The role of digital tools and emerging devices in COVID-19 contact tracing during the first 18 months of the pandemic: a systematic review.

BACKGROUND: Contact tracing is a public health intervention implemented in synergy with other preventive measures to curb epidemics, like the coronavirus pandemic. The development and use of digital devices have increased worldwide to enhance the contact tracing process. The aim of the study was to evaluate the effectiveness and impact of tracking coronavirus disease 2019 (COVID-19) patients using digital solutions. METHODS: Observational studies on digital contact tracing (DCT), published 2020-21, in English were identified through a systematic literature review performed on nine online databases. An ad hoc form was used for data extraction of relevant information. Quality assessment of the included studies was performed with validated tools. A qualitative synthesis of the findings is reported. RESULTS: Over 8000 records were identified and 37 were included in the study: 24 modelling and 13 population-based studies. DCT improved the identification of close contacts of COVID-19 cases and reduced the effective reproduction number of COVID-19-related infections and deaths by over 60%. It impacted positively on societal and economic costs, in terms of lockdowns and use of resources, including staffing. Privacy and security issues were reported in 27 studies. CONCLUSIONS: DCT contributed to curbing the COVID-19 pandemic, especially with the high uptake rate of the devices and in combination with other public health measures, especially conventional contact tracing. The main barriers to the implementation of the devices are uptake rate, security and privacy issues. Public health digitalization and contact tracing are the keys to countries' emergency preparedness for future health crises.

Providing HIV-assisted partner services to partners of partners in western Kenya: an implementation science study.

INTRODUCTION: Assisted partner services (APS), or exposure notification and HIV testing for sexual partners of persons diagnosed HIV positive (index clients), is recommended by the World Health Organization. Most APS literature focuses on outcomes among index clients and their partners. There is little data on the benefits of providing APS to partners of partners diagnosed with HIV. METHODS: We utilized data from a large-scale APS implementation project across 31 facilities in western Kenya from 2018 to 2022. Females testing HIV positive at facilities were offered APS; those who consented provided contact information for all male sexual partners in the last 3 years. Male partners were notified of their potential HIV exposure and offered HIV testing services (HTS). Males newly testing positive were also offered APS and asked to provide contact information for their female partners in the last 3 years. Female partners of male partners (FPPs) were provided exposure notification and HTS. All participants with HIV were followed up at 12 months post-enrolment to assess linkage-to antiretroviral treatment (ART) and viral suppression. We compared HIV positivity, demographics and linkage outcomes among female index clients and FPPs. RESULTS: Overall, 5708 FPPs were elicited from male partners, of whom 4951 received HTS through APS (87% coverage); 291 FPPs newly tested HIV positive (6% yield), an additional 1743 (35.2%) reported a prior HIV diagnosis, of whom 99% were on ART at baseline. At 12 months follow-up, most FPPs were taking ART (92%) with very few adverse events: <1% reported intimate partner violence or reported relationship dissolution. FPPs were more likely than female index clients to report HIV risk behaviours including no condom use at last sex (45% vs. 30%) and multiple partners (38% vs. 19%). CONCLUSIONS: Providing HIV testing via APS to FPP is a safe and effective strategy to identify newly diagnosed females and achieve high linkage and retention to ART and can be an efficient means of identifying HIV cases in the era of declining HIV incidence. The high proportion of FPPs reporting HIV risk behaviours suggests APS may help interrupt community HIV transmission via increased knowledge of HIV status and linkage to treatment.

Estimating the relative importance of epidemiological and behavioural parameters for epidemic mpox transmission: a modelling study.

BACKGROUND: Many European countries experienced outbreaks of mpox in 2022, and there was an mpox outbreak in 2023 in the Democratic Republic of Congo. There were many apparent differences between these outbreaks and previous outbreaks of mpox; the recent outbreaks were observed in men who have sex with men after sexual encounters at common events, whereas earlier outbreaks were observed in a wider population with no identifiable link to sexual contacts. These apparent differences meant that data from previous outbreaks could not reliably be used to parametrise infectious disease models during the 2022 and 2023 mpox outbreaks, and modelling efforts were hampered by uncertainty around key transmission and immunity parameters. METHODS: We developed a stochastic, discrete-time metapopulation model for mpox that allowed for sexual and non-sexual transmission and the implementation of non-pharmaceutical interventions, specifically contact tracing and pre- and post-exposure vaccinations. We calibrated the model to case data from Berlin and used Sobol sensitivity analysis to identify parameters that mpox transmission is especially sensitive to. We also briefly analysed the sensitivity of the effectiveness of non-pharmaceutical interventions to various efficacy parameters. RESULTS: We found that variance in the transmission probabilities due to both sexual and non-sexual transmission had a large effect on mpox transmission in the model, as did the level of immunity to mpox conferred by a previous smallpox vaccination. Furthermore, variance in the number of pre-exposure vaccinations offered was the dominant contributor to variance in mpox dynamics in men who have sex with men. If pre-exposure vaccinations were not available, both the accuracy and timeliness of contact tracing had a large impact on mpox transmission in the model. CONCLUSIONS: Our results are valuable for guiding epidemiological studies for parameter ascertainment and identifying key factors for success of non-pharmaceutical interventions.

Repetition in social contacts: implications in modelling the transmission of respiratory infectious diseases in pre-pandemic and pandemic settings.

The spread of viral respiratory infections is intricately linked to human interactions, and this relationship can be characterized and modelled using social contact data. However, many analyses tend to overlook the recurrent nature of these contacts. To bridge this gap, we undertake the task of describing individuals' contact patterns over time by characterizing the interactions made with distinct individuals during a week. Moreover, we gauge the implications of this temporal reconstruction on disease transmission by juxtaposing it with the assumption of random mixing over time. This involves the development of an age-structured individual-based model, using social contact data from a pre-pandemic scenario (the POLYMOD study) and a pandemic setting (the Belgian CoMix study), respectively. We found that accounting for the frequency of contacts impacts the number of new, distinct, contacts, revealing a lower total count than a naive approach, where contact repetition is neglected. As a consequence, failing to account for the repetition of contacts can result in an underestimation of the transmission probability given a contact, potentially leading to inaccurate conclusions when using mathematical models for disease control. We, therefore, underscore the necessity of acknowledging contact repetition when formulating effective public health strategies.

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.

Estimating the contribution of setting-specific contacts to SARS-CoV-2 transmission using digital contact tracing data.

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.

A pilot project harnessing surveillance systems to support clinicians providing clinical care for people diagnosed with hepatitis C in Victoria, Australia, September 2021 to 31 March 2022.

BackgroundActive follow-up of chronic hepatitis C notifications to promote linkage to care is a promising strategy to support elimination.AimThis pilot study in Victoria, Australia, explored if the Department of Health could follow-up on hepatitis C cases through their diagnosing clinicians, to assess and support linkage to care and complete data missing from the notification.MethodsFor notifications received between 1 September 2021 and 31 March 2022 of unspecified hepatitis C cases (i.e. acquired > 24 months ago or of unknown duration), contact with diagnosing clinicians was attempted. Data were collected on risk exposures, clinical and demographic characteristics and follow-up care (i.e. HCV RNA test; referral or ascertainment of previous negative testing or treatment history). Reasons for unsuccessful doctor contact and gaps in care provision were investigated. Advice to clinicians on care and resources for clinical support were given on demand.ResultsOf 513 cases where information was sought, this was able to be obtained for 356 (69.4%). Reasons for unsuccessful contact included incomplete contact details or difficulties getting in touch across three attempts, particularly for hospital diagnoses. Among the 356 cases, 307 (86.2%) had received follow-up care. Patient-management resources were requested by 100 of 286 contacted diagnosing clinicians.ConclusionsMost doctors successfully contacted had provided follow-up care. Missing contact information and the time taken to reach clinicians significantly impeded the feasibility of the intervention. Enhancing system automation, such as integration of laboratory results, could improve completeness of notifications and support further linkage to care where needed.

Identifying the interplay between protective measures and settings on the SARS-CoV-2 transmission using a Bayesian network.

Contact tracing played a crucial role in minimizing the onward dissemination of Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) in the recent pandemic. Previous studies had also shown the effectiveness of preventive measures such as mask-wearing, physical distancing, and exposure duration in reducing SARS-CoV-2 transmission. However, there is still a lack of understanding regarding the impact of various exposure settings on the spread of SARS-CoV-2 within the community, as well as the most effective preventive measures, considering the preventive measures adherence in different daily scenarios. We aimed to evaluate the effect of individual protective measures and exposure settings on the community transmission of SARS-CoV-2. Additionally, we aimed to investigate the interaction between different exposure settings and preventive measures in relation to such SARS-CoV-2 transmission. Routine SARS-CoV-2 contact tracing information was supplemented with additional data on individual measures and exposure settings collected from index patients and their close contacts. We used a case-control study design, where close contacts with a positive test for SARS-CoV-2 were classified as cases, and those with negative results classified as controls. We used the data collected from the case-control study to construct a Bayesian network (BN). BNs enable predictions for new scenarios when hypothetical information is introduced, making them particularly valuable in epidemiological studies. Our results showed that ventilation and time of exposure were the main factors for SARS-CoV-2 transmission. In long time exposure, ventilation was the most effective factor in reducing SARS-CoV-2, while masks and physical distance had on the other hand a minimal effect in this ventilation spaces. However, face masks and physical distance did reduce the risk in enclosed and unventilated spaces. Distance did not reduce the risk of infection when close contacts wore a mask. Home exposure presented a higher risk of SARS-CoV-2 transmission, and any preventive measures posed a similar risk across all exposure settings analyzed. Bayesian network analysis can assist decision-makers in refining public health campaigns, prioritizing resources for individuals at higher risk, and offering personalized guidance on specific protective measures tailored to different settings or environments.

Prevention of sexual transmission of mpox: a systematic review and qualitative evidence synthesis of approaches.

BACKGROUND: The ongoing multi-country mpox outbreak in previously unaffected countries is primarily affecting sexual networks of men who have sex with men. Evidence is needed on the effectiveness of recommended preventive interventions. To inform WHO guidelines, a systematic review and qualitative evidence synthesis were conducted on mpox preventive behavioural interventions to reduce: (i) sexual acquisition; (ii) onward sexual transmission from confirmed/probable cases; and (iii) utility of asymptomatic testing. METHODS: Medline, EMBASE, PubMed, Cochrane and WHO trial databases, grey literature and conferences were searched for English-language primary research published since 1 January 2022. A reviewer team performed screening, data extraction and bias assessment. A qualitative thematic synthesis explored views and experiences of engagement in prevention in individuals at increased risk. RESULTS: There were 16 studies: 1 on contact-tracing, 2 on sexual behaviour, and 13 on asymptomatic testing. Although MPXV was detected in varying proportions of samples (0.17%-6.5%), the testing studies provide insufficient evidence to fully evaluate this strategy. For the qualitative evidence synthesis, four studies evaluated the experiences of most affected communities. Preferences about preventive interventions were shaped by: mpox information; the diversity of sexual practices; accessibility and quality of mpox testing and care; and perceived cost to wellbeing. CONCLUSIONS: Evidence on the effectiveness of interventions to prevent the sexual transmission of mpox remains scarce. Limited qualitative evidence on values and preferences provides insight into factors influencing intervention acceptability. Given global and local inequities in access to vaccines and treatment, further research is needed to establish the effectiveness of additional interventions.

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.

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.

Surveillance of close contacts of patients with infectious tuberculosis: a prospective cohort study.

BACKGROUND: A long-term follow-up of close contacts to monitor their infection status is essential to formulate a promising screening strategy. The study aimed to assess the dynamics of tuberculosis (TB) infection using Interferon-γ release assay (IGRA) and determine risk factors associated with TB infection. METHODS: Definite TB patients were interviewed and their household contacts were screened for TB infection by IGRA during 12-month longitudinal investigation. RESULTS: We included in our analyses 184 household contacts of 92 index TB patients. 87 individuals (47.3%) in contact group progressed to TB infection, of whom 86 developed into IGRA positive within 24 weeks. Close contacts with a higher age and comorbidities are easier to exhibit TB infection. Analysis showed that risk factors for becoming IGRA-positive individuals included residence, older age, comorbidities, BCG scar and high bacterial load. Contacts with BCG scar had a lower IGRA-positive rate. CONCLUSION: IGRA conversion generally occurs within 24 weeks after exposure. The TB transmission happens since subclinical TB stage and the presence of BCG scar is an independent protective factor reducing risk of TB infection among close contacts. Repeated IGRA tests are sensible to conducted among close contacts at 24 weeks after exposure to identify the IGRA-positive individuals.

An algorithm to build synthetic temporal contact networks based on close-proximity interactions data.

Small populations (e.g., hospitals, schools or workplaces) are characterised by high contact heterogeneity and stochasticity affecting pathogen transmission dynamics. Empirical individual contact data provide unprecedented information to characterize such heterogeneity and are increasingly available, but are usually collected over a limited period, and can suffer from observation bias. We propose an algorithm to stochastically reconstruct realistic temporal networks from individual contact data in healthcare settings (HCS) and test this approach using real data previously collected in a long-term care facility (LTCF). Our algorithm generates full networks from recorded close-proximity interactions, using hourly inter-individual contact rates and information on individuals' wards, the categories of staff involved in contacts, and the frequency of recurring contacts. It also provides data augmentation by reconstructing contacts for days when some individuals are present in the HCS without having contacts recorded in the empirical data. Recording bias is formalized through an observation model, to allow direct comparison between the augmented and observed networks. We validate our algorithm using data collected during the i-Bird study, and compare the empirical and reconstructed networks. The algorithm was substantially more accurate to reproduce network characteristics than random graphs. The reconstructed networks reproduced well the assortativity by ward (first-third quartiles observed: 0.54-0.64; synthetic: 0.52-0.64) and the hourly staff and patient contact patterns. Importantly, the observed temporal correlation was also well reproduced (0.39-0.50 vs 0.37-0.44), indicating that our algorithm could recreate a realistic temporal structure. The algorithm consistently recreated unobserved contacts to generate full reconstructed networks for the LTCF. To conclude, we propose an approach to generate realistic temporal contact networks and reconstruct unobserved contacts from summary statistics computed using individual-level interaction networks. This could be applied and extended to generate contact networks to other HCS using limited empirical data, to subsequently inform individual-based epidemic models.

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.

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.

Prevalence of tuberculosis infection among contacts of drug-resistant tuberculosis patients: A systematic review and meta-analysis.

INTRODUCTION: Contact investigations with drug-susceptible tuberculosis (DS-TB) patients have demonstrated a high prevalence of tuberculosis infection (TBI). However, the prevalence of TBI among individuals in close contact with drug-resistant tuberculosis (DR-TB) patients is poorly understood. This systematic review and meta-analysis aimed to determine the prevalence of TBI among household and non-household contacts of DR-TB patients. METHOD AND ANALYSIS: We searched five databases (Medline, Embase, Scopus, Web of Science, and Cumulative Index to Nursing and Allied Health Literature (CINAHL)) from inception to 2 June 2023. All studies that reported the prevalence of TBI among DR-TB contacts were included in the study. A random-effects meta-analysis was conducted to estimate the pooled prevalence of TBI with a 95% confidence interval (CI). Sub-group analyses were conducted using study characteristics as covariates. RESULTS: Thirty studies involving 7659 study participants from 19 countries were included. The pooled prevalence of TBI among DR-TB contacts was 36.52% (95% CI: 30.27-42.77). The sub-group analysis showed considerable heterogeneity in the estimates, with the highest prevalence reported in Southeast Asia (80.74%; 95% CI: 74.09-87.39), household contacts (38.60%; 95% CI: 30.07-47.14), lower-middle-income countries (LMICs) (54.72; 95% CI: 35.90, 73.55), children (43.27%; 95% CI: 25.50, 61.04), and studies conducted between 2004 and 2012 (45.10; 95% CI: 32.44, 57.76). CONCLUSION: The prevalence of TBI among DR-TB contacts was high, with substantial regional variations. Further research is needed to determine the drug susceptibility status of TBI in DR-TB contacts. PROTOCOL REGISTRATION: The protocol is registered in PROSPERO (CRD42023390339).

The effectiveness of intervention measures on MERS-CoV transmission by using the contact networks reconstructed from link prediction data.

Introduction: Mitigating the spread of infectious diseases is of paramount concern for societal safety, necessitating the development of effective intervention measures. Epidemic simulation is widely used to evaluate the efficacy of such measures, but realistic simulation environments are crucial for meaningful insights. Despite the common use of contact-tracing data to construct realistic networks, they have inherent limitations. This study explores reconstructing simulation networks using link prediction methods as an alternative approach. Methods: The primary objective of this study is to assess the effectiveness of intervention measures on the reconstructed network, focusing on the 2015 MERS-CoV outbreak in South Korea. Contact-tracing data were acquired, and simulation networks were reconstructed using the graph autoencoder (GAE)-based link prediction method. A scale-free (SF) network was employed for comparison purposes. Epidemic simulations were conducted to evaluate three intervention strategies: Mass Quarantine (MQ), Isolation, and Isolation combined with Acquaintance Quarantine (AQ + Isolation). Results: Simulation results showed that AQ + Isolation was the most effective intervention on the GAE network, resulting in consistent epidemic curves due to high clustering coefficients. Conversely, MQ and AQ + Isolation were highly effective on the SF network, attributed to its low clustering coefficient and intervention sensitivity. Isolation alone exhibited reduced effectiveness. These findings emphasize the significant impact of network structure on intervention outcomes and suggest a potential overestimation of effectiveness in SF networks. Additionally, they highlight the complementary use of link prediction methods. Discussion: This innovative methodology provides inspiration for enhancing simulation environments in future endeavors. It also offers valuable insights for informing public health decision-making processes, emphasizing the importance of realistic simulation environments and the potential of link prediction methods.

FaceTouch: Detecting hand-to-face touch with supervised contrastive learning to assist in tracing infectious diseases.

Through our respiratory system, many viruses and diseases frequently spread and pass from one person to another. Covid-19 served as an example of how crucial it is to track down and cut back on contacts to stop its spread. There is a clear gap in finding automatic methods that can detect hand-to-face contact in complex urban scenes or indoors. In this paper, we introduce a computer vision framework, called FaceTouch, based on deep learning. It comprises deep sub-models to detect humans and analyse their actions. FaceTouch seeks to detect hand-to-face touches in the wild, such as through video chats, bus footage, or CCTV feeds. Despite partial occlusion of faces, the introduced system learns to detect face touches from the RGB representation of a given scene by utilising the representation of the body gestures such as arm movement. This has been demonstrated to be useful in complex urban scenarios beyond simply identifying hand movement and its closeness to faces. Relying on Supervised Contrastive Learning, the introduced model is trained on our collected dataset, given the absence of other benchmark datasets. The framework shows a strong validation in unseen datasets which opens the door for potential deployment.

Social contacts patterns relevant to the transmission of infectious diseases in Suzhou, China following the COVID-19 epidemic.

BACKGROUND: The COVID-19 pandemic has profoundly affected human social contact patterns, but there is limited understanding regarding the post-pandemic social contact patterns. Our objective is to quantitatively assess social contact patterns in Suzhou post-COVID-19. METHODS: We employed a diary design and conducted social contact surveys from June to October 2023, utilizing paper questionnaires. A generalized linear model was utilized to analyze the relationship between individual contacts and covariates. We examined the proportions of contact type, location, duration, and frequency. Additionally, age-related mixed matrices were established. RESULTS: The participants reported an average of 11.51 (SD 5.96) contact numbers and a total of 19.78 (SD 20.94) contact numbers per day, respectively. The number of contacts was significantly associated with age, household size, and the type of week. Compared to the 0-9 age group, those in the 10-19 age group reported a higher number of contacts (IRR = 1.12, CI: 1.01-1.24), while participants aged 20 and older reported fewer (IRR range: 0.54-0.67). Larger households (5 or more) reported more contacts (IRR = 1.09, CI: 1.01-1.18) and fewer contacts were reported on weekends (IRR = 0.95, CI: 0.90-0.99). School had the highest proportion of contact durations exceeding 4 h (49.5%) and daily frequencies (90.4%), followed by home and workplace. The contact patterns exhibited clear age-assortative mixing, with Q indices of 0.27 and 0.28. CONCLUSIONS: We assessed the characteristics of social contact patterns in Suzhou, which are essential for parameterizing models of infectious disease transmission. The high frequency and intensity of contacts among school-aged children should be given special attention, making school intervention policies a crucial component in controlling infectious disease transmission.

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.