ShareTrace: Contact Tracing with the Actor Model (original) (raw)
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Message-passing approach to epidemic tracing and mitigation with apps
Physical Review Research, 2021
With the hit of new pandemic threats, scientific frameworks are needed to understand the unfolding of the epidemic. The use of mobile apps that are able to trace contacts is of utmost importance in order to control new infected cases and contain further propagation. Here we present a theoretical approach using both percolation and message-passing techniques, to the role of contact tracing, in mitigating an epidemic wave. We show how the increase of the app adoption level raises the value of the epidemic threshold, which is eventually maximized when high-degree nodes are preferentially targeted. Analytical results are compared with extensive Monte Carlo simulations showing good agreement for both homogeneous and heterogeneous networks. These results are important to quantify the level of adoption needed for contact-tracing apps to be effective in mitigating an epidemic.
An Enhanced Autonomous Socio-Contact Tracing System of the Spread of Contiguous Diseases
Omar aboulola, 2021
COVID-19 and other neighbouring diseases spread widely, resulting in a global epidemic that was impossible to manage and control. While numerous measures have been put in place to detect an infected person and protect uninfected areas from contracting these contagious diseases, the spread of diseases like COVID-19 continues to be rapid. As of the time of writing this paper, the number of affected people has continued to rise, and there is no clear indication of the number of people who are infected but have gone undiscovered and are spreading the infections. That is why, in order to combat the threat of contiguous disease spread, this research presented an upgraded autonomous socio-contact tracing system on a mobile platform. As a result, a generic system development process was used to create a system that allows an infected person who has been tested positive to track their electromagnetic ID card in order to determine their exact location and the risk of spreading contagious diseases. by an autonomous smart assistant that assists in describing symptoms. As a result, the system is equipped with notifications alerts for the stages of social group identification, processing, and control in order to avoid the spread of contagious diseases. The development of this system is critical for controlling epidemic diseases that are spreading over the world (particularly COVID-19) and posing a threat to people's lives. Furthermore, it contributes to a greater understanding of the seriousness of epidemic diseases and how to avoid them.
Characterising contact in disease outbreaks via a network model of spatial-temporal proximity
2021
ABSTRACTContact tracing is a key tool in epidemiology to identify and control outbreaks of infectious diseases. Existing contact tracing methodologies produce contact maps of individuals based on a binary definition of contact which can be hampered by missing data and indirect contacts. Here, we present a Spatial-temporal Epidemiological Proximity (StEP) model to recover contact maps in disease outbreaks based on movement data. The StEP model accounts for imperfect data by considering probabilistic contacts between individuals based on spatial-temporal proximity of their movement trajectories, creating a robust movement network despite possible missing data and unseen transmission routes. Using real-world data we showcase the potential of StEP for contact tracing with outbreaks of multidrug-resistant bacteria and COVID-19 in a large hospital group in London, UK. In addition to the core structure of contacts that can be recovered using traditional methods of contact tracing, the StEP...
Mitigate SIR epidemic spreading via contact blocking in temporal networks
Applied Network Science, 2022
Progress has been made in how to suppress epidemic spreading on temporal networks via blocking all contacts of targeted nodes or node pairs. In this work, we develop contact blocking strategies that remove a fraction of contacts from a temporal (time evolving) human contact network to mitigate the spread of a Susceptible-Infected-Recovered epidemic. We define the probability that a contact c(i, j, t) is removed as a function of a given centrality metric of the corresponding link l(i, j) in the aggregated network and the time t of the contact. The aggregated network captures the number of contacts between each node pair. A set of 12 link centrality metrics have been proposed and each centrality metric leads to a unique contact removal strategy. These strategies together with a baseline strategy (random removal) are evaluated in empirical contact networks via the average prevalence, the peak prevalence and the time to reach the peak prevalence. We find that the epidemic spreading can ...
Efficient detection of contagious outbreaks in massive metropolitan encounter networks
Scientific Reports, 2014
Physical contact remains difficult to trace in large metropolitan networks, though it is a key vehicle for the transmission of contagious outbreaks. Co-presence encounters during daily transit use provide us with a cityscale time-resolved physical contact network, consisting of 1 billion contacts among 3 million transit users. Here, we study the advantage that knowledge of such co-presence structures may provide for early detection of contagious outbreaks. We first examine the "friend sensor" scheme-a simple, but universal strategy requiring only local information-and demonstrate that it provides significant early detection of simulated outbreaks. Taking advantage of the full network structure, we then identify advanced "global sensor sets", obtaining substantial early warning times savings over the friends sensor scheme. Individuals with highest number of encounters are the most efficient sensors, with performance comparable to individuals with the highest travel frequency, exploratory behavior and structural centrality. An efficiency balance emerges when testing the dependency on sensor size and evaluating sensor reliability; we find that substantial and reliable lead-time could be attained by monitoring only 0.01% of the population with the highest degree.
IEEE Journal of Selected Topics in Signal Processing, 2022
We study the epidemic source detection problem in contact tracing networks modeled as a graph-constrained maximum likelihood estimation problem using the susceptible-infected model in epidemiology. Based on a snapshot observation of the infection subgraph, we first study finite degree regular graphs and regular graphs with cycles separately, thereby establishing a mathematical equivalence in maximal likelihood ratio between the case of finite acyclic graphs and that of cyclic graphs. In particular, we show that the optimal solution of the maximum likelihood estimator can be refined to distances on graphs based on a novel statistical distance centrality that captures the optimality of the nonconvex problem. An efficient contact tracing algorithm is then proposed to solve the general case of finite degree-regular graphs with multiple cycles. Our performance evaluation on a variety of graphs shows that our algorithms outperform the existing state-of-the-art heuristics using contact tracing data from the SARS-CoV 2003 and COVID-19 pandemics by correctly identifying the superspreaders on some of the largest superspreading infection clusters in Singapore and Taiwan.
Automated Contact Tracing: a game of big numbers in the time of COVID-19
2020
ABSTRACTOne of the more widely advocated solutions for slowing down the spread of COVID-19 has been automated contact tracing. Since proximity data can be collected by personal mobile devices, the natural proposal has been to use this for automated contact tracing providing a major gain over a manual implementation. In this work, we study the characteristics of voluntary and automated contact tracing and its effectiveness for mapping the spread of a pandemic due to the spread of SARS-CoV-2. We highlight the infrastructure and social structures required for automated contact tracing to work. We display the vulnerabilities of the strategy to inadequate sampling of the population, which results in the inability to sufficiently determine significant contact with infected individuals. Of crucial importance will be the participation of a significant fraction of the population for which we derive a minimum threshold. We conclude that relying largely on automated contact tracing without pop...
2020 2nd Novel Intelligent and Leading Emerging Sciences Conference (NILES)
The unprecedented spread of COVID-19 pandemic has become the main challenge for several countries around the world. One of the crucial measures taken to control and manage the diffusion of COVID-19 pandemic is contact tracing. The approach is based on tracing and identifying people who have been exposed to an infected individual to prevent onward transmission by alerting those who came in contact with the positive case; thus, isolation measures and suitable precautions can be taken. By using the communication and localization technologies embedded on the smartphones, those who made contact can be effectively traced by continuously collecting the timestamped locations and contacts of the owners. In this paper, an innovative privacy preserving smartphone-based contact tracing solution named 'Tracy' is proposed to help the health facilities to limit and control the spread of COVID-19 especially with the upcoming second wave of the virus. The proposed system consists of three main components: an intelligent application installed on smartphones, a data processing platform, and a website on which the different functions of those parts are integrated to provide the required services for the contact tracing solution. The system is designed to allow individuals to investigate the possibility of them contacting a person infected with the emerging coronavirus. A novel algorithm is designed to determine the effective location points in the individual's routes in which possible contacts can happen and based on these effective points, the prior contacts are decided. The system is also designed to provide an effective communication method with the local health facilities to receive medical advice and precautionary measures required for those who have discovered the possibility of contact with COVID-19. The reliability and scalability of the proposed solution is recognized by the usage of effective contact location points to determine the point of contact with the infected rather than using all the points of the individual's route.
arXiv (Cornell University), 2022
Current approaches for modeling propagation in networks (e.g., spread of disease) are unable to adequately capture temporal properties of the data such as order and duration of evolving connections or dynamic likelihoods of propagation along these connections. Temporal models in evolving networks are crucial in many applications that need to analyze dynamic spread. For example, a disease-spreading virus has varying transmissibility based on interactions between individuals occurring over time with different frequency, proximity, and venue population density. To capture such behaviors, we first develop the Temporal Independent Cascade (T-IC) model and propose a novel spread function, that we prove to be submodular, with a hypergraph-based sampling strategy that efficiently utilizes dynamic propagation probabilities. We then introduce the notion of 'reverse spread' using the proposed TIC processes, and develop solutions to identify both sentinel/detector nodes and highly susceptible nodes. The proven guarantees of approximation quality enable scalable analysis of highly granular temporal networks. Extensive experimental results on a variety of real-world datasets show that the proposed approach significantly outperforms the alternatives in modeling both if and how spread occurs, by considering evolving network topology as well as granular contact/interaction information. Our approach has numerous applications, including its utility for the vital challenge of monitoring disease spread. Utilizing the proposed methods and TIC , we analyze the impact of various intervention strategies over real spatio-temporal contact networks. Our approach is shown also to be highly effective in quantifying the importance of superspreaders, designing targeted restrictions for controlling spread, and backward contact tracing.
The effectiveness of contact tracing in emerging epidemics
2006
Background Contact tracing plays an important role in the control of emerging infectious diseases, but little is known yet about its effectiveness. Here we deduce from a generic mathematical model how effectiveness of tracing relates to various aspects of time, such as the course of individual infectivity, the (variability in) time between infection and symptom-based detection, and delays in the tracing process. In addition, the possibility of iteratively tracing of yet asymptomatic infecteds is considered.