Contact tracing analytics
First Claim
1. A method for contact tracing during an event, the method comprising:
- connecting, by one or more processors, a plurality of disparate sources of information to an algorithm, wherein the plurality of disparate sources of information comprise;
(i) different data formats containing at least data describing body temperature readings corresponding to a plurality of individuals, data describing a plurality of travel itineraries of destinations traveled with respectively associated traveled dates and times corresponding to each of the plurality of individuals, and data describing a plurality of travel itineraries of destinations scheduled for travel with respectively associated scheduled travel dates and times corresponding to each of the plurality of individuals; and
(ii) interaction data which proliferates into a large data set;
converting, by one or more processors, the plurality of disparate sources of information into a common format amenable for analytics by the algorithm, wherein the algorithm sends the large data set to a data repository;
determining, by one or more processors, community interaction information for one or more clusters within the common format, wherein the community interaction information for a cluster correlates times and physical locations of one or more individuals within an area corresponding to the cluster;
performing, by one or more processors, the analytics on the plurality of disparate sources of information converted to the common format, in order to determine that a first individual has traveled from a first area corresponding to a first cluster to a second area corresponding to a second cluster, wherein the determination is based, at least in part, on correlated times and physical locations of the first individual; and
predicting, by one or more processors, one or more at-risk individuals by generating a list of the one or more at-risk individuals, as contained within the large data set in the data repository, based at least in part on;
the performed analytics which;
(i) account for dynamic events associated with the first cluster and the second cluster, wherein the dynamic events include a plurality of seamless interactions within the first cluster and the second cluster and between the first cluster and the second cluster, and (ii) derive discrete data from the plurality of disparate sources of information,the community interaction information of the second cluster,the data describing the plurality of travel itineraries of destinations traveled with respectively associated traveled dates and times corresponding to each of the at-risk individuals among the plurality of individuals, andthe data describing the plurality of travel itineraries of destinations scheduled for travel with respectively associated scheduled travel dates and times corresponding to each of the at-risk individuals among the plurality of individuals.
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Abstract
Contact tracing during an event is provided. Community interaction information for one or more clusters is determined. The community interaction information for a cluster correlates times and physical locations of one or more individuals within an area corresponding to the cluster. It is determined that a first individual has traveled from a first area corresponding to a first cluster to a second area corresponding to a second cluster. The determination is based, at least in part, on correlated times and physical locations of the first individual. One or more at-risk individuals is identified based, at least in part, on the community interaction information of the second cluster.
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Citations
20 Claims
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1. A method for contact tracing during an event, the method comprising:
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connecting, by one or more processors, a plurality of disparate sources of information to an algorithm, wherein the plurality of disparate sources of information comprise;
(i) different data formats containing at least data describing body temperature readings corresponding to a plurality of individuals, data describing a plurality of travel itineraries of destinations traveled with respectively associated traveled dates and times corresponding to each of the plurality of individuals, and data describing a plurality of travel itineraries of destinations scheduled for travel with respectively associated scheduled travel dates and times corresponding to each of the plurality of individuals; and
(ii) interaction data which proliferates into a large data set;converting, by one or more processors, the plurality of disparate sources of information into a common format amenable for analytics by the algorithm, wherein the algorithm sends the large data set to a data repository; determining, by one or more processors, community interaction information for one or more clusters within the common format, wherein the community interaction information for a cluster correlates times and physical locations of one or more individuals within an area corresponding to the cluster; performing, by one or more processors, the analytics on the plurality of disparate sources of information converted to the common format, in order to determine that a first individual has traveled from a first area corresponding to a first cluster to a second area corresponding to a second cluster, wherein the determination is based, at least in part, on correlated times and physical locations of the first individual; and predicting, by one or more processors, one or more at-risk individuals by generating a list of the one or more at-risk individuals, as contained within the large data set in the data repository, based at least in part on; the performed analytics which;
(i) account for dynamic events associated with the first cluster and the second cluster, wherein the dynamic events include a plurality of seamless interactions within the first cluster and the second cluster and between the first cluster and the second cluster, and (ii) derive discrete data from the plurality of disparate sources of information,the community interaction information of the second cluster, the data describing the plurality of travel itineraries of destinations traveled with respectively associated traveled dates and times corresponding to each of the at-risk individuals among the plurality of individuals, and the data describing the plurality of travel itineraries of destinations scheduled for travel with respectively associated scheduled travel dates and times corresponding to each of the at-risk individuals among the plurality of individuals. - View Dependent Claims (2, 3, 4, 5, 6, 7)
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8. A computer program product for contact tracing during an event, the computer program product comprising:
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a computer readable storage medium and program instructions stored on the computer readable storage medium, the program instructions comprising; program instructions to connect a plurality of disparate sources of information to an algorithm, wherein the plurality of disparate sources of information comprise;
(i) different data formats containing at least data describing body temperature readings corresponding to a plurality of individuals, data describing a plurality of travel itineraries of destinations traveled with respectively associated traveled dates and times corresponding to each of the plurality of individuals, and data describing a plurality of travel itineraries of destinations scheduled for travel with respectively associated scheduled travel dates and times corresponding to each of the plurality of individuals; and
(ii) interaction data which proliferates into a large data set;program instructions to convert the plurality of disparate sources of information into a common format amenable for analytics by the algorithm, wherein the algorithm stores the large data set, wherein the algorithm sends the large data set to a data repository; program instructions to determine community interaction information for one or more clusters within the common format, wherein the community interaction information for a cluster correlates times and physical locations of one or more individuals within an area corresponding to the cluster; program instructions to perform analytics on the plurality of disparate sources of information converted to the common format, in order to determine that a first individual has traveled from a first area corresponding to a first cluster to a second area corresponding to a second cluster, wherein the determination is based, at least in part, on correlated times and physical locations of the first individual; and program instructions to predict one or more at-risk individuals by generating a list of the one or more at-risk individuals, as contained within the large data set in the data repository, based at least in part on; the performed analytics which;
(i) account for dynamic events associated with the first cluster and the second cluster, wherein the dynamic events include a plurality of seamless interactions within the first cluster and the second cluster and between the first cluster and the second cluster, and (ii) derive discrete data from the plurality of disparate sources of information,the community interaction information of the second cluster; the data describing the plurality of travel itineraries of destinations traveled with respectively associated traveled dates and times corresponding to each of the at-risk individuals among the plurality of individuals, and the data describing the plurality of travel itineraries of destinations scheduled for travel with respectively associated scheduled travel dates and times corresponding to each of the at-risk individuals among the plurality of individuals. - View Dependent Claims (9, 10, 11, 12, 13, 14)
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15. A computer system for contact tracing during an event, the computer system comprising:
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one or more computer processors; one or more computer readable storage media; program instructions stored on the one or more computer readable storage media for execution by at least one of the one or more processors, the program instructions comprising; program instructions to connect a plurality of disparate sources of information to an algorithm, wherein the plurality of disparate sources of information comprise;
(i) different data formats containing at least data describing body temperature readings corresponding to a plurality of individuals, data describing a plurality of travel itineraries of destinations traveled with respectively associated traveled dates and times corresponding to each of the plurality of individuals, and data describing a plurality of travel itineraries of destinations scheduled for travel with respectively associated scheduled travel dates and times corresponding to each of the plurality of individuals; and
(ii) interaction data which proliferates into a large data set;program instructions to convert the plurality of disparate sources of information into a common format amenable for analytics by the algorithm, wherein the algorithm stores the large data set, wherein the algorithm sends the large data set to a data repository; program instructions to determine community interaction information for one or more clusters within the common format, wherein the community interaction information for a cluster correlates times and physical locations of one or more individuals within an area corresponding to the cluster; program instructions to perform analytics on the plurality of disparate sources of information converted to the common format, in order to determine that a first individual has traveled from a first area corresponding to a first cluster to a second area corresponding to a second cluster, wherein the determination is based, at least in part, on correlated times and physical locations of the first individual; and program instructions to predict one or more at-risk individuals by generating a list of the one or more at-risk individuals, as contained within the large data set in the data repository, based at least in part on; the performed analytics which;
(i) account for dynamic events associated with the first cluster and the second cluster, wherein the dynamic events include a plurality of seamless interactions within the first cluster and the second cluster and between the first cluster and the second cluster, and (ii) derive discrete data from the plurality of disparate sources of information,the community interaction information of the second cluster; the data describing the plurality of travel itineraries of destinations traveled with respectively associated traveled dates and times corresponding to each of the at-risk individuals among the plurality of individuals, and the data describing the plurality of travel itineraries of destinations scheduled for travel with respectively associated scheduled travel dates and times corresponding to each of the at-risk individuals among the plurality of individuals. - View Dependent Claims (16, 17, 18, 19, 20)
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Specification