While Digital contact tracing (DCT) has been argued to be a valuable complement to manual tracing in the containment of COVID-19, no empirical evidence of its effectiveness is available to date. ...Here, we report the results of a 4-week population-based controlled experiment that took place in La Gomera (Canary Islands, Spain) between June and July 2020, where we assessed the epidemiological impact of the Spanish DCT app Radar Covid. After a substantial communication campaign, we estimate that at least 33% of the population adopted the technology and further showed relatively high adherence and compliance as well as a quick turnaround time. The app detects about 6.3 close-contacts per primary simulated infection, a significant percentage being contacts with strangers, although the spontaneous follow-up rate of these notified cases is low. Overall, these results provide experimental evidence of the potential usefulness of DCT during an epidemic outbreak in a real population.
Dissimilatory nitrate reduction to ammonium (DNRA) competes with denitrification for nitrate (NO3−) and can result in conservation of nitrogen (N), whereas denitrification leads to gaseous losses in ...the form of nitrogen gas or the greenhouse gas nitrous oxide (N2O). Thus, promoting DNRA bacteria in agricultural soils would be tractable, but little is known about what controls them in these systems and if management or cropping regimes can affect the competition between denitrifiers and DNRA bacteria. We hypothesized that cropping systems conserving soil organic matter (SOM) and resulting in higher C/NO3− ratios would favour DNRA over denitrification, and thereby lower the N2O emissions due to shifts in the abundances of the microbial communities involved. To test this hypothesis, we compared soil of an annual cereal rotation with a ley rotation (including barley) from a long-term field experiment, each with two different N fertilizer application rates. We quantified the gross rates of denitrification and DNRA in a15N tracing experiment and quantified the abundances of the functional genes for denitrification (nirK, nirS), DNRA (nrfA) and N2O reduction (nosZI, nosZII). The annual crop rotation had changed the soil properties, whereas the ley rotation prevented depletion of SOM resulting in higher C/NO3− ratios. The abundances of both nrfA and nosZ relative to the nir genes were higher in the ley soils, which correlated with significantly higher DNRA rates and lower N2O production, compared to the annual cereal rotation. We conclude that conservation of soil N and mitigation of N2O emissions can be mediated by the soil microbiome by management of SOM.
•Management controlling soil carbon affect ratios of denitrifiers and DNRA bacteria.•Gross denitrification and DNRA rates reflected by bacterial abundances.•The soil microbiome can mediate N conservation and N2O mitigation.
We discuss the implementation of app-based contact tracing to control the coronavirus disease (COVID-19) pandemic and discuss its data protection and user acceptability aspects.
Abstract
Knowledge tracing can analyze the current knowledge level of students through the data of students’ previous learning activities. However, the existing models usually consider the features ...of exercises, ignoring the individual differences of students. It is difficult to accurately predict students’ mastery. In this paper, we propose an attentive simple recurrent unit knowledge tracing (SRU-MAKT) based on learning ability. The experimental results show that our model is superior to the existing models, and the AUC increases by 1.6%. We also conduct visualization experiments, which show that SRU-MAKT has interpretability.
Background
Reducing the transmission of severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) is a global priority. Contact tracing identifies people who were recently in contact with an ...infected individual, in order to isolate them and reduce further transmission. Digital technology could be implemented to augment and accelerate manual contact tracing. Digital tools for contact tracing may be grouped into three areas: 1) outbreak response; 2) proximity tracing; and 3) symptom tracking. We conducted a rapid review on the effectiveness of digital solutions to contact tracing during infectious disease outbreaks.
Objectives
To assess the benefits, harms, and acceptability of personal digital contact tracing solutions for identifying contacts of an identified positive case of an infectious disease.
Search methods
An information specialist searched the literature from 1 January 2000 to 5 May 2020 in CENTRAL, MEDLINE, and Embase. Additionally, we screened the Cochrane COVID‐19 Study Register.
Selection criteria
We included randomised controlled trials (RCTs), cluster‐RCTs, quasi‐RCTs, cohort studies, cross‐sectional studies and modelling studies, in general populations. We preferentially included studies of contact tracing during infectious disease outbreaks (including COVID‐19, Ebola, tuberculosis, severe acute respiratory syndrome virus, and Middle East respiratory syndrome) as direct evidence, but considered comparative studies of contact tracing outside an outbreak as indirect evidence.
The digital solutions varied but typically included software (or firmware) for users to install on their devices or to be uploaded to devices provided by governments or third parties. Control measures included traditional or manual contact tracing, self‐reported diaries and surveys, interviews, other standard methods for determining close contacts, and other technologies compared to digital solutions (e.g. electronic medical records).
Data collection and analysis
Two review authors independently screened records and all potentially relevant full‐text publications. One review author extracted data for 50% of the included studies, another extracted data for the remaining 50%; the second review author checked all the extracted data. One review author assessed quality of included studies and a second checked the assessments. Our outcomes were identification of secondary cases and close contacts, time to complete contact tracing, acceptability and accessibility issues, privacy and safety concerns, and any other ethical issue identified. Though modelling studies will predict estimates of the effects of different contact tracing solutions on outcomes of interest, cohort studies provide empirically measured estimates of the effects of different contact tracing solutions on outcomes of interest. We used GRADE‐CERQual to describe certainty of evidence from qualitative data and GRADE for modelling and cohort studies.
Main results
We identified six cohort studies reporting quantitative data and six modelling studies reporting simulations of digital solutions for contact tracing. Two cohort studies also provided qualitative data. Three cohort studies looked at contact tracing during an outbreak, whilst three emulated an outbreak in non‐outbreak settings (schools). Of the six modelling studies, four evaluated digital solutions for contact tracing in simulated COVID‐19 scenarios, while two simulated close contacts in non‐specific outbreak settings.
Modelling studies
Two modelling studies provided low‐certainty evidence of a reduction in secondary cases using digital contact tracing (measured as average number of secondary cases per index case ‐ effective reproductive number (R eff)). One study estimated an 18% reduction in R eff with digital contact tracing compared to self‐isolation alone, and a 35% reduction with manual contact‐tracing. Another found a reduction in R eff for digital contact tracing compared to self‐isolation alone (26% reduction) and a reduction in R eff for manual contact tracing compared to self‐isolation alone (53% reduction). However, the certainty of evidence was reduced by unclear specifications of their models, and assumptions about the effectiveness of manual contact tracing (assumed 95% to 100% of contacts traced), and the proportion of the population who would have the app (53%).
Cohort studies
Two cohort studies provided very low‐certainty evidence of a benefit of digital over manual contact tracing. During an Ebola outbreak, contact tracers using an app found twice as many close contacts per case on average than those using paper forms. Similarly, after a pertussis outbreak in a US hospital, researchers found that radio‐frequency identification identified 45 close contacts but searches of electronic medical records found 13. The certainty of evidence was reduced by concerns about imprecision, and serious risk of bias due to the inability of contact tracing study designs to identify the true number of close contacts.
One cohort study provided very low‐certainty evidence that an app could reduce the time to complete a set of close contacts. The certainty of evidence for this outcome was affected by imprecision and serious risk of bias. Contact tracing teams reported that digital data entry and management systems were faster to use than paper systems and possibly less prone to data loss.
Two studies from lower‐ or middle‐income countries, reported that contact tracing teams found digital systems simpler to use and generally preferred them over paper systems; they saved personnel time, reportedly improved accuracy with large data sets, and were easier to transport compared with paper forms. However, personnel faced increased costs and internet access problems with digital compared to paper systems.
Devices in the cohort studies appeared to have privacy from contacts regarding the exposed or diagnosed users. However, there were risks of privacy breaches from snoopers if linkage attacks occurred, particularly for wearable devices.
Authors' conclusions
The effectiveness of digital solutions is largely unproven as there are very few published data in real‐world outbreak settings. Modelling studies provide low‐certainty evidence of a reduction in secondary cases if digital contact tracing is used together with other public health measures such as self‐isolation. Cohort studies provide very low‐certainty evidence that digital contact tracing may produce more reliable counts of contacts and reduce time to complete contact tracing. Digital solutions may have equity implications for at‐risk populations with poor internet access and poor access to digital technology.
Stronger primary research on the effectiveness of contact tracing technologies is needed, including research into use of digital solutions in conjunction with manual systems, as digital solutions are unlikely to be used alone in real‐world settings. Future studies should consider access to and acceptability of digital solutions, and the resultant impact on equity. Studies should also make acceptability and uptake a primary research question, as privacy concerns can prevent uptake and effectiveness of these technologies.
The goal of Knowledge Tracing (KT) is to estimate how well students have mastered a concept based on their historical learning of related exercises. The benefit of knowledge tracing is that students’ ...learning plans can be better organised and adjusted, and interventions can be made when necessary. With the recent rise of deep learning, Deep Knowledge Tracing (DKT) has utilised Recurrent Neural Networks (RNNs) to accomplish this task with some success. Other works have attempted to introduce Graph Neural Networks (GNNs) and redefine the task accordingly to achieve significant improvements. However, these efforts suffer from at least one of the following drawbacks: (1) they pay too much attention to details of the nodes rather than to high-level semantic information; (2) they struggle to effectively establish spatial associations and complex structures of the nodes; and (3) they represent either concepts or exercises only, without integrating them. Inspired by recent advances in self-supervised learning, we propose a Bi-Graph Contrastive Learning based Knowledge Tracing (Bi-CLKT) to address these limitations. Specifically, we design a two-layer comparative learning scheme based on an “exercise-to-exercise” (E2E) relational subgraph. It involves node-level contrastive learning of subgraphs to obtain discriminative representations of exercises, and graph-level contrastive learning to obtain discriminative representations of concepts. Moreover, we designed a joint contrastive loss to obtain better representations and hence better prediction performance. Also, we explored two different variants, using RNN and memory-augmented neural networks as the prediction layer for comparison to obtain better representations of exercises and concepts respectively. Extensive experiments on four real-world datasets show that the proposed Bi-CLKT and its variants outperform other baseline models.
After experiencing a sharp growth in COVID-19 cases early in the pandemic, South Korea rapidly controlled transmission while implementing less stringent national social distancing measures than ...countries in Europe and the USA. This has led to substantial interest in their "test, trace, isolate" strategy. However, it is important to understand the epidemiological peculiarities of South Korea's outbreak and characterise their response before attempting to emulate these measures elsewhere.
We systematically extracted numbers of suspected cases tested, PCR-confirmed cases, deaths, isolated confirmed cases, and numbers of confirmed cases with an identified epidemiological link from publicly available data. We estimated the time-varying reproduction number, R
, using an established Bayesian framework, and reviewed the package of interventions implemented by South Korea using our extracted data, plus published literature and government sources.
We estimated that after the initial rapid growth in cases, R
dropped below one in early April before increasing to a maximum of 1.94 (95%CrI, 1.64-2.27) in May following outbreaks in Seoul Metropolitan Region. By mid-June, R
was back below one where it remained until the end of our study (July 13th). Despite less stringent "lockdown" measures, strong social distancing measures were implemented in high-incidence areas and studies measured a considerable national decrease in movement in late February. Testing the capacity was swiftly increased, and protocols were in place to isolate suspected and confirmed cases quickly; however, we could not estimate the delay to isolation using our data. Accounting for just 10% of cases, individual case-based contact tracing picked up a relatively minor proportion of total cases, with cluster investigations accounting for 66%.
Whilst early adoption of testing and contact tracing is likely to be important for South Korea's successful outbreak control, other factors including regional implementation of strong social distancing measures likely also contributed. The high volume of testing and the low number of deaths suggest that South Korea experienced a small epidemic relative to other countries. Caution is needed in attempting to replicate the South Korean response in populations with larger more geographically widespread epidemics where finding, testing, and isolating cases that are linked to clusters may be more difficult.
Abstract
Knowledge Tracing (KT) is a challenging task in personalized learning where the objective is to track the progression of students’ understanding of the concepts over time based on their ...learning history. Typically, there are many more exercises than knowledge points. Tracing the state of the concepts seems a reasonable approach but ignores the additional information contained in the exercise itself. Moreover, there are numerous relationships between knowledge points, and the modification of one point’s state can affect related points. In this paper, we introduce a method that splits exercises into constituent knowledge points, and incorporates the exercise features to improve the differential representation of the exercises containing the same concepts. We also describe two ways of propagating knowledge point states: one-way and two-way, which update the state of the current knowledge point and its related knowledge points. Our model is tested on two real datasets, and the experimental results show that our model outperforms the existing methods.
As public health professionals around the world work tirelessly to respond to the COVID-19 pandemic, it is clear that traditional methods of contact tracing need to be augmented in order to help ...address a public health crisis of unprecedented scope. Innovators worldwide are racing to develop and implement novel public-facing technology solutions, including digital contact tracing technology. These technological products may aid public health surveillance and containment strategies for this pandemic and become part of the larger toolbox for future infectious outbreak prevention and control. As technology evolves in an effort to meet our current moment, Johns Hopkins Project on Ethics and Governance of Digital Contact Tracing Technologiesa rapid research and expert consensus group effort led by Dr. Jeffrey Kahn of the Johns Hopkins Berman Institute of Bioethics in collaboration with the university's Center for Health Securitycarried out an in-depth analysis of the technology and the issues it raises. Drawing on this analysis, they produced a report that includes detailed recommendations for technology companies, policymakers, institutions, employers, and the public. The project brings together perspectives from bioethics, health security, public health, technology development, engineering, public policy, and law to wrestle with the complex interactions of the many facets of the technology and its applications. This team of experts from Johns Hopkins University and other world-renowned institutions has crafted clear and detailed guidelines to help manage the creation, implementation, and application of digital contact tracing. Digital Contact Tracing Technology for Pandemic Response is the essential resource for this fast-moving crisis.
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