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The global health crisis of COVID-19 has ushered in an era of unprecedented data generation, encompassing the virus's transmission patterns, societal consequences, and governmental responses. Data mining has emerged as a pivotal tool for extracting invaluable insights from this voluminous dataset, offering critical support for informed decision-making. While existing surveys primarily explore methodologies for detecting COVID-19 in medical imagery and official sources, this article comprehensively examines the pandemic through big data mining. We emphasize the significance of social network analysis, shedding light on the pandemic's profound influence on community socio-economic behavior. Additionally, we illuminate advancements in diverse domains, encompassing behavioral impact analysis on social media, contact tracing implications, early disease screening through medical imaging, and insights derived from health-related time-series data analytics. Our study further organizes the literature by categorizing it based on data sources, dataset types, analytical approaches, techniques, and application scenarios. Finally, we delineate prevailing challenges and forthcoming research prospects, charting the course for future investigations. [ABSTRACT FROM AUTHOR]

Among various non-pharmaceutical control measures, Digital Contact Tracing (DCT) based on information technology is an effective tool to combat COVID-19, but the penetration rates in major countries of the world are not optimal. To better prepare for any future emerging infectious disease outbreaks, it is necessary to examine the decision process of DCT acceptance among the public. Guided by the Elaboration Likelihood Model (ELM), this study compares the central and peripheral routes that motivate DCT acceptance. The research also examines how these decision processes moderate individuals' perceived susceptibility to COVID-19 and through self-efficacy. This research adopts a survey methodology for collecting 284 valid responses and utilizes structural equation modeling to verify the proposed hypotheses. The results demonstrate that perceived benefits develop along a dual route of argument quality and source credibility. Perceived benefits, in turn, enable individuals' willingness to disclose personal information to DCT. Perceived susceptibility and self-efficacy also moderate the influences of argument quality on perceived benefits. The findings imply that healthcare authorities should employ differing strategies to foster individuals' perceived benefits of DCT with the goal of boosting DCT penetration rates. [ABSTRACT FROM AUTHOR]

Purpose: Despite substantial vaccination progress, persistent measles outbreaks challenge global elimination efforts, particularly within healthcare settings. In this paper, we critically review the factors contributing to measles outbreak and effective control measures for nosocomial transmission of measles. Methods: We systematically searched electronic databases for articles up to 17th May, 2023. This was performed by two independent reviewers, with any disagreements resolved by a third reviewer. We also searched governmental and international health agencies for relevant studies. Results: Forty relevant articles were systematically reviewed, revealing key factors fuelling measles outbreak in healthcare settings, including high transmissibility capability; high intensity exposure; delayed care; failure to use protective equipment and implement control measures; vaccine failure; unclear immunisation history and lack of registries; and lacking recommendation on healthcare workers' (HCWs) measles vaccination. To combat these challenges, successful control strategies were identified which include early notification of outbreak and contact tracing; triaging all cases and setting up dedicated isolation unit; strengthening protective equipment use and physical measures; improving case detection; determining immunity status of HCWs; establishing policy for measles vaccination for HCWs; management of exposed personnel; and developing a pre-incident response plan. Conclusion: A coordinated and comprehensive approach is essential to promptly identify and manage measles cases within healthcare settings, necessitating multifactorial strategies tailored to individual settings. These findings provide a valuable foundation for refining strategies to achieve and maintain measles elimination status in healthcare environments. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has flooded open databases with population-level data. However, individual-level structured data, such as the course of disease and contact tracing information, is almost non-existent in open databases. Publish a structured and cleaned COVID-19 dataset with the course of disease and contact tracing information for easy benchmarking of COVID-19 models. We gathered data from Taiwanese open databases and daily news reports. The outcome is a structured quantitative dataset encompassing the course of the disease of Taiwanese individuals, alongside their contact tracing information. Our dataset comprises 579 confirmed cases covering the period from January 21, to November 9, 2020, when the original SARS-CoV-2 virus was most prevalent in Taiwan. The data include features such as travel history, age, gender, symptoms, contact types between cases, date of symptoms onset, confirmed, critically ill, recovered, and dead. We also include the daily summary data at population-level from January 21, 2020, to May 23, 2022. Our data can help enhance epidemiological modelling. [ABSTRACT FROM AUTHOR]

While many countries employed digital contact tracing to contain the spread of SARS-CoV-2, the contribution of cospace-time interaction (i.e., individuals who shared the same space and time) to transmission and to super-spreading in the real world has seldom been systematically studied due to the lack of systematic sampling and testing of contacts. To address this issue, we utilized data from 2230 cases and 220,878 contacts with detailed epidemiological information during the Omicron outbreak in Beijing in 2022. We observed that contact number per day of tracing for individuals in dwelling, workplace, cospace-time interactions, and community settings could be described by gamma distribution with distinct parameters. Our findings revealed that 38% of traced transmissions occurred through cospace-time interactions whilst control measures were in place. However, using a mathematical model to incorporate contacts in different locations, we found that without control measures, cospace-time interactions contributed to only 11% (95%CI: 10%–12%) of transmissions and the super-spreading risk for this setting was 4% (95%CI: 3%–5%), both the lowest among all settings studied. These results suggest that public health measures should be optimized to achieve a balance between the benefits of digital contact tracing for cospace-time interactions and the challenges posed by contact tracing within the same setting. Digital contact tracing was widely adopted during the COVID-19 pandemic to enable identification of incidental 'cospace-time' interactions with people who were not close contacts. Here the authors evaluate the effectiveness of digital contact tracing using data from Beijing, China. [ABSTRACT FROM AUTHOR]

Purpose: During the COVID-19 pandemic, the NZ COVID Tracer App (NZCTA) was released as a digital intervention to support contact tracing processes in Aotearoa New Zealand. This paper examines whether NZCTA met the data governance requirements of the Indigenous Māori people. Māori are an interesting case study as they have unique Treaty and data sovereignty rights, and a higher risk of COVID-related mortality. Methods: The NZCTA was assessed against 24 criteria drawn from the Māori Data Governance Model. The assessment drew on documentary sources and the authors' knowledge of NZCTA and contact tracing process. Each criteria was assessed as 'met', 'partially met' or 'not met'. Results: Our retrospective assessment showed a mixed performance against the Māori Data Governance Model, with NZCTA only fulfilling seven of the 24 model criteria and failing to meet nine. Conclusion: There is significant room for improvement in future digital health interventions for Māori. Much work remains to be done in the Aotearoa public sector to uphold Māori data sovereignty and address systemic barriers to genuine partnership with Māori. [ABSTRACT FROM AUTHOR]

The coronavirus disease 2019 (COVID-19) has resulted in an ongoing pandemic worldwide. Countries have adopted non-pharmaceutical interventions (NPI) to slow down the spread. This study proposes an agent-based model that simulates the spread of COVID-19 among the inhabitants of a city. The agent-based model can be accommodated for any location by integrating parameters specific to the city. The simulation gives the number of total COVID-19 cases. Considering each person as an agent susceptible to COVID-19, the model causes infected individuals to transmit the disease via various actions performed every hour. The model is validated by comparing the simulation to the real data of Ford County, KS, USA. Different interventions, including contact tracing, are applied on a scaled-down version of New York City, USA, and the parameters that lead to a controlled epidemic are determined. Our experiments suggest that contact tracing via smartphones with more than 60% of the population owning a smartphone combined with city-wide lockdown results in the effective reproduction number (Rt) to fall below 1 within 3 weeks of intervention. For 75% or more smartphone users, new infections are eliminated, and the spread is contained within 3 months of intervention. Contact tracing accompanied with early lockdown can suppress the epidemic growth of COVID-19 completely with sufficient smartphone owners. In places where it is difficult to ensure a high percentage of smartphone ownership, tracing only emergency service providers during a lockdown can go a long way to contain the spread. [ABSTRACT FROM AUTHOR]

This study investigated people’s ethical concerns of surveillance technology. By adopting the spectrum of technological utopian and dystopian narratives, how people perceive a society constructed through the compulsory use of surveillance technology was explored. This study empirically examined the anonymous online expression of attitudes toward the society-wide, compulsory adoption of a contact tracing app that affected almost every aspect of all people’s everyday lives at a societal level. By applying the structural topic modeling approach to analyze comments on four Hong Kong anonymous discussion forums, topics concerning the technological utopian, dystopian, and pragmatic views on the surveillance app were discovered. The findings showed that people with a technological utopian view on this app believed that the implementation of compulsory app use can facilitate social good and maintain social order. In contrast, individuals who had a technological dystopian view expressed privacy concerns and distrust of this surveillance technology. Techno-pragmatists took a balanced approach and evaluated its implementation practically. [ABSTRACT FROM AUTHOR]

To assess the importance of index testing in HIV case finding, we analyzed quarterly data from October 2019 to September 2021 from 371 facilities in 12 districts in South Africa. Index testing accounted for 2.6% of all HIV tests (index and non-index) (n = 163,633), but 17.8% of all HIV-positive results, with an HIV-positivity 4-times higher than non-index testing modalities (4.1%). Despite twice as many adult females ≥ 15 years accepting index testing (n = 206,715) compared to adult males ≥ 15 years (n = 102,180), females identified fewer contacts (n = 91,123) than males (n = 113,939). Slightly more than half (51.2%) of all contacts elicited were tested (n = 163,633/319,680), while 19.7% (n = 62,978) of elicited contacts were previously diagnosed as HIV-positive and not eligible for further testing. These findings indicate index testing can be effective in increasing HIV diagnoses in South Africa. Further operational research is needed to address gaps identified in the index testing cascade, including elicitation and testing of contacts. [ABSTRACT FROM AUTHOR]

Digital Contact Tracing (DCT) has been proved to be an effective tool to counteract the new SARS-CoV-2 or Covid-19. Despite this widespread effort to adopt the DCT, less attention has been paid to the organisation of the health logistics system that should support the tracing activities. Actually, the DCT poses a challenge to the logistics of the local health system in terms of number of daily tests to be collected and evaluated, especially when the spreading of the virus is soaring. In this paper we introduce a new optimisation problem called the Daily Swab Test Collection (DSTC) problem, that is the daily problem of collecting swab tests at home in such a way to guarantee a timely testing to people notified by the app to be in contact with a positive case. The problem is formulated as a variant of the team orienteering problem. The contributions of this paper are the following: (i) the new optimisation problem DSTC that complements and improves the DCT approach proposed by Ferretti et al. (Science https://doi.org/10.1126/science.abb6936, 2020), (ii) the DSCT formulation as a variant of the TOP and a literature review highlighting that this variant can have useful application in healthcare management, (iii) new realistic benchmark instances for the DSTC based on the city of Turin, (iv) two new efficient and effective hybrid algorithms capable to deal with realistic instances, (v) the managerial insights of our approach with a special regard on the fairness of the solutions. The main finding is that it possible to optimise the underlying logistics system in such a way to guarantee a timely testing to people recognised by the DCT. [ABSTRACT FROM AUTHOR]

With the outbreak of COVID-19 pandemic, contact tracing and social distance monitoring have been increasingly popular due to their capability of avoiding the spread of infectious diseases in communities. For the purpose, many studies investigate the contact events among moving objects (e.g., pedestrians, bicycles). In this light, we define the problem of Frequent Contact Detection (FCD) of moving objects on road networks. The FCD problem aims to find all frequent contact events among moving objects in a time period. We develop a two-phase solution, including contact search and frequent contact event discovery, to answer the FCD problem. For contact search, we develop the Search By Neighbor (SBN) framework and a pre-checking strategy to reduce the computation cost and further improve the efficiency of contact search. For frequent contact event discovery, we use categorized contact event set to find the objects that may form frequent contact events. Extensive experiments conducted on the two real datasets confirm the effectiveness and efficiency of our proposed solutions. [ABSTRACT FROM AUTHOR]

This research aims to determine the service quality dimensions of contact tracing applications based on user perceptions which are still lacking in the literature. This research employs a text mining approach, specifically topic modeling and sentiment analysis, on the user reviews of the PeduliLindungi contact tracing application. The process of data collection, pre-processing of the reviews, grouping of the reviews into topics, measurement of the score for each service quality dimension, and evaluation of the total score of the PeduliLindungi service quality was carried out. This research revealed four main dimensions of service quality for the contact tracing application: system efficiency, functional benefit, system availability, and emotional benefit. The service quality score was 66.5% for system efficiency, 54.4% for functional benefit, 51.5% for system availability, and 46.2% for emotional benefit. Based on the user perspective, the research highlighted that system efficiency and functional benefit were the most crucial factors in determining the service quality of a contact tracing application. The government needs to pay more attention to the emotional benefit dimension, which received the lowest score of 46.2%. Users reported feeling less excitement and encouragement from the service provided. To improve the service quality of PeduliLindungi, the government can address the users' problems, such as app crashes, unresponsive displays, inconsistent availability of vaccine certificates, and inadequate app functionalities. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has brought attention to the delicate balance between individual privacy concerns and the governance of public health emergencies. Governments are leveraging a wide range of digital methodologies to acquire individual-level data for purposes such as contact tracing, isolation protocols, and surveillance, all aimed at effectively mitigating the deleterious consequences of the epidemic. However, the surrender of individual health information depends on individuals' perception of the legitimacy of governance. In this research, our objective is to examine how individuals' perceptions of the legitimacy of governance impact their decisions regarding privacy disclosure. This study stands out by dissecting cognitive and moral legitimacy of governance, uniquely examining their influence on individuals' altruistic privacy disclosure during a crisis. Unlike previous research, our approach offers a more nuanced understanding of the interplay between governance legitimacy and privacy concessions. From July 15th to August 14th, 2022, amid the 2022 lockdown in Shanghai, China, this study utilizes surveys with established measurement scales, alongside structural equation modeling (SEM), to explore the relationship between individuals' perceptions of government legitimacy in managing the pandemic and their willingness to compromise health information. The study distinguishes between moral legitimacy (pathos) and cognitive legitimacy (logos). The results find that both cognitive and moral legitimacy positively influence altruism, thus enhancing the efficacy of voluntary disclosure of personal health information to government agencies for pandemic governance. However, it is noteworthy that education level moderates the impact of these two dimensions of legitimacy on altruism. This research provides empirical evidence to enhance our understanding of how different dimensions of citizens' perceptions of governance legitimacy in crisis situations shape their attitudes and behaviors towards privacy trade-offs. [ABSTRACT FROM AUTHOR]

In 2022, a global outbreak of mpox occurred, predominantly impacting men who have sex with men (MSM). The rapid decline of this epidemic is yet to be fully understood. We investigated the Italian outbreak by means of an individual-based mathematical model calibrated to surveillance data. The model accounts for transmission within the MSM sexual contact network, in recreational and sex clubs attended by MSM, and in households. We indicate a strong spontaneous reduction in sexual transmission (61-87%) in affected MSM communities as the possible driving factor for the rapid decline in cases. The MSM sexual contact network was the main responsible for transmission (about 80%), with clubs and households contributing residually. Contact tracing prevented about half of the potential cases, and a higher success rate in tracing contacts could significantly amplify its effectiveness. Notably, immunizing the 23% of MSM with the highest sexual activity (10 or more partners per year) could completely prevent new mpox resurgences. This research underscores the importance of augmenting contact tracing, targeted immunization campaigns of high-risk groups, and fostering reactive behavioral changes as key strategies to manage and prevent the spread of emerging sexually transmitted pathogens like mpox within the MSM community. Mpox cases in Italy rapidly declined following a peak in summer 2022. Here, the authors investigate potential reasons for the decline in cases using an individual-based model of a sexual contact network of men who have sex with men. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has left an indelible mark on societies worldwide, challenging governments to respond swiftly and effectively to mitigate its impact. India, with its vast population and complex healthcare landscape, faced unique challenges in formulating and implementing a pandemic response strategy. The article examines India's policy response to the COVID-19 pandemic and explores the valuable lessons it offers for shaping a more resilient and prepared society in a post-COVID world. It provides a comprehensive analysis of India's multifaceted approach to managing the pandemic, highlighting key elements such as lockdowns, testing and contact tracing, healthcare infrastructure, vaccination drives, and economic relief measures. By delving into both the successes and shortcomings of these policies, it seeks to extract valuable insights for policymakers and public health officials globally. As the world transitions into a post-COVID era, the lessons learned from India's experience offer a roadmap for building stronger healthcare systems, improving disaster preparedness, and enhancing social safety nets. The article underscores the importance of proactive governance, community engagement, data-driven decision-making, and international collaboration in the face of global health crises. The paper demonstrates that India's journey through the pandemic provides a wealth of knowledge that can inform policy development, foster greater resilience, and help societies better navigate the uncertainties of a post-COVID world. By reflecting on the successes and challenges of India's response, this article offers actionable insights for shaping a more equitable, sustainable, and prepared society in the wake of the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

Mycobacterium tuberculosis (Mtb) is a pathogenic bacterium which causes tuberculosis (TB). TB control programmes are facing threats from drug resistance. Multidrug-resistant (MDR) and extensively drug-resistant (XDR) Mtb strains need longer and more expensive treatment with many medications resulting in more adverse effects and decreased chances of treatment outcomes. The World Health Organization (WHO) has emphasised the development of not just new individual anti-TB drugs, but also novel medication regimens as an alternative treatment option for the drug-resistant Mtb strains. Many plants, as well as marine creatures (sponge; Haliclona sp.) and fungi, have been continuously used to treat TB in various traditional treatment systems around the world, providing an almost limitless supply of active components. Natural products, in addition to their anti-mycobacterial action, can be used as adjuvant therapy to increase the efficacy of conventional anti-mycobacterial medications, reduce their side effects, and reverse MDR Mtb strain due to Mycobacterium's genetic flexibility and environmental adaptation. Several natural compounds such as quercetin, ursolic acid, berberine, thymoquinone, curcumin, phloretin, and propolis have shown potential anti-mycobacterial efficacy and are still being explored in preclinical and clinical investigations for confirmation of their efficacy and safety as anti-TB medication. However, more high-level randomized clinical trials are desperately required. The current review provides an overview of drug-resistant TB along with the latest anti-TB medications, drug-induced hepatotoxicity and oxidative stress. Further, the role and mechanisms of action of first and second-line anti-TB drugs and new drugs have been highlighted. Finally, the role of natural compounds as anti-TB medication and hepatoprotectants have been described and their mechanisms discussed. [ABSTRACT FROM AUTHOR]

The coronavirus disease 2019 (COVID-19) presents a severe and urgent threat to global health. In response to the COVID-19 pandemic, many countries have implemented nonpharmaceutical interventions (NPIs), including national workplace and school closures, personal protection, social distancing, contact tracing, testing, home quarantine, and isolation. To evaluate the effectiveness of these NPIs in mitigating the spread of early COVID-19 and predict the epidemic trend in the United Kingdom, we developed a compartmental model to mimic the transmission with time-varying transmission rate, contact rate, disease-induced mortality rate, proportion of quarantined close contacts, and hospitalization rate. The model was fitted to the number of confirmed new cases and daily number of deaths in five stages with a Markov Chain Monte Carlo method. We quantified the effectiveness of NPIs and found that if the transmission rate, contact rate, and hospitalization rate were approximately equal to those in the second stage of the most strict NPIs, and the proportion of quarantined close contacts increased by 3%, then the epidemic would die out as early as January 12, 2021, with around 1,533,000 final cumulative number of confirmed cases, and around 55,610 final cumulative number of deaths. [ABSTRACT FROM AUTHOR]

Based on differential geometry and gear meshing theory, a meshing model of working pitch cone and pitch cylinder of crossed beveloid gear and involute cylindrical gear transmission was established for geometric parameter design. The mounting parameters of gear pair were derived, with the addition of tooth surface equation of beveloid gear and involute cylindrical gear obtained by hobbing process, tooth contact analysis (TCA) was performed. The changes of contact trace, contact ellipse and principal curvature of gear pair under different geometric parameters were investigated, then the accuracy of results was verified by loaded tooth contact analysis (LTCA). The results show that the contact region of gear pair is significantly influenced by shaft angle and cone angle of beveloid gear. The principal curvature at contact points of the gear pair is relatively sensitive to the cone angle of beveloid gear. Moreover, the length of contact trace is strongly influenced by geometric parameters. [ABSTRACT FROM AUTHOR]

Aim: To describe the process of engaging underserved communities of color that designs, conducts, and evaluates community-engaged COVID-19 Community Health Navigator training on COVID-19: Vaccination, Prevention, and Contact Tracing. Subject and methods: The project used a mixed-methods design; 18 community health navigators (CHNs) representing Asian American, Arab American, Black/African American, and Hispanic/Latinx communities completed the pretest, training, and post-test. Demographic characteristics, along with knowledge and confidence level questions regarding COVID-19 issues, were gathered in pre- and post-tests. Qualitative data were collected via open-ended questions in post-tests. Results: Findings suggest that the community health navigator training successfully increased participants' knowledge of COVID-19-related topic areas and confidence in educating community members regarding COVID-19 vaccination and prevention. Qualitative evaluation contained information learned and found most helpful, and application and utilization plans for CHNs' follow-up work. Conclusion: The process of community health navigator training and evaluation results adds important insights to the current COVID-19 pandemic workforce literature and can inform future trainings. [ABSTRACT FROM AUTHOR]

Governments around the world attempted to suppress the spread of COVID-19 using restrictions on social and economic activity. This study presents the first global analysis of job and income losses associated with those restrictions, using Gallup World Poll data from 321,000 randomly selected adults in 117 countries from July 2020 to March 2021. Nearly half of the world's adult population lost income because of COVID-19, according to our estimates, and this outcome and related measures of economic harm—such as income loss—are strongly associated with lower subjective well-being, financial hardship, and self-reported loss of subjective well-being. Our primary analysis uses a multilevel model with country and month-year levels, so we can simultaneously test for significant associations between both individual demographic predictors of harm and time-varying country-level predictors. We find that an increase of one-standard deviation in policy stringency, averaged up to the time of the survey date, predicts a 0.37 std increase in an index of economic harm (95% CI 0.24–0.51) and a 14.2 percentage point (95% CI 8.3–20.1 ppt) increase in the share of workers experiencing job loss. Similar effect sizes are found comparing stringency levels between top and bottom-quintile countries. Workers with lower-socioeconomic status—measured by within-country income rank or education—were much more likely to report harm linked to the pandemic than those with tertiary education or relatively high incomes. The gradient between harm and stringency is much steeper for workers at the bottom quintiles of the household income distribution than it is for those at the top, which we show with interaction models. Socioeconomic status is unrelated to harm where stringency is low, but highly and negatively associated with harm where it is high. Our detailed policy analysis reveals that school closings, stay-at-home orders, and other economic restrictions were strongly associated with economic harm, but other non-pharmaceutical interventions—such as contact tracing, mass testing, and protections for the elderly were not. [ABSTRACT FROM AUTHOR]

COVID-19 pandemic is an active epidemic disease and is evolving in various variants of SAR-COV-2. The most critical point for COVID-19 is to break the transmission chain by early detection and isolation. It necessitates monitoring human interactions using remote technologies as a proactive strategy to minimize the spread. This paper presents a framework for user behaviour recognition and localization in a heterogeneous environment leveraging artificial intelligence for COVID-19 contact tracing. This approach designed and developed a mobile application to acquire the mobile sensors (received signal strength indicator (RSSI), accelerometer, gyroscope, and speed), sensors response, and machine learning-based user behaviour classification to assess the spread of COVID-19 with better proximity detection. This paper presents a machine learning-based free-space path loss (FSPL) model to estimate the distance between users holding mobile devices. The framework considers comprehensive user interaction scenarios depending upon multiple parameters, including the randomness of a user's interaction (device-device communication), device position, environmental obstacles, and device type. This platform classifies user behaviour on a train, bus, walking, and sitting at one, two, and three-meter from the suspected patient. The proposed system outperforms existing models, estimates the distance up to three meters, and accurately classifies each user behaviour. This system can send alert notifications to all users within three meters of a person whose COVID-19 test is positive. This approach uses machine learning models and finds that the random forest classifier shows higher accuracy of 97.95% for user behaviour classification and uses the LSTM model for distance estimation from one to three meters between users with the highest accuracy of 98.07%. [ABSTRACT FROM AUTHOR]

Public holidays have been associated with SARS-CoV-2 incidence surges, although a firm link remains to be established. This association is sometimes attributed to events where transmissions occur at a disproportionately high rate, known as superspreading events. Here, we describe a sudden surge in new cases with the Omicron BA.1 strain amongst higher education students in Belgium. Contact tracers classed most of these cases as likely or possibly infected on New Year's Eve, indicating a direct trigger by New Year celebrations. Using a combination of contact tracing and phylogenetic data, we show the limited role of superspreading events in this surge. Finally, the numerous simultaneous transmissions allowed a unique opportunity to determine the distribution of incubation periods of the Omicron strain. Overall, our results indicate that, even under social restrictions, a surge in transmissibility of SARS-CoV-2 can occur when holiday celebrations result in small social gatherings attended simultaneously and communitywide. [ABSTRACT FROM AUTHOR]

Refugees in the United States are believed to be at high risk of COVID-19. A cross-sectional study design was utilized to collect anonymous, online surveys from refugee communities in the United States during December 2020 to January 2021. We invited bilingual community leaders to share the survey link with other refugees aged ≥18 years. We identified factors associated with COVID-19 infection and measured the distribution of contact tracing among those who tested positive. Of 435 refugees who completed the survey, 26.4% reported testing positive for COVID-19. COVID-19 infection was associated with having an infected family member and knowing people in one's immediate social environment who were infected. Among respondents who tested positive, 84.4% reported that they had been contacted for contact tracing. To prepare for future pandemics, public health authorities should continue partner with refugee community leaders and organizations to ensure efficient programs are inclusive of refugee communities. [ABSTRACT FROM AUTHOR]

Communities worldwide have used vaccines and facemasks to mitigate the COVID-19 pandemic. When an individual opts to vaccinate or wear a mask, they may lower their own risk of becoming infected as well as the risk that they pose to others while infected. The first benefit–reducing susceptibility–has been established across multiple studies, while the second–reducing infectivity–is less well understood. Using a new statistical method, we estimate the efficacy of vaccines and facemasks at reducing both types of risks from contact tracing data collected in an urban setting. We find that vaccination reduced the risk of onward transmission by 40.7% [95% CI 25.8–53.2%] during the Delta wave and 31.0% [95% CI 19.4–40.9%] during the Omicron wave and that mask wearing reduced the risk of infection by 64.2% [95% CI 5.8–77.3%] during the Omicron wave. By harnessing commonly-collected contact tracing data, the approach can broadly provide timely and actionable estimates of intervention efficacy against a rapidly evolving pathogen. [ABSTRACT FROM AUTHOR]

To complement labour-intensive conventional contact tracing, digital proximity tracing was implemented widely during the COVID-19 pandemic. However, the privacy-centred design of the dominant Google-Apple exposure notification framework has hindered assessment of its effectiveness. Between October 2021 and January 2022, we systematically collected app use and notification receipt data within a test and trace programme targeting around 50,000 university students in Leuven, Belgium. Due to low success rates in each studied step of the digital notification cascade, only 4.3% of exposed contacts (CI: 2.8-6.1%) received such notifications, resulting in 10 times more cases detected through conventional contact tracing. Moreover, the infection risk of digitally traced contacts (5.0%; CI: 3.0–7.7%) was lower than that of conventionally traced non-app users (9.8%; CI: 8.8-10.7%; p = 0.002). Contrary to common perception as near instantaneous, there was a 1.2-day delay (CI: 0.6–2.2) between case PCR result and digital contact notification. These results highlight major limitations of a digital proximity tracing system based on the dominant framework. Digital proximity tracing apps were widely used during the COVID-19 pandemic but have not been thoroughly evaluated. Here, the authors use data from students in Leuven, Belgium and estimate that apps notified only ~4% exposed contacts, had a 1–2 day delay for notification, and identified fewer infected contacts than manual contact tracing. [ABSTRACT FROM AUTHOR]

In recent years, how to prevent the widespread transmission of infectious diseases in communities has been a research hot spot. Tracing close contact with infected individuals is one of the most severe problems. In this work, we present a model called Follower Prediction Graph Network (FPGN) to identify high-risk visitors, which is known as follower prediction. The model is designed to identify visitors who may be infected with a disease by tracking their activities at the exact location of infected visitors. FPGN is inspired by the state-of-the-art temporal graph edge prediction algorithm TGN and draws on the shortcomings of existing algorithms. It utilizes graph structure information based on (α , β )-core, time interval statistics by using the statistics of timestamp information, and a GAT-based prediction module to achieve high accuracy in follower prediction. Extensive experiments are conducted on two real datasets, demonstrating the progress of FPGN. The experimental results show that FPGN can achieve the highest results compared with other SOTA baselines. Its AP scores are higher than 0.46, and its AUC scores are higher than 0.62. [ABSTRACT FROM AUTHOR]

Effective COVID-19 case investigation and contact tracing (CICT) among refugee, immigrant, and migrant (RIM) communities requires innovative approaches to address linguistic, cultural and community specific preferences. The National Resource Center for Refugees, Immigrants, and Migrants (NRC-RIM) is a CDC-funded initiative to support state and local health departments with COVID-19 response among RIM communities, including CICT. This note from the field will describe NRC-RIM and initial outcomes and lessons learned, including the use of human-centered design to develop health messaging around COVID-19 CICT; training developed for case investigators, contact tracers, and other public health professionals working with RIM community members; and promising practices and other resources related to COVID-19 CICT among RIM communities that have been implemented by health departments, health systems, or community-based organizations. [ABSTRACT FROM AUTHOR]

Introduction: With COVID-19 vaccination rolled out globally, increasing numbers of studies have shown that booster vaccines can enhance an individual's protection against the infection, hospitalization, and death caused by SARS-CoV-2. This study evaluated the effectiveness of COVID-19 vaccine BBIBP-CorV booster against being infected (susceptibility), infecting others (infectiousness), and spreading the disease from one to another (transmission). Methods: This retrospective cohort study investigated the close contacts of all officially ascertained COVID-19 confirmed cases in Urumqi, China between August 1 and September 7, 2022. Eligible records were divided into four subcohorts based on the vaccination status of both the close contact and their source case: group 2-2, 2-dose contacts seeded by 2-dose source case (as the reference level); group 2-3, 3-dose contacts seeded by 2-dose source case; group 3-2, 2-dose contacts seeded by 3-dose source case; and group 3-3, 3-dose contacts seeded by 3-dose source case. In the four subcohorts, multivariate logistic regression models were used to examine the vaccine effectiveness (VE) for the BBIBP-CorV booster dose. We adjusted for potential confounding variables, including the sex and age of source cases and close contacts, the calendar week of contact history and contact settings. We evaluated the statistical uncertainty using a 95% confidence interval (CI). In addition, we conducted subgroup analyses to evaluate VE by sex. Results: The sample sizes of groups 2-2, 2-3, 3-2, and 3-3 were 1184, 3773, 4723, and 27,136 individuals, respectively. Overall VE against susceptibility (group 2-3 vs 2-2) was 42.1% (95% CI 10.6, 62.5), VE against infectiousness (group 3-2 vs 2-2) was 62.0% (95% CI 37.2, 77.0), and VE against transmission (group 3-3 vs 2-2) was 83.7% (95% CI 75.1, 89.4). In the sex-stratified subgroups, male close contacts showed similar VE compared to the overall. However, among female close contacts, while the booster dose improved VE against infectiousness and VE against susceptibility, the VEs were not significantly different from zero. Conclusion: BBIBP-CorV vaccine booster was associated with mild to moderate levels of protection against Omicron susceptibility, infectiousness, and transmission. Real-world assessment of protective performance of COVID-19 vaccines against the risk of Omicron strains is continuously needed, and may provide information that helps vaccination strategy. [ABSTRACT FROM AUTHOR]

Digital contact tracing is used in different countries to help contain the COVID-19 pandemic. It raises challenging empirical and ethical questions due to its complexity and widespread effects calling for a broader approach in ethical evaluation. However, existing approaches often fail to include all relevant value perspectives or lack reference to empirical data on the artifact in question. In this paper, we describe the development of an interdisciplinary framework to analyze digital contact tracing from an empirical and ethical perspective. Starting with an analysis of methodological tensions in the attempt to analyze digital contact tracing, we, firstly, set out three methodological premises regarding (a) a specific view of technology, (b) a fruitful normative perspective, and (c) ways to gather empirical knowledge about the object under investigation. Secondly, we inspect consequences of these premises to develop our research methodology. In doing so, we argue for a postphenomenological perspective on technology, solidarity as an important concept to guide the ethical perspective, and an empirical approach based on qualitative social science research and the concept of affordances. We illustrate these considerations by referring to our analysis of the German Corona-Warn-App as an instance of contact tracing based on the Exposure Notification Framework by Google and Apple. We then discuss the implications of using such a framework, including considerations concerning future developments of technologies similar to or inspired by the current concepts of contact tracing. [ABSTRACT FROM AUTHOR]

Continuous top-k trajectory similarity Search (CkSearch) is now commonly required in real-time large-scale trajectory analysis, enabling the distributed stream processing engines to discover various dynamic patterns. As a fundamental operator, CkSearch empowers various applications, e.g., contact tracing during an outbreak and smart transportation. Although extensive efforts have been made to improve the efficiency of non-continuous top-k search, they do not consider dynamic capability of indexing (R1) and incremental capability of computing (R2). Therefore, in this paper, we propose a generic CkSearch-oriented framework for distributed real-time trajectory stream processing on Apache Flink, termed as Garden. To answer R1, we design a sophisticated distributed dynamic spatial index called Y-index, which consists of a real-time load scheduler and a two-layer indexing structure. To answer R2, we introduce a state reusing mechanism and index-based pruning methods that significantly reduce the computational cost. Empirical studies on real-world data validate the usefulness of our proposal and prove the huge advantage of our approach over state-of-the-art solutions in the literature. [ABSTRACT FROM AUTHOR]

Targeted public health interventions for an emerging epidemic are essential for preventing pandemics. During 2020-2022, China invested significant efforts in strict zero-COVID measures to contain outbreaks of varying scales caused by different SARS-CoV-2 variants. Based on a multi-year empirical dataset containing 131 outbreaks observed in China from April 2020 to May 2022 and simulated scenarios, we ranked the relative intervention effectiveness by their reduction in instantaneous reproduction number. We found that, overall, social distancing measures (38% reduction, 95% prediction interval 31-45%), face masks (30%, 17-42%) and close contact tracing (28%, 24-31%) were most effective. Contact tracing was crucial in containing outbreaks during the initial phases, while social distancing measures became increasingly prominent as the spread persisted. In addition, infections with higher transmissibility and a shorter latent period posed more challenges for these measures. Our findings provide quantitative evidence on the effects of public-health measures for zeroing out emerging contagions in different contexts. China maintained a 'zero-COVID' policy from early in the pandemic until late 2022 that employed various public health interventions with the aim of COVID-19 containment. Here, the authors use data from 131 outbreaks in China to estimate the effects of a range of interventions against different SARS-CoV-2 variants in diverse settings. [ABSTRACT FROM AUTHOR]

Serial intervals – the time between symptom onset in infector and infectee – are a fundamental quantity in infectious disease control. However, their estimation requires knowledge of individuals' exposures, typically obtained through resource-intensive contact tracing efforts. We introduce an alternate framework using virus sequences to inform who infected whom and thereby estimate serial intervals. We apply our technique to SARS-CoV-2 sequences from case clusters in the first two COVID-19 waves in Victoria, Australia. We find that our approach offers high resolution, cluster-specific serial interval estimates that are comparable with those obtained from contact data, despite requiring no knowledge of who infected whom and relying on incompletely-sampled data. Compared to a published serial interval, cluster-specific serial intervals can vary estimates of the effective reproduction number by a factor of 2–3. We find that serial interval estimates in settings such as schools and meat processing/packing plants are shorter than those in healthcare facilities. The serial interval (time between symptom onset in an infector and infectee) is usually estimated from contact tracing data, but this is not always available. Here, the authors develop a method for estimation of serial intervals using whole genome sequencing data and apply it data from clusters of SARS-CoV-2 in Victoria, Australia. [ABSTRACT FROM AUTHOR]

At the height of the COVID-19 pandemic, K-12 schools struggled to safely operate under the fast-changing pandemic situation. However, little is known about the impact of different school operating scenarios considering the ongoing efforts of vaccination. In this study, we deployed an agent-based simulation model to mimic disease transmission in a mid-sized community consisting of 10,000 households. A total of eight school operating scenarios were simulated, in decreasing order of restrictiveness regarding COVID-19 mitigation measures. When masks were worn at school, work, and community environments, increasing in-person education from 50% to 100% would result in only 1% increase in cumulative infections. When there were no masks nor contact tracing while schools were 100% in person, the cumulative infection increased by 86% compared to the scenario when both masking and contact tracing were in place. In the sensitivity analysis for vaccination efficacy, we found that higher vaccination efficacy was essential in reducing overall infections. Our findings showed that full in-person education was safe, especially when contact tracing, masking, and widespread vaccination were in place. If no masking nor contact tracing was practiced, the transmission would rose dramatically but eventually slow down due to herd immunity. [ABSTRACT FROM AUTHOR]

We describe the data transmitted to backend servers by the contact tracing apps now deployed in France (TousAntiCovid), Australia (CovidSafe), Singapore (TraceTogether), US/Florida (CombatCovid), Israel (HaMagen), India (Aarogya Setu) with the aim of evaluating the privacy of these contact tracing apps as actually deployed. To the best of our knowledge, the measurements we present are the first public data of this sort for the French, Australian, Israeli, Indian and Florida apps. We find that TousAntiCovid and CovidSafe are generally well-behaved with regard to privacy. TraceTogether and CombatCovid make extensive use of Google Firebase services which means that that there are two main parties involved in handling data transmitted from these apsp, namely Google and the health authority operating the app itself. HaMagen is well-behaved for uninfected users of the app but GPS location data associated with infected is publicly published on the HaMagen server. Aarogya Setu, with >160M users, is found to have a number of serious privacy issues, including silent upload of logged location data. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In this paper, we address the spectrum efficiency enhancement for Internet of Things (IoT) by introducing the graph-theory-based spectrum optimization reuse. During the spectrum reuse, how to ascertain the interference range and user's transmission status is critical. In this article, we model the interference range between IoT terminals by considering their interaction status which includes interaction frequency, duration and stability. The notion of interaction status is very similar to that of social distance among people in the context of pandemic control, which is a highly effective model for supporting contact tracing by analyzing the interaction frequency, duration and stability among people. Compared with other traditional dynamic spectrum optimization methods, introducing the concept of social distance can better evaluate the IoT user's interference and transmission status from a novel perspective, then enhancing the optimal spectrum allocation. By modeling the social distances among IoT terminals, we estimate the desirable interference range for IoT devices which serves as the basis for graph-theory-oriented spectrum optimization. Together with the actual physical distances between IoT devices, they form the basis for optimizing spectrum reuse patterns. Graph theory is further utilized to complete the final spectrum optimization. Furthermore, comparison simulation tests are conducted to evaluate the performances of our proposed solution in network benefits and system capacity.Kindly check and confirm the inserted city "Singapore" is correctly identified.CorrectPlease provide author biographys and photos.The biographys and photos are correct.Kindly check and confirm the corresponding affiliation processed is correctly identified.Corresponding author is both at Aff1 and Aff2. [ABSTRACT FROM AUTHOR]

After affecting the world in unexpected ways, the virus has started mutating which is evident with the insurgence of its new variants. The governments, hospitals, schools, industries, and humans, in general, are looking for a potential solution in the vaccine which will eventually be available, but its timeline for eradicating the virus is yet unknown. Several researchers have encouraged and recommended the use of good practices such as physical healthcare monitoring, immunity boosting, personal hygiene, mental healthcare, and contact tracing for slowing down the spread of the virus. In this article, we propose the use of smart sensors integrated with the Internet of Medical Things to cover the spectrum of good practices in an automated manner. We present hypothetical frameworks for each of the good practice modules and propose the VIrus Resistance Framework using the Internet of Medical Things (VIRFIM) to tie all the individual modules in a unified architecture. Furthermore, we validate the realization of VIRFIM framework with two case studies related to physical activity monitoring and stress detection services. We envision that VIRFIM would be influential in assisting people with the new normal for current and future pandemics as well as instrumental in halting the economic losses, respectively. We also provide potential challenges and their probable solutions in compliance with the proposed VIRFIM. [ABSTRACT FROM AUTHOR]

Background: In May 2021, the B.1.617 variant of SARS-CoV-2 emerged in Ireland, and both Delta and Kappa sub-lineages were initially deemed variants of concern (VOCs) on a precautionary basis. We describe a large outbreak of SARS-CoV-2 B.1.617.1 (Kappa mutation) linked to a private gathering among third level students in Cork, Ireland. Methods: Surveillance data were available from the Health Service Executive COVID Care Tracker. The epidemiological sequence of infection for each new case in this outbreak was tracked and whole genome sequencing was requested on all linked cases. Enhanced public health control measures were implemented by the Department of Public Health HSE-South to contain onward spread of VOCs, including retrospective contact tracing, lengthy isolation and quarantine periods for cases and close contacts. Extensive surveillance efforts were used to describe and control onward transmission. Results: There were 146 confirmed SARS-CoV-2 cases linked to the outbreak. All sequenced cases (53/146; 36%) confirmed Kappa mutation. The median age was 21 years (range 17–65). The majority (88%) had symptoms of SARS-CoV-2 infection. There were 407 close contacts; the median was 3 per case (range 0–14). There were no known hospitalisations, ICU admissions or deaths. Vaccination data was unavailable, but the outbreak pre-dated routine availability of COVID-19 vaccines among younger adults in Ireland. Conclusion: Enhanced public health control measures for new and emerging variants of SARS-CoV-2 may be burdensome for cases and close contacts. The overall public health benefit of enhanced controls may only become apparent when evidence on disease transmissibility and severity becomes more complete. [ABSTRACT FROM AUTHOR]

The COVID-19 infection cases have surged globally, causing devastations to both the society and economy. A key factor contributing to the sustained spreading is the presence of a large number of asymptomatic or hidden spreaders, who mix among the susceptible population without being detected or quarantined. Due to the continuous emergence of new virus variants, even if vaccines have been widely used, the detection of asymptomatic infected persons is still important in the epidemic control. Based on the unique characteristics of COVID-19 spreading dynamics, here we propose a theoretical framework capturing the transition probabilities among different infectious states in a network, and extend it to an efficient algorithm to identify asymptotic individuals. We find that using pure physical spreading equations, the hidden spreaders of COVID-19 can be identified with remarkable accuracy, even with incomplete information of the contract-tracing networks. Furthermore, our framework can be useful for other epidemic diseases that also feature asymptomatic spreading. [ABSTRACT FROM AUTHOR]

The wide use of IT resources to assess and manage the recent COVID-19 pandemic allows to increase the effectiveness of the countermeasures and the pervasiveness of monitoring and prevention. Unfortunately, the literature reports that IoT devices, a widely adopted technology for these applications, are characterized by security vulnerabilities that are difficult to manage at the state level. Comparable problems exist for related technologies that leverage smartphones, such as contact tracing applications, and non-medical health monitoring devices. In analogous situations, these vulnerabilities may be exploited in the cyber domain to overload the crisis management systems with false alarms and to interfere with the interests of target countries, with consequences on their economy and their political equilibria. In this paper we analyze the potential threat to an example subsystem to show how these influences may impact it and evaluate a possible consequence. [ABSTRACT FROM AUTHOR]

With the emergence of COVID-19, mobile health applications have increasingly become crucial in contact tracing, information dissemination, and pandemic control in general. Apps warn users if they have been close to an infected person for sufficient time, and therefore potentially at risk. The distance measurement accuracy heavily affects the probability estimation of being infected. Most of these applications make use of the electromagnetic field produced by Bluetooth Low Energy technology to estimate the distance. Nevertheless, radio interference derived from numerous factors, such as crowding, obstacles, and user activity can lead to wrong distance estimation, and, in turn, to wrong decisions. Besides, most of the social distance-keeping criteria recognized worldwide plan to keep a different distance based on the activity of the person and on the surrounding environment. In this study, in order to enhance the performance of the COVID-19 tracking apps, a human activity classifier based on Convolutional Deep Neural Network is provided. In particular, the raw data coming from the accelerometer sensor of a smartphone are arranged to form an image including several channels (HAR-Image), which is used as fingerprints of the in-progress activity that can be used as an additional input by tracking applications. Experimental results, obtained by analyzing real data, have shown that the HAR-Images are effective features for human activity recognition. Indeed, the results on the k-fold cross-validation and obtained by using a real dataset achieved an accuracy very close to 100%. [ABSTRACT FROM AUTHOR]

Introduction: Workers in the healthcare industry form the backbone of health systems everywhere. In the face of global health crises like the current monkeypox (mpox) outbreak, healthcare workers like doctors, dentists, pharmacists, nurses, midwives, paramedics, administrators, support staff, laboratory technicians, and community health workers all play crucial roles in providing care and containing the spread of the disease. Aim: Therefore, in the wake of concerns about mpox recurrence, we seek to shed light on the occupational transmission of mpox infection and the possible risk to healthcare personnel. Results: Contamination of the environment of the household of cases of mpox and environment of the patient care units with the viral DNA has been reported besides asymptomatic cases and detection of viral DNA in air samples; therefore, more research on non-lesion-based testing for human mpox infection for screening asymptomatic people, particularly among populations at high risk of infection, in the event of asymptomatic transmission and potential transmission via aerosols is necessary. Monitoring efforts can be aided by incorporating mpox testing into locations where people are more likely to contract illnesses and seek medical attention. We must take a precautionary infection control approach to control the spread of the virus while completing urgent research to understand better the human-to-human mpox transmission process. Conclusions: In this minireview, we discuss the potential routes of mpox transmission to healthcare and preventative strategies and measures that should be taken and considered. [ABSTRACT FROM AUTHOR]

During the COVID-19 pandemic in Senegal, contact tracing was done to identify transmission clusters, their analysis allowed to understand their dynamics and evolution. In this study, we used information from the surveillance data and phone interviews to construct, represent and analyze COVID-19 transmission clusters from March 2, 2020, to May 31, 2021. In total, 114,040 samples were tested and 2153 transmission clusters identified. A maximum of 7 generations of secondary infections were noted. Clusters had an average of 29.58 members and 7.63 infected among them; their average duration was 27.95 days. Most of the clusters (77.3%) are concentrated in Dakar, capital city of Senegal. The 29 cases identified as super-spreaders, i.e., the indexes that had the most positive contacts, showed few symptoms or were asymptomatic. Deepest transmission clusters are those with the highest percentage of asymptomatic members. The correlation between proportion of asymptomatic and degree of transmission clusters showed that asymptomatic strongly contributed to the continuity of transmission within clusters. During this pandemic, all the efforts towards epidemiological investigations, active case-contact detection, allowed to identify in a short delay growing clusters and help response teams to mitigate the spread of the disease. [ABSTRACT FROM AUTHOR]

Objective: Routinely generated surveillance data are important for monitoring the effectiveness of MDR-TB control strategies. Incidence of rifampicin-resistant tuberculosis (RR-TB) is a key indicator for monitoring MDR-TB. Methods: In a longitudinal nationwide retrospective study, 8 years (2014–2021) of sputum samples from presumptively drug-resistant tuberculosis patients from all regions of Gabon were referred to the national tuberculosis reference laboratory. Samples were analysed using GeneXpert MTB/RIF and Genotype MTBDRsl version 2/Line Probe Assay. Results: Of 3057 sputum samples from presumptive tuberculosis patients, both from local hospital and from referral patients, 334 were RR-TB. The median patient age was 33 years (interquartile range 26–43); one third was newly diagnosed drug-resistant tuberculosis patients; one-third was HIV-positive. The proportion of men with RR-TB was significantly higher than that of women (55% vs 45%; p < 0.0001). Patients aged 25–35 years were most affected (32%; 108/334). The cumulative incidence of RR-TB was 17 (95% CI 15–19)/100,000 population over 8 years. The highest incidences were observed in 2020 and 2021. A total of 281 samples were analysed for second-line drug resistance. The proportions of study participants with MDR-TB, pre-XDR-TB and XDR-TB were 90.7% (255/281), 9% (25/281) and 0.3% (1/281), respectively. The most-common mutations in fluoroquinolones resistance isolates was gyrA double mutation gyrA MUT3B and MUT3C (23%; 4/17). Most (64%; 6/8) second-line injectable drugs resistance isolates were characterised by missing both rrs WT2 and MUT2 banding. Conclusion: The increasing incidence of MDR-TB infection in Gabon is alarming. It is highest in the 25–35 years age category. The incidence of MDR-TB infection in treatment-naïve patients calls for case finding and contact tracing strategy improvement. [ABSTRACT FROM AUTHOR]

The world we live in has been taken quite surprisingly by the outbreak of a novel virus namely SARS-CoV-2. COVID-19 i.e. the disease associated with the virus, has not only shaken the world economy due to enforced lockdown but has also saturated the public health care systems of even most advanced countries due to its exponential spread. The fight against COVID-19 pandemic will continue until majority of world's population get vaccinated or herd immunity is achieved. Many researchers have exploited the Artificial intelligence (AI) knacks based IoT architecture for early detection and monitoring of potential COVID-19 cases to control the transmission of the virus. However, the main cause of the spread is that people infected with COVID-19 do not show any symptoms and are asymptomatic but can still transmit virus to the masses. Researcher have introduced contact tracing applications to automatically detect contacts that can be infected by the index case. However, these fully automated contact tracing apps have not been accepted due to issues like privacy and cross-app compatibility. In the current study, an IoT based COVID-19 detection and monitoring system with semi-automated and improved contact tracing capability namely COVICT has been presented with application of real-time data of symptoms collected from individuals and contact tracing. The deployment of COVICT, the prediction of infected persons can be made more effective and contaminated areas can be identified to mitigate the further propagation of the virus by imposing Smart Lockdown. The proposed IoT based architecture can be quite helpful for regulatory authorities for policy making to fight COVID-19. [ABSTRACT FROM AUTHOR]

Methamphetamine (MA) use is associated with HIV transmission among men who have sex with men (MSM) and lapses in medication adherence. Contingency Management (CM) is effective in reducing MA use, but studies of CM to support adherence to HIV prevention or treatment are limited. We conducted a pilot trial of a CM intervention to reduce MA use and improve PrEP/ART adherence among MSM prescribed a tenofovir (TFV)-based regimen for HIV prevention or treatment. Participants were randomly assigned to receive escalating incentives for either MA abstinence or TFV adherence (based on point-of-care urine testing), and to a monitoring schedule of either 2 or 3 visits/week for 4 weeks. 19 MSM were randomized to either CM for MA use or CM for PrEP/ART adherence (median age: 38; IQR: 28–46) and 15 were living with HIV. Participants attended 95.7% (67/70) of scheduled visits in the 2x/week arm and 74.8% (74/99) in the 3x/week arm. TFV adherence was higher among participants in the TFV adherence arm with 93.5% (n = 72/77) of urine samples positive for TFV, compared to 76.6% (n = 49/64) in the MA abstinence arm (p = 0.007). Participants in the MA abstinence arm had more urine samples negative for MA metabolites (20.3%, n = 13/64) than those receiving CM for TFV adherence (6.5%, n = 5/77; p = 0.021). A CM model for MA abstinence and PrEP/ART adherence using twice-weekly visits and urine testing for MA and TFV for MSM who use MA is feasible and potentially effective as an integrated harm reduction strategy. [ABSTRACT FROM AUTHOR]

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Given an infected host, estimating the time that has elapsed since initial exposure to the pathogen is an important problem in public health. In this paper we use longitudinal gene expression data from human challenge studies of viral respiratory illnesses for building predictive models to estimate the time elapsed since onset of respiratory infection. We apply sparsity driven machine learning to this time-stamped gene expression data to model the time of exposure by a pathogen and subsequent infection accompanied by the onset of the host immune response. These predictive models exploit the fact that the host gene expression profile evolves in time and its characteristic temporal signature can be effectively modeled using a small number of features. Predicting the time of exposure to infection to be in first 48 h after exposure produces BSR in the range of 80–90% on sequestered test data. A variety of machine learning experiments provide evidence that models developed on one virus can be used to predict exposure time for other viruses, e.g., H1N1, H3N2, and HRV. The interferon α / β signaling pathway appears to play a central role in keeping time from onset of infection. Successful prediction of the time of exposure to a pathogen has potential ramifications for patient treatment and contact tracing. [ABSTRACT FROM AUTHOR]

The noncircular face gear (NFG) with intersecting axes is composed of a spur gear and a NFG, which can realize the reduced and variable-speed integrated transmission between any intersecting axes. To study the contact characteristics of NFG with intersecting axes, the mutually different tooth surface mathematical model of the NFG with intersecting axes was established, and a tooth surface analysis method based on rotational projection was innovatively proposed. By using this method, the distribution characteristics of contact traces on different tooth profiles can be accurately compared through establishing the comparison reference of the middle plane on gear teeth. At the same time, the effects of installation errors and eccentricity on the contact characteristics of tooth surface were analyzed in detail. Results demonstrate that the change in the same kind of installation error has the same effect on the offset direction of the contact traces on each gear tooth. Under the same installation error, changing the eccentricity of the NFG will cause different changes in the offset of the contact traces on each gear tooth. Finally, a gear transmission device of NFG with intersecting axes was designed and manufactured. The correctness of the contact model and the analysis results were proven by the gear pair rolling experiment, which provides a theoretical basis for the design and installation of NFG with intersecting axes. [ABSTRACT FROM AUTHOR]

The coronavirus disease 2019 (COVID-19) was first reported in December 2019 in Wuhan, China, and then moved to almost every country showing an unprecedented outbreak. The world health organization declared COVID-19 a pandemic. Since then, millions of people were infected, and millions have lost their lives all around the globe. By the end of 2020, effective vaccines that could prevent the fast spread of the disease started to loom on the horizon. Nevertheless, isolation, social distancing, face masks, and quarantine are the best-known measures, in the time being, to fight the pandemic. On the other hand, contact tracing is an effective procedure in tracking infections and saving others' lives. In this paper, we devise a new approach using a hybrid harmony search (HHS) algorithm that casts the problem of finding strongly connected components (SCCs) to contact tracing. This new approach is named as hybrid harmony search contact tracing (HHS-CT) algorithm. The hybridization is achieved by integrating the stochastic hill climbing into the operators' design of the harmony search algorithm. The HHS-CT algorithm is compared to other existing algorithms of finding SCCs in directed graphs, where it showed its superiority over these algorithms. The devised approach provides a 77.18% enhancement in terms of run time and an exceptional average error rate of 1.7% compared to the other existing algorithms of finding SCCs. [ABSTRACT FROM AUTHOR]