Sociodemographic influences on private and professional contact behaviour during the COVID-19 pandemic in Germany: cross-sectional analysis based on a Regional Blood Donor Cohort.

OBJECTIVE: The COVID-19 pandemic has had significant health and socioeconomic impacts worldwide. Extensive measures, including contact restrictions, were implemented to control the spread of the virus. This study aims to examine the factors that influenced private and professional contact behaviour during the COVID-19 pandemic. RESULTS: We used baseline data (January-April 2021) from the SeMaCo study (Serologische Untersuchungen bei Blutspendern des Großraums Magdeburg auf Antikörper gegen SARS-CoV-2), a longitudinal, regional cohort study assessing COVID-19 seroprevalence in blood donors from Magdeburg and surrounding areas in Germany. In the blood donor cohort (n = 2,195), there was a general reduction in private contacts (by 78.9%) and professional contacts (by 54.4%) after March 18, 2020. Individuals with higher education reduced both private (by 84.1%) and professional (by 70.1%) contacts more than those with lower education levels (private contacts 59.5%; professional contacts 37%). Younger age groups (18-30 years) reduced private contacts more frequently (by 85.4%) than older individuals (61-83 years, by 68.6%) and demonstrated a higher likelihood of private contact reduction compared to older age groups (51-60 years: odds ratio (OR) 0.45 [95% [CI] 0.32-0.65]; 61-83 years: OR 0.33 [95% [CI] 0.22-0.48]).

Detecting and quantifying heterogeneity in susceptibility using contact tracing data.

The presence of heterogeneity in susceptibility, differences between hosts in their likelihood of becoming infected, can fundamentally alter disease dynamics and public health responses, for example, by changing the final epidemic size, the duration of an epidemic, and even the vaccination threshold required to achieve herd immunity. Yet, heterogeneity in susceptibility is notoriously difficult to detect and measure, especially early in an epidemic. Here we develop a method that can be used to detect and estimate heterogeneity in susceptibility given contact by using contact tracing data, which are typically collected early in the course of an outbreak. This approach provides the capability, given sufficient data, to estimate and account for the effects of this heterogeneity before they become apparent during an epidemic. It additionally provides the capability to analyze the wealth of contact tracing data available for previous epidemics and estimate heterogeneity in susceptibility for disease systems in which it has never been estimated previously. The premise of our approach is that highly susceptible individuals become infected more often than less susceptible individuals, and so individuals not infected after appearing in contact networks should be less susceptible than average. This change in susceptibility can be detected and quantified when individuals show up in a second contact network after not being infected in the first. To develop our method, we simulated contact tracing data from artificial populations with known levels of heterogeneity in susceptibility according to underlying discrete or continuous distributions of susceptibilities. We analyzed these data to determine the parameter space under which we are able to detect heterogeneity and the accuracy with which we are able to estimate it. We found that our power to detect heterogeneity increases with larger sample sizes, greater heterogeneity, and intermediate fractions of contacts becoming infected in the discrete case or greater fractions of contacts becoming infected in the continuous case. We also found that we are able to reliably estimate heterogeneity and disease dynamics. Ultimately, this means that contact tracing data alone are sufficient to detect and quantify heterogeneity in susceptibility.

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.

Characterizing participants who respond to text, email, phone calls, or postcards in a SARS-CoV-2 prevalence study.

INTRODUCTION: Multiple modalities and frequencies of contact are needed to maximize recruitment in many public health surveys. The purpose of this analysis is to characterize respondents to a statewide SARS-CoV-2 testing study whose participation followed either postcard, phone outreach or electronic means of invitation. In addition, we examine how participant characteristics differ based upon the number of contacts needed to elicit participation. METHODS: This is a cross-sectional analysis of survey data collected from participants who were randomly selected to represent Indiana residents and were invited to be tested for Covid-19 in April 2020. Participants received invitations via postcard, text/emails, and/or robocalls/texts based upon available contact information. The modality, and frequency of contacts, that prompted participation was determined by when the notification was sent and when the participant responded and subsequently registered to participate in the study. Chi square analyses were used to determine differences between groups and significant findings were analyzed using multinomial logistic regression. RESULTS: Respondents included 3,658 individuals and were stratified by postcards (7.9%), text/emails (26.5%), and robocalls/text (65.7%) with 19.7% registering after 1 contact, 47.9% after 2 contacts, and 32.4% after 3 contacts encouraging participation. Females made up 54.6% of the sample and responded at a higher rate for postcards (8.2% vs. 7.5%) and text/emails (28.1 vs. 24.6%) as compared to males (χ2 = 7.43, p = 0.025). Compared to males, females responded at a higher percentage after 1 contact (21.4 vs. 17.9%, χ2 = 7.6, p = 0.023). Those over 60 years responded most often after 2 contacts (χ2 = 27.5, p < 0.001) when compared to others at younger age groups. In regression analysis, participant sex (p = 0.036) age (p = 0.005), educational attainment (p = < 0.0001), and being motivated by "free testing" (p = 0.036) were correlated with participation in the prevalence study. DISCUSSION: Researchers should be aware that the modality of contact as well as the number of prompts used could influence differential participation in public health studies. Our findings can inform researchers developing studies that rely on selective participation by study subjects. We explore how to increase participation within targeted demographic groups using specific modalities and examining frequency of contact.

Sexual health behaviours and partner notification practices related to sexually transmitted infections in young adults in Ireland.

BACKGROUND: Sexually transmitted infections (STIs) impose burdens on individuals and communities, while their prevalence in young people has risen continually in recent years. Partner notification is an effective public health strategy which can limit STI transmission. AIMS: This study aimed to explore young adults' sexual health behaviours, attitudes toward STI testing, and feelings toward visiting a sexual health clinic. It also aimed to investigate preferences for partner notification and the role of self-efficacy in people's intentions to notify a partner for STIs including the human immunodeficiency virus (HIV). METHODS: A quantitative, cross-sectional design was applied. Participants were 400 adults aged 18-34 years (M = 23 years; SD = 4.27), recruited from the Republic of Ireland. RESULTS: Over half of the participants reported never being tested for STIs. These young people placed less importance on undergoing regular STI testing and testing after unprotected sex than their counterparts who had been tested for STIs. Self-efficacy was significantly associated with intentions to notify partner(s) for STIs including HIV. CONCLUSIONS: As STIs are becoming increasingly prevalent in young adults, it is important to gain a deeper understanding of the interventions used to break the transmission chain and how different beliefs and attitudes may affect them. Self-efficacy was a key component in PN intentions, suggesting that the belief in someone's ability or skillset to perform a sexual health behaviour is positively related to their intention to perform the behaviour.

Digital measurement of SARS-CoV-2 transmission risk from 7 million contacts.

How likely is it to become infected by SARS-CoV-2 after being exposed? Almost everyone wondered about this question during the COVID-19 pandemic. Contact-tracing apps1,2 recorded measurements of proximity3 and duration between nearby smartphones. Contacts-individuals exposed to confirmed cases-were notified according to public health policies such as the 2 m, 15 min guideline4,5, despite limited evidence supporting this threshold. Here we analysed 7 million contacts notified by the National Health Service COVID-19 app6,7 in England and Wales to infer how app measurements translated to actual transmissions. Empirical metrics and statistical modelling showed a strong relation between app-computed risk scores and actual transmission probability. Longer exposures at greater distances had risk similar to that of shorter exposures at closer distances. The probability of transmission confirmed by a reported positive test increased initially linearly with duration of exposure (1.1% per hour) and continued increasing over several days. Whereas most exposures were short (median 0.7 h, interquartile range 0.4-1.6), transmissions typically resulted from exposures lasting between 1 h and several days (median 6 h, interquartile range 1.4-28). Households accounted for about 6% of contacts but 40% of transmissions. With sufficient preparation, privacy-preserving yet precise analyses of risk that would inform public health measures, based on digital contact tracing, could be performed within weeks of the emergence of a new pathogen.

Household Transmission of Influenza A Viruses in 2021-2022.

Importance: Influenza virus infections declined globally during the COVID-19 pandemic. Loss of natural immunity from lower rates of influenza infection and documented antigenic changes in circulating viruses may have resulted in increased susceptibility to influenza virus infection during the 2021-2022 influenza season. Objective: To compare the risk of influenza virus infection among household contacts of patients with influenza during the 2021-2022 influenza season with risk of influenza virus infection among household contacts during influenza seasons before the COVID-19 pandemic in the US. Design, Setting, and Participants: This prospective study of influenza transmission enrolled households in 2 states before the COVID-19 pandemic (2017-2020) and in 4 US states during the 2021-2022 influenza season. Primary cases were individuals with the earliest laboratory-confirmed influenza A(H3N2) virus infection in a household. Household contacts were people living with the primary cases who self-collected nasal swabs daily for influenza molecular testing and completed symptom diaries daily for 5 to 10 days after enrollment. Exposures: Household contacts living with a primary case. Main Outcomes and Measures: Relative risk of laboratory-confirmed influenza A(H3N2) virus infection in household contacts during the 2021-2022 season compared with prepandemic seasons. Risk estimates were adjusted for age, vaccination status, frequency of interaction with the primary case, and household density. Subgroup analyses by age, vaccination status, and frequency of interaction with the primary case were also conducted. Results: During the prepandemic seasons, 152 primary cases (median age, 13 years; 3.9% Black; 52.0% female) and 353 household contacts (median age, 33 years; 2.8% Black; 54.1% female) were included and during the 2021-2022 influenza season, 84 primary cases (median age, 10 years; 13.1% Black; 52.4% female) and 186 household contacts (median age, 28.5 years; 14.0% Black; 63.4% female) were included in the analysis. During the prepandemic influenza seasons, 20.1% (71/353) of household contacts were infected with influenza A(H3N2) viruses compared with 50.0% (93/186) of household contacts in 2021-2022. The adjusted relative risk of A(H3N2) virus infection in 2021-2022 was 2.31 (95% CI, 1.86-2.86) compared with prepandemic seasons. Conclusions and Relevance: Among cohorts in 5 US states, there was a significantly increased risk of household transmission of influenza A(H3N2) in 2021-2022 compared with prepandemic seasons. Additional research is needed to understand reasons for this association.

The effectiveness of COVID-19 testing and contact tracing in a US city.

Although testing, contact tracing, and case isolation programs can mitigate COVID-19 transmission and allow the relaxation of social distancing measures, few countries worldwide have succeeded in scaling such efforts to levels that suppress spread. The efficacy of test-trace-isolate likely depends on the speed and extent of follow-up and the prevalence of SARS-CoV-2 in the community. Here, we use a granular model of COVID-19 transmission to estimate the public health impacts of test-trace-isolate programs across a range of programmatic and epidemiological scenarios, based on testing and contact tracing data collected on a university campus and surrounding community in Austin, TX, between October 1, 2020, and January 1, 2021. The median time between specimen collection from a symptomatic case and quarantine of a traced contact was 2 days (interquartile range [IQR]: 2 to 3) on campus and 5 days (IQR: 3 to 8) in the community. Assuming a reproduction number of 1.2, we found that detection of 40% of all symptomatic cases followed by isolation is expected to avert 39% (IQR: 30% to 45%) of COVID-19 cases. Contact tracing is expected to increase the cases averted to 53% (IQR: 42% to 58%) or 40% (32% to 47%), assuming the 2- and 5-day delays estimated on campus and in the community, respectively. In a tracing-accelerated scenario, in which 75% of contacts are notified the day after specimen collection, cases averted increase to 68% (IQR: 55% to 72%). An accelerated contact tracing program leveraging rapid testing and electronic reporting of test results can significantly curtail local COVID-19 transmission.

Time trends in social contacts of individuals according to comorbidity and vaccination status, before and during the COVID-19 pandemic.

BACKGROUND: As we are confronted with more transmissible/severe variants with immune escape and the waning of vaccine efficacy, it is particularly relevant to understand how the social contacts of individuals at greater risk of COVID-19 complications evolved over time. We described time trends in social contacts of individuals according to comorbidity and vaccination status before and during the first three waves of the COVID-19 pandemic in Quebec, Canada. METHODS: We used data from CONNECT, a repeated cross-sectional population-based survey of social contacts conducted before (2018/2019) and during the pandemic (April 2020 to July 2021). We recruited non-institutionalized adults from Quebec, Canada, by random digit dialling. We used a self-administered web-based questionnaire to measure the number of social contacts of participants (two-way conversation at a distance ≤2 m or a physical contact, irrespective of masking). We compared the mean number of contacts/day according to the comorbidity status of participants (pre-existing medical conditions with symptoms/medication in the past 12 months) and 1-dose vaccination status during the third wave. All analyses were performed using weighted generalized linear models with a Poisson distribution and robust variance. RESULTS: A total of 1441 and 5185 participants with and without comorbidities, respectively, were included in the analyses. Contacts significantly decreased from a mean of 6.1 (95%CI 4.9-7.3) before the pandemic to 3.2 (95%CI 2.5-3.9) during the first wave among individuals with comorbidities and from 8.1 (95%CI 7.3-9.0) to 2.7 (95%CI 2.2-3.2) among individuals without comorbidities. Individuals with comorbidities maintained fewer contacts than those without comorbidities in the second wave, with a significant difference before the Christmas 2020/2021 holidays (2.9 (95%CI 2.5-3.2) vs 3.9 (95%CI 3.5-4.3); P<0.001). During the third wave, contacts were similar for individuals with (4.1, 95%CI 3.4-4.7) and without comorbidities (4.5, 95%CI 4.1-4.9; P=0.27). This could be partly explained by individuals with comorbidities vaccinated with their first dose who increased their contacts to the level of those without comorbidities. CONCLUSIONS: It will be important to closely monitor COVID-19-related outcomes and social contacts by comorbidity and vaccination status to inform targeted or population-based interventions (e.g., booster doses of the vaccine).

Effectiveness of contact tracing in the control of infectious diseases: a systematic review.

BACKGROUND: Contact tracing is used for multiple infectious diseases, most recently for COVID-19, but data regarding its effectiveness in disease control are scarce. To address this knowledge gap and inform public health decision making for COVID-19, we systematically reviewed the existing literature to determine the effectiveness of contact tracing in the control of communicable illness. METHODS: We searched PubMed, Embase, and the Cochrane Library from database inception up to Nov 22, 2021, for published studies evaluating associations between provider-initiated contact tracing for transmissible infectious diseases and one of three outcomes of interest: case detection rates among contacts or at the community level, overall forward transmission, or overall disease incidence. Clinical trials and observational studies were eligible, with no language or date restrictions. Reference lists of reviews were searched for additional studies. We excluded studies without a control group, using only mathematical modelling, not reporting a primary outcome of interest, or solely examining patient-initiated contact tracing. One reviewer applied eligibility criteria to each screened abstract and full-text article, and two reviewers independently extracted summary effect estimates and additional data from eligible studies. Only data reported in published manuscripts or supplemental material was extracted. Risk of bias for each included study was assessed with the Cochrane Risk of Bias 2 tool (randomised studies) or the Newcastle-Ottawa Scale (non-randomised studies). FINDINGS: We identified 9050 unique citations, of which 47 studies met the inclusion criteria: six were focused on COVID-19, 20 on tuberculosis, eight on HIV, 12 on curable sexually transmitted infections (STIs), and one on measles. More than 2 million index patients were included across a variety of settings (both urban and rural areas and low-resource and high-resource settings). Of the 47 studies, 29 (61·7%) used observational designs, including all studies on COVID-19, and 18 (38·3%) were randomised controlled trials. 40 studies compared provider-initiated contact tracing with other interventions or evaluated expansions of provider-initiated contact tracing, and seven compared programmatic adaptations within provider-initiated contact tracing. 29 (72·5%) of the 40 studies evaluating the effect of provider-initiated contact tracing, including four (66·7%) of six COVID-19 studies, found contact tracing interventions were associated with improvements in at least one outcome of interest. 23 (48·9%) studies had low risk of bias, 22 (46·8%) studies had some risk of bias, and two (4·3%) studies (both randomised controlled trials on curable STIs) had high risk of bias. INTERPRETATION: Provider-initiated contact tracing can be an effective public health tool. However, the ability of authorities to make informed choices about its deployment might be limited by heterogenous approaches to contact tracing in studies, a scarcity of quantitative evidence on its effectiveness, and absence of specificity of tracing parameters most important for disease control. FUNDING: The Sullivan Family Foundation, Massachusetts General Hospital Executive Committee on Research, and US National Institutes of Health.

Effectiveness evaluation of digital contact tracing for COVID-19 in New South Wales, Australia.

BACKGROUND: Digital proximity tracing apps were rolled out early in the COVID-19 pandemic in many countries to complement conventional contact tracing. Empirical evidence about their benefits for pandemic response remains scarce. We evaluated the effectiveness and usefulness of COVIDSafe, Australia's national smartphone-based proximity tracing app for COVID-19. METHODS: In this prospective study, done in New South Wales (NSW), Australia, we included all individuals in the state who were older than 12 years with confirmed, locally acquired SARS-CoV-2 infection between May 4 and Nov 4, 2020. We used data from the NSW Notifiable Conditions Information Management System, the national COVIDSafe database, and information from case interviews, including information on app usage, the number of app-suggested contacts, and the number of app-suggested contacts determined by public health staff to be actual close contacts. We calculated the positive predictive value and sensitivity of COVIDSafe, its additional contact yield, and the number of averted public exposure events. Semi-structured interviews with public health staff were done to assess the app's perceived usefulness. FINDINGS: There were 619 confirmed COVID-19 cases with more than 25 300 close contacts identified by conventional contact tracing during the study period. COVIDSafe was used by 137 (22%) cases and detected 205 contacts, 79 (39%) of whom met the close contact definition. Its positive predictive value was therefore 39%. 35 (15%) of the 236 close contacts who could have been expected to have been using the app during the study period were identified by the app, making its estimated sensitivity 15%. 79 (0·3%) of the estimated 25 300 contacts in NSW were app-suggested and met the close contact definition. The app detected 17 (<0·1%) additional close contacts who were not identified by conventional contact tracing. COVIDSafe generated a substantial additional perceived workload for public health staff and was not considered useful. INTERPRETATION: The low uptake of the app among cases probably led to a reduced sensitivity estimate in our study, given that only contacts who were using the app could be detected. COVIDSafe was not sufficiently effective to make a meaningful contribution to the COVID-19 response in Australia's most populous state over a 6 month period. We provide an empirical evaluation of this digital contact tracing app that questions the potential benefits of digital contact tracing apps to the public health response to COVID-19. Effectiveness evaluations should be integrated into future implementations of proximity contact tracing systems to justify their investment. FUNDING: New South Wales Ministry of Health (Australia); National Health and Medical Research Council (Australia).

Factors associated with testing positive for syphilis among MSM who present as sexual contacts of syphilis from a clinic-based population.

OBJECTIVES: There has been a significant increase in syphilis in men who have sex with men (MSM) in the UK over the past 20 years. Partner notification strategies have increased the number of MSM attending STI clinics as sexual contacts of syphilis. Current guidelines suggest testing and consideration of presumptive antimicrobial treatment. Syphilis treatment with benzathine penicillin requires clinic resources, is painful and is associated with complications. It is important we consider strategies to rationalise presumptive antimicrobial use and promote antimicrobial stewardship. METHODS: We aimed to determine if there are any factors associated with having syphilis among MSM attending as sexual contacts of syphilis in a cross-sectional study. We examined the clinical records of MSM attending as sexual contacts of syphilis from January to December 2019. RESULTS: Of the 6613 MSM who attended for STI testing, 142 of 6613 (2.1%) presented as sexual contacts of syphilis. The median age was 40 years (IQR=31-51), 43 of 142 (30%) were HIV positive, 38 of 142 (27%) had been diagnosed and treated for syphilis in the past, and 11 of 142 (8%) presented with symptoms (possible lesions of primary or secondary syphilis). Thirteen (9%, 95% CI=4.4 to 13.9) tested positive for syphilis on the day of presentation. MSM who were symptomatic (genital ulcer or body rash), HIV positive or had a history of syphilis were significantly more likely to test positive for syphilis (OR=51.88, 95% CI: 3.01 to 893.14, p=0.007). CONCLUSIONS: We have shown that in our clinic-based population of MSM who presented as sexual contacts of syphilis, the factors associated with testing positive for syphilis were: having HIV, having a history of syphilis or presenting with symptoms (possible lesions of primary or secondary syphilis). These factors could be used to rationalise antibiotic treatment among MSM presenting as sexual contacts of syphilis. Further research is needed to validate this finding in other populations of MSM and people affected by syphilis.

Estimating the impact of interventions against COVID-19: From lockdown to vaccination.

Coronavirus disease 2019 (COVID-19) is an infectious disease of humans caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Since the first case was identified in China in December 2019 the disease has spread worldwide, leading to an ongoing pandemic. In this article, we present an agent-based model of COVID-19 in Luxembourg, and use it to estimate the impact, on cases and deaths, of interventions including testing, contact tracing, lockdown, curfew and vaccination. Our model is based on collation, with agents performing activities and moving between locations accordingly. The model is highly heterogeneous, featuring spatial clustering, over 2000 behavioural types and a 10 minute time resolution. The model is validated against COVID-19 clinical monitoring data collected in Luxembourg in 2020. Our model predicts far fewer cases and deaths than the equivalent equation-based SEIR model. In particular, with R0 = 2.45, the SEIR model infects 87% of the resident population while our agent-based model infects only around 23% of the resident population. Our simulations suggest that testing and contract tracing reduce cases substantially, but are less effective at reducing deaths. Lockdowns are very effective although costly, while the impact of an 11pm-6am curfew is relatively small. When vaccinating against a future outbreak, our results suggest that herd immunity can be achieved at relatively low coverage, with substantial levels of protection achieved with only 30% of the population fully immune. When vaccinating in the midst of an outbreak, the challenge is more difficult. In this context, we investigate the impact of vaccine efficacy, capacity, hesitancy and strategy. We conclude that, short of a permanent lockdown, vaccination is by far the most effective way to suppress and ultimately control the spread of COVID-19.

Modelling COVID-19 transmission in a hemodialysis centre using simulation generated contacts matrices.

The COVID-19 pandemic has been particularly threatening to patients with end-stage kidney disease (ESKD) on intermittent hemodialysis and their care providers. Hemodialysis patients who receive life-sustaining medical therapy in healthcare settings, face unique challenges as they need to be at a dialysis unit three or more times a week, where they are confined to specific settings and tended to by dialysis nurses and staff with physical interaction and in close proximity. Despite the importance and critical situation of the dialysis units, modelling studies of the SARS-CoV-2 spread in these settings are very limited. In this paper, we have used a combination of discrete event and agent-based simulation models, to study the operations of a typical large dialysis unit and generate contact matrices to examine outbreak scenarios. We present the details of the contact matrix generation process and demonstrate how the simulation calculates a micro-scale contact matrix comprising the number and duration of contacts at a micro-scale time step. We have used the contacts matrix in an agent-based model to predict disease transmission under different scenarios. The results show that micro-simulation can be used to estimate contact matrices, which can be used effectively for disease modelling in dialysis and similar settings.

A snapshot of the practicality and barriers to COVID-19 interventions: Public health and healthcare workers' perceptions in high and low- and middle-income countries.

BACKGROUND: In response to the COVID-19 pandemic, governments have implemented a range of non-pharmaceutical interventions (NPIs) and pharmaceutical interventions (PIs) to reduce transmission and minimise morbidity and mortality, whilst maintaining social and economic activities. The perceptions of public health workers (PHWs) and healthcare workers (HCWs) are essential to inform future COVID-19 strategies as they are viewed as trusted sources and are at the forefront of COVID-19 response. The objectives of this study were to 1) describe the practicality of implementing NPIs and PIs and 2) identify potential barriers to implementation, as perceived by HCWs and PHWs. METHODS: We conducted a cross-sectional study of PHWs and HCWs perceptions of the implementation, practicality of, and barriers to implementation of NPIs and PIs using an online survey (28/9/2020-1/11/2020) available in English, French and Portuguese. We used descriptive statistics and thematic analysis to analyse quantitative and qualitative responses. RESULTS: In total, 226 respondents (67 HCWs and 159 PHWs) from 52 countries completed the survey and 222 were included in the final analysis. Participants from low and middle-income countries (LMICs) accounted for 63% of HCWs and 67% of PHWs, with the remaining from high-income (HICs). There was little difference between the perceptions of PHWs and HCWs in HICs and LMICs, with the majority regarding a number of common NPIs as difficult to implement. However, PHWs in HICs perceived restrictions on schools and educational institutions to be more difficult to implement, with a lack of childcare support identified as the main barrier. Additionally, most contact tracing methods were perceived to be more difficult to implement in HICs than LMICs, with a range of barriers reported. A lack of public support was the most commonly reported barrier to NPIs overall across both country income and professional groups. Similarly, public fear of vaccine safety and lack of vaccine supply were the main reported barriers to implementing a COVID-19 vaccine. However, PHWs and HCWs in LMICs perceived a lack of financial support and the vaccine being manufactured in another country as additional barriers. CONCLUSION: This snapshot provides insight into the difficulty of implementing interventions as perceived by PHWs and HCWs. There is no one-size-fits-all solution to implementing interventions, and barriers in different contexts do vary. Barriers to implementing a vaccine programme expressed here by HCWs and PHCWs have subsequently come to the fore internationally.

Breaking the paradigm: Optimized Case Finding multiplies tuberculosis detection among key populations in Ukraine.

INTRODUCTION: In 2018, there were 3 million "missed" tuberculosis (TB) cases globally, much of which was disproportionally concentrated among key populations. To enhance TB case-finding, an Optimized Case Finding (OCF) strategy involving all contacts within the social network of an index TB case was introduced in five regions of Ukraine. We assessed TB detection and linkage to TB treatment using OCF in key populations. METHODOLOGY: A cohort study using routine program data (July 2018 - March 2020). OCF empowers the index TB case to identify and refer up to eight close contacts within his/her social network for TB investigations. RESULTS: Of 726 index TB cases in key populations, 6,998 close contacts were referred for TB investigations and 275 were diagnosed with TB (183 drug-sensitive and 92 drug-resistant TB). The TB case detection rate was 3,930/100,000 and the Numbers Needed to Investigate to detect one TB case was 25. TB was most frequent among people who inject drugs and homeless groups. Compared to TB detection using routine household case finding within the general population (1,090/100,000), OCF was 3.6-fold more effective and when compared to passive case finding in the general population (60/100,000), OCF was 66 times more effective. 99% (273) of TB patients were linked to care and initiated TB treatment. CONCLUSIONS: The OCF strategy among key populations is very effective in identifying TB cases and involving them for treatment through the recruitment of the contacts from the risk social networks. We advocate to scale-up this case finding strategy in Ukraine and beyond.

A language-matching model to improve equity and efficiency of COVID-19 contact tracing.

Contact tracing is a pillar of COVID-19 response, but language access and equity have posed major obstacles. COVID-19 has disproportionately affected minority communities with many non-English-speaking members. Language discordance can increase processing times and hamper the trust building necessary for effective contact tracing. We demonstrate how matching predicted patient language with contact tracer language can enhance contact tracing. First, we show how to use machine learning to combine information from sparse laboratory reports with richer census data to predict the language of an incoming case. Second, we embed this method in the highly demanding environment of actual contact tracing with high volumes of cases in Santa Clara County, CA. Third, we evaluate this language-matching intervention in a randomized controlled trial. We show that this low-touch intervention results in 1) significant time savings, shortening the time from opening of cases to completion of the initial interview by nearly 14 h and increasing same-day completion by 12%, and 2) improved engagement, reducing the refusal to interview by 4%. These findings have important implications for reducing social disparities in COVID-19; improving equity in healthcare access; and, more broadly, leveling language differences in public services.

"Hot-spotting" to improve vaccine allocation by harnessing digital contact tracing technology: An application of percolation theory.

Vaccinating individuals with more exposure to others can be disproportionately effective, in theory, but identifying these individuals is difficult and has long prevented implementation of such strategies. Here, we propose how the technology underlying digital contact tracing could be harnessed to boost vaccine coverage among these individuals. In order to assess the impact of this "hot-spotting" proposal we model the spread of disease using percolation theory, a collection of analytical techniques from statistical physics. Furthermore, we introduce a novel measure which we call the efficiency, defined as the percentage decrease in the reproduction number per percentage of the population vaccinated. We find that optimal implementations of the proposal can achieve herd immunity with as little as half as many vaccine doses as a non-targeted strategy, and is attractive even for relatively low rates of app usage.