is to be visible to moving devices 200/300 (e.g. smartphones or smartwatches) carried by people visiting the location (who are carrying compatible software on a mobile/smart phone or a wearable device such as a smartwatch).

The hub 100 preferably transmits a rotating temporary identifier. The purpose of the rotation of the temporary identifier is to avoid privacy intrusion from a malicious actor who might otherwise record a fixed identifier and examine logs in the device carried by the user to determine the places they had visited.

The moving devices (200, 300) carried by users measure their proximity to the hub 100. The purpose of this proximity measurement is that it enables the hub 100 to indicate the presence of some contextual information, such as, for example, a protective screen that is present at a retail point of sale (cash register or till). The hub 100 would be located at the cash register with such a protective screen, in this example. The fixed screen only protects encounters for those people who are physically located at the screen. To calculate risk, both proximity to the hub 100 and the use of the hub 100 to indicate the contextual information, such as a screen, are used. The hub 100 may also receive information from the movable devices carried by users, such as, for example, that a particular user is wearing protective clothing.

In particularly large and complex locations, more than one hub 100 may be placed. The purpose of this is to create a mesh, such as a Bluetooth mesh, in the location. This mesh can then be used to more accurately determine the proximity between people than would be possible without the mesh, using, for example, standard Bluetooth mesh micro navigation technologies.

The hub or hubs 100 at a location are registered in an application (400) where all relevant data about the fixed context is held. That data includes, where applicable, the information about the location and context, such as:

- the means to associate the rotating identifier seen by mobile phones and/or wearables with the actual data record for that unique hub 100; - the actual location (preferably only accessed if the application reveals that infections are occurring in the same place). The purpose of this is to identify the location acting as the source of infection;

- the type of location (restaurant, gym, office, shop, outdoors, outdoors but covered, etc). The purpose of this is to apply different risk allocation to encounters depending on the differing risks according to the type of location

- other mitigations in place at the location (e.g the presence of a protective screen or use of PPE, the presence of social distancing measures if in place, etc). The purpose this is to modify the risk allocated to an encounter according to the presence of other mitigations

- factors that might interfere with the clean propagation of Bluetooth radio signals (size of space, type of walls, density and type of furniture, etc)

- data about the Bluetooth mesh if present (the relative location of the various hubs at the location).

200. Mobile Phones. The mobile phones 200 carry an app that enables them to detect and record information about other phones 200, wearable devices 300 and the hubs 100 that the user encounters. The purpose of this is to gather the proximity and context data necessary for the application (400) to accurately calculate the risk that disease transmission may have taken place. It also enables the application to determine when infections are occurring at a common location (infection with norovirus - food poisoning, for example). This enables public health authorities to rapidly locate the source of a public health issue.

The app on the phones 200 draws on the open source software technology created in the Linux Foundation Public Health Herald project. The purpose of that project is to develop code that ensures reliable communication between two mobile phones no matter what operating systems are used and no matter what model of phones are in use. The mobile phones 200 preferably record the rotating identifiers of the hubs 100 that have been seen and this data is sent to the application 400 when necessary (e.g. when the user of the device is thought to carry an infection, or continuously if the operator of the system requires the data to monitor social mixing and hence risk levels of outbreaks should an infection occur).

300. Wearables. Wearables 300 are, for example, smart watches or badges, and the above description for mobile phones 200 also apply to such wearables 300.

400. Application. The purpose of the application 400 is to translate the data collected by the system into risk calculations, indicating the risk or probability of a person being infected by an infectious disease. Specifically, the application 400 provides the information necessary to allow the operator of the system to take steps to prevent outbreaks based on the risks that are calculated.

There are several examples of such risk calculation applications which currently exist that are designed to perform this function, and any of these can preferably be used. The preferred embodiment of the invention provides the application 400 with data concerning a fixed location where an encounter takes place, such data enabling the application 400 to make more accurate risk calculations. The application 400, once configured, also provides the means to register hubs 100 and the relevant data about those hubs.

Figure 2. Hubs.

Figure 2 shows the details regarding the hub 100 described above. Hub, or hub, 100 includes the following components.

101. Bluetooth Receiver/Transmitter. This element 101 enables the hub 100 to receive information from mobile phones 200 and/or wearables 300 and to transmit information thereto. The purpose of receiving information is in circumstances where the hub 100 can assist the mobile phone 200 or wearable 300 to determining proximity. Bluetooth signals are very noisy and so using their strength to determine proximity is inherently inaccurate. Hub 101 can be used to record the signal strength of devices carried by users, as well as the more normal process of carried devices recording signal strength. This then creates the opportunity to process the two signals in the application to determine a more accurate measurement of proximity.

In some applicationsthe hub would just act as a beacon (e.g transmit only). This may be where only the hub identifier is relevant (for example where the only relevant fixed context is to indicate an outdoor venue).

102. Memory. In circumstances where the hub/hub 101 is receiving information, memory storage is provided. The memory must be sufficient to hold the data that is being logged and must be of a type that can be repeatedly read and written to without degradation. The memory may also hold the firmware to operate the hub.

103. Processing. The purpose of the processing module (or processor) of the hub 101 is to control features such as rotating the transmitted identifier according to normal privacy standards, providing signals back to the application to demonstrate the continued normal function of the hub, and managing firmware and software upgrades.

104. Internet connection. An internet connection enables communication with the application. Whilst a continuous internet connection is not necessary for the normal function of the hub, it is advantageous for monitoring the continued functioning of the system and for automating firmware and software updates.

105. Mesh connection. Where a mesh is in place (e.g. multiple hubs) then the hub maintains a connection with the nearest hubs in the mesh. The hubs can then be used as a communications relays if necessary to enable communication with the application for hubs which otherwise would not have internet connectivity.

106. Power. The hub preferably transmits (and potential receives) continuously. A power source is therefore used to supply power to the hub. The preferable source is a fixed power source. Alternatively, the hub can be battery powered where batteries are changed or recharged periodically.

The hub (hub 101), with the fixed power source as just described, would enable the hub to also use Ultra Wide Band radio signals (UWB) which are better for determining proximity. Where a mobile phone or wearable also has UWB and sees that a hub is broadcasting, the phone or wearable can then briefly also use UWB to determine a more precise measure of proximity to the hub.

Figure 3 is a flow chart of operation of the process according to a preferred embodiment of the present invention.

The process flow starts at step 500, where the hub 100 is installed in the specific location (for example in a specific supermarket), power is connected to the hub 100 and an internet connection established (if possible). The device is now preferably transmitting a rotating identifier that will be visible to any carried devices that visit the location.

At step 501, the person who installed the hub 100 registers it using the application (400). During the registration process:

- the actual location (for example, the location of a specific supermarket, or gym) is recorded in the application;

- the hub 100's unique identification number is recorded;

- details about the location are entered (what type of venue, supermarket, gym, hospital etc)

- details about any existing mitigations are also entered, including if the specific purpose of this hub 100 is to record the presence of a protective screen

At step 502, a person visits the location carrying a mobile phone 200 or wearable 300 that is part of the system (has the functions as described above). The person then spends a period of time in the venue/location, mixing with other people who are also carrying phones 200 or wearables 300. These devices record their proximity to each other, and this provides the basis of calculating the risk of cross-infection.

At step 503, the devices (200 or 300) carried by people at the location also record their proximity to the hub (100).

At step 504, In some circumstances the hub assists the devices 200 or 300 carried, either because there is more than one hub and a Bluetooth mesh in place, or by the hub also estimating the proximity of the carried device, enabling an average of the two measurements to be taken, thus increasing the accuracy of the proximity data indicating the distance between the carried device and the hub (hub).

At step 505, the mobile phones 200 and/or wearables 300 that have visited the specific location then send the proximity data (of other carried devices 200/300 and/or of the hub 100) to the application 400. This can be near continuously for some applications (protecting a specific facility from outbreaks) where the operators wish to take preventative action by identifying and removing unnecessary transmission pathways. Or for other applications it could be just if the carrier of the device develops the disease (typically a population scale public health system). The mobile phones 200 and/or wearables 300 may also send other information to the application 400, such as any protective measures the person carrying the mobile phone or wearable has in place, such as the person is wearing PPE, or information describing the particular person who is using the mobile phone 200 or wearable 300, such as, the age of the person, the person's vaccination status, date of any vaccinations for that person, or history of that person's infection with the particular disease.

At step 506, the application 400 then uses the information registered (at step 501) about the hub and the proximity to the device (step 503) and the proximity data received by the application at step 505, to modify the riskthat would have been recorded due to the proximity between devices carried by the people visiting the space. For example, if the hub is recorded as a gym, the risk will be modified upwards, but if the record of the hub indicates that PPE is used in the space then the risk will be modified downwards. As another example, the application 400 can adjust the risk upwards or downwards depending on the location data, to take into account the relative strength of the Bluetooth signal in the particular type of location, as was described above. As a further example, the application 400 can adjust the risk upwards or downwards depending upon the information describing the particular person who is using the device 200 or 300 (the risk can be adjusted upwards for an older person who has not been vaccinated at all, for example).

The risk calculation application provides a data model of interactions and associated risk associated with an individual or a group of individuals.

Figure 4. Overall system

Figure 4 is a block diagram of the overall system which is used to obtain information regarding the variable context of an encounter between two people and to feed that information regarding such context into an application for assessing the risk of infection of a disease.

100. Sensors.

These are devices that sense the relevant variable context of the space in which they are placed. One type of sensor would preferably be a CO2 meter. The purpose of the CO2 meter is to sense the concentration of CO2, which varies considerably depending on ventilation. The background atmospheric level of CO2 is approximately 420 parts per million (ppm). Because people breathe out CO2, the concentration in a poorly ventilated space can rise to several thousand or higher ppm. An infected person will also breathe out virus particles, so CO2 concentration can be used to judge the concentration of virus particles that have accumulated in an enclosed space.

Another type of sensor would preferably be for ambient sound. People speak louder in noisy spaces, and louder speech generates significantly more fluid droplets that can carry virus from an infected people to another person. Ambient sound can then be used to improve the assessment of the risk of droplet transmission between people in close proximity. There are other variable risk factors (such as temperature, humidity etc) which are also considered to be useful, so a wider range of types of sensors would be deployed with the purpose of discovering risk factors. The sensors pass their data to an internet connected hub (200) described below.

200. Hub. This is a device (such as a Bluetooth hub) present at a specific location that contains a unique reference number forthat location. The purpose of this hub isto be visible to moving devices 300 or 400 (e.g., smart phones or wearables) carried by people visiting the location (who are carrying compatible software on a mobile phone or a wearable device such as a smart watch).

The hub 200 preferably transmits a rotating identifier. The purpose of the rotation of the temporary identifier is to avoid privacy intrusion from a malicious actor who might otherwise record a fixed identifier and examine logs in the device carried by the user to determine the places they had visited.

The hub sends data received from the sensors (100) to the application (500) that calculates the risk that infections may have taken place. The purpose of this is so the application (500) can use the sensor data to modify the risk calculations that an infection has taken place, therefore provide a more accurate risk calculation than if no input from a sensor was used at all.

In particularly large and complex locations, more than one hub 200 may be placed. The purpose of this is to create a mesh, such as a Bluetooth mesh, in the location. This mesh can then be used to more accurately determine the proximity between people than would be possible without the mesh, using, for example, standard Bluetooth mesh micro navigation technologies.

300. Mobile Phones.

The mobile phones 300 carry an app that enables them to detect and record information about other phones (300), wearable devices (400) and the hubs (200) that the user carrying the mobile phone 300 encounters. The purpose of this is to gather the proximity data of other users and the identification of the hubs necessary for the application (500) to accurately calculate the risk that disease transmission may have taken place. It also enables the application to determine when infections are occurring at a common location (infection with norovirus - food poisoning, for example). This enables public health authorities to rapidly locate the source of a public health issue.

The app on the phones draws on the open source software technology created in the Linux Foundation Public Health Herald project. The purpose of that project is to develop code that ensures reliable communication between two mobile phones no matter what operating systems are used and no matter what model of phones are in use. The mobile phones preferably record the rotating identifiers of the hubs that have been seen and this data is preferably sent to the application when necessary (e.g. when the user of the device is thought to carry an infection, or continuously if the operator of the system requires the data to monitor social mixing and hence risk levels of outbreaks should an infection occur).

400. Wearables.

Wearables 400 are, for example, smart watches or badges, and the above description for mobile phones 300 also apply to such wearables 400.

500. Application.

The purpose of the application 500 is to process and translate the data collected by the system into risk calculations, indicating the risk or probability of a person being infected by an infectious disease. Specifically, the application 500 provides the information necessary to allow the operator of the system to take steps to prevent outbreaks based on the risks that are calculated by the application.

There are several examples of software and mathematical formulations which currently exist that could be used to perform this function, and any of these can preferably be used. The preferred embodiment of the invention provides the application 500 with data that enables more accurate risk calculation. The application 500, once configured, also provides the means to register hubs 200 and the relevant data about those hubs.

Figure 5. Hub.

Figure 5 shows the details regarding the hub (200) described above. Hub 200 includes the following components:

201 Bluetooth Receiver/Transmitter. This element 201 enables the hub to receive information from mobile phones and/or wearables and to transmit information thereto.

In some applications the hub would just act as a beacon (e.g transmit only). This may be where only the hub identifier is relevant.

The hub may also act as a receiver to support other functions, such as relaying information from hubs without an internet signal, determining proximity of mobile phones and wearables to the .

Another purpose of receiving information is in circumstances where the hub can assist mobile devices in determining proximity. Bluetooth signals are very noisy and so using their strength to determine proximity is inherently inaccurate. Hubs can be used to record the signal strength of devices carried by users, as well as the more normal process of carried devices recording signal strength. This then creates the opportunity to process the two signals in the application to determine a more accurate measurement of proximity.

In some locations the hub would just act as a beacon (e.g transmit only) where no receiver functions were required (for example where the sole purpose of the hub was it indicated an outside venue for example) 202. Memory. In circumstances where the hub/hub 200 is receiving information, memory storage is provided. The memory must be sufficient to hold the data that is being logged and must be of a type that can be repeatedly read and written to without degradation. The memory may also hold the firmware to operate the hub.

203. Processing. The purpose of the processing module (or processor) of the hub is to control features such as processing and resending the sensor signals, rotating the transmitted identifier according to normal privacy standards, providing signals back to the application to demonstrate the continued normal function of the hub, and managing firmware and software upgrades.

204. Internet connection. An internet connection enables communication with the application. Whilst a continuous internet connection is not necessary for the normal function of the hub, it is advantageous for monitoring the continued functioning of the system and for automating firmware and software updates.

205. Sensor inputs. The hubs receive the inputs from the attached sensors.

206. Power. The hub preferably transmits (and potentially receives) continuously. A power source is therefore used to supply power to the hub. The preferable source is a fixed power source. Alternatively, the device can be battery powered where batteries are changed or recharged periodically.

The hub, with the fixed power source as just described, would enable the hub to also use Ultra Wide Band radio signals (UWB) which are better for determining proximity. Where a mobile phone or wearable also has UWB and sees that a hub is broadcasting, the phone or wearable can then briefly also use UWB to determine a more precise measure of proximity to the hub.

Figure 6 is a flow chart of operation of the process according to preferred embodiment of the present invention.

The process flow starts at step 600, where the hub 200 is installed in the specific location, power is connected to the hub and an internet connection established (if possible). The sensors are connected to the hub. The hub 200 is now preferably transmitting a rotating identifier that will be visible to any carried devices that visit the location.

At step 601, the person who installed the hub 200 registers it using the application (500). During the registration process:

- the hub's unique identification number is recorded (so that a unique record of that hub can be created in the application (500);

- details about the location are entered (for example the approximate volume of space, needed alongside state of ventilation to calculate the concentration of virus if the space is cooccupied by an infectious individual. For example if an infected persons spent 2 hours in a poorly ventilated small space then the concentration of virus and hence transmission risk to other people in the same space would be high. But if the same person spent 2 hours in a well ventilated larger space then the concentration would be much lower) - details about the connected sensors (such as CO2, ambient noise, temperature and humidity) are entered.

602. A person visits the location carrying a mobile phone or wearable that is part of the system (has the functions as described above). The person then spends a period of time in the venue/location, mixing with other people who are also carrying phones or wearables. These devices record their proximity to each other, and this provides the basis of calculating the risk of cross-infection via droplet.

603. The devices (mobile phones or wearables) carried by people also record their proximity to the hub. The mobile phones or wearables (and/or the hub) also record the time that each mobile phone or wearable spends in proximity to the hub. This is also done for other people carrying a mobile phone or wearable. The time spent in the same space as someone who is infected forms the basis of the calculation of the risk of cross-infection via airborne transmission.

604. The registered hub (601) sends data from the sensors to the application (500). The data from the sensors has previously been communicated from the sensors to the hub.

In some circumstances the hub assists the mobile phones or wearables, either because there is more than one hub and a Bluetooth mesh in place, or by the hub also estimating the proximity of the carried device, enabling an average of the two measurements to be taken, thus increasing the accuracy of the proximity data indicating the distance between the carried device and the hub (hub).

605. The mobile phones and/or wearables then send the proximity data (of other carried devices and/or of the hub) to the application. This can be near continuously for some applications (protecting a specific facility from outbreaks) where the operators wish to take preventative action by identifying and removing unnecessary transmission pathways. Or for other applications it could be just if the carrier of the device develops the disease (typically a population scale public health system).

606. The application (500) then uses the sensor data (604) and the proximity data to modify the risk that would otherwise have been recorded based on the proximity of people carrying the devices (droplet risk) and based on the time people have spent in the same space (airborne risk). For example, if the sensor data shows that the space is poorly ventilated then the airborne risk is modified upwards, and if other sensor data indicates that the ambient noise is high then the droplet risk, calculated from the proximity data (605) will be modified upwards for encounters in that space. As another example, the application 500 can adjust the risk upwards or downwards depending on the location data, to take into account the relative strength of the Bluetooth signal in the particular type of location, as was described above. As a further example, the application 500 can adjust the risk upwards or downwards depending upon the information describing the particular person who is using the device (mobile phone or wearable) (the risk can be adjusted upwards for a person who has not been vaccinated at all, for example). The risk calculation application provides a data model of interactions and associated risk associated with an individual or a group of individuals.

Figure 7. Overall system

Figure 7 is a block diagram of the overall system which is used to detect an encounter between two people and to feed that information regarding such encounter into an application for assessing the risk of infection of a disease.

100. Mobile Phones.

The mobile phones 100 carry an app that enables them to detect and record information about other phones (100), wearable devices (200) that the user carrying the mobile phone 100 encounters in a particular physical location. The purpose of this is to gatherthe proximity data of other users for the application (500) to accurately calculate the risk that disease transmission may have taken place at the particular physical location.

The app on the phones draws on the open source software technology created in the Linux Foundation Public Health Herald project. The purpose of that project is to develop code that ensures reliable communication between two mobile phones no matter what operating systems are used and no matter what model of phones are in use.

200. Wearables.

Wearables 200 are, for example, smart watches or badges, and the above description for mobile phones 300 also apply to such wearables 200.

300. Separation Detection Module.

The software in the phone app or wearable device monitors motion. The purpose of the Separation Detection Module is to detect whether the phone or wearable device is completely static. A prolonged static period is used to indicate that the phone or device is no longer with (has become separated from) the user.

400. Disregarding Proximity Module.

A phone or device may be briefly static (or stationary) even though it is being carried by the user. And a phone or wearable may be static for a few seconds to minutes if someone is sitting particularly still. But if the phone is still for a length of time beyond a threshold, then separation is deemed to have occurred. The purpose of the threshold is to avoid disregarding proximity data when someone is briefly static or briefly particularly still but genuinely in close proximity to another user.

Having passed the threshold, the software then goes back to the beginning of the static period to mark the data as a separation event. The purpose of this is to avoid false proximity readings from the beginning of the static period until the point the threshold was passed. Once the threshold is passed, the software can also shut down any unnecessary processes (for example related to reading proximity of other devices). The purpose of this is to avoid unnecessary battery drain. When motion is detected the software resumes normal operation, including recording the proximity of other devices.

500. Application.

The purpose of the application is to process and translate the data collected by the system into risk calculations, indicating the risk or probability of a person being infected by an infectious disease. Specifically, the application 500 provides the information necessary to allow the operator of the system to take steps to prevent outbreaks based on the risks that are calculated by the application.

There are several examples of software that currently exists that is capable of performing this mathematical function, and any of these can preferably be used. The preferred embodiment of the invention provides the application 500 with data that enables more accurate risk calculation.

A phone or device will still transmit data from beginning of the static period up until the threshold to the application should data be uploaded to the application. The purpose of this is to enable the application to confirm that the threshold is set correctly. For example, if evidence emerged that infections occurred beyond the threshold when a device had been classified as separated, then the threshold in the system can be changed to improve accuracy.

Figure 8. Flow Chart of Operation

Figure 2 is flow chart of operation of the system according to a preferred embodiment of the present invention.

Step 600. This process starts when the software detects that a mobile phone or wearable running the software becomes static.

Step 601. The device records the beginning of a time period in which the mobile phone or wearable has become static but the device continues to operate normally and logs the proximity data of other nearby devices. The purpose of this is to record potentially genuine proximity events between people if the reason the device is static is that the user is sitting very still.

Step 602. The software detects that the device has remained static (or stationary) beyond a threshold time. The threshold time is the time beyond which it is not credible that a person would remain completely still for that long (for example, this time period may be ten minutes). It is then assumed that the device and user have become separated at the start of the static period.

Step 603. The software then marks the data collected from the beginning of the static period with the assumption that the user and the device became separated at that time. The purpose of this is to enable that data to be disregarded in subsequent calculations of transmission risk between the users (since the user was not with the device).

Step 604. The software also closes unnecessary processes to preserve battery usage. This will depend on the characteristics of the device (the software must be able to restart when motion is detected).

Step 605. When sustained (rather than fleeting) motion is detected the software restarts normal operation.

Step 606. The mobile phones and/or wearables then sends (uploads) the proximity data (of other carried devices detected to be in proximity to a mobile phone or wearable) to the application. In some use cases this might be uploaded every few hours (a corporate use for example). In others the data may only be uploaded if the users report symptoms or tests positive (a public health use for example). In either case, the upload would include the data from the start of the static period to the threshold. The purpose of this is to check that infections are not occurring between users during this period. If infections did occur it would indicate that separation had not happened and that the threshold was set too early. The threshold could then be corrected. Equally if no infections were detected to ever occur after half the threshold time had passed then the threshold is too long and can be adjusted downwards to reduce battery consumption.

The application 500 then disregards any proximity data where it has been determined/assumed that the people who normally carry or wear the relevant devices have become separated from their devices.

A machine learning algorithm can also be used to determine a measure of likelihood that the person has become separated from the mobile device based on location history. Further, the risk calculation application can provide a data model of interactions and associated risk.

The method described above could include a step of providing a measure of risk associated with an individual or a group of individuals, and a step of providing an identification of a location associated with a risk above a threshold.

Clauses

The following numbered clauses include further technical aspects which are here disclosed.

1. A method of collecting information for providing to a risk calculation application which calculates the risk of a person contracting an infectious disease, comprising the steps of: collecting first information from a mobile device which has been carried by a first person who has entered a particular physical location, the information including:

(e) first proximity data indicating a distance between the first person and at least one other person who has also entered the particular physical location and carried a mobile device;

(f) second proximity data indicating a distance between the first person and a hub located at the particular physical location; collecting second information from the hub, said second information comprising data received from one or more sensors that sense the variable context of the particular physical location in which the one or more sensors are placed; and providing the collected first and second information to the risk calculation application which performs the further step of calculating a risk of a person contracting an infectious disease, wherein the first and second information is used by the risk calculation application to vary the risk associated with a given proximity.

2. The method of clause 1, wherein the hub is a Bluetooth receiver/transmitter.

3. The method of clause 1, wherein the mobile device is a mobile phone or smart phone.

4. The method of clause 1, wherein the mobile device is a smart watch or wearable computer.

5. The method of clause 1, wherein the first information also includes information specific to the first person.

6. The method of clause 5, wherein the information specific to the first person includes a vaccination status of the first person, an age of the first person or historical data regarding the first person's infection with the infectious disease.

7. The method of clause 1, wherein the one or more sensors include a carbon dioxide, CO2, meter.

8. The method of clause 1, wherein the one or more sensors include a sensor for ambient sound. 9. The method of clause 1, wherein the one or more sensors include a sensor for temperature.

10. The method of clause 1, wherein the one or more sensors include a sensor for humidity.

11. The method of clause 1, wherein the second information also includes proximity data indicating the distance between the first person and the hub, and wherein an average is taken of: e) the proximity data included in the first information, indicating the distance between the first person and the hub, and f) the proximity data included in the second information, indicating the distance between the first person and the hub.

12. The method of clause 1, wherein the hub registers details of the sensors with the application.

13. The method of clause 1, wherein ultra wide band, UWB, radio signals are used to determine the first and second proximity data.

14. The method of clause 11, wherein the hub uses ultra wide band, UWB, radio signals to determine the proximity data indicating the distance between the first person and the hub.

15. The method of clause 1, wherein a plurality of hubs are present at the particular physical location, and the plurality of hubs are used as a communications relay to communicate with the application.

16. The method of clause 1, wherein the hub transmits a rotating temporary identifier.

17. The method of clause 1, wherein the hub records a signal strength of the mobile device.

18. The method of clause 1, wherein the hub records information regarding the location where the hub is located.

19. The method of clause 18, wherein the information regarding the location where the hub is located includes the volume of space associated with the location.

20. The method of clause 1, wherein the second proximity data also includes a period of time that the first person spends in proximity to the hub. 21. The method of clause 1, wherein the risk calculation application provides a data model of interactions and associated risk associated with an individual or a group of individuals

22. A system comprising means adapted for carrying out all the steps of the method according to any preceding method clause.

23. A computer program comprising instructions for carrying out all the steps of the method according to any preceding method clause, when said computer program is executed on a computer system.

24. A method of collecting information for providing to a risk calculation application which calculates the risk of a person contracting an infectious disease, where the method involves the collection of information from a mobile device which has been carried by a first person who has entered a particular physical location, the information including proximity data indicating a distance between the first person and at least one other person who has also entered the particular physical location and carried a mobile device, the method comprising the steps of: detecting that the first person has become separated from the mobile device; and providing the collected information to the risk calculation application which, upon receiving the collected information, performs the further step of calculating a risk of a person contracting an infectious disease, where the information is provided such that the application disregards the collected information corresponding to the detection that the first person has become separated from the mobile device.

25. The method of clause 24, wherein the mobile device is a mobile phone or smart phone.

26. The method of clause 24, wherein the mobile device is a smart watch or wearable computer.

27. The method of clause 24, wherein the step of detecting that the first person has become separated from the mobile device includes detecting that the mobile device has remained stationary fora time period which is longer than a threshold time period.

28. The method of any preceding clause 24-27 wherein a machine learning algorithm is used to determine a measure of likelihood that the person has become separated from the mobile device based on location history.

29. The method of any preceding clause 24-28 wherein the risk calculation application provides a data model of interactions and associated risk.

30. The method of any preceding clause 24-29 further comprising providing a measure of risk associated with an individual or a group of individuals. The method of any preceding clause 24-30 further comprising providing an identification of a location associated with a risk above a threshold. A system comprising means adapted for carrying out all the steps of the method according to any preceding method clause 24-31. A computer program comprising instructions for carrying out all the steps of the method according to any preceding method clause 24-31, when said computer program is executed on a computer system.

Claims

(g) first proximity data indicating a distance between the first person and at least one other person who has also entered the particular physical location and carried a mobile device;

(h) second proximity data indicating a distance between the first person and a hub located at the particular physical location; collecting second information from the hub, said second information indicating a specific context of the particular physical location; and providing the collected first and second information to the risk calculation application which performs the further step of calculating a risk of a person contracting an infectious disease, wherein the first and second information is used by the risk calculation application in modifying the risk associated with a given proximity.

2. The method of claim 1, wherein the hub is a Bluetooth receiver/transmitter.

3. The method of claim 1, wherein the mobile device is a mobile phone or smart phone.

4. The method of claim 1, wherein the mobile device is a smart watch or wearable computer.

5. The method of claim 1, wherein the first information also includes information specific to the first person.

6. The method of claim 5, wherein the information specific to the first person includes a vaccination status of the first person, an age of the first person or historical data regarding the first person's infection with the infectious disease.

7. The method of claim 1, wherein the specific context of the particular physical location is an indication of a type of venue of the particular physical location.

8. The method of claim 1, wherein the specific context of the particular physical location is an indication of whether a physical protective device is present at the particular physical location.

9. The method of claim 8, wherein the physical protective device is a screen for separating people at the particular physical location.

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10. The method of claim 1, further comprising the step of the mobile device providing information to the application regarding protective measures the person carrying the mobile device has in place while visiting the particular physical location.

11. The method of claim 1, wherein the second information also includes proximity data indicating the distance between the first person and the hub, and wherein an average is taken of: g) the proximity data included in the first information, indicating the distance between the first person and the hub, and h) the proximity data included in the second information, indicating the distance between the first person and the hub.

12. The method of claim 1, wherein the hub registers its particular physical location with the application.

13. The method of claim 1, wherein ultra wide band, UWB, radio signals are used to determine the first and second proximity data.

14. The method of claim 11, wherein the hub uses ultra wide band, UWB, radio signals to determine the proximity data indicating the distance between the first person and the hub.

15. The method of claim 1, wherein a plurality of hubs are present at the particular physical location, and the plurality of hubs are used as a communications relay to communicate with the application.

16. The method of claim 1, wherein the hub transmits a rotating temporary identifier.

17. The method of claim 1, wherein the hub records a signal strength of the mobile device.

18. The method of claim 1, wherein the risk calculation application provides a data model of interactions and associated risk associated with an individual or a group of individuals.

19. A system comprising means adapted for carrying out all the steps of the method according to any preceding method claim.

20. A computer program comprising instructions for carrying out all the steps of the method according to any preceding method claim, when said computer program is executed on a computer system.