Since the outbreak of the COVID-19 pandemic in spring 2020, the concept of test, trace, and isolate (TTI) was used as a non-pharmaceutical intervention against further spreading of the disease. Hereby, recent contact partners of newly confirmed SARS-CoV-2 infected persons were identified and isolated along with the originally detected case to avoid potential secondary infections. While the policy is, given the compliance of the traced persons, generally deemed efficient, not much is known about network-specific impact factors. In this work, we aim to evaluate the effectiveness of the TTI strategy when used (1) for diseases with different infectiousness levels and (2) on different contact networks. For the prior, we vary the infection probability per contact, for the latter, we analyse different clustering coefficients. Our goal is to test the validity of two hypotheses: First, we expect the policy to be more efficient if the infectiousness of the disease is small, since the time delay for isolating persons is crucial. Second, due to the implications of the friendship paradox, we expect the policy to be more effective if the clustering coefficient of the underlying contact network is high. We make use of an agent-based network model consisting of three intertwined model parts: an epidemiological SEIR model, a quarantine model and a contact-tracing model. To test the hypotheses, the disease parameters and the clustering coefficient of the underlying contact network are varied. The simulation results show that, indeed, tracing seems to have a slightly larger containment impact for networks with higher clustering, in particular for fast-spreading diseases. Yet, the effects are small compared to the impact of the infectiousness of the disease. Therefore, we find a significant decrease of the policy effectiveness the higher the transmission probability. The latter implies that the containment impact of tracing and isolating contacts becomes more efficient, if supported by additional measures that limit the infection probability or if applied in periods with low negative seasonality effects.

UNASSIGNED: Provider-initiated contact tracing (CT) is an important measure to slow down the spread of infectious diseases such as COVID-19. However, carrying out effective CT depends on the collaboration between the patient and the contact tracer. To improve CT, it is important to understand which factors influence contact tracers in being able to carry out CT during large pandemics.
UNASSIGNED: We performed individual semi-structured interviews with nine contact tracers working for the COVID-19 unit of the Public Health Service (PHS) Rotterdam-Rijnmond, the Netherlands, to explore their experiences with carrying out CT. Data were collected between July 2020 and December 2020. The interview protocol was structured based on the CT tasks and guided by the literature and the framework explaining adherence to clinical practice guidelines.
UNASSIGNED: In general, CT seemed to be carried out satisfactorily. Individual factors (interviewing techniques and skills, attitude towards the patient and attitude towards CT), factors related to the patient (cooperativeness and engagement, emotions, language and culture and (mis)information), guideline-related factors (characteristics) and factors related to the organisation (interactions with colleagues, support from management, workload and training) were found to influence the carrying out of CT.
UNASSIGNED: To be well prepared for future pandemics, it is important to explore strategies that can be effective to support the contact tracer in performing CT, support patients in feeling comfortable to be engaged and ways to reach more consistency in policies and protocols.

Although computational linguistic methods-such as topic modelling, sentiment analysis and emotion detection-can provide social media researchers with insights into online public discourses, it is not inherent as to how these methods should be used, with a lack of transparent instructions on how to apply them in a critical way. There is a growing body of work focusing on the strengths and shortcomings of these methods. Through applying best practices for using these methods within the literature, we focus on setting expectations, presenting trajectories, examining with context and critically reflecting on the diachronic Twitter discourse of two case studies: the longitudinal discourse of the NHS Covid-19 digital contact-tracing app and the snapshot discourse of the Ofqual A Level grade calculation algorithm, both related to the UK. We identified difficulties in interpretation and potential application in all three of the approaches. Other shortcomings, such the detection of negation and sarcasm, were also found. We discuss the need for further transparency of these methods for diachronic social media researchers, including the potential for combining these approaches with qualitative ones-such as corpus linguistics and critical discourse analysis-in a more formal framework.

This study adopts a public value perspective to examine the eHealth services deployed by national and regional governments to contain the coronavirus (Covid-19) pandemic, including symptoms checkers, information portals and contact-tracing applications. We analyse 50 cases of eHealth applications adopted in 25 European Economic Area (EEA) and outline how these systems and technologies map against four dimensions of public value: user orientation, participation, legality and equity. Our findings reveal that the public value of the eHealth applications adopted in the context of the current pandemic is affected by both endogenous and exogenous factors that undermine their ability to improve the quality of healthcare services and social wellbeing. We conclude by suggesting areas for further research to address such factors and the trade-offs emerging between different dimensions of public value.

BACKGROUND: More than 78 countries have developed COVID contact-tracing apps to limit the spread of coronavirus. However, many experts and scientists cast doubt on the effectiveness of those apps. For each app, a large number of reviews have been entered by end-users in app stores.
OBJECTIVE: Our goal is to gain insights into the user reviews of those apps, and to find out the main problems that users have reported. Our focus is to assess the \"software in society\" aspects of the apps, based on user reviews.
METHODS: We selected nine European national apps for our analysis and used a commercial app-review analytics tool to extract and mine the user reviews. For all the apps combined, our dataset includes 39,425 user reviews.
RESULTS: Results show that users are generally dissatisfied with the nine apps under study, except the Scottish (\"Protect Scotland\") app. Some of the major issues that users have complained about are high battery drainage and doubts on whether apps are really working.
CONCLUSIONS: Our results show that more work is needed by the stakeholders behind the apps (e.g., app developers, decision-makers, public health experts) to improve the public adoption, software quality and public perception of these apps.

Background: The contact tracing and isolation of contagious individuals are cornerstones in the control of the COVID-19 pandemic. Strategies to identify household contacts who should be isolated around index children that tested positive for SARS-CoV-2 remain to be clarified. We aimed to compare contact tracing strategies around an index child positive for SARS-CoV-2 using serological rapid diagnostic testing (RDT, chromatography immunoassay). Methods: We conducted a contact tracing study in households of index cases children in the Paris region, France, between May 8 and July 27, 2020. We compared two strategies, one using SARS-CoV-2 reverse transcriptase polymerase chain reaction (RT-PCR) and one combining RT-PCR and serological RDT, initiated once RDT was available. The contacts RT-PCR-/RDT+ were considered to have been previously infected and not requiring quarantine. The primary outcome was the proportion of contacts that could avoid quarantine with the two screening strategies. Results: We included 34 children as index cases. Median age was 7 years. They generated 184 contacts (111 adults, 73 children) tested by RT-PCR: 24/184 (13%) were positive. The strategy combining RDT and RT-PCR was performed in 120/184 contacts (77 adults, 43 children) of 26 index children: 16/120 (13%) were RT-PCR+ and 47/120 (39%) were RDT+. Among the 16 individuals who were RT-PCR+, 14 (87%) were also RDT+. Among the 104 individuals who were RT-PCR-, 33 were RDT+. Hence 33/120 (27%) individuals were not isolated. Conclusions: Following the diagnosis of SARS-CoV-2 infection in children, a strategy combining serological RDT and nasopharyngeal RT-PCR enabled us to identify around one fourth of contacts with past infection and avoid unnecessary quarantine of these individuals.

The emergence of novel COVID-19 causes an over-load in health system and high mortality rate. The key priority is to contain the epidemic and prevent the infection rate. In this context, many countries are now in some degree of lockdown to ensure extreme social distancing of entire population and hence slowing down the epidemic spread. Furthermore, authorities use case quarantine strategy and manual second/third contact-tracing to contain the COVID-19 disease. However, manual contact-tracing is time-consuming and labor-intensive task which tremendously over-load public health systems. In this paper, we developed a smartphone-based approach to automatically and widely trace the contacts for confirmed COVID-19 cases. Particularly, contact-tracing approach creates a list of individuals in the vicinity and notifying contacts or officials of confirmed COVID-19 cases. This approach is not only providing awareness to individuals they are in the proximity to the infected area, but also tracks the incidental contacts that the COVID-19 carrier might not recall. Thereafter, we developed a dashboard to provide a plan for policymakers on how lockdown/mass quarantine can be safely lifted, and hence tackling the economic crisis. The dashboard used to predict the level of lockdown area based on collected positions and distance measurements of the registered users in the vicinity. The prediction model uses k-means algorithm as an unsupervised machine learning technique for lockdown management.

A 14-day quarantine is implemented in many countries in response to the coronavirus disease pandemic. Korea implemented a mandatory quarantine for those who had close contact with infected patients and those returning from abroad. The present study explored the implications of mandatory coronavirus disease 2019 testing before releasing individuals from the 14-day quarantine in Incheon, Korea. From February 11 to July 5, 2020, 19,296 people were self-quarantined, and 56 (0.3%) of them were confirmed cases of COVID-19. Twenty (35.7%) were identified through the reporting of symptoms during quarantine, and 32 (57.1%) were identified using mandatory pre-release RT-PCR tests. Among the 32, 14 (25%) individuals reported mild symptoms and 18 (32.1%) were asymptomatic. It is suggested that mandatory diagnostic testing prior to release and the symptom-based surveillance after the 14-day quarantine may help control delayed or asymptomatic COVID-19 cases.

Several countries in Africa have either deployed or considering using digital contact-tracing (DCT) as part of their Covid-19 containment strategy, amidst calls for the use of technology to improve the efficiency of traditional contact-tracing. We discuss some of the complexities entailed in using DCT in Africa. Adopting a socio-technical perspective, we argue that if DCT design and deployment are not well thought out, it can lead to unintended consequences, particularly in a continent like Africa with disproportionate levels of digital divides and other structural inequalities. We suggest that any adoption of DCT by African countries must take account of their compatibility with local resources, values, social structure, and domestic political factors. Accordingly, we propose a process of translation whereby DCT adaptation is made to accommodate the unique institutional and technological characteristics of African countries by leveraging local practices learned from previous pandemics like Ebola to develop a blended epidemiological approach to (digital) contact-tracing.

A pandemic of a novel Coronavirus emerged in December of 2019 (COVID-19), causing devastating public health impact across the world. In the absence of a safe and effective vaccine or antivirals, strategies for controlling and mitigating the burden of the pandemic are focused on non-pharmaceutical interventions, such as social-distancing, contact-tracing, quarantine, isolation, and the use of face-masks in public. We develop a new mathematical model for assessing the population-level impact of the aforementioned control and mitigation strategies. Rigorous analysis of the model shows that the disease-free equilibrium is locally-asymptotically stable if a certain epidemiological threshold, known as the reproduction number (denoted by ℛc), is less than unity. Simulations of the model, using data relevant to COVID-19 transmission dynamics in the US state of New York and the entire US, show that the pandemic burden will peak in mid and late April, respectively. The worst-case scenario projections for cumulative mortality (based on the baseline levels of anti-COVID non-pharmaceutical interventions considered in the study) decrease dramatically by 80% and 64%, respectively, if the strict social-distancing measures implemented are maintained until the end of May or June, 2020. The duration and timing of the relaxation or termination of the strict social-distancing measures are crucially-important in determining the future trajectory of the COVID-19 pandemic. This study shows that early termination of the strict social-distancing measures could trigger a devastating second wave with burden similar to those projected before the onset of the strict social-distancing measures were implemented. The use of efficacious face-masks (such as surgical masks, with estimated efficacy ≥ 70%) in public could lead to the elimination of the pandemic if at least 70% of the residents of New York state use such masks in public consistently (nationwide, a compliance of at least 80% will be required using such masks). The use of low efficacy masks, such as cloth masks (of estimated efficacy less than 30%), could also lead to significant reduction of COVID-19 burden (albeit, they are not able to lead to elimination). Combining low efficacy masks with improved levels of the other anti-COVID-19 intervention strategies can lead to the elimination of the pandemic. This study emphasizes the important role social-distancing plays in curtailing the burden of COVID-19. Increases in the adherence level of social-distancing protocols result in dramatic reduction of the burden of the pandemic, and the timely implementation of social-distancing measures in numerous states of the US may have averted a catastrophic outcome with respect to the burden of COVID-19. Using face-masks in public (including the low efficacy cloth masks) is very useful in minimizing community transmission and burden of COVID-19, provided their coverage level is high. The masks coverage needed to eliminate COVID-19 decreases if the masks-based intervention is combined with the strict social-distancing strategy.