scholarly journals IoT-based Contact Tracing Systems for Infectious Diseases: Architecture and Analysis

2020 ◽  
Author(s):  
Peng Hu
Keyword(s):  
Infectious Diseases ◽  
Contact Tracing

scholarly journals TraceAll: A Real-Time Processing for Contact Tracing Using Indoor Trajectories

Information ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 202
Author(s):  
Louai Alarabi ◽  
Saleh Basalamah ◽  
Abdeltawab Hendawi ◽  
Mohammed Abdalla
Keyword(s):  
Infectious Diseases ◽  
Infected Patient ◽  
Public Health Problem ◽  
Real Data ◽  
Exposure Period ◽  
Contact Tracing ◽  
Data Sets ◽  
Major Public Health Problem ◽  
Real Time Processing ◽  
Recent Developments

The rapid spread of infectious diseases is a major public health problem. Recent developments in fighting these diseases have heightened the need for a contact tracing process. Contact tracing can be considered an ideal method for controlling the transmission of infectious diseases. The result of the contact tracing process is performing diagnostic tests, treating for suspected cases or self-isolation, and then treating for infected persons; this eventually results in limiting the spread of diseases. This paper proposes a technique named TraceAll that traces all contacts exposed to the infected patient and produces a list of these contacts to be considered potentially infected patients. Initially, it considers the infected patient as the querying user and starts to fetch the contacts exposed to him. Secondly, it obtains all the trajectories that belong to the objects moved nearby the querying user. Next, it investigates these trajectories by considering the social distance and exposure period to identify if these objects have become infected or not. The experimental evaluation of the proposed technique with real data sets illustrates the effectiveness of this solution. Comparative analysis experiments confirm that TraceAll outperforms baseline methods by 40% regarding the efficiency of answering contact tracing queries.

scholarly journals An Egocentric Network Contact Tracing Experiment: Testing Different Procedures to Elicit Contacts and Places

2021 ◽  
Vol 18 (4) ◽  
pp. 1466
Author(s):  
Andrew Pilny ◽  
C. Joseph Huber
Keyword(s):  
Social Network ◽  
Infectious Diseases ◽  
Effect Size ◽  
Survey Methods ◽  
Contact Tracing ◽  
Medium Effect ◽  
Physiological Variables ◽  
Medium Effect Size ◽  
Egocentric Network ◽  
Survey Instruments

Contact tracing is one of the oldest social network health interventions used to reduce the diffusion of various infectious diseases. However, some infectious diseases like COVID-19 amass at such a great scope that traditional methods of conducting contact tracing (e.g., face-to-face interviews) remain difficult to implement, pointing to the need to develop reliable and valid survey approaches. The purpose of this research is to test the effectiveness of three different egocentric survey methods for extracting contact tracing data: (1) a baseline approach, (2) a retrieval cue approach, and (3) a context-based approach. A sample of 397 college students were randomized into one condition each. They were prompted to anonymously provide contacts and populated places visited from the past four days depending on what condition they were given. After controlling for various demographic, social identity, psychological, and physiological variables, participants in the context-based condition were significantly more likely to recall more contacts (medium effect size) and places (large effect size) than the other two conditions. Theoretically, the research supports suggestions by field theory that assume network recall can be significantly improved by activating relevant activity foci. Practically, the research contributes to the development of innovative social network data collection methods for contract tracing survey instruments.

scholarly journals Evaluating the Effectiveness of COVID-19 Bluetooth-Based Smartphone Contact Tracing Applications

2020 ◽  
Vol 10 (20) ◽  
pp. 7113 ◽  
Author(s):  
Enrique Hernández-Orallo ◽  
Carlos T. Calafate ◽  
Juan-Carlos Cano ◽  
Pietro Manzoni
Keyword(s):  
Infectious Diseases ◽  
Epidemic Model ◽  
Contact Tracing ◽  
Reproductive Number ◽  
Smartphone Applications ◽  
Social Distancing ◽  
Implementation Model ◽  
Decentralized Model ◽  
Centralized Model

One of the strategies to control the spread of infectious diseases is based on the use of specialized applications for smartphones. These apps offer the possibility, once individuals are detected to be infected, to trace their previous contacts in order to test and detect new possibly-infected individuals. This paper evaluates the effectiveness of recently developed contact tracing smartphone applications for COVID-19 that rely on Bluetooth to detect contacts. We study how these applications work in order to model the main aspects that can affect their performance: precision, utilization, tracing speed and implementation model (centralized vs. decentralized). Then, we propose an epidemic model to evaluate their efficiency in terms of controlling future outbreaks and the effort required (e.g., individuals quarantined). Our results show that smartphone contact tracing can only be effective when combined with other mild measures that can slightly reduce the reproductive number R0 (for example, social distancing). Furthermore, we have found that a centralized model is much more effective, requiring an application utilization percentage of about 50% to control an outbreak. On the contrary, a decentralized model would require a higher utilization to be effective.

scholarly journals Taiwan’s Successful COVID-19 Mitigation and Containment Strategy: Achieving Quasi Population Immunity

2020 ◽  
pp. 1-4 ◽  
Author(s):  
Li-Chien Chien ◽  
Christian K. Beÿ ◽  
Kristi L. Koenig
Keyword(s):  
Public Health ◽  
Infectious Diseases ◽  
Health Management ◽  
Mainland China ◽  
Emerging Infectious Diseases ◽  
Contact Tracing ◽  
Population Immunity ◽  
Containment Strategy ◽  
Local Transmission ◽  
Pharmaceutical Interventions

ABSTRACT The authors describe Taiwan’s successful strategy in achieving control of coronavirus disease (COVID-19) without economic shutdown, despite the prediction that millions of infections would be imported from travelers returning from Chinese New Year celebrations in Mainland China in early 2020. As of September 2, 2020, Taiwan reports 489 cases, 7 deaths, and no locally acquired COVID-19 cases for the last 135 days (greater than 4 months) in its population of over 23.8 million people. Taiwan created quasi population immunity through the application of established public health principles. These non-pharmaceutical interventions, including public masking and social distancing, coupled with early and aggressive identification, isolation, and contact tracing to inhibit local transmission, represent a model for optimal public health management of COVID-19 and future emerging infectious diseases.

scholarly journals When might Host Heterogeneity Drive the Evolution of Asymptomatic, Pandemic Coronaviruses?

2020 ◽  
Author(s):  
Kenichi W. Okamoto ◽  
Virakbott Ong ◽  
Robert G. Wallace ◽  
Rodrick Wallace ◽  
Luis Fernando Chaves
Keyword(s):  
Public Health ◽  
Infectious Diseases ◽  
Disease Control ◽  
Disease Surveillance ◽  
Fitness Landscape ◽  
Emerging Infectious Diseases ◽  
Disease Suppression ◽  
Contact Tracing ◽  
Pathogen Strains ◽  
Host Heterogeneity

For most emerging infectious diseases, including SARS-Coronavirus-2 (SARS-CoV-2), pharmaceutical intervensions such as drugs and vaccines are not available, and disease surveillance followed by isolating, contact-tracing and quarantining infectious individuals is critical for controlling outbreaks. These interventions often begin by identifying symptomatic individuals. However, by actively removing pathogen strains likely to be symptomatic, such interventions may inadvertently select for strains less likely to result in symptomatic infections. Additionally, the pathogen's fitness landscape is structured around a heterogeneous host pool. In particular, uneven surveillance efforts and distinct transmission risks across host classes can drastically alter selection pressures. Here we explore this interplay between evolution caused by disease control efforts, on the one hand, and host heterogeneity in the efficacy of public health interventions on the other, on the potential for a less symptomatic, but widespread, pathogen to evolve. We use an evolutionary epidemiology model parameterized for SARS-CoV-2, as the widespread potential for silent transmission by asymptomatic hosts has been hypothesized to account, in part, for its rapid global spread. We show that relying on symptoms-driven reporting for disease control ultimately shifts the pathogen's fitness landscape and can cause pandemics. We find such outcomes result when isolation and quarantine efforts are intense, but insufficient for suppression. We further show that when host removal depends on the prevalence of symptomatic infections, intense isolation efforts can select for the emergence and extensive spread of more asymptomatic strains. The severity of selection pressure on pathogens caused by these interventions likely lies somewhere between the extremes of no intervention and thoroughly successful eradication. Identifying the levels of public health responses that facilitate selection for asymptomatic pathogen strains is therefore critical for calibrating disease suppression and surveillance efforts and for sustainably managing emerging infectious diseases.

scholarly journals UTLDR: an agent-based framework for modeling infectious diseases and public interventions

2021 ◽  
Author(s):  
Giulio Rossetti ◽  
Letizia Milli ◽  
Salvatore Citraro ◽  
Virginia Morini
Keyword(s):  
Infectious Diseases ◽  
Data Driven ◽  
Contact Tracing ◽  
Mobility Patterns ◽  
Epidemic Modeling ◽  
Specific Data ◽  
Agent Based ◽  
Software Libraries

AbstractDue to the SARS-CoV-2 pandemic, epidemic modeling is now experiencing a constantly growing interest from researchers of heterogeneous study fields. Indeed, due to such an increased attention, several software libraries and scientific tools have been developed to ease the access to epidemic modeling. However, only a handful of such resources were designed with the aim of providing a simple proxy for the study of the potential effects of public interventions (e.g., lockdown, testing, contact tracing). In this work, we introduce UTLDR, a framework that, overcoming such limitations, allows to generate “what if” epidemic scenarios incorporating several public interventions (and their combinations). UTLDR is designed to be easy to use and capable to leverage information provided by stratified populations of agents (e.g., age, gender, geographical allocation, and mobility patterns…). Moreover, the proposed framework is generic and not tailored for a specific epidemic phenomena: it aims to provide a qualitative support to understanding the effects of restrictions, rather than produce forecasts/explanation of specific data-driven phenomena.

scholarly journals Evaluating How Smartphone Contact Tracing Technology Can Reduce the Spread of Infectious Diseases: The Case of COVID-19

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 99083-99097 ◽  
Author(s):  
Enrique Hernandez-Orallo ◽  
Pietro Manzoni ◽  
Carlos Tavares Calafate ◽  
Juan-Carlos Cano
Keyword(s):  
Infectious Diseases ◽  
Contact Tracing

scholarly journals Workshop: Reaching out to engaging the risk groups: online respondent-driven methods for public health

2019 ◽  
Vol 29 (Supplement_4) ◽  
Author(s):  
Keyword(s):  
Public Health ◽  
Infectious Diseases ◽  
Essential Element ◽  
Contact Tracing ◽  
Population Estimates ◽  
Successful Implementation ◽  
Snowball Sampling ◽  
Convenience Sample ◽  
Effective Prevention ◽  
Hard To Reach Populations

Abstract Hard-to-reach populations (i.e. those stigmatised, marginalised, underrepresented, or otherwise disadvantaged) such as men who have sex with men and immigrants are at higher risk for infectious diseases. Reaching these populations, studying their behaviour and/or testing individuals for infectious diseases is essential for developing effective prevention programmes and disease surveillance. These populations, however, lack sampling frames making it difficult to collect representative quantitative data using common probability-based sampling methods. Respondent-driven sampling (RDS), a variant of snowball sampling, is an effective method to recruit these populations and to make unbiased population estimates using a statistical model. RDS starts with recruiting a convenience sample of the target population (so-called “seeds”). These seeds are then asked to recruit a number of other eligible individuals of their social network. This process continues which leads to chains of recruitment, with several waves of recruits. The process of respondent-driven recruitment is very similar to the way infectious diseases such as influenza and mumps transmit through populations. This provides opportunities to use the method with a different aim: the detection of cases within networks. Finding infectious cases is an essential element for prevention of further spread in the population and individual health consequences. Essential as it is to public health, conventional contact tracing is a rather timely, costly and, up to a certain degree, really frustrating activity. Studying and making use of social networks may help to understand and control the spread of infectious diseases transmitted via direct contact. These diseases do not spread at random through a population, but follow the underlying patterns of contact networks. This entails that cases tend to cluster by time and space, and their contact persons are at a higher risk for infection. Same as with RDS, respondent-driven detection (RDD) starts with individuals being asked to recruit relevant contact persons from their network. These contact persons are then asked to do the same, resulting in successive waves of contact persons. A case is reached through contact with a known case, similar to pathogens spreading through these contact relationships. RDD may therefore enhance conventional contact tracing, providing further insight in the extent of outbreaks, in a quick and less laborious manner for public health professionals. Using three examples from public health practice, this workshop provides participants insights in the methodology of online respondent-driven methods (RDS and RDD), how these provide behavioural and epidemiological knowledge on networks and the spread of infectious diseases, and highlights pre-requisites for successful implementation in practice. Lastly, an interactive discussion will be held with the audience on how attendees can apply these methods for their own public health challenges. Key messages RDS is used to sample hard-to-reach populations to collect their social, sexual and behavioural information, and to make unbiased population estimates. RDD is used to detect infectious cases or clusters of disease.

scholarly journals An Egocentric Network Contact Tracing Experiment

2020 ◽  
Author(s):  
Andrew Pilny ◽  
C. Joseph Huber
Keyword(s):  
Infectious Diseases ◽  
Effect Size ◽  
Survey Methods ◽  
Contact Tracing ◽  
Medium Effect ◽  
Face To Face ◽  
Physiological Variables ◽  
Medium Effect Size ◽  
Egocentric Network ◽  
Survey Instruments

Contact tracing is one of the oldest social network health interventions used to reduce the diffusion of various infectious diseases. However, some infectious diseases like COVID-19 amass at such a great scope that traditional methods of conducting contact tracing (e.g., face-to-face interviews) remain difficult to implement, pointing the need to develop reliable and valid survey approaches. The purpose of this research is to test the effectiveness of three different egocentric survey methods for extracting contact tracing data: (1) a baseline approach, (2) a retrieval cue approach, and (3) a context-based approach. A sample of 397 college students were randomized into one of each condition and were prompted to anonymously provide contacts and populated places visited from the past four days. After controlling for various demographic, social identity, psychological, and physiological variables, participants in the context-based condition were significantly more likely recall more contacts (medium effect size) and places (large effect size) than the other two conditions. Theoretically, the research supports suggestions by field theory that assume network recall can be significantly improved by activating relevant activity foci. Practically, the research contributes to developing innovative social network data collection methods for contract tracing survey instruments.

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