Differences in social activity increase efficiency of contact tracing.
Ontology highlight
ABSTRACT: Abstract
Digital contact tracing has been suggested as an effective strategy for controlling an epidemic without severely limiting personal mobility. Here, we use smartphone proximity data to explore how social structure affects contact tracing of COVID-19. We model the spread of COVID-19 and find that the effectiveness of contact tracing depends strongly on social network structure and heterogeneous social activity. Contact tracing is shown to be remarkably effective in a workplace environment and the effectiveness depends strongly on the minimum duration of contact required to initiate quarantine. In a realistic social network, we find that forward contact tracing with immediate isolation can reduce an epidemic by more than 70%. In perspective, our findings highlight the necessity of incorporating social heterogeneity into models of mitigation strategies.Graphic abstract
Supplementary information
The online version supplementary material available at 10.1140/epjb/s10051-021-00222-8.
SUBMITTER: Nielsen BF
PROVIDER: S-EPMC8523203 | biostudies-literature | 2021
REPOSITORIES: biostudies-literature
Publications
Differences in social activity increase efficiency of contact tracing.
Nielsen Bjarke Frost BF Sneppen Kim K Simonsen Lone L Mathiesen Joachim J
The European physical journal. B 20211019 10
<h4>Abstract</h4>Digital contact tracing has been suggested as an effective strategy for controlling an epidemic without severely limiting personal mobility. Here, we use smartphone proximity data to explore how social structure affects contact tracing of COVID-19. We model the spread of COVID-19 and find that the effectiveness of contact tracing depends strongly on social network structure and heterogeneous social activity. Contact tracing is shown to be remarkably effective in a workplace envi ...[more]
Similar Datasets
Project description:As the COVID-19 pandemic has demonstrated, identifying the origin of a pandemic remains a challenging task. The search for patient zero may benefit from the widely-used and well-established toolkit of contact tracing methods, although this possibility has not been explored to date. We fill this gap by investigating the prospect of performing the source detection task as part of the contact tracing process, i.e., the possibility of tuning the parameters of the process in order to pinpoint the origin of the infection. To this end, we perform simulations on temporal networks using a recent diffusion model that recreates the dynamics of the COVID-19 pandemic. We find that increasing the budget for contact tracing beyond a certain threshold can significantly improve the identification of infected individuals but has diminishing returns in terms of source detection. Moreover, disease variants of higher infectivity make it easier to find the source but harder to identify infected individuals. Finally, we unravel a seemingly-intrinsic trade-off between the use of contact tracing to either identify infected nodes or detect the source of infection. This trade-off suggests that focusing on the identification of patient zero may come at the expense of identifying infected individuals.
| S-EPMC9801158 | biostudies-literature
Project description: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.
| S-EPMC8241027 | biostudies-literature
Project description:Standard contact tracing practice for COVID-19 is to identify persons exposed to an infected person during the contagious period, assumed to start two days before symptom onset or diagnosis. In the first large cohort study on backward contact tracing for COVID-19, we extended the contact tracing window by 5 days, aiming to identify the source of the infection and persons infected by the same source. The risk of infection amongst these additional contacts was similar to contacts exposed during the standard tracing window and significantly higher than symptomatic individuals in a control group, leading to 42% more cases identified as direct contacts of an index case. Compared to standard practice, backward traced contacts required fewer tests and shorter quarantine. However, they were identified later in their infectious cycle if infected. Our results support implementing backward contact tracing when rigorous suppression of viral transmission is warranted.
| S-EPMC9375086 | biostudies-literature
Project description:Contact tracing is currently one of the most effective measures to contain the COVID-19 pandemic. In order to identify persons that would otherwise not be known or remembered and to keep the time delay when reporting an infection and when contacting people as short as possible, digital contact tracing using smartphones seems to be a reasonable measure additional to manual contact tracing. Although first modelling studies predicted a positive effect in terms of prompt contact tracing, no empirically reliable data are as yet available, neither on the population-wide benefit nor on the potential risks of contact tracing apps. Risk-benefit assessment of such an app includes investigating whether such an app fulfils its purpose, as also research on the effectiveness, risks and side effects, and implementation processes (e.?g. planning and inclusion of different participants). The aim of this article was to give an overview of possible public health benefits as well as technical, social, legal and ethical aspects of a contact-tracing app in the context of the COVID-19 pandemic. Furthermore, conditions for the widest possible use of the app are presented.
| S-EPMC7536383 | biostudies-literature
Project description:A plethora of measures are being combined in the attempt to reduce SARS-CoV-2 spread. Due to its sustainability, contact tracing is one of the most frequently applied interventions worldwide, albeit with mixed results. We evaluate the performance of digital contact tracing for different infection detection rates and response time delays. We also introduce and analyze a novel strategy we call contact prevention, which emits high exposure warnings to smartphone users according to Bluetooth-based contact counting. We model the effect of both strategies on transmission dynamics in SERIA, an agent-based simulation platform that implements population-dependent statistical distributions. Results show that contact prevention remains effective in scenarios with high diagnostic/response time delays and low infection detection rates, which greatly impair the effect of traditional contact tracing strategies. Contact prevention could play a significant role in pandemic mitigation, especially in developing countries where diagnostic and tracing capabilities are inadequate. Contact prevention could thus sustainably reduce the propagation of respiratory viruses while relying on available technology, respecting data privacy, and most importantly, promoting community-based awareness and social responsibility. Depending on infection detection and app adoption rates, applying a combination of digital contact tracing and contact prevention could reduce pandemic-related mortality by 20-56%.
| S-EPMC8277769 | biostudies-literature
Project description:BACKGROUND:Contact tracing data of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic is used to estimate basic epidemiological parameters. Contact tracing data could also be potentially used for assessing the heterogeneity of transmission at the individual patient level. Characterization of individuals based on different levels of infectiousness could better inform the contact tracing interventions at field levels. METHODS:Standard social network analysis methods used for exploring infectious disease transmission dynamics was employed to analyze contact tracing data of 1959 diagnosed SARS-CoV-2 patients from a large state of India. Relational network data set with diagnosed patients as "nodes" and their epidemiological contact as "edges" was created. Directed network perspective was utilized in which directionality of infection emanated from a "source patient" towards a "target patient". Network measures of " degree centrality" and "betweenness centrality" were calculated to identify influential patients in the transmission of infection. Components analysis was conducted to identify patients connected as sub- groups. Descriptive statistics was used to summarise network measures and percentile ranks were used to categorize influencers. RESULTS:Out-degree centrality measures identified that of the total 1959 patients, 11.27% (221) patients have acted as a source of infection to 40.19% (787) other patients. Among these source patients, 0.65% (12) patients had a higher out-degree centrality (>?=?10) and have collectively infected 37.61% (296 of 787), secondary patients. Betweenness centrality measures highlighted that 7.50% (93) patients had a non-zero betweenness (range 0.5 to 135) and thus have bridged the transmission between other patients. Network component analysis identified nineteen connected components comprising of influential patient's which have overall accounted for 26.95% of total patients (1959) and 68.74% of epidemiological contacts in the network. CONCLUSIONS:Social network analysis method for SARS-CoV-2 contact tracing data would be of use in measuring individual patient level variations in disease transmission. The network metrics identified individual patients and patient components who have disproportionately contributed to transmission. The network measures and graphical tools could complement the existing contact tracing indicators and could help improve the contact tracing activities.
| S-EPMC7494745 | biostudies-literature
Project description:Globally, the current coronavirus disease 2019 (COVID-19) pandemic is resulting in high fatality rates. Consequently, the prevention of further transmission is very vital. Until vaccines are widely available, the only available infection prevention methods include the following: contact tracing, case isolation and quarantine, social (physical) distancing, and hygiene measures (washing of hands with soap and water and using alcohol-based hand sanitizers). Contact tracing, which is key in preventing the spread of COVID-19, refers to the process of finding unreported people who maybe infected by using a verified case to trace back possible infections of contacts. Consequently, the wide and fast spread of COVID-19 requires computational approaches which utilize innovative algorithms that build a memory of proximity contacts of cases that are positive. In this paper, a recommender algorithm called socially aware recommendation of people probably infected with COVID-19 (SARPPIC) is proposed. SARPPIC initially utilizes betweenness centrality in a social network to measure the number of target contact points (nodes/users) who have come into contact with an infected contact point (COVID-19 patient). Then, using contact durations and contact frequencies, tie strengths of the same contact points above are also computed. Finally, the above algorithmic computations are hybridized through profile integration to generate results for effective contact tracing recommendations of possible COVID-19-infected patients who will require testing in a healthcare facility. Benchmarking experimental results in the paper demonstrate that, using two interconnected relevant real-world datasets, SARPPIC outperforms other relevant methods in terms of suitable evaluation metrics such as precision, recall, and F-measure.
| S-EPMC7667519 | biostudies-literature
Project description:The COVID-19 pandemic has spurred an unprecedented demand for interventions that can reduce disease spread without excessively restricting daily activity, given negative impacts on mental health and economic outcomes. Digital contact tracing (DCT) apps have emerged as a component of the epidemic management toolkit. Existing DCT apps typically recommend quarantine to all digitally-recorded contacts of test-confirmed cases. Over-reliance on testing may, however, impede the effectiveness of such apps, since by the time cases are confirmed through testing, onward transmissions are likely to have occurred. Furthermore, most cases are infectious over a short period; only a subset of their contacts are likely to become infected. These apps do not fully utilize data sources to base their predictions of transmission risk during an encounter, leading to recommendations of quarantine to many uninfected people and associated slowdowns in economic activity. This phenomenon, commonly termed as "pingdemic," may additionally contribute to reduced compliance to public health measures. In this work, we propose a novel DCT framework, Proactive Contact Tracing (PCT), which uses multiple sources of information (e.g. self-reported symptoms, received messages from contacts) to estimate app users' infectiousness histories and provide behavioral recommendations. PCT methods are by design proactive, predicting spread before it occurs. We present an interpretable instance of this framework, the Rule-based PCT algorithm, designed via a multi-disciplinary collaboration among epidemiologists, computer scientists, and behavior experts. Finally, we develop an agent-based model that allows us to compare different DCT methods and evaluate their performance in negotiating the trade-off between epidemic control and restricting population mobility. Performing extensive sensitivity analysis across user behavior, public health policy, and virological parameters, we compare Rule-based PCT to i) binary contact tracing (BCT), which exclusively relies on test results and recommends a fixed-duration quarantine, and ii) household quarantine (HQ). Our results suggest that both BCT and Rule-based PCT improve upon HQ, however, Rule-based PCT is more efficient at controlling spread of disease than BCT across a range of scenarios. In terms of cost-effectiveness, we show that Rule-based PCT pareto-dominates BCT, as demonstrated by a decrease in Disability Adjusted Life Years, as well as Temporary Productivity Loss. Overall, we find that Rule-based PCT outperforms existing approaches across a varying range of parameters. By leveraging anonymized infectiousness estimates received from digitally-recorded contacts, PCT is able to notify potentially infected users earlier than BCT methods and prevent onward transmissions. Our results suggest that PCT-based applications could be a useful tool in managing future epidemics.
| S-EPMC10010527 | biostudies-literature
Project description:BackgroundThe German Corona-Warn-App (CWA) is a contact tracing app to mitigate the spread of SARS-CoV-2. As of today, it has been downloaded approximately 45 million times.ObjectiveThis study aims to investigate the influence of (non)users' social environments on the usage of the CWA during 2 periods with relatively lower death rates and higher death rates caused by SARS-CoV-2.MethodsWe conducted a longitudinal survey study in Germany with 833 participants in 2 waves to investigate how participants perceive their peer groups' opinion about making use of the German CWA to mitigate the risk of SARS-CoV-2. In addition, we asked whether this perceived opinion, in turn, influences the participants with respect to their own decision to use the CWA. We analyzed these questions with generalized estimating equations. Further, 2 related sample tests were performed to test for differences between users of the CWA and nonusers and between the 2 points in time (wave 1 with the highest death rates observable during the pandemic in Germany versus wave 2 with significantly lower death rates).ResultsParticipants perceived that peer groups have a positive opinion toward using the CWA, with more positive opinions by the media, family doctors, politicians, and virologists/Robert Koch Institute and a lower, only slightly negative opinion originating from social media. Users of the CWA perceived their peer groups' opinions about using the app as more positive than nonusers do. Furthermore, the perceived positive opinion of the media (P=.001) and politicians (P<.001) was significantly lower in wave 2 compared with that in wave 1. The perceived opinion of friends and family (P<.001) as well as their perceived influence (P=.02) among nonusers toward using the CWA was significantly higher in the latter period compared with that in wave 1. The influence of virologists (in Germany primarily communicated via the Robert Koch Institute) had the highest positive effect on using the CWA (B=0.363, P<.001). We only found 1 decreasing effect of the influence of politicians (B=-0.098, P=.04).ConclusionsOpinions of peer groups play an important role when it comes to the adoption of the CWA. Our results show that the influence of virologists/Robert Koch Institute and family/friends exerts the strongest effect on participants' decisions to use the CWA while politicians had a slightly negative influence. Our results also indicate that it is crucial to accompany the introduction of such a contact tracing app with explanations and a media campaign to support its adoption that is backed up by political decision makers and subject matter experts.
| S-EPMC10234420 | biostudies-literature
Project description:Diagnostic and contact tracing apps are a needed weapon to contain contagion during a pandemic. We study how the content of the messages used to promote the apps influence adoption by running a survey experiment on approximately 23,000 Mexican adults. Respondents were randomly assigned to one of three different prompts, or a control condition, before stating their willingness to adopt a diagnostic app and contact tracing app. The prompt emphasizing government efforts to ensure data privacy, which has been one of the most common strategies, reduced willingness to adopt the apps by about 4 pp and 3 pp, respectively. An effective app promotion policy must understand individuals' reservations and be wary of unintended reactions to naïve reassurances.
| S-EPMC8321141 | biostudies-literature