Generally, the invention relates to prediction analytics for spread of
infectious diseases. More specifically, the invention relates to a system and method
for determining a risk level for a potential spread of infectious disease.
BACKGROUND OF INVENTION
[002] An outbreak of pandemic may affect all spheres of life, socially as well
as economically. Example of pandemics may include, but not limited to, Influenza,
Severe Acute Respiratory Syndrome (SARS), Ebola, and COVID-19 (Corona Virus).
Moreover, organizations, commercial establishments, and enterprises may be
negatively impacted with the outbreak of the pandemic.
[003] Further, different corporate and government guidelines may be adhered
to, during the pandemic. In order to respond to the situations during the pandemic,
capabilities of contact tracing, identification of areas with high risk of disease
transmission, and identification of frequently accessed areas may be important. In
certain scenarios, systems may keep a track of contacts, locations, and people.
However, such systems may not consider multiple parameters to rate risk of locations
and thereby provide imprecise results.
[004] Accordingly, there is a need to identify risk level of locations effectively
during pandemic.
SUMMARY OF INVENTION
[005] In one embodiment, a method for determining a risk level for a potential
spread of infectious disease is disclosed. The method may include receiving data
associated with each of a plurality of locations within an institutional region in real time.
The method may further include determining a set of risk metrics for each of the
plurality of locations, based on the data associated with the each of the plurality of
locations. The set of risk metrics may include an occupancy metric, a social distancing
violation metric, and a mask compliance metric. The set of risk metrics may also
optionally include a temperature metric and a health risk assessment metric. The
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method may further include computing a cumulative location risk score for each of the
plurality of locations, based on the set of risk metrics for the each of the plurality of
locations. The method may further include determining a relative risk score for each of
the plurality of locations based on the cumulative risk score of the each of the plurality
of locations with respect to the cumulative risk scores of the plurality of locations The
method may further include determining the risk level for the potential spread of
infectious disease for each of the plurality of locations based on the corresponding
relative risk score.
[006] In another embodiment, a system for determining a risk level for a
potential spread of infectious disease is disclosed. The system may include a
processor and a memory communicatively coupled to the processor. The memory may
store processor-executable instructions, which, on execution, may cause the
processor to receive data associated with each of a plurality of locations within an
institutional region in real time. The processor-executable instructions, on execution,
may further cause the processor to determine a set of risk metrics for each of the
plurality of locations, based on the data associated with the each of the plurality of
locations. The set of risk metrics may include an occupancy metric, a social distancing
violation metric, and a mask compliance metric. The set of risk metrics may also
optionally include a temperature metric and a health risk assessment metric. The
processor-executable instructions, on execution, may further cause the processor to
compute a cumulative location risk score for each of the plurality of locations, based
on the set of risk metrics for the each of the plurality of locations. The processorexecutable instructions, on execution, may further cause the processor to determine
a relative risk score for each of the plurality of locations based on the cumulative risk
score of the each of the plurality of locations with respect to the cumulative risk scores
of the plurality of locations. The processor-executable instructions, on execution, may
further cause the processor to determine the risk level for the potential spread of
infectious disease for each of the plurality of locations based on the corresponding
relative risk score.
[007] It is to be understood that both the foregoing general description and the
following detailed description are exemplary and explanatory only and are not
restrictive of the invention, as claimed.
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BRIEF DESCRIPTION OF THE DRAWINGS
[008] The present application can be best understood by reference to the
following description taken in conjunction with the accompanying drawing figures, in
which like parts may be referred to by like numerals
[009] FIG. 1 illustrates a block diagram of an exemplary system in a network
environment for determining a risk level for a potential spread of infectious disease, in
accordance with some embodiments of the present disclosure.
[010] FIG. 2 illustrates a functional block diagram of an exemplary risk
determination system, in accordance with some embodiments of the present
disclosure.
[011] FIG. 3 illustrates a flow diagram of an exemplary process for determining
a risk level for a potential spread of infectious disease, in accordance with some
embodiments of the present disclosure.
[012] FIG. 4A and 4B illustrates an exemplary scenario for determining a risk
level for a potential spread of infectious disease, in accordance with some
embodiments of the present disclosure.
DETAILED DESCRIPTION OF THE DRAWINGS
[013] The following description is presented to enable a person of ordinary skill
in the art to make and use the invention and is provided in the context of particular
applications and their requirements. Various modifications to the embodiments will be
readily apparent to those skilled in the art, and the generic principles defined herein
may be applied to other embodiments and applications without departing from the spirit
and scope of the invention. Moreover, in the following description, numerous details
are set forth for the purpose of explanation. However, one of ordinary skill in the art
will realize that the invention might be practiced without the use of these specific
details. In other instances, well-known structures and devices are shown in block
diagram form in order not to obscure the description of the invention with unnecessary
detail. Thus, the present invention is not intended to be limited to the embodiments
shown, but is to be accorded the widest scope consistent with the principles and
features disclosed herein.
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[014] While the invention is described in terms of particular examples and
illustrative figures, those of ordinary skill in the art will recognize that the invention is
not limited to the examples or figures described. Those skilled in the art will recognize
that the operations of the various embodiments may be implemented using hardware,
software, firmware, or combinations thereof, as appropriate. For example, some
processes can be carried out using processors or other digital circuitry under the
control of software, firmware, or hard-wired logic. (The term “logic” herein refers to
fixed hardware, programmable logic and/or an appropriate combination thereof, as
would be recognized by one skilled in the art to carry out the recited functions.)
Software and firmware can be stored on computer-readable storage media. Some
other processes can be implemented using analog circuitry, as is well known to one of
ordinary skill in the art. Additionally, memory or other storage, as well as
communication components, may be employed in embodiments of the invention.
[015] Referring now to FIG. 1, a block diagram of a system 100 for determining
a risk level for a potential spread of infectious disease is illustrated, in accordance with
some embodiments of the present disclosure. In an embodiment, a risk determination
system 102 may be used to resolve aforementioned problems by considering various
parameters, for example, a number of people at a location, people without masks,
social distancing violations, elevated body temperature, and the like. The risk
determination system 102 may use data associated with a plurality of locations and
based on that determines risk level of each location of the plurality of locations.
Subsequently, the risk determination system 102 may provide information of risk level
through a map (for example, a heat map).
[016] The risk determination system 102 may include a memory 104, a
processor 106, input/output (I/O) devices 108. The I/O devices 108 may further include
a user interface 110. A user or an administrator may interact with the risk determination
system 102 and vice versa through the user interface 110. By way of an example, the
user interface 110 may be used to provide results of analysis performed by the risk
determination system 102, to the user.
[017] Examples of the I/O devices 108 may include a speaker, a computing
device, and a display device. In accordance with an embodiment, the speaker may be
used to provide a warning or a notification to the user or management authorities via
an audio/ voice note and the display device may show a message corresponding to
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the audio/ voice note. By way of an example, the speaker and the display device may
instruct the user to avoid areas with high risk level for preventing spread of the
infectious disease. By way of another example, the speaker and the display device
may also be used to provide directions to the user to maintain social distancing or to
wear mask.
[018] As will be described in greater detail herein below, in order to determine
the risk level for the potential spread of infectious disease, the risk determination
system 102 may acquire information (e.g., occupancy data, social distancing violation
data, mask compliance data, health risk data, and temperature data) via
communicatively connected a location access management system 112a, a video
surveillance system 112b, a thermal imaging camera 112c, and a network system
112d. Additionally, in some embodiments, the risk determination system 102 may
extract information (e.g. thermal images) from a server 114, which is further
communicatively coupled to a database 116.
[019] The memory 104 and the processor 106 of the risk determination system
102 may perform various functions including, but not limited to, receiving data,
determining metrics, computing risk scores, generating heat map, determining risk
categories, determining risk level, and notifying the user. The memory 104 may store
instructions that, when executed by the processor 106, cause the processor 106 to
determine the risk level for a potential spread of the infectious disease, in accordance
with some embodiments of the present invention. In accordance with an embodiment,
the memory 104 may also store various data (e.g. thermal images, occupancy data,
social distancing violation data, mask compliance data, health risk data, and
temperature data) that may be captured, processed, generated, and/or required by the
risk determination system 102. The memory 104 may be a non-volatile memory (e.g.,
flash memory, Read Only Memory (ROM), Programmable ROM (PROM), Erasable
PROM (EPROM), Electrically EPROM (EEPROM) memory, etc.) or a volatile memory
(e.g., Dynamic Random Access Memory (DRAM), Static Random-Access memory
(SRAM), etc.).
[020] In some embodiments, the risk determination system 102 may interact
with the user or management authorities via external devices 118 over a
communication network 120. In such embodiments, the risk determination system 102
may render the results to the user or to the management authorities via the user
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interface 110 over the external devices 118. For example, the users and the
management authorities may get a notification over the external devices 118 to take
required actions. The one or more external devices 118 may include, but not limited
to, a desktop, a laptop, a notebook, a netbook, a tablet, a smartphone, a remote server,
a mobile phone, or another computing system/device. The communication network
120 may be any wired or wireless communication network and the examples may
include, but may be not limited to, the Internet, Wireless Local Area Network (WLAN),
Wi-Fi, Long Term Evolution (LTE), Worldwide Interoperability for Microwave Access
(WiMAX), and General Packet Radio Service (GPRS).
[021] Further, the risk determination system 102 may interact with the external
devices 118 and/or the server 114 for sending/receiving various data, via the
communication network 120. The server 114 may be directly coupled to the database
116, which may store the data captured by the location access management system
112a, video surveillance system 112b, thermal imaging camera 112c, and network
system 112d. In accordance with an embodiment, the server 114 may be
communicatively coupled to the database 116, via the communication network 120
(not shown in FIG. 1). In accordance with an embodiment, the risk determination
system 102 may be communicatively coupled to the location access management
system 112a, video surveillance system 112b, thermal imaging camera 112c, and
network system 112d, via the communication network 120 (not shown in FIG. 1).
[022] Referring now to FIG. 2, a functional block diagram of an exemplary risk
determination system 200 (similar to the risk determination system 102) is illustrated,
in accordance with some embodiments of the present disclosure. FIG. 2 is explained
in conjunction with FIG. 1. The risk determination system 200 may be configured to
determine risk level of a plurality of locations for a potential spread of infectious
disease. The risk level may be determined based on data 204 associated with the
plurality of locations within an institutional region. The institutional region may
correspond to, but not limited to, an organized establishment, a foundation, a society,
or a region that buildings occupy. The data 204 may be captured by one or more of a
location access management system 202a, a video surveillance system 202b, a
thermal imaging camera 202c, and a network system 202d.
[023] The location access management system 202a may be configured to
manage restricting entrance to a property, a building, or a room to authorized persons
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in the institutional region. Data from the location access management system 202a
may be aggregated and used by the risk determination system 200 to provide
significant scale insights into people movement. The video surveillance system 202b
may correspond to a surveillance system capable of capturing images and videos that
can be stored or sent over communication networks, such as the communication
network 120. The video surveillance system 202b may be used to manage data
streamed from security cameras. The video surveillance system 202b may be
configured to monitor building entry points, support remote access / employee access
control, setup motion detection. The thermal imaging camera 202c may be configured
to capture, at building entry points, and create an image of an object (such as, a user)
by using infrared radiation emitted from the object. The created image may represent
temperature of the object. The risk determination system 200 may be configured to
collect data associated with the temperature of the object (such as, the user 218) from
the thermal imaging camera 202c.
[024] The risk determination system 200 may perform various operations to
provide information related to a risk level of the plurality of locations to the user 218.
Further, to perform various operations, the risk determination system 200 may include
a metrics determination module 206, a cumulative score determination module 208, a
relative score determination module 210, a risk level determination module 212, and
a heat map generation module 214. Additionally, the risk determination system 200
may also include a data store 216 to store various intermediate data and results
generated while determining the risk level.
[025] The metrics determination module 206 may be configured to receive the
data 204 associated with each of a plurality of locations within the institutional region.
Further, the metrics determination module 206 may be configured to determine a set
of risk metrics for each of the plurality of locations, based on the data associated with
the each of the plurality of locations. The set of metrics may include an occupancy
metric, a social distancing violation metric, a mask compliance metric, a health risk
metric, and a temperature metric. In some embodiments, the occupancy metric, the
social distancing violation metric, the mask compliance metric, the health risk metric,
and the temperature metric may correspond to occupancy ratio, social distancing ratio,
and mask compliance ratio, the health risk ratio, and the temperature ratio,
respectively. The set of metrics is explained in detailed description of FIG. 3. The data
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204 associated with each of the plurality of locations may include occupancy data for
a location, social distancing violation data in the location, and mask compliance data
in the location. In addition to this, the data 204 associated with each of the plurality of
locations may also include other data, such as health risk data and temperature data
of each person in the location.
[026] In accordance with an embodiment, the occupancy data may be
captured by identifying a number of people within the location. Further, the occupancy
data may include access log data, network log data, and a number of people in video
data. In other words, the number of people within the location may be identified based
on access log data, network log data, and a number of people in the video data. For
example, the occupancy data may be used by the metrics determination module 206
to identify busiest locations, or analyze space usage in a building of the institutional
region. It should be noted that the access log data may be captured by the location
access management system 202a. Also, it should be noted that the network log data
and the video data may be captured by the network system 202d and the video
surveillance system 202b, respectively
[027] The social distancing violation data in the location may indicate violation
of minimum distance between any two people in the location. It should be noted that
value of the minimum distance may be pre-defined. However, values for the minimum
distance may be changed in certain scenarios. The social distancing violation data
may be derived from the video data captured by the video surveillance system 202b.
[028] Further, the mask compliance data in the location may refer to a number
of people without masks (i.e., not wearing masks) in the location. The mask
compliance data may also be derived based on the video data acquired by the video
surveillance system 202b.
[029] The health risk data may correspond to a number of people with
exposure to health risk which may be acquired based on health risk assessment
reports from a health enterprise or a government authority (not shown in FIG. 2). The
health risk may be categorized into a high risk, a medium risk and a low risk.
[030] The temperature data may be captured by the thermal imaging camera
202c. It should be noted that the temperature data may correspond to a number of
people with Elevated Body Temperature (EBT). Further, the metrics determination
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module 206 may be communicatively coupled to the cumulative score determination
module 208 and the data store 216 to further transmit the determined set of metrics.
[031] The cumulative score determination module 208 may be configured to
compute cumulative location risk scores. Based on the set of risk metrics, the
cumulative score determination module 208 may be configured to determine a
cumulative location risk score for each of the plurality of locations. The cumulative
location risk score may also be referred as Comprehensive Location Risk Scoring
during Pandemic (CLRSP). The cumulative score determination module 208 may be
configured to determine cumulative sum based on determined set metrics for each
location separately. The CLRSP may correspond to a ratio of the cumulative sum of
metrics and the maximum sum of metrics. By way of an example, consider two
locations within the institutional region and determined set of metrics for a first location
may be ‘α1’, ‘β1’, ‘γ1’, ‘δ1’, and ‘ε1’. Similarly, determined set of metrics for a location 2
may be ‘α2’, ‘β2’, ‘γ2’, ‘δ2’, and ‘ε2’. The cumulative sum of metrics for the location 1
may be ‘α1+β1+γ1+δ1+ε1’ and for location 2 may be ‘α2+β2+γ2+δ2+ε2’. In that case, the
maximum sum of the metrics may be ‘5’ (i.e., maximum value of each metric may be
‘1’). This is explained in detailed description of FIG. 3. Further, the cumulative score
determination module 208 may be communicatively coupled to the relative score
determination module 210 and the data store 216.
[032] The relative score determination module 210 may be configured to
receive the output generated by the cumulative score determination module 208
directly or through the data store 216. Further, the relative score determination module
210 may be configured to determine a relative risk score for each of the plurality of
locations based on the CLRSP of the each of the plurality of locations with respect to
the CLRSP of the plurality of locations. In some embodiments, the relative risk score
may correspond to ‘z’ score. The ‘z’ score may be calculated in terms of standard
deviation from mean. The ‘z’ score may be positive ‘z’ score or negative ‘z’ score. This
may be further explained in detail in conjunction with FIG. 3 and FIG. 4.
[033] Further, the relative score determination module 210 may be
communicatively coupled to the risk level determination module 212 and the data store
216.
[034] The risk level determination module 212 may determine the risk level for
the potential spread of infectious disease for each of the plurality of locations based
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on the corresponding relative risk score. The risk level determination module 212 may
further include a risk category determination module 212a. The risk category
determination module 212a may determine a risk category for the risk level, from a
plurality of predefined risk categories, for each of the plurality of locations. The risk
category may be determined based on the relative risk score for each of the plurality
of locations. The risk category may be at least one of a high risk, a medium risk, a low
risk and a no risk category.
[035] The heat map generation module 214 may be operatively coupled to the
risk level determination module 212. The heat map generation module 214 may
generate a heat map based on the determined risk category of the each of the plurality
of locations. The heat map may correspond to a representation of data associated with
risk level for the potential spread of infectious disease for each of the plurality of
locations, in the form of a map or diagram in which data values are represented as
colours. The heat map may be further rendered on a user device associated with the
user 218. In some embodiments, notifications may be generated by a notification
module (not shown in FIG. 2). The notifications may be transmitted to associated
management authorities or the user 218 controlling containment of the potential
spread of the infectious disease, based on the risk level above a predefined threshold
value.
[036] It should be noted that the risk determination system 102 may be
implemented in programmable hardware devices such as programmable gate arrays,
programmable array logic, programmable logic devices, or the like. Alternatively, the
risk determination system 102 may be implemented in software for execution by
various types of processors. An identified engine/module of executable code may, for
instance, include one or more physical or logical blocks of computer instructions which
may, for instance, be organized as a component, module, procedure, function, or other
construct. Nevertheless, the executables of an identified engine/module need not be
physically located together but may include disparate instructions stored in different
locations which, when joined logically together, comprise the identified engine/module
and achieve the stated purpose of the identified engine/module. Indeed, an engine or
a module of executable code may be a single instruction, or many instructions, and
may even be distributed over several different code segments, among different
applications, and across several memory devices.
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[037] As will be appreciated by one skilled in the art, a variety of processes
may be employed for determining a risk level for a potential spread of infectious
disease. For example, the exemplary system 100 and associated risk determination
system 102 may determine the risk level, by the process discussed herein. In
particular, as will be appreciated by those of ordinary skill in the art, control logic and/or
automated routines for performing the techniques and steps described herein may be
implemented by the system 100 and the associated risk determination system 102
either by hardware, software, or combinations of hardware and software. For example,
suitable code may be accessed and executed by the one or more processors on the
system 100 to perform some or all of the techniques described herein. Similarly,
application specific integrated circuits (ASICs) configured to perform some or all the
processes described herein may be included in the one or more processors on the
system 100.
[038] Referring now to FIG. 3, an exemplary process for determining a risk
level for a potential spread of infectious disease is depicted via a flow diagram 300, in
accordance with some embodiments of the present disclosure. Each step of the
process may be performed by a risk determination system (similar to the risk
determination system 102 and 200). FIG. 3 is explained in conjunction with FIG. 1 and
FIG. 2.
[039] At step 302, data associated with each of a plurality of locations may be
received in real time. The plurality of locations may be within an institutional region.
For example, in some embodiments, the plurality of locations may be within at least
one of a coworking space, a hospital, a plaza, a school, a shopping center, a ship, a
long-term care center or other private or public buildings. The data associated with
each of the plurality of locations may include occupancy data for a location, social
distancing violation data in the location, and mask compliance data in the location. The
data may further include health risk data and temperature data. Further, the occupancy
data for the location may correspond to a number of people in the location. The
occupancy data may be acquired from at least one of access log data for the location
from location access management system (for example, the location access
management system 112a), network log data for the location from network system (for
example, the network system 112d), a number of people in a video acquired at the
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location by the video surveillance system (for example, the video surveillance system
112b).
[040] Further, the social distancing violation data in the location may
correspond to a violation of minimum distance between any two people in the location.
This may be derived from the video acquired at the location by the video surveillance
system. And, the mask compliance data in the location may correspond to a number
of people without masks in the location which may be also be derived from the video
acquired at the location by the video surveillance system.
[041] At step 304, a set of risk metrics for each of the plurality of locations may
be determined. It should be noted that determination of the set of risk metrics may be
performed based on the data associated with the each of the plurality of locations.
Further, the set of risk metrics may include an occupancy metric, a social distancing
violation metric, and a mask compliance metric. It should be noted that the set of risk
metrics may be determined using a metrics determination module (analogous to the
metrics determination module 206). The occupancy metric ‘α’ may be determined as
per equation (1), given below:
� = � (�, �, �, �) = ��������� �����/� equation (1)
where, ��������� ����� = ���(�(�): � = �, �, �)
Here, ‘a’ represents a number of devices as per network logs, ‘b’ represents a number
of persons as per access logs, ‘c’ represents a number of persons as per video feed,
and ‘d’ represents capacity of the location.
[042] Further, the social distancing violation metric ‘β’, may be determined as
per equation (2), given below:
� = �(�, �) = �/������� ���������� equation (2)
where, ������� ���������� = >
�
2
@ = �!/2! (� − 2)!
In the above equation (2), ‘a’ indicates a number of violations occurred within a
particular location, and ‘b’ indicates a total number of persons present at the location.
[043] The mask compliance metric ‘�’ may be determined as per equation (3),
given below:
� = �(�, �) = �/� equation (3)
where, a = number of persons without mask, and
b = total number of persons present at the location/Occupancy Count
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[044] Further, the data associated with each of the plurality of locations may
also include health risk data and temperature data of each person in a location. The
health risk data may correspond to a number of people with exposure to health risk
which may be based on health risk assessment reports from a health enterprise or a
government authority. For example, a person entering a location of the plurality of
locations within the institutional region may come from a containment zone or area.
Thus, this may increase chances of disease spread. And, the temperature data may
correspond to a number of people with Elevated Body Temperature (EBT). The
temperature data may be derived from thermal images captured by thermal cameras
(for example, the thermal imagining camera 112c) installed at entry points of the
location.
[045] Thus, in addition, some other metrics such as, a health risk metric, and
a temperature metric may also be determined based on the health risk data and
temperature data. The health risk metric ‘δ’ and temperature metric ‘ε’ may be
determined as per below given equations (4) and (5), respectively.
δ (Health Risk Metric) = �(�, �, �) = (� + �)/� equation (4)
Where, a = number of persons with high risk, b = total number of persons/occupancy
count, c = number of persons with medium risk, d = number of persons with low risk.
ε (Temperature Metric ) = �(�, �) = �/� equation (5)
Where, a = number of persons with EBT and b = total number of persons/occupancy
count.
[046] At step 306, a cumulative location risk score for each of the plurality of
locations may be computed. To compute the CLRSP, the set of risk metrics for the
each of the plurality of locations may be considered. It should be noted that a
cumulative score determination module (similar to the cumulative score determination
module 208) may be employed to compute the CLRSP. The CLRSP may correspond
to a ratio of cumulative sum of the set of metrics and maximum sum of the set of
metrics. The CLRSP may be computed as per equation (6), given below:
�(�, �, �, �, �) = (� + � + � + � + �)/5 equation (6)
[047] Thereafter, at step 308, a relative risk score for each of the plurality of
locations may be determined using a relative score determination module (same as
the relative score determination module 210). The relative risk score for each of the
plurality of locations may be determined based on the CLRSP with respect to the
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CLRSP. The relative risk score may correspond to z-score. The ‘z’ score may be
computed as per equation (7), given below:
� ����� ��� �������� 2 = ("#$%&)!( *
+ equation (7)
where, µ (����) = ("#$%&)",("#$%&)!,⋯……("#$%&)#
/ , and
σ (Standard ���������) = d∑ (����� − µ) /
0
1
�
where, (CLRSP)1 = CLRSP with respect to location ‘1’, (CLRSP)2 = CLRSP with
respect to location ‘2’, and (CLRSP)n = CLRSP with respect to location ‘n’, and n =
Total number of locations. This may be further explained in detail in conjunction with
FIG. 4.
[048] At step 310, the risk level for the potential spread of infectious disease
for each of the plurality of locations may be determined based on the corresponding
relative risk score. In some embodiments, a risk category for the risk level may be
determined from a plurality of predefined risk categories. It should be noted that the
risk category may be determined for each of the plurality of locations. The risk category
may be determined based on the relative risk score for each of the plurality of locations.
The risk category may be at least one of a high risk, a medium risk, a low risk and a
no risk category. Additionally, in some other embodiments, a heat map may be
generated. The heat map may be generated based on the determined risk category of
the each of the plurality of locations.
[049] Further, in some embodiments, notifications may be transmitted to
management authorities controlling containment of the potential spread of infectious
disease. For example, when the risk level exceeds a predefined threshold value the
risk determination system 200 may generate a notification and consequently transmit
it to associated management authorities. It should be noted that the notification may
be a warning message. The notification may be in the form of a text message, an audio
note, a video.
[050] Referring now to FIG. 4A and 4B, an exemplary scenario 400 for
determining a risk level for a plurality of locations within an institutional region 402 is
illustrated, in accordance with some embodiments of the present disclosure. As
illustrated in FIG. 4A, the plurality of locations may correspond to different floors in
towers of the institutional region 402. In particular, the institutional region 402 may
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include total three towers, that is, a tower 404, a tower 406, and a tower 408. Further,
the tower 404 and the tower 406 may include three floors each. For example, the tower
404 may have floor 404a, floor 404b, floor 404c, and tower 406 may have floor 406a,
floor 406b, floor 406c. The tower 408 may include two floors, namely, floor 408a and
floor 408b.
[051] Further, FIG 4A shows data capturing systems 410 associated with the
institutional region 402 to acquire data associated with the floors 404a, 404b, 404c,
406a, 406b, 406c, 408a, 408b of respective towers (tower 404, tower 406, tower 408).
The data capturing systems 410 may include a video surveillance system 410a, a
network system 410b, a location access management system 410c, and a thermal
imaging camera 410d. Further, in some embodiments, the data may be acquired from
health enterprise 412. The data acquired from the data capturing systems 410 and the
health enterprise 412 may be transmitted to a risk determination system 414
associated with the institutional region 402. The risk determination system 414 may
determine a risk level for a potential spread of infectious disease for each of the floors
404a, 404b, 404c, 406a, 406b, 406c, 408a, 408b. Further, the risk determination
system 414 may generate a heat map as represented in FIG. 4B.
[052] In FIG. 4B, a risk level at different floors of towers 404, 406, and 408 is
illustrated via the heat map. Rows of the heat map represent floors of the towers 404,
406, and 408, and columns represent time period. A color 416 is used to indicate high
risk category, a color 418 may represent medium risk category, a color 420 may
represent a low risk category and a color 422 may represent a no risk category. There
may be some other risk categories, for example highest risk category and lowest risk
category (not shown). By way of an example, on 30 July, a risk category for the floor
404a of tower 404 may be the high-risk category. However, for other floors of the tower
404 (i.e., 404b and 404c) may be the medium risk category, for the same day.
Similarly, risk categories of the floors 404a, 404b, 404c, 406a, 406b, 406c, 408a, and
408b, from 30 June to 06 July is represented in FIG. 4B.
[053] By way of an example, the risk category may be a medium risk category,
for a ‘z’ score or relative risk score value lying in a range of (-0.5) ≤ (+0.5). If the ‘z’
score value determined by the risk determination system 414 lies in between (-1.0) ≤
(-0.5), then the corresponding risk category may be considered as the low risk
category. Moreover, if the determined ‘z’ score value lies in between (+0.5) ≤ (+1.0),
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then the corresponding risk category may be considered as the high-risk category.
Further, the determined risk category by the risk determination system 414 may be a
no risk category if the value of ‘z’ score is less than (-0.5). These ranges are exemplary
and may be changed depending requirement or application, by a facility head.
[054] Thus, the present disclosure may overcome drawbacks of traditional
systems discussed before. The disclosed method and system in the present disclosure
provide crucial information of the risk level for each location to users (such as,
employees), separately, based on data elements. Further, the disclosure provides
various advantages such as, control access to a given location, assistance to other
stakeholders like employees and service providers by sharing information with them,
enables building management, enriches ratings with other source information. Hence,
the disclosed system performs adequate steps to lower the risk prevalent at certain
locations. The disclosed method and system of the present disclosure may consider
locations within a premise as a centre point and use metrics associated with
occupancy, social distancing, mask compliance, metrics from thermal camera to
determine risk score of locations and not individuals, thereby computing
Comprehensive Location Risk Scoring during Pandemic to understand the level of risk
for each location.
[055] It will be appreciated that, for clarity purposes, the above description has
described embodiments of the invention with reference to different functional units and
processors. However, it will be apparent that any suitable distribution of functionality
between different functional units, processors or domains may be used without
detracting from the invention. For example, functionality illustrated to be performed by
separate processors or controllers may be performed by the same processor or
controller. Hence, references to specific functional units are only to be seen as
references to suitable means for providing the described functionality, rather than
indicative of a strict logical or physical structure or organization.
[056] Although the present invention has been described in connection with
some embodiments, it is not intended to be limited to the specific form set forth herein.
Rather, the scope of the present invention is limited only by the claims. Additionally,
although a feature may appear to be described in connection with particular
embodiments, one skilled in the art would recognize that various features of the
described embodiments may be combined in accordance with the invention.
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[057] Furthermore, although individually listed, a plurality of means, elements
or process steps may be implemented by, for example, a single unit or processor.
Additionally, although individual features may be included in different claims, these
may possibly be advantageously combined, and the inclusion in different claims does
not imply that a combination of features is not feasible and/or advantageous. Also, the
inclusion of a feature in one category of claims does not imply a limitation to this
category, but rather the feature may be equally applicable to other claim categories,
as appropriate.

CLAIMS
What is claimed is:
1. A method (300) for determining a risk level for a potential spread of infectious
disease, the method (300) comprising:
receiving (302), by a risk determination system (102), data associated with each
of a plurality of locations within an institutional region in real time;
determining (304), by the risk determination system (102), a set of risk metrics
for each of the plurality of locations, based on the data associated with the each of the
plurality of locations, wherein the set of risk metrics comprises an occupancy metric,
a social distancing violation metric, and a mask compliance metric;
computing (306), by the risk determination system (102), a cumulative location
risk score for each of the plurality of locations, based on the set of risk metrics for the
each of the plurality of locations;
determining (308), by the risk determination system (102), a relative risk score
for each of the plurality of locations based on the cumulative risk score of the each of
the plurality of locations with respect to the cumulative risk scores of the plurality of
locations; and
determining (310), by the risk determination system (102), the risk level for the
potential spread of infectious disease for each of the plurality of locations based on
the corresponding relative risk score.
2. The method (300) as claimed in claim 1, wherein determining (310) the risk level
for each of the plurality of locations comprises:
determining a risk category for the risk level, from a plurality of predefined risk
categories, for each of the plurality of locations, based on the relative risk score for
each of the plurality of locations, wherein the risk category comprises one of a high
risk, a medium risk, a low risk and a no risk category; and
generating a heat map based on the determined risk category of the each of
the plurality of locations.
3. The method (300) as claimed in claim 1, comprising transmitting notifications to
management authorities controlling containment of the potential spread of infectious
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disease, based on the risk level above a predefined threshold value, wherein the
notifications comprise warning messages.
4. The method (300) as claimed in claim 1, wherein the data associated with each of
the plurality of locations comprises occupancy data for a location, social distancing
violation data in the location, and mask compliance data in the location, and wherein:
the occupancy data for the location correspond to a number of people in the
location and is based on at least one of access log data for the location from location
access management system (112a), network log data for the location from network
system (112d), a number of people in a video acquired at the location by the video
surveillance system (112b);
the social distancing violation data in the location correspond to a violation of
minimum distance between any two people in the location and is derived from the
video acquired at the location by the video surveillance system (112b); and
the mask compliance data in the location correspond to a number of people
without masks in the location and is derived from the video acquired at the location by
the video surveillance system (112b).
5. The method (300) as claimed in claim 1, wherein the set of risk metrics for each of
the plurality of locations comprises a health risk metric and a temperature metric,
wherein the data associated with each of the plurality of locations comprises health
risk data and temperature data of each person in a location, and wherein:
the health risk data correspond to a number of people with exposure to health
risk and is based on health risk assessment reports from a health enterprise or a
government authority; and
the temperature data correspond to a number of people with Elevated Body
Temperature (EBT) and is derived from thermal images captured by thermal cameras
(112c) installed at entry points of the location.
6. A system (100) for determining a risk level for a potential spread of infectious
disease, the system (100) comprising:
a processor (106); and
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a memory (104) communicatively coupled to the processor (106), wherein the
memory (104) stores processor-executable instructions, which, on execution, causes
the processor (106) to:
receive (302) data associated with each of a plurality of locations within
an institutional region in real time;
determine (304) a set of risk metrics for each of the plurality of locations,
based on the data associated with the each of the plurality of locations, wherein
the set of risk metrics comprises an occupancy metric, a social distancing
violation metric, and a mask compliance metric;
compute (306) a cumulative location risk score for each of the plurality
of locations, based on the set of risk metrics for the each of the plurality of
locations;
determine (308) a relative risk score for each of the plurality of locations
based on the cumulative risk score of the each of the plurality of locations with
respect to the cumulative risk scores of the plurality of locations; and
determine (310) the risk level for the potential spread of infectious
disease for each of the plurality of locations based on the corresponding relative
risk score.
7. The system (100) as claimed in claim 6, wherein the processor-executable
instructions cause the processor (106) to determine (310) the risk level for each of the
plurality of locations by:
determining a risk category for the risk level, from a plurality of predefined risk
categories, for each of the plurality of locations, based on the relative risk score for
each of the plurality of locations, and wherein the risk category comprises one of a
high risk, a medium risk, a low risk and a no risk category; and
generating a heat map based on the determined risk category of the each of
the plurality of locations.
8. The system (100) as claimed in claim 6, wherein the processor-executable
instructions cause the processor (106) to transmit notifications to management
authorities controlling containment of the potential spread of infectious disease, based
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on the risk level above a predefined threshold value, wherein the notifications
comprise warning messages.
9. The system (100) as claimed in claim 6, wherein the data associated with each of
the plurality of locations comprises occupancy data for a location, social distancing
violation data in the location, and mask compliance data in the location, and wherein:
the occupancy data for the location correspond to a number of people in the
location and is based on at least one of access log data for the location from location
access management system (112a), network log data for the location from network
system (112d), a number of people in a video acquired at the location by the video
surveillance system (112b);
the social distancing violation data in the location correspond to a violation of
minimum distance between any two people in the location and is derived from the
video acquired at the location by the video surveillance system (112b); and
the mask compliance data in the location correspond to a number of people
without masks in the location and is derived from the video acquired at the location by
the video surveillance system (112b).
10. The system (100) as claimed in claim 6, wherein the set of risk metrics for each
of the plurality of locations comprises a health risk metric and a temperature metric,
wherein the data associated with each of the plurality of locations comprises health
risk data and temperature data of each person in a location, and wherein:
the health risk data correspond to a number of people with exposure to health
risk and is based on health risk assessment reports from a health enterprise or a
government authority; and
the temperature data correspond to a number of people with Elevated Body
Temperature (EBT) and is derived from thermal images captured by thermal cameras
(112c) installed at entry points of the location.