Contact tracing for COVID-19 in England operated from May 2020 to February 2022. The clinical, demographic and exposure information collected on cases and their contacts offered a unique opportunity to study secondary transmission. We aimed to quantify the relative impact of host factors and exposure settings on secondary COVID-19 transmission risk using 550,000 sampled transmission links between cases and their contacts. Links, or ‘contact episodes’, were established where a contact subsequently became a case, using an algorithm accounting for incubation period, setting, and contact date. A mixed-effects logistic regression model was used to estimate adjusted odds of transmission. Of sampled episodes, 8.7% resulted in secondary cases. Living with a case (71% episodes) was the most significant risk factor (aOR = 2.6, CI = 1.9–3.6). Other risk factors included unvaccinated status (aOR = 1.2, CI = 1.2–1.3), symptoms, and older age (66–79 years; aOR = 1.4, CI = 1.4–1.5). Whilst global COVID-19 strategies emphasized protection outside the home, including education, travel, and gathering restrictions, this study evidences the relative importance of household transmission. There is a need to reconsider the contribution of household transmission to future control strategies and the requirement for effective infection control within households.

In an effort to respond to the large surge in COVID-19 cases in Arizona that began between May and July 2020, the Arizona State University (ASU) Student Outbreak Response Team (SORT) formed a remote, volunteer-based case investigation team that worked in partnership with a local public health department through delegated public health authority.

Tuberculosis (TB) contact tracing and TB preventive treatment are key tools in preventing the transmission of TB with the aim of eliminating the disease. Our study seeks to demonstrate how the infection spread from an individual patient to the entire community and how proactive contact tracing facilitated prompt diagnosis and treatment. Our work was conducted as a retrospective analysis of the spread of TB infection within the Roma community in the Czech Republic, following the case of an index patient who succumbed to pulmonary TB. Several levels of care and preventive and treatment measures are outlined. Confirming the identity of the Mycobacterium tuberculosis strain was achieved using molecular methods. Among the 39 individuals examined, TB disease was detected in eight patients and TB infection was detected in six patients. The investigation of contacts within this group yielded positive results in 36% of cases, necessitating treatment. The study’s findings provide evidence that actively tracing individuals at risk can lead to early detection of cases, prompt treatment, and prevention of further disease transmission. The study also indicates that the highest risk of infection occurs within the sick person’s household and that young children under the age of 5 are most susceptible to falling ill.

Governments all over the world are rapidly embracing digital technologies for information collection, governance, and social control. Recent studies suggest citizens may accept or even support digital surveillance. By using an online survey dataset on public opinion about facial recognition technology, contact tracing apps, and the social credit system in China, Germany, the US, and the UK, this article shows that these studies have overlooked a small yet significant group of digital technology doubters. Our results show that while up to 10% of Chinese citizens belong to the group of “digital doubters,” this group is the largest in Germany with 30% of citizens. The US and the UK are in the middle with approximately 20%. While citizens who belong to this group of digital doubters worry about privacy and surveillance issues, their attitudes can also be explained by them not being convinced of the benefits of digital technologies, including improved efficiency, security, or convenience. We find that the more citizens lack trust in their government, the more likely they are to belong to the group of digital doubters. Our findings demonstrate that in both democratic and authoritarian states, there are citizens opposing the adoption of certain digital technologies. This underscores the importance of initiating societal debate to determine the appropriate regulations that align with these societal preferences.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron variant (B.1.1.529) rapidly replaced Delta (B.1.617.2) to become dominant in England. Our study assessed differences in transmission between Omicron and Delta using two independent data sources and methods. Omicron and Delta cases were identified through genomic sequencing, genotyping and S-gene target failure in England from 5–11 December 2021. Secondary attack rates for named contacts were calculated in household and non-household settings using contact tracing data, while household clustering was identified using national surveillance data. Logistic regression models were applied to control for factors associated with transmission for both methods. For contact tracing data, higher secondary attack rates for Omicron vs. Delta were identified in households (15.0% vs. 10.8%) and non-households (8.2% vs. 3.7%). For both variants, in household settings, onward transmission was reduced from cases and named contacts who had three doses of vaccine compared to two, but this effect was less pronounced for Omicron (adjusted risk ratio, aRR 0.78 and 0.88) than Delta (aRR 0.62 and 0.68). In non-household settings, a similar reduction was observed only in contacts who had three doses vs. two doses for both Delta (aRR 0.51) and Omicron (aRR 0.76). For national surveillance data, the risk of household clustering, was increased 3.5-fold for Omicron compared to Delta (aRR 3.54 (3.29–3.81)). Our study identified increased risk of onward transmission of Omicron, consistent with its successful global displacement of Delta. We identified a reduced effectiveness of vaccination in lowering risk of transmission, a likely contributor for the rapid propagation of Omicron.

Quantitative information on epidemiological quantities such as the incubation period and generation time of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants is scarce. We analysed a dataset collected during contact tracing activities in the province of Reggio Emilia, Italy, throughout 2021. We determined the distributions of the incubation period for the Alpha and Delta variants using information on negative polymerase chain reaction tests and the date of last exposure from 282 symptomatic cases. We estimated the distributions of the intrinsic generation time using a Bayesian inference approach applied to 9724 SARS-CoV-2 cases clustered in 3545 households where at least one secondary case was recorded. We estimated a mean incubation period of 4.9 days (95% credible intervals, CrI, 4.4–5.4) for Alpha and 4.5 days (95% CrI 4.0–5.0) for Delta. The intrinsic generation time was estimated to have a mean of 7.12 days (95% CrI 6.27–8.44) for Alpha and of 6.52 days (95% CrI 5.54–8.43) for Delta. The household serial interval was 2.43 days (95% CrI 2.29–2.58) for Alpha and 2.74 days (95% CrI 2.62–2.88) for Delta, and the estimated proportion of pre-symptomatic transmission was 48–51% for both variants. These results indicate limited differences in the incubation period and intrinsic generation time of SARS-CoV-2 variants Alpha and Delta compared to ancestral lineages.

Symptoms are currently used as testing indicators for SARS-CoV-2 in England. In this study, we analysed national contact tracing data for England (NHS Test and Trace) for the period 1 December to 28 December 2021 to explore symptom differences between the variants, Delta and Omicron. We found that at least one of the symptoms currently used as indicators (fever, cough and loss of smell and taste) were reported in 61.5% of Omicron cases and 72.2% in Delta cases, suggesting that these symptoms are less predictive of Omicron infections. Nearly 40% of Omicron infections did not report any of the three key indicative symptoms, reinforcing the importance of the entire spectrum of symptoms for targeted testing. After adjusting for potential confounding factors, fever and cough were more commonly associated with Omicron infections compared to Delta, showing the importance of considering age and vaccination status when assessing symptom profiles. Sore throat was also more commonly reported in Omicron infections, and loss of smell and taste more commonly reported in Delta infections. Our study shows the value of continued monitoring of symptoms associated with SARS-CoV-2, as changes may influence the effectiveness of testing policy and case ascertainment approaches.

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.

Since the beginning of the coronavirus (COVID-19) global pandemic, digital contact-tracing applications (apps) have been at the centre of attention as a digital tool to enable citizens to monitor their social distancing, which appears to be one of the leading practices for mitigating the spread of airborne infectious diseases. Many countries have been working towards developing suitable digital contact-tracing apps to allow the measurement of the physical distance between citizens and to alert them when contact with an infected individual has occurred. However, the adoption of digital contact-tracing apps has faced several challenges so far, including interoperability between mobile devices and users’ privacy concerns. There is a need to reach a trade-off between the achievable technical performance of new technology, false-positive rates, and social and behavioural factors. This paper reviews a wide range of factors and classifies them into three categories of technical, epidemiological and social ones, and incorporates these into a compact mathematical model. The paper evaluates the effectiveness of digital contact-tracing apps based on received signal strength measurements. The results highlight the limitations, potential and challenges of the adoption of digital contact-tracing apps.

Several digital contact tracing smartphone applications have been developed worldwide in the effort to combat COVID-19 that warn users of potential exposure to infectious patients and generate big data that helps in early identification of hotspots, complementing the manual tracing operations. In most democracies, concerns over a breach in data privacy have resulted in severe opposition toward their mandatory adoption. This paper examines India as a noticeable exception, where the compulsory installation of such a government-backed application, the “Aarogya Setu” has been deemed mandatory in certain situations. We argue that the mandatory app requirement constitutes a legitimate public health intervention during a public health emergency.

We used social network analysis (SNA) to study the novel coronavirus (COVID-19) outbreak in Karnataka, India, and to assess the potential of SNA as a tool for outbreak monitoring and control. We analysed contact tracing data of 1147 COVID-19 positive cases (mean age 34.91 years, 61.99% aged 11–40, 742 males), anonymised and made public by the Karnataka government. Software tools, Cytoscape and Gephi, were used to create SNA graphics and determine network attributes of nodes (cases) and edges (directed links from source to target patients). Outdegree was 1–47 for 199 (17.35%) nodes, and betweenness, 0.5–87 for 89 (7.76%) nodes. Men had higher mean outdegree and women, higher mean betweenness. Delhi was the exogenous source of 17.44% cases. Bangalore city had the highest caseload in the state (229, 20%), but comparatively low cluster formation. Thirty-four (2.96%) ‘super-spreaders’ (outdegree ⩾ 5) caused 60% of the transmissions. Real-time social network visualisation can allow healthcare administrators to flag evolving hotspots and pinpoint key actors in transmission. Prioritising these areas and individuals for rigorous containment could help minimise resource outlay and potentially achieve a significant reduction in COVID-19 transmission.

Mass gatherings and high-density activities, such as sporting events, conventions, and theme parks, are consistently included among highest-risk activities given the increased potential for widespread coronavirus disease 2019 (COVID-19) transmission. A more balanced risk management approach is required because absolute suppression of risk is unrealistic in all facets of life. Contact tracing remains a limiting factor in achieving such a balance. The use of Bluetooth or pairing devices is proposed to address this challenge. This simple approach, when applied in a manner that satisfies privacy and trust concerns, would allow high-risk encounters to be quickly identified, namely those where participants have spent 15 minutes or more within 6 ft of each other per current guidelines. If an attendee later tests positive for COVID-19 and tracing is required, the event organizer can provide a limited list of potential close contacts rather than an exhaustive list of all attendees. Contact tracers can, therefore, limit efforts to this concise group rather than needing to contact thousands of people or conduct mass media communications. Such a system, if institutionalized, supports risk assurance and safety measures for businesses by demonstrating a commitment to staff, customer protection, and ensuring high-risk encounters are logged, reinforcing longer-term societal pandemic resilience.

Extensively drug-resistant (XDR) tuberculosis (TB) poses a threat to public health due to its complicated, expensive and often unsuccessful treatment. A cluster of three XDR TB cases was detected among foreign medical students of a Romanian university. The contact investigations included tuberculin skin testing or interferon gamma release assay, chest X-ray, sputum smear microscopy, culture, drug susceptibility testing, genotyping and whole-genome sequencing (WGS), and were addressed to students, personnel of the university, family members or other close contacts of the cases. These investigations increased the total number of cases to seven. All confirmed cases shared a very similar WGS profile. Two more cases were epidemiologically linked, but no laboratory confirmation exists. Despite all the efforts done, the source of the outbreak was not identified, but the transmission was controlled. The investigation was conducted by a team including epidemiologists and microbiologists from five countries (Finland, Israel, Romania, Sweden and the UK) and from the European Centre for Disease Prevention and Control. Our report shows how countries can collaborate to control the spread of XDR TB by exchanging information about cases and their contacts to enable identification of additional cases and transmission and to perform the source investigation.

The Middle East respiratory syndrome coronavirus (MERS-CoV) is caused by a novel coronavirus discovered in 2012. Since then, 1806 cases, including 564 deaths, have been reported by the Kingdom of Saudi Arabia (KSA) and affected countries as of 1 June 2016. Previous literature attributed increases in MERS-CoV transmission to camel breeding season as camels are likely the reservoir for the virus. However, this literature review and subsequent analysis indicate a lack of seasonality. A retrospective, epidemiological cluster analysis was conducted to investigate increases in MERS-CoV transmission and reports of household and nosocomial clusters. Cases were verified and associations between cases were substantiated through an extensive literature review and the Armed Forces Health Surveillance Branch's Tiered Source Classification System. A total of 51 clusters were identified, primarily nosocomial (80·4%) and most occurred in KSA (45·1%). Clusters corresponded temporally with the majority of periods of greatest incidence, suggesting a strong correlation between nosocomial transmission and notable increases in cases.

This paper is concerned with a stochastic model for the spread of an epidemic with a contact tracing scheme, in which diagnosed individuals may name some of their infectious contacts, who are then removed if they have not been already. Traced individuals may or may not also be asked to name their own contacts. The epidemic is studied by considering an approximating, modified birth-death process with intersibling dependencies, for which a threshold parameter and expressions from which extinction probabilities may be calculated are derived. When all individuals can name their contacts, it is shown that this threshold parameter depends on the infectious period distribution only through its mean. Numerical studies show that the infectious period distribution choice can have a material effect on the threshold behaviour of an epidemic, while the dependencies help reduce spread.

Alexandra Hospital (AH) was one of the public hospitals in Singapore that responded to the severe acute respiratory syndrome (SARS) crisis. Being the only public hospital that remained “SARS-free”, i.e., with no documented intra-institutional spread of disease, AH had to tackle a sudden, two-fold surge in hospital attendances and patient volume. Being the oldest hospital with a traditional open ward design and lack of proper isolation facilities, tough command and control policies had to be implemented to reduce the risk of a SARS outbreak. Stringent infection control measures, screening and triage, clinical procedures, and administrative policies all were important factors in helping the hospital balance the need to run routine operations while “fighting” SARS. Staff and people management also were crucial in keeping the workforce healthy and maintaining their morale and confidence during this difficult period.