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.

Smear-positive adults with tuberculosis are the main source of childhood tuberculosis. The evaluation of children exposed to tuberculosis and determination of the disease stages are the cornerstones of managing childhood tuberculosis.
To determine the frequency of tuberculous contact, latent tuberculosis infection and tuberculosis disease in children who were in contact with smear-positive adults.
This is a single-centre, retrospective study. The medical records of children exposed to tuberculosis (<18 years old) between 2014 and 2018 were investigated. After diagnosing the index cases, the children were referred to the hospital. To identify the children in contact with adults with tuberculosis, a careful medical history, demographic features and physical examination, tuberculin skin test, postero-anterior and lateral chest radiographs, and, if necessary, chest computed tomography and microbiological tests were undertaken. The children\'s final diagnosis, treatment regimens and follow-up were documented. The sensitivity, specificity and positive and negative predictive values, tuberculin skin test and chest radiograph imaging were assessed and compared with computed tomography results.
A total of 150 paediatric patients were exposed to 88 index cases. These were fathers in 29.3% of cases and mothers in 10% of cases. Of the children, 131 (87.3%) were asymptomatic, and physical examination was normal in all children, apart from one who had respiratory symptoms. The tuberculin skin test results were positive in 60 (43%) patients and chest radiograph was abnormal in 100 (66%) children. Findings were consistent with tuberculosis in 34 (40%) of the 84 patients who underwent computed tomography. Fifty (38.5%) of the remaining children were defined as having been in contact with a case of tuberculosis, 41 (31.5%) had latent tuberculous infection and 39 (30%) had tuberculosis disease.
Pulmonary tuberculosis is asymptomatic in most children but with meticulous use of computed tomography it can be detected in asymptomatic children who have had close contact with tuberculosis.Abbreviation: AFB: acid-fast bacilli; AUC: area under the curve; BCG: bacillus Calmette-Guérin; CI: confidence interval; CT: computed tomography; CXR: chest radiograph; HIV: human immunodeficiency virus; ICD-10: International Classification of Diseases 10; LTBI: latent tuberculosis infection; MDR-TB: multi-drug-resistant tuberculosis; NPV: negative predictive value; PCR: polymerase chain reaction; PPV: positive predictive value; ROC: receiver operating characteristics; SD: standard deviation; TB: tuberculosis; TST: tuberculin skin test; XDR-TB: extensively drug-resistant tuberculosis.

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.

The COVID-19 pandemic highlighted the impact of social inequalities in health (SIH). Various studies have shown significant inequalities in mortality and morbidity associated with COVID-19 and the influence of social determinants of health. The objective of this qualitative case study was to analyze the consideration of SIH in the design of two key COVID-19 prevention and control interventions in France: testing and contact tracing. Interviews were conducted with 36 key informants involved in the design of the intervention and/or the government response to the pandemic as well as relevant documents (n = 15) were reviewed. We applied data triangulation and a hybrid deductive and inductive analysis to analyze the data. Findings revealed the divergent understandings and perspectives about SIH, as well as the challenges associated with consideration for these at the beginning stages of the pandemic. Despite a shared concern for SIH between the participants, an epidemiological frame of reference dominated the design of the intervention. It resulted in a model in which consideration for SIH appeared as a complement, with a clinical goal of the intervention: breaking the chain of COVID-19 transmission. Although the COVID-19 health crisis highlighted the importance of SIH, it did not appear to be an opportunity to further their consideration in response efforts. This article provides original insights into consideration for SIH in the design of testing and contact-tracing interventions based upon a qualitative investigation.
The COVID-19 pandemic has highlighted the importance of social inequalities in health (SIH) and the disproportionate burden of the pandemic and its consequences related to socioeconomic status, ethnicity and race, among other determinants of health. Public health interventions are likely to increase SIH when they are not considered in the design phase. Through a qualitative case study, we analyzed the design of one of the first local initiative providing testing and contact tracing offer to the general population in the Île-de-France region (Paris region, France) in response to the COVID-19 pandemic. This article discusses the uncertainty and challenges associated with consideration for SIH in the intervention design. It explores the diverse understandings of SIH among the actors and the complexities of cross-sectoral partnerships addressing SIH in times of health crisis. Despite a consensual concern for this issue among the respondents, an epidemiological frame of reference dominated the intervention design. It resulted in a model in which consideration for SIH appeared as a complement, with a clinical goal of the intervention: breaking the chain of COVID-19 transmission.

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: The COVID-19 pandemic has caused changes in technology use worldwide, both socially and economically. This pandemic crisis has brought additional measures such as contact-tracing apps (CTAs) to help fight against spread of the virus. Unfortunately, the low adoption rate of these apps affected their success. There could be many reasons for the low adoption, including concerns of security and privacy, along with reported issues of trust in CTAs. Some concerns are related with how CTAs could be used as surveillance tools or their potential threats to privacy as they involve health data. For example, in Estonia, the CTA named HOIA had approximately 250,000 downloads in the middle of January 2021. However, in 2021, only 4.7% of the population used HOIA as a COVID-19 CTA. The reasons for the low adoption include lack of competency, and privacy and security concerns. This lower adoption and the lack of trustworthiness persist despite efforts of the European Union in building ethics and trustworthy artificial intelligence (AI)-based apps.
OBJECTIVE: The aim of this study was to understand how to measure trust in health technologies. Specifically, we assessed the usefulness of the Human-Computer Trust Scale (HCTS) to measure Estonians\' trust in the HOIA app and the causes for this lack of trust.
METHODS: The main research question was: Can the HCTS be used to assess citizens\' perception of trust in health technologies? We established four hypotheses that were tested with a survey. We used a convenience sample for data collection, including sharing the questionnaire on social network sites and using the snowball method to reach all potential HOIA users in the Estonian population.
RESULTS: Among the 78 respondents, 61 had downloaded the HOIA app with data on usage patterns. However, 20 of those who downloaded the app admitted that it was never opened despite most claiming to regularly use mobile apps. The main reasons included not understanding how it works, and privacy and security concerns. Significant correlations were found between participants\' trust in CTAs in general and their perceived trust in the HOIA app regarding three attributes: competency (P<.001), risk perception (P<.001), and reciprocity (P=.01).
CONCLUSIONS: This study shows that trust in the HOIA app among Estonian residents did affect their predisposition to use the app. Participants did not generally believe that HOIA could help to control the spread of the virus. The result of this work is limited to HOIA and health apps that use similar contact-tracing methods. However, the findings can contribute to gaining a broader understanding and awareness of the need for designing trustworthy technologies. Moreover, this work can help to provide design recommendations that ensure trustworthiness in CTAs, and the ability of AI to use highly sensitive data and serve society.

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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.