Tracing Tool Research Articles

The Characteristics of Student SARS-CoV-2 Cases on an Urban University Campus: Observational Study.

BackgroundAcademic institutions are central hubs for young adults, laden with academic and social interactions and communal living arrangements, heightening the risk of transmission of many communicable diseases, including COVID-19. Shortly after the start of the fall 2020 academic year, institutions of higher learning were identified as hot spots for rises in COVID-19 incidence among young adults.ObjectiveThis study aims to identify the characteristics of student SARS-CoV-2 cases, identify the extent to which the student population adhered to preventative strategies, and examine behaviors that would put them at higher risk of contracting or spreading COVID-19.MethodsThis observational study comprises 1175 university students at The George Washington University in Washington, DC, with a confirmed COVID-19 diagnosis between August 3, 2020, and November 30, 2021. Case investigation and contact tracing tools were developed by the Campus COVID-19 Support Team and captured in REDCap (Research Electronic Data Capture). Trained case investigators were notified of a case and attempted to contact all cases within 24 hours of the case receiving their lab result. Associations between case characteristics and number of contacts were examined using Wilcoxon rank sum tests. Knowledge of exposure, behaviors since exposure, student residence status, and fraternity and sorority life affiliation were examined using chi-square tests.ResultsPositive student cases reported a median of 3 close contacts, and 84.6% (993/1175) reported at least one symptom with a median of 4 COVID-19 symptoms. Congestion (628/1175, 53.4%), cough (530/1175, 45.1%), and headache (484/1175, 41.2%) were the most frequently reported symptoms. Moreover, 36% (415/1160) reported that they did not know how they were exposed to the virus. Among those aware of contact with a COVID-19 confirmed case, 55.1% (109/198) reported the contact was a close friend or family member, and 25.3% (50/198) reported that it was someone with whom they lived. Athlete (vs nonathlete; P<.001), on-campus (vs off-campus; P<.001), and undergraduate (vs graduate; P=.01) students all reported a significantly higher number of contacts. Students living on campus were more likely to report attending campus events in the 2 days prior to symptom onset or positive test result (P=.004). Students with fraternity or sorority affiliation were more likely to report attending campus events in the 2 days prior to symptom onset or positive test result (P<.001).ConclusionsCOVID-19 cases have not yet stabilized to a predictable state, but this study provides case characteristics and insights for how academic institutions might prepare to mitigate outbreaks on their campuses as the world plans for the transition from pandemic to endemic COVID-19.

Circuit analysis reveals a neural pathway for light avoidance in Drosophila larvae

Understanding how neural circuits underlie behaviour is challenging even in the connectome era because it requires a combination of anatomical and functional analyses. This is exemplified in the circuit underlying the light avoidance behaviour displayed by Drosophila melanogaster larvae. While this behaviour is robust and the nervous system relatively simple, the circuit is only partially delineated with some contradictions among studies. Here, we devise trans-Tango MkII, an offshoot of the transsynaptic circuit tracing tool trans-Tango, and implement it in anatomical tracing together with functional analysis. We use neuronal inhibition to test necessity of particular neuronal types in light avoidance and selective neuronal activation to examine sufficiency in rescuing light avoidance deficiencies exhibited by photoreceptor mutants. Our studies reveal a four-order circuit for light avoidance connecting the light-detecting photoreceptors with a pair of neuroendocrine cells via two types of clock neurons. This approach can be readily expanded to studying other circuits.

Widening or narrowing inequalities? The equity implications of digital tools to support COVID-19 contact tracing: A qualitative study.

BackgroundAs digital tools are increasingly used to support COVID‐19 contact tracing, the equity implications must be considered. As part of a study to understand the public's views of digital contact tracing tools developed for the national ‘Test and Protect’ programme in Scotland, we aimed to explore the views of groups often excluded from such discussions. This paper reports on their views about the potential for contact tracing to exacerbate inequalities.MethodsA qualitative study was carried out; interviews were conducted with key informants from organizations supporting people in marginalized situations, followed by interviews and focus groups with people recruited from these groups. Participants included, or represented, minority ethnic groups, asylum seekers and refugees and those experiencing multiple disadvantage including severe and enduring poverty.ResultsA total of 42 people participated: 13 key informants and 29 members of the public. While public participants were supportive of contact tracing, key informants raised concerns. Both sets of participants spoke about how contact tracing, and its associated digital tools, might increase inequalities. Barriers included finances (inability to afford smartphones or the data to ensure access to the internet); language (digital tools were available only in English and required a degree of literacy, even for English speakers); and trust (many marginalized groups distrusted statutory organizations and there were concerns that data may be passed to other organizations). One strength was that NHS Scotland, the data guardian, is seen as a generally trustworthy organization. Poverty was recognized as a barrier to people's ability to self‐isolate. Some participants were concerned about giving contact details of individuals who might struggle to self‐isolate for financial reasons.ConclusionsThe impact of contact tracing and associated digital tools on marginalized populations needs careful monitoring. This should include the contact tracing process and the ability of people to self‐isolate. Regular clear messaging from trusted groups and community members could help maintain trust and participation in the programme.Patient and Public ContributionOur patient and public involvement coapplicant, L. L., was involved in all aspects of the study including coauthorship. Interim results were presented to our local Public and Patient Involvement and Engagement Group, who commented on interpretation and made suggestions about further recruitment.

Developing digital contact tracing tailored to haulage in East Africa to support COVID-19 surveillance: a protocol

IntroductionAt the peak of Uganda’s first wave of SARS-CoV-2 in May 2020, one in three COVID-19 cases was linked to the haulage sector. This triggered a mandatory requirement for a...

The complete chloroplast genomes of Tetrastigma hemsleyanum (Vitaceae) from different regions of China: molecular structure, comparative analysis and development of DNA barcodes for its geographical origin discrimination

BackgroundTetrastigma hemsleyanum is a valuable traditional Chinese medicinal plant widely distributed in the subtropical areas of China. It belongs to the Cayratieae tribe, family Vitaceae, and exhibited significant anti-tumor and anti-inflammatory activities. However, obvious differences were observed on the quality of T. hemsleyanum root from different regions, requiring the discrimination strategy for the geographical origins.ResultThis study characterized five complete chloroplast (cp) genomes of T. hemsleynum samples from different regions, and conducted a comparative analysis with other representing species from family Vitaceae to reveal the structural variations, informative markers and phylogenetic relationships. The sequenced cp genomes of T. hemsleyanum exhibited a conserved quadripartite structure with full length ranging from 160,124 bp of Jiangxi Province to 160,618 bp of Zhejiang Province. We identified 112 unique genes (80 protein-coding, 28 tRNA and 4 rRNA genes) in the cp genomes of T. hemsleyanum with highly similar gene order, content and structure. The IR contraction/expansion events occurred on the junctions of ycf1, rps19 and rpl2 genes with different degrees, causing the differences of genome sizes in T. hemsleyanum and Vitaceae plants. The number of SSR markers discovered in T. hemsleyanum was 56–57, exhibiting multiple differences among the five geographic groups. Phylogenetic analysis based on conserved cp genome proteins strongly grouped the five T. hemsleyanum species into one clade, showing a sister relationship with T. planicaule. Comparative analysis of the cp genomes from T. hemsleyanum and Vitaceae revealed five highly variable spacers, including 4 intergenic regions and one protein-coding gene (ycf1). Furthermore, five mutational hotspots were observed among T. hemsleyanum cp genomes from different regions, providing data for designing DNA barcodes trnL and trnN. The combination of molecular markers of trnL and trnN clustered the T. hemsleyanum samples from different regions into four groups, thus successfully separating specimens of Sichuan and Zhejiang from other areas.ConclusionOur study obtained the chloroplast genomes of T. hemsleyanum from different regions, and provided a potential molecular tracing tool for determining the geographical origins of T. hemsleyanum, as well as important insights into the molecular identification approach and and phylogeny in Tetrastigma genus and Vitaceae family.

A multi-proxy, bioavailable strontium isotope baseline for southern Almería, Spain: Using modern environmental samples to constrain the isotopic range of bioavailable strontium

The Spanish region of Almería is well known for its rich geological records and its richness in archaeological remains. Sr isotopes have been applied in archaeology as a powerful tracing tool for individual human and animal mobility, but their application requires extensive regional baselines as reference against which the target materials can be compared. This study presents Sr concentrations and 87Sr/86Sr values of modern environmental proxies (plants, soils and surface waters) from southern Almería to establish such a bioavailable Sr isotope baseline for this region. Additionally, this study evaluates differences in bioavailable 87Sr/86Sr signatures of the plants, soil leachates and surface waters and tests three soil leaching agents, ultrapure water (mq), 1M NH4NO3 and 0.1M HNO3, to better understand variations of Sr isotope signatures captured by the different proxies and to evaluate their suitability for baseline constructions. Our results define a wide range of 87Sr/86Sr values ranging from 0.70836 to 0.71630. Our data reveals a strong influence of the local surface lithology on bioavailable 87Sr/86Sr compositions. While the plants and soil leachates generally returned similar 87Sr/86Sr values, surface waters from the same sites sometimes returned significantly less radiogenic values likely due to transported, carbonate-derived Sr from their catchment areas. The different soil leaching procedures returned leachable fractions with similar 87Sr/86Sr values, but a slight bias was observed for soils with a carbonate component signifying the overall strong control of bioavailable Sr by carbonates. We propose to define bioavailable Sr isotope baselines as the average bioavailable 87Sr/86Sr ratio ± double standard deviation (x̅± 2σ) of 1) plants and soil leachates and 2) surface waters for each surface lithology. The soil and plant-based baselines define the narrowest range in 87Sr/86Sr for areas dominated by Cenozoic volcanic rocks while the widest range is seen in areas dominated by high grade Paleozoic metamorphic rocks. Due to the scarcity of surface water run-off in the arid region of southern Almería, surface water based 87Sr/86Sr baselines could only be defined for sites dominated by Cenozoic sediments and high grade Paleozoic metamorphic rocks.

The value of social interactions and incentives on the use of a digital contact tracing tool post COVID-19 lockdown in Singapore

We assessed the preferences and trade-offs for social interactions, incentives, and being traced by a digital contact tracing (DCT) tool post lockdown in Singapore by a discrete choice experiment (DCE) among 3839 visitors of a large public hospital in Singapore between July 2020 – February 2021. Respondents were sampled proportionately by gender and four age categories (21 – 80 years). The DCE questionnaire had three attributes (1. Social interactions, 2. Being traced by a DCT tool, 3. Incentives to use a DCT tool) and two levels each. Panel fixed conditional logit model was used to analyse the data. Respondents were more willing to trade being traced by a DCT tool for social interactions than incentives and unwilling to trade social interactions for incentives. The proportion of respondents preferring no incentives and could only be influenced by their family members increases with age. Among proponents of monetary incentives, the preferred median value for a month’s usage of DCT tools amounted to S$10 (USD7.25) and S$50 (USD36.20) for subsidies and lucky draw. In conclusion, DCE can be used to elicit profile-specific preferences to optimize the uptake of DCT tools during a pandemic. Social interactions are highly valued by the population, who are willing to trade them for being traced by a DCT tool during the COVID-19 pandemic. Although a small amount of incentive is sufficient to increase the satisfaction of using a DCT tool, incentives alone may not increase DCT tool uptake.

Ros2_tracing: Multipurpose Low-Overhead Framework for Real-Time Tracing of ROS 2

Testing and debugging have become major obstacles for robot software development, because of high system complexity and dynamic environments. Standard, middleware-based data recording does not provide sufficient information on internal computation and performance bottlenecks. Other existing methods also target very specific problems and thus cannot be used for multipurpose analysis. Moreover, they are not suitable for real-time applications. In this letter, we present <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ros2_tracing</monospace> , a collection of flexible tracing tools and multipurpose instrumentation for ROS 2. It allows collecting runtime execution information on real-time distributed systems, using the low-overhead LTTng tracer. Tools also integrate tracing into the invaluable ROS 2 orchestration system and other usability tools. A message latency experiment shows that the end-to-end message latency overhead, when enabling all ROS 2 instrumentation, is on average 0.0033 ms, which we believe is suitable for production real-time systems. ROS 2 execution information obtained using <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ros2_tracing</monospace> can be combined with trace data from the operating system, enabling a wider range of precise analyses, that help understand an application execution, to find the cause of performance bottlenecks and other issues.

Optimal Power Scheduling Using Data-Driven Carbon Emission Flow Modelling for Carbon Intensity Control

Regarding the continuing increase of anthropogenic carbon emissions in the power system with growing energy consumption, researchers have focused on managing the demand side to achieve low-carbon transition. However, such a low-carbon transition cannot be achieved effectively without the proper incentive scheme. Moreover, it is desired to improve the computation quality of carbon tracing tools and extend their applications by state-of-art techniques. To fulfil these existing research gaps, this paper proposes a low-carbon optimal scheduling model with demand response (DR) based carbon intensity control. It aims to reduce the dependence of customers on energy sources with high carbon intensities while decreasing reasonable energy consumption. Likewise, a data-driven approach conducted with the Bayesian interfere regression is proposed to carry out the carbon emission flow (CEF) model to cope with the drawbacks of the conventional emission calculation. Moreover, the storage emission and relevant dynamic impacts to the system are fully considered. The proposed data-driven approach is tested on a series of IEEE systems with different sizes, its high accuracy and efficiency are proved by the simulation results. Further, simulation results indicate that the proposed scheduling model can effectively achieve carbon emission mitigation.

EPH137 Digital Health Solutions for Workers: Engagement with a Digital Tool for Management of SARS-COV-2 Exposure and Infection

EPH137 Digital Health Solutions for Workers: Engagement with a Digital Tool for Management of SARS-COV-2 Exposure and Infection

A novel technology for in vivo detection of cell type-specific neural connection with AQP1-encoding rAAV2-retro vector and metal-free MRI

A mammalian brain contains numerous neurons with distinct cell types for complex neural circuits. Virus-based circuit tracing tools are powerful in tracking the interaction among the different brain regions. However, detecting brain-wide neural networks in vivo remains challenging since most viral tracing systems rely on postmortem optical imaging. We developed a novel approach that enables in vivo detection of brain-wide neural connections based on metal-free magnetic resonance imaging (MRI). The recombinant adeno-associated virus (rAAV) with retrograde ability, the rAAV2-retro, encoding the human water channel aquaporin 1 (AQP1) MRI reporter gene was generated to label neural connections. The mouse was micro-injected with the virus at the Caudate Putamen (CPU) region and subjected to detection with Diffusion-weighted MRI (DWI). The prominent structure of the CPU-connected network was clearly defined. In combination with a Cre-loxP system, rAAV2-retro expressing Cre-dependent AQP1 provides a CPU-connected network of specific type neurons. Here, we established a sensitive, metal-free MRI-based strategy for in vivo detection of cell type-specific neural connections in the whole brain, which could visualize the dynamic changes of neural networks in rodents and potentially in non-human primates.

Internet of Healthcare Things (IoHTs) Technology to Strengthen Nigeria Health Systems: A predictive technique for Syndromic Surveillance of Suspected cases of Filovirus Diseases in Africa.

Infectious disease should be early recognised, treated timely and adequately, otherwise it spreads and leads to many other complications that is capable of causing health hazards, which can relatively become a cause of multiple cases of death. The emergence and re-emerging of Filovirus diseases especially in African continent are alarmingly threatening the healthcare workers and poses greater fatality rate. Ebola Virus Disease (EVD) and Marburg Virus Disease (MVD) are in the family of Filovirus infectious disease, that is predicted to continue emerging especially in regions where the reservoir host species are known. The upsurge of Internet of Things (IoTs) in Healthcare has provision very many technological advancement and positive solutions to healthcare challenges across the globe especially in many developed countries of the world. Internet of Healthcare Things (IoHTS) is a pool of innovative technological medical related devices that are connected together to provide seemingly control management, monitoring and surveillance, predictive detection and information sharing, etc that is capable of improving quality of healthcare delivery. Thus, early recognition, and timely detection and response to highly contagious and deadly virus is critically important in this modern era. This research study employs the Case-Study methodology to present an investigative framework of the use of Internet of Healthcare Things (IoHTs) technique to detect, predict and real-time report of -re-emerging Filovirus diseases in order to provide early recognition, quick response and intervention management with improved contact tracing tool of Geographical Information System (GIS) for active surveillance for informed decision-making on emergency healthcare delivery while minimizing the spread of the viruses. The study proposes to apply the emerging IoTs innovations to symptomatically detect and predict particular Filovirus (MVD or EVD) based on the defined physiological condition systematically presented. The objectives of the study are not limited to properly review the existing frameworks suitable for the research study, and design the propose architectural framework for the performance improvement of the system. The outcome is to provide a scientific way of detecting and predicting re-emerging Filovirus Diseases, and reporting such data in real-time for improve rapid response to emergency health intervention. The system structure is logically abreast with the technique to provide Suspected individuals with opportunity to early, easily and speedily detect and predict (know) their health condition during any Filovirus (EVD and MVD) outbreaks, and significantly minimize the spread of the diseases. Keywords : Ebola Virus Disease, Marburg Virus Disease, Internet of Healthcare Things, Smart Health Care, Filovirus, Predictive Detection, Syndromic Surveillance and Real-Time Reporting. DOI: 10.7176/CEIS/13-2-05 Publication date: April 30 th 2022

Assessment of the feasibility of pool testing for SARS-CoV-2 infection screening

Background SARS-CoV-2 pandemic represented a huge challenge for national health systems worldwide. Pooling nasopharyngeal (NP) swabs seems to be a promising strategy, saving time and resources, but it could reduce the sensitivity of the RT-PCR and exacerbate samples management in terms of automation and tracing. In this study, taking advantage of the routine implementation of a screening plan on health workers, we evaluated the feasibility of pool testing for SARS-CoV-2 infection diagnosis in the presence of low viral load samples. Method Pools were prepared with an automated instrument, mixing 4, 6 or 20 NP specimens, including one, two or none positive samples. Ct values of positive samples were on average about 35 for the four genes analyzed. Results The overall sensitivity of 4-samples and 6-samples pools was 93.1 and 90.0%, respectively. Focussing on pools including one sample with Ct value ≥35 for all analyzed genes, sensitivity decreased to 77.8 and 75.0% for 4- and 6-samples, respectively; pools including two positive samples, resulted positive in any size as well as pools including positive samples with Ct values <35. Conclusion Pool testing strategy should account the balance between cost-effectiveness, dilution effect and prevalence of the infection. Our study demonstrated the good performances in terms of sensitivity and saving resources of pool testing mixing 4 or 6 samples, even including low viral load specimens, in a real screening context possibly affected by prevalence fluctuation. In conclusion, pool testing strategy represents an efficient and resources saving surveillance and tracing tool, especially in specific context like schools, even for monitoring changes in prevalence associated to vaccination campaign.

Novel Techniques Targeting Fibroblasts after Ischemic Heart Injury.

The great plasticity of cardiac fibroblasts allows them to respond quickly to myocardial injury and to contribute to the subsequent cardiac remodeling. Being the most abundant cell type (in numbers) in the heart, and a key participant in the several phases of tissue healing, the cardiac fibroblast is an excellent target for treating cardiac diseases. The development of cardiac fibroblast-specific approaches have, however, been difficult due to the lack of cellular specific markers. The development of genetic lineage tracing tools and Cre-recombinant transgenics has led to a huge acceleration in cardiac fibroblast research. Additionally, the use of novel targeted delivery approaches like nanoparticles and modified adenoviruses, has allowed researchers to define the developmental origin of cardiac fibroblasts, elucidate their differentiation pathways, and functional mechanisms in cardiac injury and disease. In this review, we will first characterize the roles of fibroblasts in the different stages of cardiac repair and then examine novel techniques targeting fibroblasts post-ischemic heart injury.

Determinants of the acceptance and adoption of a digital contact tracing tool during the COVID-19 pandemic in Singapore.

The motivations that govern the adoption of digital contact tracing (DCT) tools are complex and not well understood. Hence, we assessed the factors influencing the acceptance and adoption of Singapore's national DCT tool - TraceTogether - during the COVID-19 pandemic. We surveyed 3943 visitors of Tan Tock Seng Hospital from July 2020 to February 2021 and stratified the analyses into three cohorts. Each cohort was stratified based on the time when significant policy interventions were introduced to increase the adoption of TraceTogether. Binary logistic regression was preceded by principal components analysis to reduce the Likert items. Respondents who 'perceived TraceTogether as useful and necessary' had higher likelihood of accepting it but those with 'Concerns about personal data collected by TraceTogether' had lower likelihood of accepting and adopting the tool. The injunctive and descriptive social norms were also positively associated with both the acceptance and adoption of the tool. Liberal individualism was mixed in the population and negatively associated with the acceptance and adoption of TraceTogether. Policy measures to increase the uptake of a national DCT bridged the digital divide and accelerated its adoption. However, good public communications are crucial to address the barriers of acceptance to improve voluntary uptake widespread adoption.

Open Tracing Tools: Overview and Critical Comparison

Open Tracing Tools: Overview and Critical Comparison

A Ray Tracing Tool for Propagation Modeling in Layered Media: A Case Study at the Chip Scale

Nowadays, many wireless applications require the exchange of electromagnetic waves through propagation environments that look like to stratified media to some extent. This may concern both natural scenarios, e.g., air/vegetation/ground, and artificial structures, like meta-materials, photonic devices, solar cells or systems-on-chip. The characterization of the layered propagation channel is therefore important for the design and deployment of effective devices and systems. To this aim, the Dyadic Green Function method has been often leveraged, although its use is usually limited to simple sources, like short dipoles or current elements. Furthermore, it seems unsuited to assess the dispersive properties of the channel, which are known to become important when high data-rate must be conveyed. In this paper, a ray-based approach to propagation inside layers is proposed. As the layered environment can be quite reverberating, a great number of rays may be needed, to the extent that the corresponding computational burden might be hardly afforded by general purpose ray tracing tools. Therefore, the ray tracing engine here presented is specifically conceived for the layered case, and mostly relies on analytical formulation. The accuracy of the model is checked against measurement carried out at chip scale at optical frequency, as a reference study case.

How Do Industry 4.0 Technologies Boost Collaborations in Buyer-Supplier Relationships?

Overview: Business leaders often consider digital technologies an enabler of new business models and market opportunities, but they often overlook their potential impact on the entire value chain. Considering three Industry 4.0 technologies—big data analytics and cloud computing, track and tracing, and simulation and modeling software—we identify the opportunities and challenges that emerge in the context of managing supply chain relationships. This study uses data from an international survey to test how these three Industry 4.0 technologies increase visibility and integration between buyers and suppliers and how they impact supply chain performance. Our results show mixed evidence: although all three technologies directly improve supply chain performance, big data analytics and cloud computing and simulation and modeling also fully support collaborative supply chain models, while track and tracing tools create more visible supply chains but are detrimental to obtaining higher process integration with suppliers. Surprisingly, buyer-supplier collaboration, in terms of visibility and integration, matters more than the technologies themselves.

TraceRank: Abnormal service localization with dis‐aggregated end‐to‐end tracing data in cloud native systems

AbstractModern cloud native applications are generally built with a microservice architecture. To tackle various performance problems among a large number of services and machines, an end‐to‐end tracing tool is always equipped in these systems to track the execution path of every single request. However, it is nontrivial to conduct root cause analysis of anomalies with such a large volume of tracing data. This paper proposes a novel system named TraceRank to identify and locate abnormal services causing performance problems with dis‐aggregated end‐to‐end traces. TraceRank mainly includes an anomaly detection module and a root cause analysis module. The root cause analysis procedure is triggered when an anomaly is detected. To fully leverage the information provided by the tracing data, both the spectrum analysis and the PageRank‐based random walk methods are introduced to pinpoint abnormal services. The experiments in TrainTicket and Bookinfo microservice benchmarks and a real‐world system show that TraceRank can locate root causes with 90% in Precision and 86% in Recall. TraceRank has up to 10% improvement compared with several state‐of‐the‐art approaches in both Precision and Recall. Finally, TraceRank has good scalability and a low overhead to adapt to large‐scale microservice systems.

Epidemics with asymptomatic transmission: Subcritical phase from recursive contact tracing.

The challenges presented by the COVID-19 epidemic have created a renewed interest in the development of new methods to combat infectious diseases, and it has shown the importance of preparedness for possible future diseases. A prominent property of the SARS-CoV-2 transmission is the significant fraction of asymptomatic transmission. This may influence the effectiveness of the standard contact tracing procedure for quarantining potentially infected individuals. However, the effects of asymptomatic transmission on the epidemic threshold of epidemic spreading on networks have rarely been studied explicitly. Here we study the critical percolation transition for an arbitrary disease with a nonzero asymptomatic rate in a simple epidemic network model in the presence of a recursive contact tracing algorithm for instant quarantining. We find that, above a certain fraction of asymptomatic transmission, standard contact tracing loses its ability to suppress spreading below the epidemic threshold. However, we also find that recursive contact tracing opens a possibility to contain epidemics with a large fraction of asymptomatic or presymptomatic transmission. In particular, we calculate the required fraction of network nodes participating in the contact tracing for networks with arbitrary degree distributions and for varying recursion depths and discuss the influence of recursion depth and asymptomatic rate on the epidemic percolation phase transition. We anticipate recursive contact tracing to provide a basis for digital, app-based contact tracing tools that extend the efficiency of contact tracing to diseases with a large fraction of asymptomatic transmission.