scholarly journals The impact of digital contact tracing on the SARS-CoV-2 pandemic—a comprehensive modelling study

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Tina R. Pollmann ◽  
Stefan Schönert ◽  
Johannes Müller ◽  
Julia Pollmann ◽  
Elisa Resconi ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Stochastic Models ◽  
Cell Phone ◽  
Contact Tracing ◽  
Reproductive Number ◽  
Random Testing ◽  
Deterministic Models ◽  
The Cost ◽  
The Impact ◽  
Testing Protocols

AbstractContact tracing is one of several strategies employed in many countries to curb the spread of SARS-CoV-2. Digital contact tracing (DCT) uses tools such as cell-phone applications to improve tracing speed and reach. We model the impact of DCT on the spread of the virus for a large epidemiological parameter space consistent with current literature on SARS-CoV-2. We also model DCT in combination with random testing (RT) and social distancing (SD).Modelling is done with two independently developed individual-based (stochastic) models that use the Monte Carlo technique, benchmarked against each other and against two types of deterministic models.For current best estimates of the number of asymptomatic SARS-CoV-2 carriers (approximately 40%), their contagiousness (similar to that of symptomatic carriers), the reproductive number before interventions (${R_{0}}$ R 0 at least 3) we find that DCT must be combined with other interventions such as SD and/or RT to push the reproductive number below one. At least 60% of the population would have to use the DCT system for its effect to become significant. On its own, DCT cannot bring the reproductive number below 1 unless nearly the entire population uses the DCT system and follows quarantining and testing protocols strictly. For lower uptake of the DCT system, DCT still reduces the number of people that become infected.When DCT is deployed in a population with an ongoing outbreak where $\mathcal{O}$ O (0.1%) of the population have already been infected, the gains of the DCT intervention come at the cost of requiring up to 15% of the population to be quarantined (in response to being traced) on average each day for the duration of the epidemic, even when there is sufficient testing capability to test every traced person.

scholarly journals The impact of digital contact tracing on the SARS-CoV-2 pandemic - a comprehensive modelling study

2020 ◽  
Author(s):  
Tina R Pollmann ◽  
Julia Pollmann ◽  
Christoph Wiesinger ◽  
Christian Haack ◽  
Lolian Shtembari ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Stochastic Models ◽  
Cell Phone ◽  
Contact Tracing ◽  
Reproductive Number ◽  
Random Testing ◽  
Deterministic Models ◽  
The Cost ◽  
The Impact ◽  
Testing Protocols

Contact tracing is one of several strategies employed in many countries to curb the spread of SARS-CoV-2. Digital contact tracing (DCT) uses tools such as cell-phone applications to improve tracing speed and reach. We model the impact of DCT on the spread of the virus for a large epidemiological parameter space consistent with current literature on SARS-CoV-2. We also model DCT in combination with random testing (RT) and social distancing (SD). Modelling is done with two independently developed individual-based (stochastic) models that use the Monte Carlo technique, benchmarked against each other and against two types of deterministic models. For current best estimates of the number of asymptomatic SARS-CoV-2 carriers (approximately 40\%), their contagiousness (similar to that of symptomatic carriers), the reproductive number before interventions (R0 at least 3) we find that DCT must be combined with other interventions such as SD and/or RT to push the reproductive number below one. At least 60\% of the population would have to use the DCT system for its effect to become significant. On its own, DCT cannot bring the reproductive number below 1 unless nearly the entire population uses the DCT system and follows quarantining and testing protocols strictly. For lower uptake of the DCT system, DCT still reduces the number of people that become infected. When DCT is deployed in a population with an ongoing outbreak where O(0.1\%) of the population have already been infected, the gains of the DCT intervention come at the cost of requiring up to 15% of the population to be quarantined (in response to being traced) on average each day for the duration of the epidemic, even when there is sufficient testing capability to test every traced person.

scholarly journals Digital proximity tracing on empirical contact networks for pandemic control

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
G. Cencetti ◽  
G. Santin ◽  
A. Longa ◽  
E. Pigani ◽  
A. Barrat ◽  
...  
Keyword(s):  
Large Scale ◽  
Disease Outbreaks ◽  
Physical Distance ◽  
Contact Tracing ◽  
Reproductive Number ◽  
Modeling Framework ◽  
Infectious Disease Outbreaks ◽  
Contact Patterns ◽  
Contact Data ◽  
The Impact

AbstractDigital contact tracing is a relevant tool to control infectious disease outbreaks, including the COVID-19 epidemic. Early work evaluating digital contact tracing omitted important features and heterogeneities of real-world contact patterns influencing contagion dynamics. We fill this gap with a modeling framework informed by empirical high-resolution contact data to analyze the impact of digital contact tracing in the COVID-19 pandemic. We investigate how well contact tracing apps, coupled with the quarantine of identified contacts, can mitigate the spread in real environments. We find that restrictive policies are more effective in containing the epidemic but come at the cost of unnecessary large-scale quarantines. Policy evaluation through their efficiency and cost results in optimized solutions which only consider contacts longer than 15–20 minutes and closer than 2–3 meters to be at risk. Our results show that isolation and tracing can help control re-emerging outbreaks when some conditions are met: (i) a reduction of the reproductive number through masks and physical distance; (ii) a low-delay isolation of infected individuals; (iii) a high compliance. Finally, we observe the inefficacy of a less privacy-preserving tracing involving second order contacts. Our results may inform digital contact tracing efforts currently being implemented across several countries worldwide.

scholarly journals Uncertainty propagation for deterministic models of biochemical networks using moment equations and the extended Kalman filter

2021 ◽  
Vol 18 (181) ◽  
pp. 20210331
Author(s):  
Tamara Kurdyaeva ◽  
Andreas Milias-Argeitis
Keyword(s):  
Monte Carlo ◽  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Biochemical Networks ◽  
Moment Closure ◽  
Moment Equations ◽  
Deterministic Models ◽  
Model Predictions ◽  
The Impact ◽  
Low Order

Differential equation models of biochemical networks are frequently associated with a large degree of uncertainty in parameters and/or initial conditions. However, estimating the impact of this uncertainty on model predictions via Monte Carlo simulation is computationally demanding. A more efficient approach could be to track a system of low-order statistical moments of the state. Unfortunately, when the underlying model is nonlinear, the system of moment equations is infinite-dimensional and cannot be solved without a moment closure approximation which may introduce bias in the moment dynamics. Here, we present a new method to study the time evolution of the desired moments for nonlinear systems with polynomial rate laws. Our approach is based on solving a system of low-order moment equations by substituting the higher-order moments with Monte Carlo-based estimates from a small number of simulations, and using an extended Kalman filter to counteract Monte Carlo noise. Our algorithm provides more accurate and robust results compared to traditional Monte Carlo and moment closure techniques, and we expect that it will be widely useful for the quantification of uncertainty in biochemical model predictions.

scholarly journals Digital Proximity Tracing in the COVID-19 Pandemic on Empirical Contact Networks

2020 ◽  
Author(s):  
Giulia Cencetti ◽  
Gabriele Santin ◽  
Antonio Longa ◽  
Emanuele Pigani ◽  
Alain Barrat ◽  
...  
Keyword(s):  
Disease Outbreaks ◽  
Contact Tracing ◽  
Reproductive Number ◽  
Modeling Framework ◽  
Effective Measure ◽  
Epidemic Dynamics ◽  
Contact Patterns ◽  
Contact Data ◽  
High Level ◽  
The Impact

Abstract Digital contact tracing is increasingly considered as a tool to control infectious disease outbreaks. As part of a broader test, trace, isolate, and quarantine strategy, digital contract tracing apps have been proposed to alleviate lock-downs, and to return societies to a more normal situation in the ongoing COVID-19 crisis. Early work evaluating digital contact tracing did not consider important features and heterogeneities present in real-world contact patterns which impact epidemic dynamics. Here, we fill this gap by considering a modeling framework informed by empirical high-resolution contact data to analyze the impact of digital contact tracing apps in the COVID-19 pandemic. We investigate how well contact tracing apps, coupled with the quarantine of identified contacts, can mitigate the spread of COVID-19 in realistic scenarios such as a university campus, a workplace, or a high school. We find that restrictive policies are more effective in confining the epidemics but come at the cost of quarantining a large part of the population. It is possible to avoid this effect by considering less strict policies, which only consider contacts with longer exposure and at shorter distance to be at risk. Our results also show that isolation and tracing can help keep re-emerging outbreaks under control provided that hygiene and social distancing measures limit the reproductive number to 1.5. Moreover, we confirm that a high level of app adoption is crucial to make digital contact tracing an effective measure. Our results may inform app-based contact tracing efforts currently being implemented across several countries worldwide.

scholarly journals Maximizing and evaluating the impact of test-trace-isolate programs: A modeling study

PLoS Medicine ◽  
2021 ◽  
Vol 18 (4) ◽  
pp. e1003585
Author(s):  
Kyra H. Grantz ◽  
Elizabeth C. Lee ◽  
Lucy D’Agostino McGowan ◽  
Kyu Han Lee ◽  
C. Jessica E. Metcalf ◽  
...  
Keyword(s):  
Control Strategies ◽  
R Package ◽  
Sensitivity Analyses ◽  
Contact Tracing ◽  
Reproductive Number ◽  
Case Detection ◽  
Modeling Framework ◽  
Online Application ◽  
Almost All ◽  
The Impact

Background Test-trace-isolate programs are an essential part of Coronavirus Disease 2019 (COVID-19) control that offer a more targeted approach than many other nonpharmaceutical interventions. Effective use of such programs requires methods to estimate their current and anticipated impact. Methods and findings We present a mathematical modeling framework to evaluate the expected reductions in the reproductive number, R, from test-trace-isolate programs. This framework is implemented in a publicly available R package and an online application. We evaluated the effects of completeness in case detection and contact tracing and speed of isolation and quarantine using parameters consistent with COVID-19 transmission (R0: 2.5, generation time: 6.5 days). We show that R is most sensitive to changes in the proportion of cases detected in almost all scenarios, and other metrics have a reduced impact when case detection levels are low (<30%). Although test-trace-isolate programs can contribute substantially to reducing R, exceptional performance across all metrics is needed to bring R below one through test-trace-isolate alone, highlighting the need for comprehensive control strategies. Results from this model also indicate that metrics used to evaluate performance of test-trace-isolate, such as the proportion of identified infections among traced contacts, may be misleading. While estimates of the impact of test-trace-isolate are sensitive to assumptions about COVID-19 natural history and adherence to isolation and quarantine, our qualitative findings are robust across numerous sensitivity analyses. Conclusions Effective test-trace-isolate programs first need to be strong in the “test” component, as case detection underlies all other program activities. Even moderately effective test-trace-isolate programs are an important tool for controlling the COVID-19 pandemic and can alleviate the need for more restrictive social distancing measures.

scholarly journals It's Not the Flu: Popular Perceptions of the Impact of COVID-19 in the U.S.

2021 ◽  
Vol 12 ◽  
Author(s):  
Laura Niemi ◽  
Kevin M. Kniffin ◽  
John M. Doris
Keyword(s):  
Causes Of Death ◽  
Severe Disease ◽  
Moral Values ◽  
Health Impacts ◽  
Contact Tracing ◽  
Automobile Accidents ◽  
The Public ◽  
Seasonal Flu ◽  
The Impact ◽  
Testing Protocols

Messaging from U.S. authorities about COVID-19 has been widely divergent. This research aims to clarify popular perceptions of the COVID-19 threat and its effects on victims. In four studies with over 4,100 U.S. participants, we consistently found that people perceive the threat of COVID-19 to be substantially greater than that of several other causes of death to which it has recently been compared, including the seasonal flu and automobile accidents. Participants were less willing to help COVID-19 victims, who they considered riskier to help, more contaminated, and more responsible for their condition. Additionally, politics and demographic factors predicted attitudes about victims of COVID-19 above and beyond moral values; whereas attitudes about the other kinds of victims were primarily predicted by moral values. The results indicate that people perceive COVID-19 as an exceptionally severe disease threat, and despite prosocial inclinations, do not feel safe offering assistance to COVID-19 sufferers. This research has urgent applied significance: the findings are relevant to public health efforts and related marketing campaigns working to address extended damage to society and the economy from the pandemic. In particular, efforts to educate the public about the health impacts of COVID-19, encourage compliance with testing protocols and contact tracing, and support safe, prosocial decision-making and risk assessment, will all benefit from awareness of these findings. The results also suggest approaches, such as engaging people's stable values rather than their politicized perspectives on COVID-19, that may reduce stigma and promote cooperation in response to pandemic threats.

scholarly journals Dynamic Cost-Optimal Assessment of Complementary Diurnal Electricity Storage Capacity in High PV Penetration Grid

Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4496
Author(s):  
Samuel Matthew G. Dumlao ◽  
Keiichi N. Ishihara
Keyword(s):  
Monte Carlo ◽  
Solar Energy ◽  
Optimal Trajectory ◽  
Storage Capacity ◽  
Growth Trajectory ◽  
Growth Trajectories ◽  
Financial Assessment ◽  
The Cost ◽  
Pv Penetration ◽  
The Impact

Solar Photovoltaics (PV) is seen as one of the renewable energy technologies that could help reduce the world’s dependence on fossil fuels. However, since it is dependent on the sun, it can only generate electricity in the daytime, and this restriction is exacerbated in electricity grids with high PV penetration, where solar energy must be curtailed due to the mismatch between supply and demand. This study conducts a techno-economic analysis to present the cost-optimal storage growth trajectory that could support the dynamic integration of solar PV within a planning horizon. A methodology for cost-optimal assessment that incorporates hourly simulation, Monte Carlo random sampling, and a proposed financial assessment is presented. This approach was tested in Japan’s southernmost region since it is continuously increasing its solar capacity and is at the precipice of high PV curtailment scenario. The results show the existence of a cost-optimal storage capacity growth trajectory that balances the cost penalty from curtailment and the additional investment cost from storage. This optimal trajectory reduces the impact of curtailment on the energy generation cost to manageable levels and utilizes more solar energy potential that further reduces CO2 emissions. The results also show that the solar capacity growth rate and storage cost significantly impact the optimal trajectory. The incorporation of the Monte Carlo method significantly reduced the computational requirement of the analysis enabling the exploration of several growth trajectories, and the proposed financial assessment enabled the time-bound optimization of these trajectories. The approach could be used to calculate the optimal growth trajectories in other nations or regions, provided that historical hourly temperature, irradiance, and demand data are available.

scholarly journals Epidemiology and Transmission of COVID-19 in Shenzhen China: Analysis of 391 cases and 1,286 of their close contacts

2020 ◽  
Author(s):  
Qifang Bi ◽  
Yongsheng Wu ◽  
Shujiang Mei ◽  
Chenfei Ye ◽  
Xuan Zou ◽  
...  
Keyword(s):  
General Population ◽  
Disease Transmission ◽  
Clinical Severity ◽  
Transmission Risk ◽  
Contact Tracing ◽  
Initial Assessment ◽  
Reproductive Number ◽  
Risk Of Infection ◽  
The Impact ◽  
Close Contacts

AbstractBackgroundRapid spread of SARS-CoV-2 in Wuhan prompted heightened surveillance in Shenzhen and elsewhere in China. The resulting data provide a rare opportunity to measure key metrics of disease course, transmission, and the impact of control.MethodsThe Shenzhen CDC identified 391 SARS-CoV-2 cases from January 14 to February 12, 2020 and 1286 close contacts. We compare cases identified through symptomatic surveillance and contact tracing, and estimate the time from symptom onset to confirmation, isolation, and hospitalization. We estimate metrics of disease transmission and analyze factors influencing transmission risk.FindingsCases were older than the general population (mean age 45) and balanced between males (187) and females (204). Ninety-one percent had mild or moderate clinical severity at initial assessment. Three have died, 225 have recovered (median time to recovery is 21 days). Cases were isolated on average 4.6 days after developing symptoms; contact tracing reduced this by 1.9 days. Household contacts and those travelling with a case where at higher risk of infection (ORs 6 and 7) than other close contacts. The household secondary attack rate was 15%, and children were as likely to be infected as adults. The observed reproductive number was 0.4, with a mean serial interval of 6.3 days.InterpretationOur data on cases as well as their infected and uninfected close contacts provide key insights into SARS-CoV-2 epidemiology. This work shows that heightened surveillance and isolation, particularly contact tracing, reduces the time cases are infectious in the community, thereby reducing R. Its overall impact, however, is uncertain and highly dependent on the number of asymptomatic cases. We further show that children are at similar risk of infection as the general population, though less likely to have severe symptoms; hence should be considered in analyses of transmission and control.

scholarly journals The cost-effectiveness of accelerated partner therapy (APT) compared to standard contact tracing for people with chlamydia: an economic evaluation based on the LUSTRUM population-based chlamydia transmission model

2021 ◽  
Author(s):  
Eleanor V. Williams ◽  
Chidubem B. Okeke Ogwulu ◽  
Claudia S. Estcourt ◽  
Alison R. Howarth ◽  
Andrew Copas ◽  
...  
Keyword(s):  
United Kingdom ◽  
Cost Effectiveness ◽  
Population Based ◽  
Contact Tracing ◽  
Chlamydia Infection ◽  
The United Kingdom ◽  
Intervention Cost ◽  
Standard Contact ◽  
The Cost ◽  
The Impact

Objective: To investigate the cost-effectiveness of accelerated partner therapy (APT) compared with standard contact tracing for people with sexually transmitted chlamydia infection in the United Kingdom Design: Economic evaluation using a model consisting of two components: a population-based chlamydia transmission component, to estimate the impact of APT on chlamydia prevalence, and an economic component, to estimate the impact of APT on healthcare costs and health outcomes. Setting: United Kingdom Participants: Hypothetical heterosexual population of 50,000 men and 50,000 women aged 16-34 years. Main Outcome Measures: Cost-effectiveness based on quality-adjusted life years (QALYs) gained and major outcomes averted (MOA), defined as mild pelvic inflammatory disease (PID), severe PID, chronic pelvic pain, ectopic pregnancy, tubal factor infertility and epididymitis. Results: For a model population of 50,000 men and 50,000 women and an APT intervention lasting 5 years, the intervention cost of APT (&pound135,201) is greater than the intervention cost of standard contact tracing (&pound116,334). When the costs of complications arising from chlamydia are considered, the total cost of APT (&pound370,657) is lower than standard contact tracing (&pound379,597). Thus, APT yields a total cost saving of approximately &pound9000 and leads to 73 fewer major outcomes and 21 fewer QALYs lost. Hence, APT is the dominant PN strategy. APT remained cost-effective across the full range of sensitivity analyses. Conclusions: Based on cost-effectiveness grounds APT is likely to be recommended as an alternative to standard contact tracing for chlamydia infection in the United Kingdom

scholarly journals Contact tracing efficiency, transmission heterogeneity, and accelerating COVID-19 epidemics

2021 ◽  
Vol 17 (6) ◽  
pp. e1009122
Author(s):  
Billy J. Gardner ◽  
A. Marm Kilpatrick
Keyword(s):  
Symptom Onset ◽  
High Capacity ◽  
Contact Tracing ◽  
Easy Access ◽  
Reproductive Number ◽  
Infection Dynamics ◽  
Difficult Challenge ◽  
The Impact ◽  
The Relationship ◽  
Heterogeneous Transmission

Simultaneously controlling COVID-19 epidemics and limiting economic and societal impacts presents a difficult challenge, especially with limited public health budgets. Testing, contact tracing, and isolating/quarantining is a key strategy that has been used to reduce transmission of SARS-CoV-2, the virus that causes COVID-19 and other pathogens. However, manual contact tracing is a time-consuming process and as case numbers increase a smaller fraction of cases’ contacts can be traced, leading to additional virus spread. Delays between symptom onset and being tested (and receiving results), and a low fraction of symptomatic cases being tested and traced can also reduce the impact of contact tracing on transmission. We examined the relationship between increasing cases and delays and the pathogen reproductive number Rt, and the implications for infection dynamics using deterministic and stochastic compartmental models of SARS-CoV-2. We found that Rt increased sigmoidally with the number of cases due to decreasing contact tracing efficacy. This relationship results in accelerating epidemics because Rt initially increases, rather than declines, as infections increase. Shifting contact tracers from locations with high and low case burdens relative to capacity to locations with intermediate case burdens maximizes their impact in reducing Rt (but minimizing total infections may be more complicated). Contact tracing efficacy decreased sharply with increasing delays between symptom onset and tracing and with lower fraction of symptomatic infections being tested. Finally, testing and tracing reductions in Rt can sometimes greatly delay epidemics due to the highly heterogeneous transmission dynamics of SARS-CoV-2. These results demonstrate the importance of having an expandable or mobile team of contact tracers that can be used to control surges in cases. They also highlight the synergistic value of high capacity, easy access testing and rapid turn-around of testing results, and outreach efforts to encourage symptomatic cases to be tested immediately after symptom onset.

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