Epidemics, such as the present Covid-19 pandemic, usually spread at a rapid rate. Standard models, e.g., the SIR model, have stressed on the importance of finding the susceptible cases to flatten the growth rate of the spread of infection as early as possible. In the present scientific world, location traces in the form of GPS coordinates are logged by mobile device manufacturing and their operating systems developing companies, such as Apple, Samsung, Google etc. However, due to the sensitive nature of this data, it is usually not shared with URLanisations, mainly to protect individual privacy. However, in disaster situations, such as epidemics, data in the form of location traces of a community of people can potentially be helpful to proactively locate susceptible people from the community and enforce quarantine on them as early as possible. Since procuring such data for the purpose of restricted use is difficult (time-consuming) due to the sensitive nature of the data, a strong case needs to be made that how could such data be useful in disaster situations. The aim of this article is to to demonstrate a proof-of-the-concept that with the availability of massive amounts of real check-in data , it is feasible to develop a scalable system that is both effective (in terms of identifying the susceptible people) and efficient (in terms of the time taken to do so). We believe that this proof-of-the-concept will encourage sharing (with restricted use) of such sensitive data in order to help mitigate disaster situations. In this article, we describe a software resource to efficiently (consuming a small run-time) locate a set of susceptible persons given a global database of user check-ins and a set of infected people. Specifically, we describe a system, named InfecTracer, that seeks to find out cases of close proximity of a person with another infected person .
Amidst an increasing number of infected cases during the Covid-19 pandemic, it is essential to trace, as early as possible, the susceptible people who might have been infected by the disease due to their close proximity with people who were tested positive for the virus. This early contact tracing is likely to limit the rate of spread of the infection within a locality. In this paper, we investigate how effectively and efficiently can such a list of susceptible people be found given a list of infected persons and their locations. To address this problem from an information retrieval (search) perspective, we represent the location of each person at each time instant as a point in a vector space. By using the locations of the given list of infected persons as queries, we investigate the feasibility of applying approximate nearest neighbour (ANN) based indexing and retrieval approaches to obtain a list of top-k suspected users in real-time. Since leveraging information from true user location data can lead to security and privacy concerns, we also investigate what effects does distance-preserving encoding methods have on the effectiveness of the ANN methods. Experiments conducted on real and synthetic datasets demonstrate that the top-k retrieved lists of susceptible users retrieved with existing ANN approaches (KD-tree and HNSW) yield satisfactory precision and recall values, thus indicating that ANN approaches can potentially be applied in practice to facilitate real-time contact tracing even under the presence of imposed privacy constraints .
R
Epidemics, such as the present
Amidst an increasing number of infected cases during the
R
usually spread at a rapid rate. Standard models, e.g., the SIR model, have stressed on the importance of finding the susceptible cases to flatten the growth rate of the spread of infection as early as possible. In the present scientific world, location traces in the form of GPS coordinates are logged by mobile device manufacturing and their operating systems developing companies, such as Apple, Samsung, Google etc. However, due to the sensitive nature of this data, it is usually not shared with URLanisations, mainly to protect individual privacy. However, in disaster situations, such as epidemics, data in
it is essential to trace, as early as possible, the susceptible people who might have been infected by the disease due to their close proximity with people who were tested positive for the virus. This early contact tracing is likely to limit the rate of spread of the infection within a locality. In this paper, we investigate how effectively and efficiently can such a list of susceptible people be found given a list of infected persons and their locations. To address this problem from an information retrieval (search) perspective, we represent the location of each person at each time instant as a point in a vector space. By using the locations of
R
form of location traces of a community of people can potentially be helpful to proactively locate susceptible people from the community and enforce quarantine on them as early as possible. Since procuring such data for the purpose of restricted use is difficult (time-consuming) due to the sensitive nature of the data, a strong case needs to be made that how could such data be useful in disaster situations. The aim of this article is to to demonstrate a proof-of-the-concept that with the availability of massive amounts of real check-in data , it is feasible to develop a scalable system that is both effective (in terms of identifying the susceptible people) and efficient (in terms of the time taken to do so). We believe that this proof-of-the-concept will encourage sharing (with restricted use) of such sensitive data in order to help mitigate disaster situations. In this article, we describe a software resource to efficiently (consuming a small run-time) locate a set of susceptible persons given a global database of user check-ins and a set of infected people. Specifically, we describe a system, named InfecTracer, that seeks to find out cases of close proximity of a person with another infected person
given list of infected persons as queries, we investigate the feasibility of applying approximate nearest neighbour (ANN) based indexing and retrieval approaches to obtain a list of top-k suspected users in real-time. Since leveraging information from true user location data can lead to security and privacy concerns, we also investigate what effects does distance-preserving encoding methods have on the effectiveness of the ANN methods. Experiments conducted on real and synthetic datasets demonstrate that the top-k retrieved lists of susceptible users retrieved with existing ANN approaches (KD-tree and HNSW) yield satisfactory precision and recall values, thus indicating that ANN approaches can potentially be applied in practice to facilitate real-time contact tracing even under the presence of imposed privacy constraints