scholarly journals Potential reduction in transmission of COVID-19 by digital contact tracing systems

2020 ◽  
Author(s):  
Michael J Plank ◽  
Alex James ◽  
Audrey Lustig ◽  
Nicholas Steyn ◽  
Rachelle N Binny ◽  
...  
Keyword(s):  
Uptake Rate ◽  
Process Model ◽  
Reproduction Number ◽  
Contact Tracing ◽  
Location Tracking ◽  
Public Health Agencies ◽  
Proximity Detection ◽  
Health Agencies ◽  
Alternative Approaches ◽  
Age Structured

Digital tools are being developed to support contact tracing as part of the global effort to control the spread of COVID-19. These include smartphone apps, Bluetooth-based proximity detection, location tracking, and automatic exposure notification features. Evidence on the effectiveness of alternative approaches to digital contact tracing is so far limited. We use an age-structured branching process model of the transmission of COVID-19 in different settings to estimate the potential of manual contact tracing and digital tracing systems to help control the epidemic. We investigate the effect of the uptake rate and proportion of contacts recorded by the digital system on key model outputs: the effective reproduction number, the mean outbreak size after 30 days, and the probability of elimination. We show that effective manual contact tracing can reduce the effective reproduction number from 2.4 to around 1.5. The addition of a digital tracing system with a high uptake rate over 75% could further reduce the effective reproduction number to around 1.1. Fully automated digital tracing without manual contact tracing is predicted to be much less effective. We conclude that, for digital tracing systems to make a significant contribution to the control of COVID-19, they need be designed in close conjunction with public health agencies to support and complement manual contact tracing by trained professionals.

scholarly journals Bidirectional contact tracing dramatically improves COVID-19 control

2020 ◽  
Author(s):  
William J. Bradshaw ◽  
Ethan C. Alley ◽  
Jonathan H. Huggins ◽  
Alun L. Lloyd ◽  
Kevin M. Esvelt
Keyword(s):  
Process Model ◽  
Branching Process ◽  
Reproduction Number ◽  
Contact Tracing ◽  
Test Sensitivity ◽  
Outbreak Control ◽  
Case Ascertainment ◽  
Health Agencies

AbstractContact tracing is critical to controlling COVID-19, but most protocols only “forward-trace” to notify people who were recently exposed. Using a stochastic branching-process model, we show that “bidirectional” tracing to identify infector individuals and their other infectees robustly improves outbreak control, reducing the effective reproduction number (Reff) by at least ∼0.3 while dramatically increasing resilience to low case ascertainment and test sensitivity. Adding smartphone-based exposure notification can further reduce Reff by 0.25, but only if nearly all smartphones can detect exposure events. Our results suggest that with or without digital approaches, implementing bidirectional tracing will enable health agencies to control COVID-19 more effectively without requiring high-cost interventions.

scholarly journals COVID-19 Pandemic & Bureaucracy: The Crisis Inside the Crisis

2021 ◽  
Vol 9 ◽  
Author(s):  
Katalyn Roßmann ◽  
Heike Wegner ◽  
Hans Stark ◽  
Gerd Großmann ◽  
Andreas Jansen ◽  
...  
Keyword(s):  
Public Health ◽  
Armed Forces ◽  
Contact Tracing ◽  
Public Health Approach ◽  
Outbreak Management ◽  
Public Health Agencies ◽  
German Armed Forces ◽  
Health Agencies ◽  
Crisis Support ◽  
Second Wave

The Medical Intelligence and Information (MI2) Unit of the German Armed Forces (Bundeswehr) is experienced in crisis support in military missions since several years. It gained additional experiences during the current coronavirus 2019 (COVID-19) pandemic on different levels of the response to crisis and was requested to share the findings and expertise with the overloaded civil public health agencies inside Germany. Since the beginning of the pandemic, the unit is constantly developing new products for crisis communication, knowledge sharing techniques in new databases, dashboards for leadership, and training for laypersons in contact tracing. Hence, trying to innovate in crisis since the first severe acute respiratory syndrome coronavirus (SARS-CoV)-2-disease wave. During the second wave, the unit was requested to evaluate the outbreak management of different national civil public health agencies in southern Germany, and to support the development of dashboards in a comprehensive public health approach as a necessary start toward digitalization.

scholarly journals The feasibility of targeted test-trace-isolate for the control of SARS-CoV-2 variants

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 291
Author(s):  
William Bradshaw ◽  
Jonathan Huggins ◽  
Alun Lloyd ◽  
Kevin Esvelt
Keyword(s):  
Process Model ◽  
Branching Process ◽  
Large Fraction ◽  
Contact Tracing ◽  
Rt Pcr ◽  
Health Agencies ◽  
The Common ◽  
Ancestral Strain

The SARS-CoV-2 variant B.1.1.7 reportedly exhibits substantially higher transmission than the ancestral strain and may generate a major surge of cases before vaccines become widely available, while the P.1 and B.1.351 variants may be equally transmissible and also resist vaccines. All three variants can be sensitively detected by RT-PCR due to an otherwise rare del11288-11296 mutation in orf1ab; B.1.1.7 can also be detected using the common TaqPath kit. Testing, contact tracing, and isolation programs overwhelmed by SARS-CoV-2 could slow the spread of the new variants, which are still outnumbered by tracers in most countries. However, past failures and high rates of mistrust may lead health agencies to conclude that tracing is futile, dissuading them from redirecting existing tracers to focus on the new variants. Here we apply a branching-process model to estimate the effectiveness of implementing a variant-focused testing, contact tracing, and isolation strategy with realistic levels of performance. Our model indicates that bidirectional contact tracing can substantially slow the spread of SARS-CoV-2 variants even in regions where a large fraction of the population refuses to cooperate with contact tracers or to abide by quarantine and isolation requests.

scholarly journals An SEIR Model with Contact Tracing and Age-Structured Social Mixing for COVID-19 outbreak

2020 ◽  
Author(s):  
Ali Teimouri
Keyword(s):  
Reproduction Number ◽  
Contact Tracing ◽  
Seir Model ◽  
Expectation Value ◽  
Proposed Model ◽  
Social Mixing ◽  
The World ◽  
Age Structured ◽  
And Control ◽  
Explicit Relation

AbstractIn December 2019 a severe acute respiratory syndrome now known as SARS-CoV-2 began to surge in Wuhan, China. The virus soon spread throughout the world to become a pandemic. Since the outbreak various measures were put in place to contain and control the spread, these interventions were mostly based on compartmental models in epidemiology with the main goal of controlling and monitoring the rate of the basic and effective reproduction number. In this paper, we propose an SEIR model where we incorporate contact tracing and age-structured social mixing. We show the explicit relation between contact tracing and social mixing and other relevant parameters of the proposed model. We derive a formula for the effective reproduction number which is expressed in terms of reported cases, tracing quantities and social mixing. We use this formula to determine the expectation value of the effective reproduction number in London, UK.

scholarly journals Survey of Decentralized Solutions with Mobile Devices for User Location Tracking, Proximity Detection, and Contact Tracing in the COVID-19 Era

Data ◽  
2020 ◽  
Vol 5 (4) ◽  
pp. 87 ◽  
Author(s):  
Viktoriia Shubina ◽  
Sylvia Holcer ◽  
Michael Gould ◽  
Elena Simona Lohan
Keyword(s):  
Mobile Devices ◽  
Large Scale ◽  
Data Science ◽  
Research Field ◽  
Mobile Internet ◽  
Contact Tracing ◽  
Future Research ◽  
Location Tracking ◽  
Proximity Detection ◽  
User Location

Some of the recent developments in data science for worldwide disease control have involved research of large-scale feasibility and usefulness of digital contact tracing, user location tracking, and proximity detection on users’ mobile devices or wearables. A centralized solution relying on collecting and storing user traces and location information on a central server can provide more accurate and timely actions than a decentralized solution in combating viral outbreaks, such as COVID-19. However, centralized solutions are more prone to privacy breaches and privacy attacks by malevolent third parties than decentralized solutions, storing the information in a distributed manner among wireless networks. Thus, it is of timely relevance to identify and summarize the existing privacy-preserving solutions, focusing on decentralized methods, and analyzing them in the context of mobile device-based localization and tracking, contact tracing, and proximity detection. Wearables and other mobile Internet of Things devices are of particular interest in our study, as not only privacy, but also energy-efficiency, targets are becoming more and more critical to the end-users. This paper provides a comprehensive survey of user location-tracking, proximity-detection, and digital contact-tracing solutions in the literature from the past two decades, analyses their advantages and drawbacks concerning centralized and decentralized solutions, and presents the authors’ thoughts on future research directions in this timely research field.

scholarly journals The feasibility of targeted test-trace-isolate for the control of SARS-CoV-2 variants

2021 ◽  
Author(s):  
William J. Bradshaw ◽  
Jonathan H. Huggins ◽  
Alun L. Lloyd ◽  
Kevin M. Esvelt
Keyword(s):  
Process Model ◽  
Branching Process ◽  
Large Fraction ◽  
Contact Tracing ◽  
Rt Pcr ◽  
Health Agencies ◽  
The Common ◽  
Ancestral Strain

AbstractThe SARS-CoV-2 variant B.1.1.7 reportedly exhibits substantially higher transmission than the ancestral strain and may generate a major surge of cases before vaccines become widely available, while the P.1 and B.1.351 variants may be equally transmissible and also resist vaccines. All three variants can be sensitively detected by RT-PCR due to an otherwise rare del11288-11296 mutation in orf1ab; B.1.1.7 can also be detected using the common TaqPath kit. Testing, contact tracing, and isolation programs overwhelmed by SARS-CoV-2 could slow the spread of the new variants, which are still outnumbered by tracers in most countries. However, past failures and high rates of mistrust may lead health agencies to conclude that tracing is futile, dissuading them from redirecting existing tracers to focus on the new variants. Here we apply a branching-process model to estimate the effectiveness of implementing a variant-focused testing, contact tracing, and isolation strategy with realistic levels of performance. Our model indicates that bidirectional contact tracing can substantially slow the spread of SARS-CoV-2 variants even in regions where a large fraction of the population refuses to cooperate with contact tracers or to abide by quarantine and isolation requests.

scholarly journals Isolation and Contact Tracing Can Tip the Scale to Containment of COVID-19 in Populations With Social Distancing

2021 ◽  
Vol 8 ◽  
Author(s):  
Mirjam E. Kretzschmar ◽  
Ganna Rozhnova ◽  
Michiel van Boven
Keyword(s):  
Growth Rates ◽  
Process Model ◽  
Reproduction Number ◽  
Health Care Systems ◽  
Contact Tracing ◽  
Explicit Calculation ◽  
Social Distancing ◽  
Household Contacts ◽  
Care Systems ◽  
Doubling Times

SARS-CoV-2 has established itself in all parts of the world, and many countries have implemented social distancing as a measure to prevent overburdening of health care systems. Here we evaluate whether and under which conditions containment of SARS-CoV-2 is possible by isolation and contact tracing in settings with various levels of social distancing. To this end we use a branching process model in which every person generates novel infections according to a probability distribution that is affected by the incubation period distribution, distribution of the latent period, and infectivity. The model distinguishes between household and non-household contacts. Social distancing may affect the numbers of the two types of contacts differently, for example while work and school contacts are reduced, household contacts may remain unchanged. The model allows for an explicit calculation of the basic and effective reproduction numbers, and of exponential growth rates and doubling times. Our findings indicate that if the proportion of asymptomatic infections in the model is larger than 30%, contact tracing and isolation cannot achieve containment for a basic reproduction number (ℛ0) of 2.5. Achieving containment by social distancing requires a reduction of numbers of non-household contacts by around 90%. If containment is not possible, at least a reduction of epidemic growth rate and an increase in doubling time may be possible. We show for various parameter combinations how growth rates can be reduced and doubling times increased by contact tracing. Depending on the realized level of contact reduction, tracing and isolation of only household contacts, or of household and non-household contacts are necessary to reduce the effective reproduction number to below 1. In a situation with social distancing, contact tracing can act synergistically to tip the scale toward containment. These measures can therefore be a tool for controlling COVID-19 epidemics as part of an exit strategy from lock-down measures or for preventing secondary waves of COVID-19.

Contact Tracing and Consequences

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Martin Kulldorff
Keyword(s):  
Public Health ◽  
Disease Outbreaks ◽  
Contact Tracing ◽  
Public Health Agencies ◽  
The Public ◽  
Health Agencies

COVID-19 contact tracing programs are eroding trust between the public and public health agencies—with potential dire consequences for future disease outbreaks in which contact tracing could be critical.

scholarly journals Addressing COVID-19 Among People Experiencing Homelessness: Description, Adaptation, and Early Findings of a Multiagency Response in Boston

2020 ◽  
Vol 135 (4) ◽  
pp. 435-441 ◽  
Author(s):  
Travis P. Baggett ◽  
Melanie W. Racine ◽  
Elizabeth Lewis ◽  
Denise De Las Nueces ◽  
James J. O’Connell ◽  
...  
Keyword(s):  
Disease Surveillance ◽  
Service Providers ◽  
Focal Point ◽  
Adult Population ◽  
Contact Tracing ◽  
Care Model ◽  
Public Health Agencies ◽  
Symptom Screening ◽  
Contact Investigation ◽  
Health Agencies

People experiencing homelessness are at high risk for coronavirus disease 2019 (COVID-19). In March 2020, Boston Health Care for the Homeless Program, in partnership with city and state public health agencies, municipal leaders, and homeless service providers, developed and implemented a citywide COVID-19 care model for this vulnerable population. Components included symptom screening at shelter front doors, expedited testing at pop-up sites, isolation and management venues for symptomatic people under investigation and for people with confirmed disease, quarantine venues for asymptomatic exposed people, and contact investigation and tracing. Real-time disease surveillance efforts in a large shelter outbreak of COVID-19 during the third week of operations illustrated the need for several adaptations to the care model to better respond to the local epidemiology of illness among people experiencing homelessness. Symptom screening was de-emphasized given the high number of asymptomatic or minimally symptomatic infections discovered during mass testing; contact tracing and quarantining were phased out under the assumption of universal exposure among the sheltered population; and isolation and management venues were rapidly expanded to accommodate a surge in people with newly diagnosed COVID-19. During the first 6 weeks of operation, 429 of 1297 (33.1%) tested people were positive for COVID-19; of these, 395 people were experiencing homelessness at the time of testing, representing about 10% of the homeless adult population in Boston. Universal testing, as resources permit, is a focal point of ongoing efforts to mitigate the effect of COVID-19 on this vulnerable group of people.

scholarly journals Applying particle filtering in both aggregated and age-structured population compartmental models of pre-vaccination measles

2018 ◽  
Author(s):  
Xiaoyan Li ◽  
Alexander Doroshenko ◽  
Nathaniel D. Osgood
Keyword(s):  
Public Health ◽  
Young Children ◽  
Particle Filtering ◽  
Sequential Monte Carlo ◽  
Causes Of Death ◽  
Dynamic Models ◽  
Public Health Agencies ◽  
Health Agencies ◽  
Age Structured ◽  
Measles Outbreaks

AbstractMeasles is a highly transmissible disease and is one of the leading causes of death among young children under 5 globally. While the use of ongoing surveillance data and – recently – dynamic models offer insight on measles dynamics, both suffer notable shortcomings when applied to measles outbreak prediction. In this paper, we apply the Sequential Monte Carlo approach of particle filtering, incorporating reported measles incidence for Saskatchewan during the pre-vaccination era, using an adaptation of a previously contributed measles compartmental model. To secure further insight, we also perform particle filtering on an age structured adaptation of the model in which the population is divided into two interacting age groups – children and adults. The results indicate that, when used with a suitable dynamic model, particle filtering can offer high predictive capacity for measles dynamics and outbreak occurrence in a low vaccination context. We have investigated five particle filtering models in this project. Based on the most competitive model as evaluated by predictive accuracy, we have performed prediction and outbreak classification analysis. The prediction results demonstrated that this model could predict the measles transmission patterns and classify whether there will be an outbreak or not in the next month (Area under the ROC Curve of 0.89). We conclude that anticipating the outbreak dynamics of measles in low vaccination regions by applying particle filtering with simple measles transmission models, and incorporating time series of reported case counts, is a valuable technique to assist public health authorities in estimating risk and magnitude of measles outbreaks. Such approach offer particularly strong value proposition for other pathogens with little-known dynamics, critical latent drivers, and in the context of the growing number of high-velocity electronic data sources. Strong additional benefits are also likely to be realized from extending the application of this technique to highly vaccinated populations.Author summaryMeasles is a highly infectious disease and is one of the leading causes of death among young children globally. In 2016, close to 90,000 people died from measles. Measles can cause outbreaks particularly in people who did not receive protective vaccine. Understanding how measles outbreaks unfold can help public health agencies to design intervention strategies to prevent and control this potentially deadly infection. Although traditional methods – including the use of ongoing monitoring of infectious diseases trends by public health agencies and simulation of such trends using scientific technique of mathematical modeling – offer insight on measles dynamics, both have shortcomings when applied to our ability to predict measles outbreaks. We seek to enhance the accuracy with which we can understand the current measles disease burden as well as number of individuals who may develop measles because of lack of protection and predict future measles trends. We do this by applying a machine learning technique that combines the best features of insights from ongoing observations and mathematical models while minimizing important weaknesses of each. Our results indicate that, coupled with a suitable mathematical model, this technique can predict future measles trends and measles outbreaks in areas with low vaccination coverage.

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