Effective Contact Tracing Research Articles

Mpox across countries from Central and Eastern Europe - 2022 outbreak

BackgroundThe aim of the study was to assess socio-demographical characteristics, clinical presentation, and outcomes in patients diagnosed with mpox. MethodsA survey on patients diagnosed with mpox was performed in 14 countries from Central and Eastern Europe. Data was compared according to HIV status and country of origin (EU vs. non-EU). Mpox diagnosis was confirmed by RT-PCR from oropharyngeal swabs, skin lesions, and other body fluids. ResultsOut of 154 patients confirmed with mpox in 2022, 99.3% were males, with a median age (years) of 35 (IQR 30–39), 90.2% MSM and 48.7% PLWH. Compared to HIV-negative subjects, PLWH had more frequent high-risk behaviours:chemsex (p = 0.015), group sex (p = 0.027), and a history of sexually transmitted infections (STIs) (p = 0.004). Persons from EU were more often PLWH (p = 0.042), MSM (p < 0.0001), had multiple sexual partners (p = 0.025), practiced chemsex (p = 0.008) or group-sex (p = 0.005) and had more often history of STIs (p < 0.0001). The median CD4 cell count/mL at mpox diagnosis was 713 (IQR 486–996) and 73.5% had undetectable HIV VL. The commonest clinical features were fever (108 cases), lymphadenopathy (78), and vesiculo-pustular rash: penile (76), perianal (48), limbs (67). Fifty-one (31%) persons were hospitalized due to complications or epidemiological reasons. Three patients received tecovirimat or cidofovir. The outcome was favorable for all patients, including 4 with severe forms. ConclusionsMpox was diagnosed predominantly in young MSM, with high-risk behaviors and history of STIs. Effective contact tracing and vaccination are important strategic pillars to control mpox outbreaks.

Protect Your Score: Contact-Tracing with Differential Privacy Guarantees

The pandemic in 2020 and 2021 had enormous economic and societal consequences, and studies show that contact tracing algorithms can be key in the early containment of the virus. While large strides have been made towards more effective contact tracing algorithms, we argue that privacy concerns currently hold deployment back. The essence of a contact tracing algorithm constitutes the communication of a risk score. Yet, it is precisely the communication and release of this score to a user that an adversary can leverage to gauge the private health status of an individual. We pinpoint a realistic attack scenario and propose a contact tracing algorithm with differential privacy guarantees against this attack. The algorithm is tested on the two most widely used agent-based COVID19 simulators and demonstrates superior performance in a wide range of settings. Especially for realistic test scenarios and while releasing each risk score with epsilon=1 differential privacy, we achieve a two to ten-fold reduction in the infection rate of the virus. To the best of our knowledge, this presents the first contact tracing algorithm with differential privacy guarantees when revealing risk scores for COVID19.

Taiwan ended third COVID-19 community outbreak as forecasted

Accurate forecasting of community outbreaks is crucial for governments to allocate healthcare resources correctly and implement suitable non-pharmaceutical interventions. Additionally, companies must address critical questions about stock and staff management. Society’s key concern is when businesses and organizations can resume normal operations. Between December 31st 2019 and 2021, Taiwan experienced three separate COVID-19 community outbreaks with significant time intervals in between, suggesting that each outbreak eventually came to an end. We identified the ratio of the 7-day average of local & unknown confirmed to suspected cases as the key control variable and forecasted the end of the third outbreak by the exponential model. We forecasted the end of the third outbreak on Aug. 16th with threshold ratios of 1.2·10-4\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$1.2\\cdot 10^{-4}$$\\end{document}. The real observations crossed the threshold on Aug. 27th, eleven days later than forecasted, with the last case of the third outbreak confirmed and quarantined on Sept. 20th. This demonstrated the accuracy of the proposed forecasting method in predicting the end of a local outbreak. Furthermore, we highlight that the ratio reflects the effectiveness of contact tracing. Effective contact tracing together with testing and isolation of infected individuals is crucial for ending community outbreaks.

Suppression of the alpha, delta, and omicron variants of SARS-Cov-2 in Taiwan.

Taiwan was a coronavirus disease 2019 (COVID-19) outlier, with an extraordinarily long transmission-free record: 253 days without locally transmitted infections while the rest of the world battled wave after wave of infection. The appearance of the alpha variant in May 2021, closely followed by the delta variant, disrupted this transmission-free streak. However, despite low vaccination coverage (<1%), outbreaks were well-controlled. This study analyzed the time to border closure and conducted one-sample t test to compare between Taiwan and Non-Taiwan countries prior to vaccine introduction. The study also collected case data to observe the dynamics of omicron transmission. Time-varying reproduction number,Rt, was calculated and was used to reflect infection impact at specified time points and model trends of future incidence. The study analyzed and compare the time to border closure in Taiwan and non-Taiwan countries. The mean times to any border closure from the first domestic case within each country were -21 and 5.98 days, respectively (P < .0001). The Taiwanese government invested in quick and effective contact tracing with a precise quarantine strategy in lieu of a strict lockdown. Residents followed recommendations based on self-discipline and unity. The self-discipline in action is evidenced in Google mobility reports. The central and local governments worked together to enact non-pharmaceutical interventions (NPIs), including universal masking, social distancing, limited unnecessary gatherings, systematic contact tracing, and enhanced quarantine measures. The people cooperated actively with pandemic-prevention regulations, including vaccination and preventive NPIs. This article describes four key factors underlying Taiwan's success in controlling COVID-19 transmission: quick responses; effective control measures with new technologies and rolling knowledge updates; unity and cooperation among Taiwanese government agencies, private companies and organizations, and individual citizens; and Taiwanese self-discipline.

Contact Tracing Over Uncertain Indoor Positioning Data

Pandemics often cause dramatic losses of human lives and impact our societies in many aspects such as public health, tourism, and economy. To contain the spread of an epidemic like COVID-19, efficient and effective contact tracing is important, especially in indoor venues where the risk of infection is higher. In this work, we formulate and study a novel query called Indoor Contact Query ( <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ICQ</small> ) over raw, uncertain indoor positioning data that digitalizes people's movements indoors. Given a query object <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$o$</tex-math></inline-formula> , e.g., a person confirmed to be a virus carrier, an <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ICQ</small> analyzes uncertain indoor positioning data to find objects that most likely had close contact with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$o$</tex-math></inline-formula> for a long period of time. To process <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ICQ</small> , we propose a set of techniques. First, we design an enhanced indoor graph model to organize different types of data necessary for <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ICQ</small> . Second, for indoor moving objects, we devise methods to determine uncertain regions and to derive positioning samples missing in the raw data. Third, we propose a query processing framework with a close contact determination method, a search algorithm, and the acceleration strategies. We conduct extensive experiments on synthetic and real datasets to evaluate our proposals. The results demonstrate the efficiency and effectiveness of our proposals.

Emerging Technology-Driven Hybrid Models for Preventing and Monitoring Infectious Diseases: A Comprehensive Review and Conceptual Framework.

The emergence of the infectious diseases, such as the novel coronavirus, as a significant global health threat has emphasized the urgent need for effective treatments and vaccines. As infectious diseases become more common around the world, it is important to have strategies in place to prevent and monitor them. This study reviews hybrid models that incorporate emerging technologies for preventing and monitoring infectious diseases. It also presents a comprehensive review of the hybrid models employed for preventing and monitoring infectious diseases since the outbreak of COVID-19. The review encompasses models that integrate emerging and innovative technologies, such as blockchain, Internet of Things (IoT), big data, and artificial intelligence (AI). By harnessing these technologies, the hybrid system enables secure contact tracing and source isolation. Based on the review, a hybrid conceptual framework model proposes a hybrid model that incorporates emerging technologies. The proposed hybrid model enables effective contact tracing, secure source isolation using blockchain technology, IoT sensors, and big data collection. A hybrid model that incorporates emerging technologies is proposed as a comprehensive approach to preventing and monitoring infectious diseases. With continued research on and the development of the proposed model, the global efforts to effectively combat infectious diseases and safeguard public health will continue.

Bodily surveillance: Singapore’s COVID-19 app and technological opportunism

ABSTRACT Singapore won early kudos for its ‘gold standard’ handling of the COVID-19 pandemic back in February 2020. It was praised globally for its ability to activate an effective contact tracing system. Riding on this success, the government introduced ‘TraceTogether’, a mobile phone app to enhance contact tracing efforts, using a technology that leverages the Bluetooth feature on smartphones to track proximity between users and record their physical encounters. This paper contends that the roll-out of the app is a form of ‘technological opportunism’ to enhance greater bodily surveillance over its citizens during a time of crisis. The low number of downloads of the app initially (at 20%), before persuasion-coercion strategies were applied to lift the take-up rate to 90%, belies the assumption that surveillance is genuinely widely accepted. This paper details key responses to the app in Singapore, and the government’s decision to make it mandatory during the heart of the pandemic between 2020 to 2022. It considers the implications of technological opportunism, taking advantage of a pandemic to continue in the journey of turning citizens into what Michel Foucault would refer to as ‘subjectified bodies’ to be traced, tracked and codified.

Contact tracing for vancomycin-resistant Enterococcus faecium (VRE): evaluation of the Dutch policy of quintuple screening cultures

Detection of vancomycin-resistant Enterococcus faecium (VRE) is hampered by low sensitivity of rectal swab cultures. This study aimed to define the number of screening cultures needed to increase sensitivity to detect VRE transmission, and to determine time from presumed exposure to detectable colonization. In a tertiary care setting, we retrospectively analyzed data from 9 VRE outbreaks. As a proxy or estimation for time to detectable colonization, the time between first positive culture of the presumed index patient and that of their contacts was determined. Only 64% of secondary cases were positive in the first out of five cultures. By using the first three out of five rectal swabs, 89% (95%CI: 78–95%) of all secondary cases would have been identified. The median number of days between the positive culture of the index patient and the first positive culture of secondary cases was 9 days. Eleven percent of secondary cases would have been missed if only three rectal samples would have been obtained. Furthermore, our results show that one or more rectal swabs taken around day 9 after presumed exposure should at least be included in the screening approach. In our setting, obtaining a fourth and a fifth rectal swab showed a relevant additional value compared to only one to three swabs. Our findings are useful for determining the most effective VRE contact tracing approach to prevent transmission.

Challenges and opportunities for improved contact tracing in Ghana: experiences from Coronavirus disease-2019-related contact tracing in the Bono region

BackgroundIn Ghana, contact tracing received heightened attention in the fight against the COVID-19 pandemic during its peak period. Despite the successes achieved, numerous challenges continue to limit the efforts of contact tracing in completely curtailing the effect of the pandemic. Despite these challenges, there are still opportunities that could be harnessed from the COVID-19 contact tracing experience for future eventualities. This study thus identified the challenges and opportunities associated with COVID-19 contact tracing in the Bono Region of Ghana.MethodsUsing a focus group discussion (FGD) approach, an exploratory qualitative design was conducted in six selected districts of the Bono region of Ghana in this study. The purposeful sampling technique was employed to recruit 39 contact tracers who were grouped into six focus groups. A thematic content analysis approach via ATLAS ti version 9.0 software was used to analyse the data and presented under two broad themes.ResultsThe discussants reported twelve (12) challenges that hindered effective contact tracing in the Bono region. These include inadequate personal protective equipment, harassment by contacts, politicisation of the discourse around the disease, stigmatization, delays in processing test results, poor remuneration and lack of insurance package, inadequate staffing, difficulty in locating contacts, poor quarantine practices, poor education on COVID-19, language barrier and transportation challenges. Opportunities for improving contact tracing include cooperation, awareness creation, leveraging on knowledge gained in contact tracing, and effective emergency plans for future pandemics.ConclusionThere is a need for health authorities, particularly in the region, and the state as a whole to address contact tracing-related challenges while simultaneously harnessing the recommended opportunities for improved contact tracing in the future for effective pandemic control.

Recent outbreaks of Marburg virus in Africa: an urgent call for public concern

Recent outbreaks of Marburg virus in Africa: an urgent call for public concern

Using Rational Choice Theory to Explore Factors Impacting Contact Tracing Application Adoption

ABSTRACT Effective contact tracing is an important tool in trying to control the spread of highly infectious diseases. Utilizing the theory of reasoned action and rational choice theory, this study explores factors impacting the adoption of mobile contact tracing application. Findings indicate that perceived benefits of usage and perceived costs of non-usage positively impact an individual’s attitude toward usage intention, while information security concern as perceived cost of usage has a negative effect.

A descriptive study of COVID-19 cases in primary and secondary schools in the Maltese islands: a nationwide experience.

As part of the measures to contain the initial cases of Coronavirus Disease (COVID-19) in 2020, all educational facilities were closed in March 2020 and remained so for the remainder of that scholastic year. When they reopened in October 2020, most educational facilities on the Maltese islands did so with various mitigation measures in place. A Schools Contact Tracing Team (SCTT) dedicated to the management of COVID-19 cases within schools was set up and networks established between the Ministries responsible for Health and Education to facilitate timely communication and, consequently, effective contact tracing. All cases pertaining to educational facilities, be they students, teaching or non-teaching staff were assessed and managed by this Team. Between October 2020 and June 2021, the SCTT assessed 2603 COVID-19 cases within educational facilities in Malta. The highest rate of cases overall was observed in teaching staff (56.53/1000). In 72.45% of cases, no contacts were identified as high risk and thus nobody was placed in quarantine. In 3.07% of school cases >21 high-risk contacts were placed in mandatory quarantine together with their household members. Only 11% of the cases were epi-linked to another positive case within school. The strong collaboration between the health and education authorities combined with strict measures observed in schools ensured that schools remained open throughout most of this pandemic. This study describes the processes by which contact tracing for COVID-19 cases in Maltese schools was carried out and analyses the data collected throughout the scholastic year 2020-21.

Implementation and effectiveness of a COVID-19 case investigation and contact tracing program at a large, urban midwestern university

BackgroundThe University of Illinois Chicago (UIC) COVID-19 Contact Tracing and Epidemiology Program was critical to the university's COVID-19 incident response during the 2020-2021 academic year. We are a team of epidemiologists and student contact tracers who perform COVID-19 contact tracing among campus members. Literature is sparse on models for mobilizing non-clinical students as contact tracers; therefore, we aim to disseminate strategies that are adaptable by other institutions. MethodsWe described essential aspects of our program including surveillance testing, staffing and training models, interdepartmental partnerships, and workflows. Additionally, we analyzed the epidemiology of COVID-19 at UIC and measures of contact tracing effectiveness. ResultsThe program was responsible for promptly quarantining 120 cases prior to converting and potentially infecting others, thereby preventing at least 132 downstream exposures and 22 COVID-19 infections from occurring. DiscussionFeatures central to program success included routine data translation and dissemination and utilizing students as indigenous campus contact tracers. Major operational challenges included high staff turnover and adjusting to rapidly evolving public health guidance. ConclusionsInstitutes of higher education provide fertile ground for effective contact tracing, particularly when comprehensive networks of partners facilitate compliance with institution-specific public health requirements.

PIVOT: Private and Effective Contact Tracing

We propose, design, and evaluate PIVOT, a privacy-enhancing and effective contact tracing solution that aims to strike a balance between utility and privacy: one that does not collect sensitive information yet allowing effective tracing and notifying the close contacts of diagnosed users. PIVOT requires a considerably low degree of trust in the entities involved compared to centralized alternatives while retaining the necessary utility. To protect users’ privacy, it uses local proximity tracing based on broadcasting and recording constantly changing anonymous public keys via short-range communication. These public keys are used to establish a shared secret key between two people in close contact. The three keys (i.e., the two public keys and the established shared key) are then used to generate two unique per-user-per-contact hashes: one for infection registration and one for exposure score query. These hashes are never revealed to the public. To improve utility, user exposure score computation is performed centrally, which provides health authorities with minimal, yet insightful and actionable data. Data minimization is achieved by the use of per-user-per-contact hashes and by enforcing role separation: the health authority act as a mixing node, while the matching between reported and queried hashes is outsourced to a third entity, an independent matching service (MS). This separation ensures that out-of-scope information, such as users’ social interactions, is hidden from the health authorities, whereas the MS does not learn users’ sensitive information. To sustain our claims, we conduct a practical evaluation that encompasses anonymity guarantees and energy requirements.

Outcomes of a Community Engagement and Information Gathering Program to Support Telephone-Based COVID-19 Contact Tracing: Descriptive Analysis.

Contact tracing is an important public health tool for curbing the spread of infectious diseases. Effective and efficient contact tracing involves the rapid identification of individuals with infection and their exposed contacts and ensuring their isolation or quarantine, respectively. Manual contact tracing via telephone call and digital proximity app technology have been key strategies in mitigating the spread of COVID-19. However, many people are not reached for COVID-19 contact tracing due to missing telephone numbers or nonresponse to telephone calls. The New York City COVID-19 Trace program augmented the efforts of telephone-based contact tracers with information gatherers (IGs) to search and obtain telephone numbers or residential addresses, and community engagement specialists (CESs) made home visits to individuals that were not contacted via telephone calls. The aim of this study was to assess the contribution of information gathering and home visits to the yields of COVID-19 contact tracing in New York City. IGs looked for phone numbers or addresses when records were missing phone numbers to locate case-patients or contacts. CESs made home visits to case-patients and contacts with no phone numbers or those who were not reached by telephone-based tracers. Contact tracing management software was used to triage and queue assignments for the telephone-based tracers, IGs, and CESs. We measured the outcomes of contact tracing-related tasks performed by the IGs and CESs from July 2020 to June 2021. Of 659,484 cases and 861,566 contact records in the Trace system, 28% (185,485) of cases and 35% (303,550) of contacts were referred to IGs. IGs obtained new phone numbers for 33% (61,804) of case-patients and 11% (31,951) of contacts; 50% (31,019) of the case-patients and 46% (14,604) of the contacts with new phone numbers completed interviews; 25% (167,815) of case-patients and 8% (72,437) of contacts were referred to CESs. CESs attempted 80% (132,781) of case and 69% (49,846) of contact investigations, of which 47% (62,733) and 50% (25,015) respectively, completed interviews. An additional 12,192 contacts were identified following IG investigations and 13,507 following CES interventions. Gathering new or missing locating information and making home visits increased the number of case-patients and contacts interviewed for contact tracing and resulted in additional contacts. When possible, contact tracing programs should add information gathering and home visiting strategies to increase COVID-19 contact tracing coverage and yields as well as promote equity in the delivery of this public health intervention.

DISTERNING: Distance Estimation Using Machine Learning Approach for COVID-19 Contact Tracing and Beyond

Since the coronavirus disease 19 (COVID-19) outbreak, the epidemiological analysis has raised a strong requirement for more effective and accurate contact tracing solution. However, the existing contact tracing solutions either lacked the evaluation of tracing proximity or the features used for the tracing proximity evaluation were susceptible to certain negative environmental factors (e.g., body shielding). In this article, we propose a novel distance estimation algorithm based on machine learning for contact tracing: DISTERNING, where we leverage machine learning algorithms including Learning Vector Quantization, Regression, and Deep Feed-forward (DFF) Neural Network, data processing methods, and digital filters to process the Bluetooth signal information collected by the mobile phone for contact distance estimation. A contact tracing scheme based on edge computing is also proposed for algorithm deployment due to the requirements of the computational power. Compared with the existing contact tracing solutions, our algorithm considers the factors that have significant negative influence on the Bluetooth signal for distance estimation in reality. The evaluation results show that when the collected Bluetooth signal is influenced by real-world negative environmental factors, employing our proposed algorithm DISTERNING can keep the accuracy of the estimated distance reliable. The output distance can be combined with some medical models to conduct infection risk assessments.

COVID-19 Contact Tracing and Privacy: A Longitudinal Study of Public Opinion

There is growing use of technology-enabled contact tracing, the process of identifying potentially infected COVID-19 patients by notifying all recent contacts of an infected person. Governments, technology companies, and research groups alike have been working towards releasing smartphone apps, using IoT devices, and distributing wearable technology to automatically track “close contacts” and identify prior contacts in the event an individual tests positive. However, there has been significant public discussion about the tensions between effective technology-based contact tracing and the privacy of individuals. To inform this discussion, we present the results of seven months of online surveys focused on contact tracing and privacy, each with 100 participants. Our first surveys were on April 1 and 3, 2020, before the first peak of the virus in the US, and we continued to conduct the surveys weekly for 10 weeks (through June), and then fortnightly through November, adding topical questions to reflect current discussions about contact tracing and COVID-19. Our results present the diversity of public opinion and can inform policy makers, technologists, researchers, and public health experts on whether and how to leverage technology to reduce the spread of COVID-19, while considering potential privacy concerns.

Accelerated partner therapy should be part of the suite of contact tracing options

Contact tracing, or partner notification, is a key pillar for controlling infectious diseases, such as chlamydia. If performed effectively, it can break chains of transmission, reduce the index patient's risk of repeat infection, and reduce the risk of complications for the index patient and their sexual partner(s).1 However, effective contact tracing is challenging because partners can be hard to reach or anonymous, and stigma limits contact and impedes timely testing and treatment.2,3

The Emergence of Monkeypox: A Global Health Threat.

Monkeypox (MPX) is a zoonotic disease caused by the monkeypox virus (MPXV) belonging to the Orthopoxvirus genus. It results in a smallpox-like disease in humans. Recently, MPX has been declared a public health emergency of international concern. The disease is characterized by fever, muscle ache, malaise, and pustules. The presence of characteristic significant lymphadenopathy helps it to be differentiated from other similar illnesses. Early detection of cases and effective contact tracing is necessary for breaking the chain of transmission. Diagnosis can be confirmed by polymerase chain reaction (PCR) testing of the lesions or by demonstrating the virus in other body fluids. There is no specific treatment for monkeypox, although the smallpox vaccine is thought to have high levels of protection. In this review, we have tried to collect all relevant information about the current outbreak, including epidemiological data, modalities of diagnosis, and treatment options

A State Health Department and Health Information Exchange Partnership: an Effective Collaboration for a Data-Driven Response for COVID-19 Contact Tracing in Maryland.

Accurate, complete, timely data were essential to effective contact tracing for COVID-19. Maryland Department of Health partnered with Maryland's designated health information exchange, Chesapeake Regional Information System for Our Patients (CRISP), to establish data enhancement processes that provided the foundation for Maryland's successful contact tracing program. Hourly, electronic positive COVID-19 test results were routed through CRISP to the contact tracing data platform. CRISP matched reports against its master patient index to enhance the record with demographic, locating, fatality, vaccination, and hospitalization data. Records were de-duplicated and flagged if associated with a congregate setting, select state universities, or recent international travel. Chi-square tests were used to assess if CRISP-added phone numbers resulted in better contact tracing outcomes. During June 15, 2020-September 1, 2021, CRISP pushed 531,094 records to the state's contact tracing data platform within an hour of receipt; of those eligible for investigation, 99% had a phone number. CRISP matched 521,731 (98%) records to their master patient index, allowing for deduplication and enrichment. CRISP flagged 15,615 cases in congregate settings and 3,304 cases as university students; these records were immediately routed for outbreak investigation. Records with an added phone number were significantly more likely to be successfully reached compared to cases with no added phone number (p = 0.01). CRISP enhanced COVID-19 electronic laboratory reports with a near-instant impact on public health actions. The partnership and data processing workflows can serve as a blueprint for data modernization in public health agencies across the United States.