Transmission from vaccinated individuals in a large SARS-CoV-2 Delta variant outbreak.

An outbreak of over 1,000 COVID-19 cases in Provincetown, Massachusetts (MA), in July 2021-the first large outbreak mostly in vaccinated individuals in the US-prompted a comprehensive public health response, motivating changes to national masking recommendations and raising questions about infection and transmission among vaccinated individuals. To address these questions, we combined viral genomic and epidemiological data from 467 individuals, including 40% of outbreak-associated cases. The Delta variant accounted for 99% of cases in this dataset; it was introduced from at least 40 sources, but 83% of cases derived from a single source, likely through transmission across multiple settings over a short time rather than a single event. Genomic and epidemiological data supported multiple transmissions of Delta from and between fully vaccinated individuals. However, despite its magnitude, the outbreak had limited onward impact in MA and the US overall, likely due to high vaccination rates and a robust public health response.

Preventing Outbreaks through Interactive, Experiential Real-Life Simulations.

Operation Outbreak (OO) is a Bluetooth-based simulation platform that teaches students how pathogens spread and the impact of interventions, thereby facilitating the safe reopening of schools. OO also generates data to inform epidemiological models and prevent future outbreaks. Before SARS-CoV-2 was reported, we repeatedly simulated a virus with similar features, correctly predicting many human behaviors later observed during the pandemic.

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.

The epidemiological impact of the NHS COVID-19 app.

The COVID-19 pandemic has seen the emergence of digital contact tracing to help to prevent the spread of the disease. A mobile phone app records proximity events between app users, and when a user tests positive for COVID-19, their recent contacts can be notified instantly. Theoretical evidence has supported this new public health intervention1-6, but its epidemiological impact has remained uncertain7. Here we investigate the impact of the National Health Service (NHS) COVID-19 app for England and Wales, from its launch on 24 September 2020 to the end of December 2020. It was used regularly by approximately 16.5 million users (28% of the total population), and sent approximately 1.7 million exposure notifications: 4.2 per index case consenting to contact tracing. We estimated that the fraction of individuals notified by the app who subsequently showed symptoms and tested positive (the secondary attack rate (SAR)) was 6%, similar to the SAR for manually traced close contacts. We estimated the number of cases averted by the app using two complementary approaches: modelling based on the notifications and SAR gave an estimate of 284,000 (central 95% range of sensitivity analyses 108,000-450,000), and statistical comparison of matched neighbouring local authorities gave an estimate of 594,000 (95% confidence interval 317,000-914,000). Approximately one case was averted for each case consenting to notification of their contacts. We estimated that for every percentage point increase in app uptake, the number of cases could be reduced by 0.8% (using modelling) or 2.3% (using statistical analysis). These findings support the continued development and deployment of such apps in populations that are awaiting full protection from vaccines.

Effect of non-pharmaceutical interventions to contain COVID-19 in China.

On 11 March 2020, the World Health Organization (WHO) declared coronavirus disease 2019 (COVID-19) a pandemic1. The strategies based on non-pharmaceutical interventions that were used to contain the outbreak in China appear to be effective2, but quantitative research is still needed to assess the efficacy of non-pharmaceutical interventions and their timings3. Here, using epidemiological data on COVID-19 and anonymized data on human movement4,5, we develop a modelling framework that uses daily travel networks to simulate different outbreak and intervention scenarios across China. We estimate that there were a total of 114,325 cases of COVID-19 (interquartile range 76,776-164,576) in mainland China as of 29 February 2020. Without non-pharmaceutical interventions, we predict that the number of cases would have been 67-fold higher (interquartile range 44-94-fold) by 29 February 2020, and we find that the effectiveness of different interventions varied. We estimate that early detection and isolation of cases prevented more infections than did travel restrictions and contact reductions, but that a combination of non-pharmaceutical interventions achieved the strongest and most rapid effect. According to our model, the lifting of travel restrictions from 17 February 2020 does not lead to an increase in cases across China if social distancing interventions can be maintained, even at a limited level of an on average 25% reduction in contact between individuals that continues until late April. These findings improve our understanding of the effects of non-pharmaceutical interventions on COVID-19, and will inform response efforts across the world.

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.

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.

Transmission Dynamics and Rare Clustered Transmission Within an Urban University Population Before Widespread Vaccination.

BACKGROUND: Universities returned to in-person learning in 2021 while SARS-CoV-2 spread remained high. At the time, it was not clear whether in-person learning would be a source of disease spread. METHODS: We combined surveillance testing, universal contact tracing, and viral genome sequencing to quantify introductions and identify likely on-campus spread. RESULTS: Ninety-one percent of viral genotypes occurred once, indicating no follow-on transmission. Less than 5% of introductions resulted in >3 cases, with 2 notable exceptions of 40 and 47 cases. Both partially overlapped with outbreaks defined by contact tracing. In both cases, viral genomics eliminated over half the epidemiologically linked cases but added an equivalent or greater number of individuals to the transmission cluster. CONCLUSIONS: Public health interventions prevented within-university transmission for most SARS-CoV-2 introductions, with only 2 major outbreaks being identified January to May 2021. The genetically linked cases overlap with outbreaks identified by contact tracing; however, they persisted in the university population for fewer days and rounds of transmission than estimated via contact tracing. This underscores the effectiveness of test-trace-isolate strategies in controlling undetected spread of emerging respiratory infectious diseases. These approaches limit follow-on transmission in both outside-in and internal transmission conditions.

Adapting COVID-19 Contact Tracing Protocols to Accommodate Resource Constraints, Philadelphia, Pennsylvania, USA, 2021.

Because of constrained personnel time, the Philadelphia Department of Public Health (Philadelphia, PA, USA) adjusted its COVID-19 contact tracing protocol in summer 2021 by prioritizing recent cases and limiting staff time per case. This action reduced required staff hours to prevent each case from 21-30 to 8-11 hours, while maintaining program effectiveness.

Chest Radiograph Screening for Detecting Subclinical Tuberculosis in Asymptomatic Household Contacts, Peru.

The World Health Organization's end TB strategy promotes the use of symptom and chest radiograph screening for tuberculosis (TB) disease. However, asymptomatic early states of TB beyond latent TB infection and active disease can go unrecognized using current screening criteria. We conducted a longitudinal cohort study enrolling household contacts initially free of TB disease and followed them for the occurrence of incident TB over 1 year. Among 1,747 screened contacts, 27 (52%) of the 52 persons in whom TB subsequently developed during follow-up had a baseline abnormal radiograph. Of contacts without TB symptoms, persons with an abnormal radiograph were at higher risk for subsequent TB than persons with an unremarkable radiograph (adjusted hazard ratio 15.62 [95% CI 7.74-31.54]). In young adults, we found a strong linear relationship between radiograph severity and time to TB diagnosis. Our findings suggest chest radiograph screening can extend to detecting early TB states, thereby enabling timely intervention.

Using contact network dynamics to implement efficient interventions against pathogen spread in hospital settings: A modelling study.

BACKGROUND: Long-term care facilities (LTCFs) are hotspots for pathogen transmission. Infection control interventions are essential, but the high density and heterogeneity of interindividual contacts within LTCF may hinder their efficacy. Here, we explore how the patient-staff contact structure may inform effective intervention implementation. METHODS AND FINDINGS: Using an individual-based model (IBM), we reproduced methicillin-resistant Staphylococcus aureus colonisation transmission dynamics over a detailed contact network recorded within a French LTCF of 327 patients and 263 staff over 3 months. Simulated baseline cumulative colonisation incidence was 21 patients (prediction interval: 11, 31) and 35 staff (prediction interval: 19, 54). We examined the potential impact of 3 types of interventions against transmission (reallocation reducing the number of unique contacts per staff, reinforced contact precautions, and hypothetical vaccination protecting against acquisition), targeted towards specific populations. All 3 interventions were effective when applied to all nurses or healthcare assistants (median reduction in MRSA colonisation incidence up to 35%), but the benefit did not exceed 8% when targeting any other single staff category. We identified "supercontactor" individuals with most contacts ("frequency-based," overrepresented among nurses, porters, and rehabilitation staff) or with the longest cumulative time spent in contact ("duration-based," overrepresented among healthcare assistants and patients in elderly care or persistent vegetative state (PVS)). Targeting supercontactors enhanced interventions against pathogen spread in the LTCF. With contact precautions, targeting frequency-based staff supercontactors led to the highest incidence reduction (20%, 95% CI: 19, 21). Vaccinating a mix of frequency- and duration-based staff supercontactors led to a higher reduction (23%, 95% CI: 22, 24) than all other approaches. Although based on data from a single LTCF, when varying epidemiological parameters to extend to other pathogens, our results suggest that targeting supercontactors is always the most effective strategy, indicating this approach could be applied to prevent transmission of other nosocomial pathogens. CONCLUSIONS: By characterising the contact structure in hospital settings and identifying the categories of staff and patients more likely to be supercontactors, with either more or longer contacts than others, interventions against nosocomial spread could be more effective. We find that the most efficient implementation strategy depends on the intervention (reallocation, contact precautions, vaccination) and target population (staff, patients, supercontactors). Importantly, both staff and patients may be supercontactors, highlighting the importance of including patients in measures to prevent pathogen transmission in LTCF.

Digital interventions for STI and HIV partner notification: a scoping review.

BACKGROUND: Partner notification (PN) is key to the control of sexually transmitted infections (STIs) and human immunodeficiency virus (HIV). Digital interventions have been used to facilitate PN. A scoping review was conducted to describe the interventions used, user preferences and acceptability of digital PN interventions from patient and partner perspectives. METHODS: A systematic literature search was conducted of eight databases for articles published in English, available online with digital PN outcome data. Articles were assessed using the Mixed Methods Appraisal Tool. Quantitative and qualitative data were synthesised and analysed using thematic analysis. RESULTS: Twenty-six articles met the eligibility criteria. Articles were heterogeneous in quality and design, with the majority using quantitative methods. Nine articles focused solely on bacterial STIs (five on syphilis; four on chlamydia), one on HIV, two on syphilis and HIV, and 14 included multiple STIs, of which 13 included HIV. There has been a shift over time from digital PN interventions solely focusing on notifying partners, to interventions including elements of partner management, such as facilitation of partner testing and treatment, or sharing of STI test results (between index patients and tested sex partners). Main outcomes measured were number of partners notified (13 articles), partner testing/consultation (eight articles) and treatment (five articles). Relationship type and STI type appeared to affect digital PN preferences for index patients with digital methods preferred for casual rather than established partner types. Generally, partners preferred face-to-face PN. CONCLUSION: Digital PN to date mainly focuses on notifying partners rather than comprehensive partner management. Despite an overall preference for face-to-face PN with partners, digital PN could play a useful role in improving outcomes for certain partner types and infections. Further research needs to understand the impact of digital PN interventions on specific PN outcomes, their effectiveness for different infections and include health economic evaluations.

Community tuberculosis screening, testing and care, Uganda.

Objective: To assess the effectiveness of a community-based tuberculosis and leprosy intervention in which village health teams and health workers conduct door-to-door tuberculosis screening, targeted screenings and contact tracing. Methods: We conducted a before-and-after implementation study in Uganda to assess the effectiveness of the community tuberculosis intervention by looking at reach, outputs, adoption and effectiveness of the intervention. Campaign 1 was conducted in March 2022 and campaign 2 in September 2022. We calculated percentages of targets achieved and compared case notification rates during the intervention with corresponding quarters in the previous year. We also assessed the leprosy screening. Findings: Over 5 days, campaign 1 screened 1 289 213 people (2.9% of the general population), of whom 179 144 (13.9%) fulfilled the presumptive tuberculosis criteria, and 4043 (2.3%) were diagnosed with bacteriologically-confirmed tuberculosis; 3710 (91.8%) individuals were linked to care. In campaign 2, 5 134 056 people (11.6% of the general population) were screened, detecting 428 444 (8.3%) presumptive tuberculosis patients and 8121 (1.9%) bacteriologically-confirmed tuberculosis patients; 5942 individuals (87.1%) were linked to care. The case notification rate increased from 48.1 to 59.5 per 100 000 population in campaign 1, with a case notification rate ratio of 1.24 (95% confidence interval, CI: 1.22-1.26). In campaign 2, the case notification rate increased from 45.0 to 71.6 per 100 000 population, with a case notification rate ratio of 1.59 (95% CI: 1.56-1.62). Of the 176 patients identified with leprosy, 137 (77.8%) initiated treatment. Conclusion: This community tuberculosis screening initiative is effective. However, continuous monitoring and adaptations are needed to overcome context-specific implementation challenges.

Assisted Partner Notification Services in Namibia: Comparison of Case-Finding in Persons With New and Previously Diagnosed Human Immunodeficiency Virus, and Success as a Platform for PrEP Referral.

BACKGROUND: Assisted partner notification services (APS) are widely implemented throughout sub-Saharan Africa. The effectiveness of APS among persons with previously diagnosed human immunodeficiency virus (HIV) infection is uncertain, and there are few published data on the success of integrating referrals for HIV preexposure prophylaxis (PrEP) into APS. METHODS: Staff in 22 Namibian Ministry of Health and Social Service clinics offered APS to patients newly and previously diagnosed with HIV (index cases [ICs]) between October 2019 and June 2021. Counselors used a structured interview guide to elicit ICs' sex partners and biological children and assisted ICs to arrange testing of contacts. Contacts testing HIV-positive were linked to HIV services and those 14 years or older testing negative were offered PrEP. The primary outcome was the case-finding index (contacts testing HIV-positive ÷ ICs receiving APS). RESULTS: Staff provided APS to 1222 (78%) of 1557 newly diagnosed ICs eliciting 1155 sex partners and 649 biological children. Among 280 previously diagnosed ICs, 279 sex partners and 158 biological children were elicited. The case-finding index was higher among ICs with newly diagnosed HIV compared with previously diagnosed HIV (0.14 vs 0.09, P = 0.46), though this difference was not statistically significant. Most sex partners testing HIV-negative were initiated on PrEP (67% in sex partners from newly diagnosed ICs; 74% in sex partners from previously diagnosed ICs). CONCLUSIONS: Assisted partner notification services successfully identified sex partners and biological children with undiagnosed HIV infection when provided to both newly and previously diagnosed ICs. Integration of referral to PrEP resulted in many HIV-negative partners initiating PrEP.

TB index case tracing in the Roma community in the Czech Republic.

Tuberculosis (TB) contact tracing and TB preventive treatment are key tools in preventing the transmission of TB with the aim of eliminating the disease. Our study seeks to demonstrate how the infection spread from an individual patient to the entire community and how proactive contact tracing facilitated prompt diagnosis and treatment. Our work was conducted as a retrospective analysis of the spread of TB infection within the Roma community in the Czech Republic, following the case of an index patient who succumbed to pulmonary TB. Several levels of care and preventive and treatment measures are outlined. Confirming the identity of the Mycobacterium tuberculosis strain was achieved using molecular methods. Among the 39 individuals examined, TB disease was detected in eight patients and TB infection was detected in six patients. The investigation of contacts within this group yielded positive results in 36% of cases, necessitating treatment. The study's findings provide evidence that actively tracing individuals at risk can lead to early detection of cases, prompt treatment, and prevention of further disease transmission. The study also indicates that the highest risk of infection occurs within the sick person's household and that young children under the age of 5 are most susceptible to falling ill.

Population and contact tracer uptake of New Zealand's QR-code-based digital contact tracing app for COVID-19.

This study aimed to understand the population and contact tracer uptake of the quick response (QR)-code-based function of the New Zealand COVID Tracer App (NZCTA) used for digital contact tracing (DCT). We used a retrospective cohort of all COVID-19 cases between August 2020 and February 2022. Cases of Asian and other ethnicities were 2.6 times (adjusted relative risk (aRR) 2.58, 99 per cent confidence interval (95% CI) 2.18, 3.05) and 1.8 times (aRR 1.81, 95% CI 1.58, 2.06) more likely than Maori cases to generate a token during the Delta period, and this persisted during the Omicron period. Contact tracing organization also influenced location token generation with cases handled by National Case Investigation Service (NCIS) staff being 2.03 (95% CI 1.79, 2.30) times more likely to generate a token than cases managed by clinical staff at local Public Health Units (PHUs). Public uptake and participation in the location-based system independent of contact tracer uptake were estimated at 45%. The positive predictive value (PPV) of the QR code system was estimated to be close to nil for detecting close contacts but close to 100% for detecting casual contacts. Our paper shows that the QR-code-based function of the NZCTA likely made a negligible impact on the COVID-19 response in New Zealand (NZ) in relation to isolating potential close contacts of cases but likely was effective at identifying and notifying casual contacts.

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.

Lessons Learned from the Launch and Implementation of the COVID-19 Contact Tracing Program in New York City: a Qualitative Study.

On June 1, 2020, NYC Health + Hospitals, in partnership with the NYC Department of Health and Mental Hygiene, other city agencies, and a large network of community partners, launched the New York City Test & Trace (T2) COVID-19 response program to identify and isolate cases, reduce transmission through contact tracing, and provide support to residents during isolation or quarantine periods. In this paper, we describe lessons learned with respect to planning and implementation of case notification and contact tracing. Our findings are based on extensive document review and analysis of 74 key informant interviews with T2 leadership and frontline staff, cases, and contacts conducted between January and September 2022. Interviews elicited respondent background, history of program development, program leadership and structure, goals of the program, program evolution, staffing, data systems, elements of community engagement, trust with community, program reach, timeliness, equity, general barriers and challenges, general facilitators and best practices, and recommendations/improvement for the program. Facilitators and barriers revealed in the interviews primarily revolved around hiring and managing staff, data and technology, and quality of interactions with the public. Based on these facilitators and barriers, we identify suggestions to support effective planning and response for future case notification and contact tracing programs, including recommendations for planning during latent periods, case management and data systems, and processes for outreach to cases and contacts.

Meeting Social Needs in a Crisis Context: Lessons Learned from Integrating the 'Take Care Initiative' into New York City's Testing and Contact Tracing Program.

The COVID-19 pandemic highlighted the importance of addressing social needs in a crisis context. Some US jurisdictions integrated a social service component into case investigation and contact tracing (CI/CT) programs, including the New York City (NYC) Test & Trace (T2) Program; the Take Care initiative referred NYC residents who tested positive or were exposed to COVID-19 to services to support isolation and quarantine and meet basic needs. More research is needed to determine effective implementation strategies for integrating social needs provision into CI/CT programs. To identify barriers and facilitators to the implementation of the Take Care initiative, we conducted key informant interviews with program staff, community-based organization partners, and cases and contacts as part of a larger evaluation of the T2 program. Interviews were recorded, transcribed, and analyzed using rapid qualitative methods. Key facilitators to implementation included utilizing a case management software system, employing strategies to encourage service uptake, leveraging cross-agency collaborations, and partnering with community-based organizations for resource navigation. Barriers identified included external management of the software system, challenges reaching and engaging the public, administrative complications due to shifting collaborations, and management of CBO partners' structure and hiring. Based on our findings, we provide recommendations to support effective planning and implementation of social needs service provision in a crisis context. Future research should focus on testing promising implementation strategies highlighted in this study and applying them to varied contexts and crisis situations.

Cryptic connections illuminate pathogen transmission within community networks.

Understanding host interactions that lead to pathogen transmission is fundamental to the prediction and control of epidemics1-5. Although the majority of transmissions often occurs within social groups6-9, the contribution of connections that bridge groups and species to pathogen dynamics is poorly understood10-12. These cryptic connections-which are often indirect or infrequent-provide transmission routes between otherwise disconnected individuals and may have a key role in large-scale outbreaks that span multiple populations or species. Here we quantify the importance of cryptic connections in disease dynamics by simultaneously characterizing social networks and tracing transmission dynamics of surrogate-pathogen epidemics through eight communities of bats. We then compared these data to the invasion of the fungal pathogen that causes white-nose syndrome, a recently emerged disease that is devastating North American bat populations13-15. We found that cryptic connections increased links between individuals and between species by an order of magnitude. Individuals were connected, on average, to less than two per cent of the population through direct contact and to only six per cent through shared groups. However, tracing surrogate-pathogen dynamics showed that each individual was connected to nearly fifteen per cent of the population, and revealed widespread transmission between solitarily roosting individuals as well as extensive contacts among species. Connections estimated from surrogate-pathogen epidemics, which include cryptic connections, explained three times as much variation in the transmission of the fungus that causes white-nose syndrome as did connections based on shared groups. These findings show how cryptic connections facilitate the community-wide spread of pathogens and can lead to explosive epidemics.