Genomic characterization of human respiratory syncytial virus circulating in Islamabad, Pakistan, during an outbreak in 2022-2023.

In this study, conducted at the National Institute of Health, Islamabad, during an outbreak of human respiratory syncytial virus (hRSV) from December 2022 to January 2023, the first whole-genome sequences of hRSV isolates from Islamabad, Pakistan, were determined. Out of 10 positive samples, five were sequenced, revealing the presence of two genotypes: RSV-A (GA2.3.5, ON1 strain) and RSV-B (GB5.0.5.a, BA-10 strain). A rare non-synonymous substitution (E232G) in G the protein and N276S in the F protein were found in RSV-A. In RSV-B, the unique mutations K191R, Q209R, and I206M were found in the F protein. These mutations could potentially influence vaccine efficacy and viral pathogenicity. This research underscores the importance of genomic surveillance for understanding RSV diversity and guiding public health responses in Pakistan.

Phylogeography and gene pool analysis of highly pathogenic avian influenza H5N1 viruses reported in India from 2006 to 2021.

India has reported highly pathogenic avian influenza (HPAI) H5N1 virus outbreaks since 2006, with the first human case reported in 2021. These included viruses belonging to the clades 2.2, 2.2.2, 2.2.2.1, 2.3.2.1a, and 2.3.2.1c. There are currently no data on the gene pool of HPAI H5N1 viruses in India. Molecular clock and phylogeography analysis of the HA and NA genes; and phylogenetic analysis of the internal genes of H5N1 viruses from India were carried out. Sequences reported from 2006 to 2015; and sequences from 2021 that were available in online databases were used in the analysis. Five separate introductions of H5N1 viruses into India were observed, via Indonesia or Korea (2002), Bangladesh (2009), Bhutan (2010), and China (2013, 2018) (clades 2.2, 2.2.2, 2.2.2.1, 2.3.2.1a, 2.3.2.1c, and 2.3.4.4b). Phylogenetic analysis revealed eight reassortant genotypes. The H5N1 virus isolated from the human case showed a unique reassortant genotype. Amino acid markers associated with adaptation to mammals were also present. This is the first report of the spatio-temporal origins and gene pool analysis of H5N1 viruses from India, highlighting the need for increased molecular surveillance.

Travel surveillance uncovers dengue virus dynamics and introductions in the Caribbean.

Dengue is the most prevalent mosquito-borne viral disease in humans, and cases are continuing to rise globally. In particular, islands in the Caribbean have experienced more frequent outbreaks, and all four dengue virus (DENV) serotypes have been reported in the region, leading to hyperendemicity and increased rates of severe disease. However, there is significant variability regarding virus surveillance and reporting between islands, making it difficult to obtain an accurate understanding of the epidemiological patterns in the Caribbean. To investigate this, we used travel surveillance and genomic epidemiology to reconstruct outbreak dynamics, DENV serotype turnover, and patterns of spread within the region from 2009-2022. We uncovered two recent DENV-3 introductions from Asia, one of which resulted in a large outbreak in Cuba, which was previously under-reported. We also show that while outbreaks can be synchronized between islands, they are often caused by different serotypes. Our study highlights the importance of surveillance of infected travelers to provide a snapshot of local introductions and transmission in areas with limited local surveillance and suggests that the recent DENV-3 introductions may pose a major public health threat in the region.

A molecular epidemiological investigation of contagious caprine pleuropneumonia in goats and captive Arabian sand gazelle (Gazella marica) in Oman.

BACKGROUND: Contagious caprine pleuropneumonia (CCPP) is a fatal WOAH-listed, respiratory disease in small ruminants with goats as primary hosts that is caused by Mycoplasma capricolum subspecies capripneumoniae (Mccp). Twelve CCPP outbreaks were investigated in 11 goat herds and a herd of captive Arabian sand gazelle (Gazella marica) in four Omani governorates by clinical pathological and molecular analysis to compare disease manifestation and Mccp genetic profiles in goats and wild ungulates. RESULTS: The CCPP forms in diseased and necropsied goats varied from peracute (5.8%), acute (79.2%) and chronic (4.5%) while all of the five necropsied gazelles showed the acute form based on the clinical picture, gross and histopathological evaluation. Colonies of Mccp were recovered from cultured pleural fluid, but not from lung tissue samples of one gazelle and nine goats and all the isolates were confirmed by Mccp-specific real time PCR. Whole genome-single nucleotide polymorphism (SNP) analysis was performed on the ten isolates sequenced in this study and twenty sequences retrieved from the Genbank database. The Mccp strains from Oman clustered all in phylogroup A together with strains from East Africa and one strain from Qatar. A low variability of around 125 SNPs was seen in the investigated Omani isolates from both goats and gazelles indicating mutual transmission of the pathogen between wildlife and goats. CONCLUSION: Recent outbreaks of CCPP in Northern Oman are caused by Mccp strains of the East African Phylogroup A which can infect goats and captive gazelles likewise. Therefore, wild and captive ungulates should be considered as reservoirs and included in CCPP surveillance measures.

Coordination and modularization: the experience of the joint prevention and control mechanism to COVID emergencies in China.

Background: The integration of disparate emergency resources and the improvement of emergency response teamwork are the underlying trends and shared requirements for building resilience in an era of multiple global public health crises. Objective: This study investigated the emergency response with emergency collaboration networks of each functional module and the overall Joint Epidemic Prevention and Control Mechanism (JPCM) network in China's COVID outbreak prevention and control. Methods: The study employed a scholarly framework of "the integration of JPCM coordination and emergency collaborative modularization" to explore the attributes of JPCM using social network analysis. The data were obtained from administrative records from JPCM's official website, spanning January 2020 to December 2022. Results: The study examined the JPCM coordination and found several functional working modules of JPCM, such as Interrupt Spread, Manage Supply, Medical Rescue, Restore Work and Production, and Implement Responsibility modules. The network structure indicators showed that the Manage Supply module had the most extensive network connectivity, the shortest communication distance, and the most consistent collaboration. The E-I index of the overall JPCM network and the Manage Supply network were - 0.192 and - 0.452, respectively (at p < 0.001 and p < 0.05), indicating more internal relationships than external relationships. The E-I index of the Medical Rescue and Implement Responsibility collaboration networks were 0.122 and 0.147, respectively (at p < 0.001 and p < 0.05), indicating more external relationships than internal relationships. The QAP regression analysis showed that the most vital driver on the overall JPCM network was the Interrupt Spread module, followed by the Implement Responsibility and Medical Rescue modules. Discussion: The Interrupt Spread module initiated emergency coordination with most departments and agencies. The Manage Supply module ensured the flow of medical supplies and survival essentials, while the Medical Rescue module addressed the core aspects of the health emergency response. The Restore Work and Production module repaired the halt in production and livelihoods caused by the outbreak, strengthening and developing emergency coordination and roles across emergency organizations. The Implement Responsibility module provided more heterogeneous emergency response resources for the overall JPCM coordination, complementing the COVID cross-organizational emergency response coordination. Conclusion: The study on the JPCM case in China improves public health emergency management and aids informed decision-making.

Wastewater based epidemiology as a surveillance tool during the current COVID-19 pandemic on a college campus (East Carolina University) and its accuracy in predicting SARS-CoV-2 outbreaks in dormitories.

The COVID-19 outbreak led governmental officials to close many businesses and schools, including colleges and universities. Thus, the ability to resume normal campus operation required adoption of safety measures to monitor and respond to COVID-19. The objective of this study was to determine the efficacy of wastewater-based epidemiology as a surveillance method in monitoring COVID-19 on a college campus. The use of wastewater monitoring as part of a surveillance program to control COVID-19 outbreaks at East Carolina University was evaluated. During the Spring and Fall 2021 semesters, wastewater samples (N = 830) were collected every Monday, Wednesday, and Friday from the sewer pipes exiting the dormitories on campus. Samples were analyzed for SARS-CoV-2 and viral quantification was determined using qRT-PCR. During the Spring 2021 semester, there was a significant difference in SARS-CoV-2 virus copies in wastewater when comparing dorms with the highest number student cases of COVID-19 and those with the lowest number of student cases, (p = 0.002). Additionally, during the Fall 2021 semester it was observed that when weekly virus concentrations exceeded 20 copies per ml, there were new confirmed COVID-19 cases 85% of the time during the following week. Increases in wastewater viral concentration spurred COVID-19 swab testing for students residing in dormitories, aiding university officials in effectively applying COVID testing policies. This study showed wastewater-based epidemiology can be a cost-effective surveillance tool to guide other surveilling methods (e.g., contact tracing, nasal/salvia testing, etc.) to identify and isolate afflicted individuals to reduce the spread of pathogens and potential outbreaks within a community.

DeepDynaForecast: Phylogenetic-informed graph deep learning for epidemic transmission dynamic prediction.

In the midst of an outbreak or sustained epidemic, reliable prediction of transmission risks and patterns of spread is critical to inform public health programs. Projections of transmission growth or decline among specific risk groups can aid in optimizing interventions, particularly when resources are limited. Phylogenetic trees have been widely used in the detection of transmission chains and high-risk populations. Moreover, tree topology and the incorporation of population parameters (phylodynamics) can be useful in reconstructing the evolutionary dynamics of an epidemic across space and time among individuals. We now demonstrate the utility of phylodynamic trees for transmission modeling and forecasting, developing a phylogeny-based deep learning system, referred to as DeepDynaForecast. Our approach leverages a primal-dual graph learning structure with shortcut multi-layer aggregation, which is suited for the early identification and prediction of transmission dynamics in emerging high-risk groups. We demonstrate the accuracy of DeepDynaForecast using simulated outbreak data and the utility of the learned model using empirical, large-scale data from the human immunodeficiency virus epidemic in Florida between 2012 and 2020. Our framework is available as open-source software (MIT license) at github.com/lab-smile/DeepDynaForcast.

Literature Review of Pathogen Agnostic Molecular Testing of Clinical Specimens From Difficult-to-Diagnose Patients: Implications for Public Health.

To better identify emerging or reemerging pathogens in patients with difficult-to-diagnose infections, it is important to improve access to advanced molecular testing methods. This is particularly relevant for cases where conventional microbiologic testing has been unable to detect the pathogen and the patient's specimens test negative. To assess the availability and utility of such testing for human clinical specimens, a literature review of published biomedical literature was conducted. From a corpus of more than 4,000 articles, a set of 34 reports was reviewed in detail for data on where the testing was being performed, types of clinical specimens tested, pathogen agnostic techniques and methods used, and results in terms of potential pathogens identified. This review assessed the frequency of advanced molecular testing, such as metagenomic next generation sequencing that has been applied to clinical specimens for supporting clinicians in caring for difficult-to-diagnose patients. Specimen types tested were from cerebrospinal fluid, respiratory secretions, and other body tissues and fluids. Publications included case reports and series, and there were several that involved clinical trials, surveillance studies, research programs, or outbreak situations. Testing identified both known human pathogens (sometimes in new sites) and previously unknown human pathogens. During this review, there were no apparent coordinated efforts identified to develop regional or national reports on emerging or reemerging pathogens. Therefore, development of a coordinated sentinel surveillance system that applies advanced molecular methods to clinical specimens which are negative by conventional microbiological diagnostic testing would provide a foundation for systematic characterization of emerging and underdiagnosed pathogens and contribute to national biodefense strategy goals.

Assessment of using Google Trends for real-time monitoring of infectious disease outbreaks: a measles case study.

Measles remains a significant threat to children worldwide despite the availability of effective vaccines. The COVID-19 pandemic exacerbated the situation by leading to the postponement of supplementary measles immunization activities. Along with this postponement, measles surveillance also deteriorated, with the lowest number of submitted specimens in over a decade. In this study, we focus on measles as a challenging case study due to its high vaccination coverage, which leads to smaller outbreaks and potentially weaker signals on Google Trends. Our research aimed to explore the feasibility of using Google Trends for real-time monitoring of infectious disease outbreaks. We evaluated the correlation between Google Trends searches and clinical case data using the Pearson correlation coefficient and Spearman's rank correlation coefficient across 30 European countries and Japan. The results revealed that Google Trends was most suitable for monitoring acute disease outbreaks at the regional level in high-income countries, even when there are only a few weekly cases. For example, from 2017 to 2019, the Pearson correlation coefficient was 0.86 (p-value< 0.05) at the prefecture level for Okinawa, Japan, versus 0.33 (p-value< 0.05) at the national level for Japan. Furthermore, we found that the Pearson correlation coefficient may be more suitable than Spearman's rank correlation coefficient for evaluating the correlations between Google Trends search data and clinical case data. This study highlighted the potential of utilizing Google Trends as a valuable tool for timely public health interventions to respond to infectious disease outbreaks, even in the context of diseases with high vaccine coverage.

Markov chain-based impact analysis of the pandemic Covid-19 outbreak on global primary energy consumption mix.

The historic evolution of global primary energy consumption (GPEC) mix, comprising of fossil (liquid petroleum, gaseous and coal fuels) and non-fossil (nuclear, hydro and other renewables) energy sources while highlighting the impact of the novel corona virus 2019 pandemic outbreak, has been examined through this study. GPEC data of 2005-2021 has been taken from the annually published reports by British Petroleum. The equilibrium state, a property of the classical predictive modeling based on Markov chain, is employed as an investigative tool. The pandemic outbreak has proved to be a blessing in disguise for global energy sector through, at least temporarily, reducing the burden on environment in terms of reducing demand for fossil energy sources. Some significant long term impacts of the pandemic occurred in second and third years (2021 and 2022) after its outbreak in 2019 rather than in first year (2020) like the penetration of other energy sources along with hydro and renewable ones in GPEC. Novelty of this research lies within the application of the equilibrium state feature of compositional Markov chain based prediction upon GPEC mix. The analysis into the past trends suggests the advancement towards a better global energy future comprising of cleaner fossil resources (mainly natural gas), along with nuclear, hydro and renewable ones in the long run.

Assessment of the integrated disease surveillance and response system implementation in health zones at risk for viral hemorrhagic fever outbreaks in North Kivu, Democratic Republic of the Congo, following a major Ebola outbreak, 2021.

BACKGROUND: The Democratic Republic of the Congo (DRC) experienced its largest Ebola Virus Disease Outbreak in 2018-2020. As a result of the outbreak, significant funding and international support were provided to Eastern DRC to improve disease surveillance. The Integrated Disease Surveillance and Response (IDSR) strategy has been used in the DRC as a framework to strengthen public health surveillance, and full implementation could be critical as the DRC continues to face threats of various epidemic-prone diseases. In 2021, the DRC initiated an IDSR assessment in North Kivu province to assess the capabilities of the public health system to detect and respond to new public health threats. METHODS: The study utilized a mixed-methods design consisting of quantitative and qualitative methods. Quantitative assessment of the performance in IDSR core functions was conducted at multiple levels of the tiered health system through a standardized questionnaire and analysis of health data. Qualitative data were also collected through observations, focus groups and open-ended questions. Data were collected at the North Kivu provincial public health office, five health zones, 66 healthcare facilities, and from community health workers in 15 health areas. RESULTS: Thirty-six percent of health facilities had no case definition documents and 53% had no blank case reporting forms, limiting identification and reporting. Data completeness and timeliness among health facilities were 53% and 75% overall but varied widely by health zone. While these indicators seemingly improved at the health zone level at 100% and 97% respectively, the health facility data feeding into the reporting structure were inconsistent. The use of electronic Integrated Disease Surveillance and Response is not widely implemented. Rapid response teams were generally available, but functionality was low with lack of guidance documents and long response times. CONCLUSION: Support is needed at the lower levels of the public health system and to address specific zones with low performance. Limitations in materials, resources for communication and transportation, and workforce training continue to be challenges. This assessment highlights the need to move from outbreak-focused support and funding to building systems that can improve the long-term functionality of the routine disease surveillance system.

Methodological issues of retrospective surveys for measuring mortality of highly clustered diseases: case study of the 2014-16 Ebola outbreak in Bo District, Sierra Leone.

BACKGROUND: There is a lack of empirical data on design effects (DEFF) for mortality rate for highly clustered data such as with Ebola virus disease (EVD), along with a lack of documentation of methodological limitations and operational utility of mortality estimated from cluster-sampled studies when the DEFF is high. OBJECTIVES: The objectives of this paper are to report EVD mortality rate and DEFF estimates, and discuss the methodological limitations of cluster surveys when data are highly clustered such as during an EVD outbreak. METHODS: We analysed the outputs of two independent population-based surveys conducted at the end of the 2014-2016 EVD outbreak in Bo District, Sierra Leone, in urban and rural areas. In each area, 35 clusters of 14 households were selected with probability proportional to population size. We collected information on morbidity, mortality and changes in household composition during the recall period (May 2014 to April 2015). Rates were calculated for all-cause, all-age, under-5 and EVD-specific mortality, respectively, by areas and overall. Crude and adjusted mortality rates were estimated using Poisson regression, accounting for the surveys sample weights and the clustered design. RESULTS: Overall 980 households and 6,522 individuals participated in both surveys. A total of 64 deaths were reported, of which 20 were attributed to EVD. The crude and EVD-specific mortality rates were 0.35/10,000 person-days (95%CI: 0.23-0.52) and 0.12/10,000 person-days (95%CI: 0.05-0.32), respectively. The DEFF for EVD mortality was 5.53, and for non-EVD mortality, it was 1.53. DEFF for EVD-specific mortality was 6.18 in the rural area and 0.58 in the urban area. DEFF for non-EVD-specific mortality was 1.87 in the rural area and 0.44 in the urban area. CONCLUSION: Our findings demonstrate a high degree of clustering; this contributed to imprecise mortality estimates, which have limited utility when assessing the impact of disease. We provide DEFF estimates that can inform future cluster surveys and discuss design improvements to mitigate the limitations of surveys for highly clustered data.

Main findings: For humanitarian organizations it is imperative to document the methodological limitations of cluster surveys and discuss the utility.Added knowledge: This paper adds new knowledge on cluster surveys for highly clustered data such us in Ebola virus disease.Global health impact of policy and action: We provided empirical estimates and discuss design improvements to inform future study.

Spatiotemporal dynamics of cholera epidemics in Ethiopia: 2015-2021.

Since the onset of the seventh cholera pandemic, Ethiopia has been affected by recurrent epidemics. However, the epidemiology of cholera in this country remains poorly understood. This study aimed to describe cholera outbreak characteristics in Ethiopia from 2015 to 2021. During this period, Ethiopia experienced four epidemic waves. The first wave involved nationwide outbreaks during the second half of 2016 followed by outbreaks predominantly affecting Somali Region in 2017. The second wave primarily affected Tigray and Afar Regions. During the third wave, multiple smaller-scale outbreaks occurred during 2019. The fourth wave was limited to Bale Zone (Oromia Region) in 2021. Overall, a north to south shift was observed over the course of the study period. Major cholera transmission factors included limited access to safe water and sanitation facilities. Severe weather events (drought and flooding) appear to aggravate cholera diffusion. Cholera transmission between Ethiopia and nearby countries (Kenya and Somalia), likely plays a major role in regional cholera dynamics. Overall, this study provides the first understanding of recent spatiotemporal cholera dynamics in Ethiopia to inform cholera control and elimination strategies.

Comparison of analysis methods to classify cholera hotspots in Ethiopia from 2015 to 2021.

Cholera continues to represent a major public health concern in Ethiopia. The country has developed a Multi-sectoral National Cholera Elimination Plan in 2022, which targets prevention and control interventions in cholera hotspots. Multiple methods to classify cholera hotspots have been used in several countries. Since 2014, a classification method developed by United Nations Children's Fund has been applied to guide water, sanitation and hygiene interventions throughout Sub-Saharan Africa based on three outbreak parameters: frequency, duration and standardized attack rate. In 2019, the Global Task Force on Cholera Control (GTFCC) proposed a method based on two parameters: average annual cholera incidence and persistence. In 2023, an updated GTFCC method for multisectoral interventions considers three epidemiological indicators (cumulative incidence, cumulative mortality and persistence,) and a cholera-case confirmation indicator. The current study aimed to classify cholera hotspots in Ethiopia at the woreda level (equivalent to district level) applying the three methods and comparing the results to optimize the hotspot targeting strategy. From 2015 to 2021, cholera hotspots were located along major routes between Addis Ababa and woredas adjacent to the Kenya and Somalia borders, throughout Tigray Region, around Lake Tana, and in Afar Region. The multi-method comparison enables decision makers to prioritize interventions according to a sub-classification of the highest-priority areas.

Demographic and clinical characteristics of people diagnosed with active sexually transmitted infections among monkeypox cases in Brazil: the 2022 outbreak.

The worldwide monkeypox (mpox) outbreak in 2022 showed a high frequency of sexually transmitted infections (STI). A cross-sectional study was carried out using secondary data from the Brazilian official mpox surveillance systems. A total of 10,169 mpox cases were identified, with a median age of 32 years. Among them, 92.3% were male at birth and 57.5% were men who have sex with other men (MSM). Approximately 11% were diagnosed with STI, including 5.8% with syphilis and 2.5% with genital herpes. Individuals aged from 25 to 34 years, MSM, individuals with HIV-positive status, and those manifesting skin eruptions or penile edema were associated with STI. Laboratory investigation for mpox must be implemented as a priority in STI clinics (especially for MSM) to mitigate neglected cases, ensure appropriate treatments, and prevent misdiagnoses.

Predicting malaria outbreaks using earth observation measurements and spatiotemporal deep learning modelling: a South Asian case study from 2000 to 2017.

BACKGROUND: Malaria remains one the leading communicable causes of death. Approximately half of the world's population is considered at risk of infection, predominantly in African and South Asian countries. Although malaria is preventable, heterogeneity in sociodemographic and environmental risk factors over time and across diverse geographical and climatological regions make outbreak prediction challenging. Data-driven approaches accounting for spatiotemporal variability could offer potential for location-specific early warning tools for malaria. METHODS: In this case study, we developed and internally validated a data fusion approach to predict malaria incidence in Pakistan, India, and Bangladesh using geo-referenced environmental factors. For 2000-17, district-level malaria incidence rates for each country were obtained from the US Agency for International Development's Demographic and Health Survey datasets. Environmental factors included average annual temperature, rainfall, and normalised difference vegetation index, obtained from the Advancing Research on Nutrition and Agriculture (known as AReNA) project conducted by the International Food Policy Research Institute in 2020. Data on night-time light intensity was derived from two satellites of the National Oceanic and Atmospheric Administration Defense Meteorological Satellite Program-Operational Linescan System: Nighttime Lights Time Series Version 4, and VIIRS Nighttime Day/Night Band Composites version 1. A multi-dimensional spatiotemporal long short-term memory (M-LSTM) model was developed using data from 2000-16 and internally validated for the year 2017. The M-LSTM model consisted of four hidden layers, each with 100 LSTM units; a fully connected layer was used, followed by linear regression, to predict the malaria incidence rate for 2017 using spatiotemporal partitioning. Model performance was measured using accuracy and root mean squared error. Country-specific models were produced for Bangladesh, India, and Pakistan. Bivariate geospatial heatmaps were produced for a qualitative comparison of univariate environmental factors with malaria rates. FINDINGS: Malaria incidence was predicted with 80·6% accuracy in districts across Pakistan, 76·7% in districts across India, and 99·1% in districts across Bangladesh. The root mean squared error was 7 × 10-4 for Pakistan, 4·86 × 10-6 for India, and 1·32 × 10-5 for Bangladesh. Bivariate maps showed an inverse relationship between night-time lights and malaria rates; whereas high malaria rates were found in areas with high temperature, rainfall, and vegetation. INTERPRETATION: Malaria outbreaks could be forecasted using remotely measured environmental factors. Modelling techniques that enable simultaneously forecasting ahead in time as well as across large geographical areas might potentially empower regional decision makers to manage outbreaks early. FUNDING: NIHR Oxford Biomedical Research Centre Programme and The Higher Education Commission of Pakistan.

Factors associated with meningitis vaccine awareness and engagement among Latino men who have sex with men in South Florida.

Awareness and uptake of the meningitis vaccine remains low among marginalized groups, such as Latino men who have sex with men (LMSM), potentially due to structural and psychosocial barriers in accessing preventative healthcare. The current study explored awareness and uptake of meningitis vaccines among a group of LMSM (N = 99) living in South Florida. A three-pronged variable selection approach was utilized prior to conducting regression models (linear and logistic). Overall, 48.5% of the participants reported little to no knowledge about meningitis vaccines, and 20.2% reported being vaccinated. Living with HIV (OR = 10.48) and time since outbreak (OR = 1.03) were significant predictors of meningitis vaccine uptake. No significant correlates of meningitis vaccine awareness were identified. More research is needed to identify other important factors associated with meningitis vaccine awareness and uptake among LMSM, a multiple marginalized group.

Indirect vaccine effectiveness in an outbreak of Alpha B.1.1.7 variant in a California state Prison, May 2021.

Incarcerated populations experienced high rates of SARS-CoV-2 infection and death during early phases of the COVID-19 pandemic. To evaluate vaccine effectiveness in the carceral context, we investigated the first outbreak of COVID-19 in a California state prison following widespread rollout of vaccines to residents in early 2021. We identified a cohort of 733 state prison residents presumed to be exposed between May 14 and June 22, 2021. 46.9 % (n = 344) were vaccinated, primarily with two doses of mRNA-1273 (n = 332, 93.6 %). In total, 92 PCR-positive cases were identified, of which 14 (14.5 %) occurred among mRNA-1273 vaccinated residents. No cases required hospitalization. All nine isolates collected belonged to the Alpha (B.1.1.7) variant. We used Cox proportional hazard regression to estimate vaccine effectiveness for at least one dose of any vaccine at the start of the outbreak. Vaccine effectiveness was 86 % (95 % CI: 75 %-97 %) against PCR-confirmed infection, with similar results for symptomatic infection. Higher rates of building-level vaccine uptake were associated with a lower overall rate of PCR-confirmed infection and symptomatic infection among unvaccinated residents. Among unvaccinated residents who lived in shared cells at the time of presumed exposure, exposure to a vaccinated cellmate was associated with a 38% (95% CI: 0.37, 1.04) lower hazard rate of PCR-confirmed infection over the study period. In this outbreak involving the Alpha SARS-CoV-2 variant, vaccination conferred direct and possibly indirect protection against SARS-CoV-2 infection and symptomatic COVID-19. Our results support the importance of vaccine uptake in mitigating outbreaks and severe disease in the prison setting and the consideration of community vaccination levels in policy and infection response.