Stochastic EM algorithm for partially observed stochastic epidemics with individual heterogeneity.

We develop a stochastic epidemic model progressing over dynamic networks, where infection rates are heterogeneous and may vary with individual-level covariates. The joint dynamics are modeled as a continuous-time Markov chain such that disease transmission is constrained by the contact network structure, and network evolution is in turn influenced by individual disease statuses. To accommodate partial epidemic observations commonly seen in real-world data, we propose a stochastic EM algorithm for inference, introducing key innovations that include efficient conditional samplers for imputing missing infection and recovery times which respect the dynamic contact network. Experiments on both synthetic and real datasets demonstrate that our inference method can accurately and efficiently recover model parameters and provide valuable insight at the presence of unobserved disease episodes in epidemic data.

The public health impact of COVID-19 variants of concern on the effectiveness of contact tracing in Vermont, United States.

Case investigation and contact tracing (CICT) are public health measures that aim to break the chain of pathogen transmission. Changes in viral characteristics of COVID-19 variants have likely affected the effectiveness of CICT programs. We estimated and compared the cases averted in Vermont when the original COVID-19 strain circulated (Nov. 25, 2020-Jan. 19, 2021) with two periods when the Delta strain dominated (Aug. 1-Sept. 25, 2021, and Sept. 26-Nov. 20, 2021). When the original strain circulated, we estimated that CICT prevented 7180 cases (55% reduction in disease burden), compared to 1437 (15% reduction) and 9970 cases (40% reduction) when the Delta strain circulated. Despite the Delta variant being more infectious and having a shorter latency period, CICT remained an effective tool to slow spread of COVID-19; while these viral characteristics did diminish CICT effectiveness, non-viral characteristics had a much greater impact on CICT effectiveness.

Improving COVID-19 contact tracing and testing of exposed individuals in Cameroon using digital health technology: a cluster randomised trial.

Background: Contact tracing was described as a key strategy to contribute to controlling the spread of severe acute respiratory syndrome of Coronavirus 2 (SARS-CoV-2) but implementing it can be a challenge. Digitalisation of contact tracing is among the proposed solutions being explored in sub-Saharan African settings. We assessed the effectiveness of a digital tool to expand SARS-CoV-2 testing in exposed individuals in Cameroon. Methods: We conducted a cluster-randomised (1:1) trial in eight health districts, including 22 facilities and SARS-CoV-2 testing units, randomly assigned to a digital (intervention) or standard (control) contact tracing approach. The intervention consisted of a contact tracing module added to the digital platform "Mamal PRO" used for monitoring and coordination of Coronavirus Disease 2019 pandemic response in Cameroon. The primary outcome was the proportion of contacts declared by SAR-CoV-2 index patients who were successfully traced and tested for SARS-CoV-2 evaluated with a Poisson regression model with cluster adjustment. This study is registered with ClinicalTrials.gov (NCT05684887). Findings: Between October 18, 2022, and March 31, 2023, we enrolled 164 index patients in the intervention arm and 149 in the control arm, who identified 854 and 849 contacts, respectively. In the intervention arm, 93.8% (801/854) of identified contacts were successfully reached by the tracing unit versus 54.5% (463/849) in the control arm. The intervention significantly increased the likelihood of successfully tracing contacts (adjusted relative risks (RR) 1.72 [95% CI: 1.00-2.95], p = 0.049). The median (interquartile range, IQR) time to successfully tracing contacts was 0 days [IQR: 0, 1] in the intervention and 1 day [IQR: 0, 2] in the control arm. In the intervention arm, 21.3% (182/854) of identified contacts received SARS-CoV-2 testing compared to 14.5% (123/849) in the control arm (adjusted RR 1.47 [95% CI: 0.44-4.90], p = 0.530). Interpretation: Digitalising the contact tracing process improved exposure notification and facilitated the tracing of a greater number of contacts of individuals infected with SARS-CoV-2 in resource-limited settings. Funding: The study was funded by FIND, United Kingdom (FCDO 40105983), Switzerland (81066910), Netherlands (SDD 4000004160), Canada (DFATD 7429348), The Kingdom of Saudi Arabia (FIND-ACT-A DX PARTNERSHIP 20.08.2020), The Rockefeller Foundation (2020 HTH 059), Germany (BMZ Covid-19 Diagnostic and Surveillance Response 27.07.2021), Australia (DFAT 76442), Kuwait (M239/2020), The Government of Portugal and Partners (ANF, BCP, CGF, APIFARMA) and The BlackRock Foundation (Grant Agreement as of April 20, 2022).

Comparison of the efficacy of COVID-19 responses in South Korea and the United States.

BACKGROUND: The COVID-19 pandemic devastated many countries worldwide by causing large numbers of fatalities. In our research, we wanted to answer the question: Why was there such a large difference in the mortality rate between South Korea and the United States? This is because many East Asian countries, such as Korea, had a lower mortality rate than many countries, including developed ones, across the world - the mortality rate of South Korea was about five times lower than the United States. METHODS: This study comprehensively compares strategies used to address the COVID-19 pandemic in two different countries: South Korea and the United States. The various aspects of these two countries' responses are examined, including initial response, information dissemination and public compliance, mitigation strategies, and vaccine rollout and their impacts. RESULTS: Early and widespread testing, rigorous contact tracing, the clear release of government information, and an organized vaccine rollout powered a proactive approach in South Korea. The United States had a contrasting response consisting of delayed and more decentralized measures, where testing lagged due to varying policies and the political controversies facing vaccine distribution. CONCLUSIONS: We signify the gravity of rapid response and testing, clear communication, and efficient vaccine distribution, as we believe this could correlate with a lower mortality rate. In addition, we discuss future directions, including the need for a specific health infrastructure and protocol against highly infectious outbreaks.

Main findings: The study suggests strategies that may be effective ways to reduce fatalities during a pandemic through a comparative analysis of COVID-19 responses in South Korea and the United States.Added knowledge: This review further consolidates the importance of an effective defense strategy involving testing, contact tracing, information dissemination, and vaccine rollouts.Global health impact for policy and action: There is a need for the development of a specific pandemic response infrastructure against fast-spreading and fatal viruses involving effective policies that take into account both the freedom and health of citizens.

Identifying Barriers to the Adoption of Digital Contact Tracing Apps in England: Semistructured Interview Study With Professionals Involved in the Pandemic Response.

BACKGROUND: The NHS (National Health Service) COVID-19 app was a digital contact tracing app (DCTA) used in England in response to the COVID-19 pandemic. The aim of which was to limit the spread of COVID-19 by providing exposure alerts. At the time of the pandemic, questions were raised regarding the effectiveness and cost of the NHS COVID-19 app and whether DCTAs have a role in future pandemics. OBJECTIVE: This study aims to explore key barriers to DCTAs in England during the COVID-19 pandemic. METHODS: This is a qualitative study using semistructured video interviews conducted with professionals in public health, digital health, clinicians, health care law, and health executives who had an active role in the COVID-19 pandemic. These interviews aimed to explore the perspective of different experts involved in the pandemic response and gauge their opinions on the key barriers to DCTAs in England during the COVID-19 pandemic. The initial use of maximum variation sampling combined with a snowball sampling approach ensured diversity within the cohort of interviewees. Interview transcripts were then analyzed using Braun and Clarke's 6 steps for thematic analysis. RESULTS: Key themes that acted as barriers to DCTAs were revealed by interviewees such as privacy concerns, poor communication, technological accessibility, digital literacy, and incorrect use of the NHS COVID-19 app. Interviewees believed that some of these issues stemmed from poor governmental communication and a lack of transparency regarding how the NHS COVID-19 app worked, resulting in decreased public trust. Moreover, interviewees highlighted that a lack of social support integration within the NHS COVID-19 app and delayed app notification period also contributed to the poor adoption rates. CONCLUSIONS: Qualitative findings from interviews highlighted barriers to the NHS COVID-19 app, which can be applied to DCTAs more widely and highlight some important implications for the future use of DCTAS. There was no consensus among interviewees as to whether the NHS COVID-19 app was a success; however, all interviewees provided recommendations for improvements in creating and implementing DCTAs in the future.

Construction of effective reproduction number of infectious disease individuals based on spatiotemporal discriminant search model: take hand-foot-mouth disease as an example.

OBJECTIVE: In order to facilitate the tracing of infectious diseases in a small area and to effectively carry out disease control and epidemiological investigations, this research proposes a novel spatiotemporal model to estimate effective reproduction number(Re)for infectious diseases, based on the fundamental concept of contact tracing. METHODS: This study utilizes the incidence of hand, foot, and mouth disease (HFMD) among children in Bishan District, Chongqing, China from 2015 to 2019. The study incorporates the epidemiological characteristics of HFMD and aims to construct a Spatiotemporal Correlation Discrimination of HFMD. Utilizing ARC ENGINE and C# programming for the creation of a spatio-temporal database dedicated to HFMD to facilitate data collection and analysis. The scientific validity of the proposed method was verified by comparing the effective reproduction number obtained by the traditional SEIR model. RESULTS: We have ascertained the optimal search radius for the spatiotemporal search model to be 1.5 km. Upon analyzing the resulting Re values, which range from 1.14 to 4.75, we observe a skewed distribution pattern from 2015 to 2019. The median and quartile Re value recorded is 2.42 (1.98, 2.72). Except for 2018, the similarity coefficient r of the years 2015, 2016, 2017, and 2019 were all close to 1, and p <0.05 in the comparison of the two models, indicating that the Re values obtained by using the search model and the traditional SEIR model are correlated and closely related. The results exhibited similarity between the Re curves of both models and the epidemiological characteristics of HFMD. Finally, we illustrated the regional distribution of Re values obtained by the search model at various time intervals on Geographic Information System (GIS) maps which highlighted variations in the incidence of diseases across different communities, neighborhoods, and even smaller areas. CONCLUSION: The model comprehensively considers both temporal variation and spatial heterogeneity in disease transmission and accounts for each individual's distinct time of onset and spatial location. This proposed method differs significantly from existing mathematical models used for estimating Re in that it is founded on reasonable scientific assumptions and computer algorithms programming that take into account real-world spatiotemporal factors. It is particularly well-suited for estimating the Re of infectious diseases in relatively stable mobile populations within small geographical areas.

Utilizing rapid qualitative assessment and thematic analysis methods to identify and share promising case investigation and contact tracing practices with people in refugee, immigrant, and migrant communities during COVID-19.

In the early months of the COVID-19 pandemic The National Resource Center for Refugees, Immigrants and Migrants (NRC-RIM) was established. NRC-RIM initially sought to rapidly identify promising case investigation and contact tracing (CICT) practices within refugee, immigrant, and migrant communities. Between September 2020 and April 2021, the team conducted 60 interviews with individuals from cross-sector organizations (i.e., public health, health systems, community experts/organizations) working with refugee, immigrant and migrant communities in health and public health capacities related to COVID-19. The overarching aim was to identify and amplify innovative promising and best practices for CICT with refugee, immigrant, and migrant communities, including an exploration of barriers and facilitators. We utilized layered methods to rapidly assess, summarize and disseminate promising practices while simultaneously completing four thematic analyses including: (1) public health organizations; (2) health system organizations; (3) community leaders and organizations; and (4) vaccine planning and access across the three sectors. The primary objective of this article is to describe the project design, applied methods, and team science approach we utilized. We found that rapid identification and dissemination of promising practices, and barriers and facilitators for CICT with refugee, immigrant and migrant communities was feasible during a public health emergency. This approach was essential for identifying and widely sharing culturally and linguistically concordant public health practices.

'Barely keeping the wheels on the trolley': A qualitative study of the New Zealand COVID Tracer App.

Digital contact tracing apps were developed to help control the spread of COVID-19 but research exploring these apps has underrepresented both 'at-risk' communities and contact tracers. Our study examines perspectives of the New Zealand COVID Tracer app among 53 participants, comprising policy advisors, contact tracers, and Maori, Pacific, and disability stakeholders, underpinned by the theory of social construction of which positions technology within the social context in which it evolves, operates, and is negotiated. Although community stakeholders believed the app helped safeguard communities from COVID-19, the health officials' views on the app's usefulness in contact tracing varied. Participants who oversaw the app's technical development generally perceived it as being more useful, particularly regarding Bluetooth proximity detection, in contrast with contact tracers' perceptions. The findings highlight a disconnection between public sentiment and operational reality in the use of the app and the need for improved collaboration and consultation in future contact tracing responses.

Epidemic exposure risk assessment in digital contact tracing: A fuzzy logic approach.

Background: Bluetooth low energy (BLE)-based contact-tracing applications were widely used during the COVID-19 pandemic. However, the use of only the received signal strength feature for proximity calculations may not be adaptable to different virus variants or scalable for other potential epidemic diseases. Objective: This study presents a novel framework in regard to evaluating and classifying personal exposure risk that considers both contact features, which include distance and length of contact, and environment features, which include crowd size and the number of recently infected cases in the environment. The framework utilizes a fuzzy expert system that is adaptable to different virus variants. Methods: The proposed method was tested on two viruses with different close contact features, which used four membership functions and 256 fuzzy rule sets. Results: The proposed framework classified personal exposure risks into four classes, which include low, medium, high, and too high risk. The empirical results showed that the fuzzy logic-based approach reduced the number of false positive cases and demonstrated better accuracy and precision than the current BLE-only approaches. Conclusions: The proposed framework provides a more practical and adaptable method in regard to assessing exposure risks in real-world scenarios. It has the potential to be scalable and adaptable to different virus variants and other potential epidemic diseases by considering both contact and environment features. These findings may be useful in order to develop more effective digital contact-tracing applications and policies.

A Wireless Wearable Ecosystem for Social Network Analysis in Free-living Animals.

Studying animal social systems requires understanding variations in contact and interaction, influenced by factors like environmental conditions, resource availability, and predation risk. Traditional observational methods have limitations, but advancements in sensor technologies and data analytics provide new opportunities. We developed a wireless wearable sensor system, "Juxta," with features such as modular battery packs and a smartphone app for data collection. A pilot study on free-living prairie voles (Microtus ochrogaster), a species with complex social behavior, demonstrated Juxta's potential for studying social networks and behavior. We propose a framework for merging temporal, spatial, and event-driven data, which can help explore complex social dynamics across species and environments.

Estimating the human bottleneck for contact tracing.

The SARS-CoV-2 pandemic has highlighted the importance of contact tracing for epidemiological mitigation. Contact tracing interviews (CTIs) typically rely on episodic memory, which is prone to decline over time. Here, we provide a quantitative estimate of reporting decline for age- and gender-representative samples from the United Kingdom and Germany, emulating >15,000 CTIs. We find that the number of reported contacts declines as a power function of recall delay and is significantly higher for younger subjects and for those who used memory aids, such as a scheduler. We further find that these factors interact with delay: Older subjects and those who made no use of memory aids have steeper decline functions. These findings can inform epidemiological modeling and policies in the context of infectious diseases.

Uncovering COVID-19 transmission tree: identifying traced and untraced infections in an infection network.

Introduction: This paper presents a comprehensive analysis of COVID-19 transmission dynamics using an infection network derived from epidemiological data in South Korea, covering the period from January 3, 2020, to July 11, 2021. The network illustrates infector-infectee relationships and provides invaluable insights for managing and mitigating the spread of the disease. However, significant missing data hinder conventional analysis of such networks from epidemiological surveillance. Methods: To address this challenge, this article suggests a novel approach for categorizing individuals into four distinct groups, based on the classification of their infector or infectee status as either traced or untraced cases among all confirmed cases. The study analyzes the changes in the infection networks among untraced and traced cases across five distinct periods. Results: The four types of cases emphasize the impact of various factors, such as the implementation of public health strategies and the emergence of novel COVID-19 variants, which contribute to the propagation of COVID-19 transmission. One of the key findings is the identification of notable transmission patterns in specific age groups, particularly in those aged 20-29, 40-69, and 0-9, based on the four type classifications. Furthermore, we develop a novel real-time indicator to assess the potential for infectious disease transmission more effectively. By analyzing the lengths of connected components, this indicator facilitates improved predictions and enables policymakers to proactively respond, thereby helping to mitigate the effects of the pandemic on global communities. Conclusion: This study offers a novel approach to categorizing COVID-19 cases, provides insights into transmission patterns, and introduces a real-time indicator for better assessment and management of the disease transmission, thereby supporting more effective public health interventions.

Effects of User Experience in Automated Information Processing on Perceived Usefulness of Digital Contact-Tracing Apps: Cross-Sectional Survey Study.

BACKGROUND: In pandemic situations, digital contact tracing (DCT) can be an effective way to assess one's risk of infection and inform others in case of infection. DCT apps can support the information gathering and analysis processes of users aiming to trace contacts. However, users' use intention and use of DCT information may depend on the perceived benefits of contact tracing. While existing research has examined acceptance in DCT, automation-related user experience factors have been overlooked. OBJECTIVE: We pursued three goals: (1) to analyze how automation-related user experience (ie, perceived trustworthiness, traceability, and usefulness) relates to user behavior toward a DCT app, (2) to contextualize these effects with health behavior factors (ie, threat appraisal and moral obligation), and (3) to collect qualitative data on user demands for improved DCT communication. METHODS: Survey data were collected from 317 users of a nationwide-distributed DCT app during the COVID-19 pandemic after it had been in app stores for >1 year using a web-based convenience sample. We assessed automation-related user experience. In addition, we assessed threat appraisal and moral obligation regarding DCT use to estimate a partial least squares structural equation model predicting use intention. To provide practical steps to improve the user experience, we surveyed users' needs for improved communication of information via the app and analyzed their responses using thematic analysis. RESULTS: Data validity and perceived usefulness showed a significant correlation of r=0.38 (P<.001), goal congruity and perceived usefulness correlated at r=0.47 (P<.001), and result diagnosticity and perceived usefulness had a strong correlation of r=0.56 (P<.001). In addition, a correlation of r=0.35 (P<.001) was observed between Subjective Information Processing Awareness and perceived usefulness, suggesting that automation-related changes might influence the perceived utility of DCT. Finally, a moderate positive correlation of r=0.47 (P<.001) was found between perceived usefulness and use intention, highlighting the connection between user experience variables and use intention. Partial least squares structural equation modeling explained 55.6% of the variance in use intention, with the strongest direct predictor being perceived trustworthiness (ß=.54; P<.001) followed by moral obligation (ß=.22; P<.001). Based on the qualitative data, users mainly demanded more detailed information about contacts (eg, place and time of contact). They also wanted to share information (eg, whether they wore a mask) to improve the accuracy and diagnosticity of risk calculation. CONCLUSIONS: The perceived result diagnosticity of DCT apps is crucial for perceived trustworthiness and use intention. By designing for high diagnosticity for the user, DCT apps could improve their support in the action regulation of users, resulting in higher perceived trustworthiness and use in pandemic situations. In general, automation-related user experience has greater importance for use intention than general health behavior or experience.

Non-adherence to tuberculosis contact screening and its associated factors in Kuching, Sarawak: A cross-sectional study.

Introduction: Tuberculosis (TB) contacts in Malaysia undergo follow-up screening to reduce their risk of active or latent TB. However, adherence to this screening is low. Limited studies have explored the factors contributing to non-adherence to follow-up screening. This study aimed to determine the non-adherence rate and reasons in a government health clinic. Methods: Participants were TB contacts due for their 2nd contact screening (including those who attended their first contact screening at Petra Jaya Health Clinic from November 2018 to March 2019), were aged at least 18 years and were able to understand English or Malay. Data were collected during the second contact screening from August 2019 to January 2020. Results: A total of 383 TB contacts were included. Of them, 56.6% (n=217) were aged 20-39 years, and the sex distribution was equal (men: 44.1%, n=169). The majority were non-household contacts (82.2%, n=315). The rate of non-adherence to follow-up screening was 19.1% (n=73). Approximately 52.1% (n=36) reported forgetting their scheduled appointment date as the primary reason for non-adherence. The influencing factors included employment and ethnicity. Only 39.1% (n=27) were aware of their risk for active TB, while 49.5% (n=189) were unsure whether TB can be cured with proper treatment. Conclusion: The findings highlight the need to improve the reminder system for TB contacts. Although direct association between knowledge and adherence could not be established, the low percentage of correct answers to most basic knowledge questions associated with TB indicates a need to improve health education for TB contacts.

Vaccine Effectiveness against SARS-CoV-2 among Household Contacts during Omicron BA.2-Dominant Period, Japan.

We calculated attack rates for household contacts of COVID-19 patients during the SARS-CoV-2 Omicron BA.2-dominant period in Japan. Attack rates among household contacts without recent (<3 months) vaccination was lower for contacts of index patients with complete vaccination than for contacts of index patients without complete vaccination, demonstrating indirect vaccine effectiveness.

The Use of Contact Tracing Technologies for Infection Prevention and Control Purposes in Nosocomial Settings: A Systematic Literature Review.

BACKGROUND: Pandemic management and preparedness are more needed than ever before and there is widespread governmental interest in learning from the COVID-19 pandemic in order to ensure the availability of evidence-based Infection Prevention and Control measures. Contact tracing is integral to Infection Prevention and Control, facilitating breaks in the chain of transmission in a targeted way, identifying individuals who have come into contact with an infected person, and providing them with instruction/advice relating to testing, medical advice and/or self-isolation. AIM: This study aims to improve our understanding of the use of contact tracing technologies in healthcare settings. This research seeks to contribute to the field of Infection Prevention and Control by investigating how these technologies can mitigate the spread of nosocomial infections. Ultimately, this study aims to improve the quality and safety of healthcare delivery. METHODS: A systematic literature review was conducted, and journal articles investigating the use of contact tracing technologies in healthcare settings were retrieved from databases held on the OvidSP platform between March and September 2022, with no date for a lower limit. RESULTS: In total, 277 studies were retrieved and screened, and 14 studies were finally included in the systematic literature review. Most studies investigated proximity sensing technologies, reporting promising results. However, studies were limited by small sample sizes and confounding factors, revealing contact tracing technologies remain at a nascent stage. Investment in research and development of new testing technologies is necessary to strengthen national and international contact tracing capabilities. CONCLUSION: This review aims to contribute to those who intend to create robust surveillance systems and implement infectious disease reporting protocols.

The Role of Transparency in Digital Contact Tracing During COVID-19: Insights from an Expert Survey.

Health technologies such as apps for digital contract tracing [DCT] played a crucial role in containing and combating infections during the COVID-19 pandemic. Their primary function was to prevent the spread of SARS-CoV-2 by consistently generating and disseminating information related to various events such as encounters, vaccinations or infections. While the functionality of DCT has been well researched, the necessity of transparency in the use of DCT and the consent to share sensitive information such as users' health, vaccination and location status remains unclear. On one hand, DCT enabled the continuous monitoring of various risk factors, including data-based calculations of infection probabilities. On the other hand, digital monitoring of health risks was closely associated with various uncertainties, such as the ambiguous storage of personal data and its potential future misuse, e.g., by tech companies or health authorities. Our contribution aims to retrospectively analyze the COVID-19 pandemic from a post-pandemic perspective and utilize it as a case study for the implementation of new technological measures. We argue that under the condition of voluntary use of DCT, transparency plays a key role in convincing individuals to install health technologies on their mobile devices, keep them activated and consent to the sharing of sensitive data. We support our argument with qualitative data from an expert survey conducted between 2020 and 2021 and analyzed according to the principles of Grounded Theory.

An SEIR network epidemic model with manual and digital contact tracing allowing delays.

We consider an SEIR epidemic model on a network also allowing random contacts, where recovered individuals could either recover naturally or be diagnosed. Upon diagnosis, manual contact tracing is triggered such that each infected network contact is reported, tested and isolated with some probability and after a random delay. Additionally, digital tracing (based on a tracing app) is triggered if the diagnosed individual is an app-user, and then all of its app-using infectees are immediately notified and isolated. The early phase of the epidemic with manual and/or digital tracing is approximated by different multi-type branching processes, and three respective reproduction numbers are derived. The effectiveness of both contact tracing mechanisms is numerically quantified through the reduction of the reproduction number. This shows that app-using fraction plays an essential role in the overall effectiveness of contact tracing. The relative effectiveness of manual tracing compared to digital tracing increases if: more of the transmission occurs on the network, when the tracing delay is shortened, and when the network degree distribution is heavy-tailed. For realistic values, the combined tracing case can reduce R0 by 20%-30%, so other preventive measures are needed to reduce the reproduction number down to 1.2-1.4 for contact tracing to make it successful in avoiding big outbreaks.

New framework to assess tracing and testing based on South Korea's response to COVID-19.

South Korea's remarkable success in controlling the spread of COVID-19 during the pre-Omicron period was based on extensive contact tracing and large-scale testing. Here we suggest a general criterion for tracing and testing based on South Korea's experience, and propose a new framework to assess tracing and testing. We reviewed papers on South Korea's response to COVID-19 to capture its concept of tracing and testing. South Korea expanded its testing capabilities to enable group tracing combined with preemptive testing, and to conduct open testing. According to our proposed model, COVID-19 cases are classified into 4 types: confirmed in quarantine, source known, source unknown, and unidentified. The proportion of the first two case types among confirmed cases is defined as "traced proportion", and used as the indicator of tracing and testing effectiveness. In conclusion, South Korea successfully suppressed COVID-19 transmission by maintaining a high traced proportion (> 60%) using group tracing in conjunction with preemptive testing as a complementary strategy to traditional contact tracing.

Evaluation of Mpox Exposures and Outcomes in Workplaces, 6 Jurisdictions, June 1-August 31, 2022.

OBJECTIVES: The risk for mpox virus (MPXV) transmission in most workplaces has not been thoroughly assessed in the context of the 2022 global mpox outbreak. Our objectives were to describe mpox case patients who worked while infectious and the subsequent workplace contact tracing efforts, risk assessments, and outcomes. METHODS: The Centers for Disease Control and Prevention requested information from health departments in the United States in September 2022 to identify people with confirmed or probable mpox who worked outside the home while infectious, either before or after diagnosis, from June 1 through August 31, 2022. We collected and summarized data on demographic, clinical, and workplace characteristics of case patients and workplace contact investigations. We stratified data by industry and occupation categories. RESULTS: In total, 102 case patients were reported by 6 jurisdictions. The most common industries were accommodation and food services (19.8%) and professional business, management, and technical services (17.0%). Contact investigations identified 178 total contacts; 54 cases (52.9%) had no contacts identified. Of 178 contacts, 54 (30.3%) were recommended to receive postexposure prophylaxis (PEP) and 18 (10.1%) received PEP. None of the contacts developed a rash or were tested for orthopox or mpox, and none were reported to have confirmed or probable mpox. CONCLUSION: Data from 6 jurisdictions suggest that the risk of MPXV transmission from workers to others in workplace settings in many industries is low. These findings might support future updates to exposure risk classifications and work activity recommendations for patients. These findings also demonstrate the importance of collecting and analyzing occupation and industry data in case reports to better understand risks in workplaces.