Protein representations: Encoding biological information for machine learning in biocatalysis.

Enzymes offer a more environmentally friendly and low-impact solution to conventional chemistry, but they often require additional engineering for their application in industrial settings, an endeavour that is challenging and laborious. To address this issue, the power of machine learning can be harnessed to produce predictive models that enable the in silico study and engineering of improved enzymatic properties. Such machine learning models, however, require the conversion the complex biological information to a numerical input, also called protein representations. These inputs demand special attention to ensure the training of accurate and precise models, and, in this review, we therefore examine the critical step of encoding protein information to numeric representations for use in machine learning. We selected the most important approaches for encoding the three distinct biological protein representations - primary sequence, 3D structure, and dynamics - to explore their requirements for employment and inductive biases. Combined representations of proteins and substrates are also introduced as emergent tools in biocatalysis. We propose the division of fixed representations, a collection of rule-based encoding strategies, and learned representations extracted from the latent spaces of large neural networks. To select the most suitable protein representation, we propose two main factors to consider. The first one is the model setup, which is influenced by the size of the training dataset and the choice of architecture. The second factor is the model objectives such as consideration about the assayed property, the difference between wild-type models and mutant predictors, and requirements for explainability. This review is aimed at serving as a source of information and guidance for properly representing enzymes in future machine learning models for biocatalysis.

GASP: A Pan-Specific Predictor of Family 1 Glycosyltransferase Acceptor Specificity Enabled by a Pipeline for Substrate Feature Generation and Large-Scale Experimental Screening.

Glycosylation represents a major chemical challenge; while it is one of the most common reactions in Nature, conventional chemistry struggles with stereochemistry, regioselectivity, and solubility issues. In contrast, family 1 glycosyltransferase (GT1) enzymes can glycosylate virtually any given nucleophilic group with perfect control over stereochemistry and regioselectivity. However, the appropriate catalyst for a given reaction needs to be identified among the tens of thousands of available sequences. Here, we present the glycosyltransferase acceptor specificity predictor (GASP) model, a data-driven approach to the identification of reactive GT1:acceptor pairs. We trained a random forest-based acceptor predictor on literature data and validated it on independent in-house generated data on 1001 GT1:acceptor pairs, obtaining an AUROC of 0.79 and a balanced accuracy of 72%. The performance was stable even in the case of completely new GT1s and acceptors not present in the training data set, highlighting the pan-specificity of GASP. Moreover, the model is capable of parsing all known GT1 sequences, as well as all chemicals, the latter through a pipeline for the generation of 153 chemical features for a given molecule taking the CID or SMILES as input (freely available at https://github.com/degnbol/GASP). To investigate the power of GASP, the model prediction probability scores were compared to GT1 substrate conversion yields from a newly published data set, with the top 50% of GASP predictions corresponding to reactions with >50% synthetic yields. The model was also tested in two comparative case studies: glycosylation of the antihelminth drug niclosamide and the plant defensive compound DIBOA. In the first study, the model achieved an 83% hit rate, outperforming a hit rate of 53% from a random selection assay. In the second case study, the hit rate of GASP was 50%, and while being lower than the hit rate of 83% using expert-selected enzymes, it provides a reasonable performance for the cases when an expert opinion is unavailable. The hierarchal importance of the generated chemical features was investigated by negative feature selection, revealing properties related to cyclization and atom hybridization status to be the most important characteristics for accurate prediction. Our study provides a GT1:acceptor predictor which can be trained on other data sets enabled by the automated feature generation pipelines. We also release the new in-house generated data set used for testing of GASP to facilitate the future development of GT1 activity predictors and their robust benchmarking.

Target and Nontarget Screening to Support Capacity Scaling for Substance Use Assessment through a Statewide Wastewater Surveillance Network in New York.

Wastewater-based epidemiology (WBE) has been widely implemented around the world as a complementary tool to conventional surveillance techniques to inform and improve public health responses. Currently, wastewater surveillance programs in the U.S. are evaluating integrated approaches to address public health challenges across multiple domains, including substance abuse. In this work, we demonstrated the potential of online solid-phase extraction coupled with liquid chromatography-high-resolution mass spectrometry to support targeted quantification and nontargeted analysis of psychoactive and lifestyle substances as a step toward understanding the operational feasibility of a statewide wastewater surveillance program for substance use assessment in New York. Target screening confirmed 39 substances in influent samples collected from 10 wastewater treatment plants with varying sewershed characteristics and is anticipated to meet the throughput demands as the statewide program scales up to full capacity. Nontarget screening prioritized additional compounds for identification at three confidence levels, including psychoactive substances, such as opioid analgesics, phenethylamines, and cathinone derivatives. Consumption rates of 12 target substances detected in over 80% of wastewater samples were similar to those reported by previous U.S.-based WBE studies despite the uncertainty associated with back-calculations. For selected substances, the relative bias in consumption estimates was sensitive to variations in monitoring frequency, and factors beyond human excretion (e.g., as indicated by the parent-to-metabolite ratios) might also contribute to their prevalence at the sewershed scale. Overall, our study marks the initial phase of refining analytical workflows and data interpretation in preparation for the incorporation of substance use assessment into the statewide wastewater surveillance program in New York.

The carbon monoxide prodrug oCOm-21 increases Ca2+ sensitivity of the cardiac myofilament.

Patients undergoing cardiopulmonary bypass procedures require inotropic support to improve hemodynamic function and cardiac output. Current inotropes such as dobutamine, can promote arrhythmias, prompting a demand for improved inotropes with little effect on intracellular Ca2+ flux. Low-dose carbon monoxide (CO) induces inotropic effects in perfused hearts. Using the CO-releasing pro-drug, oCOm-21, we investigated if this inotropic effect results from an increase in myofilament Ca2+ sensitivity. Male Sprague Dawley rat left ventricular cardiomyocytes were permeabilized, and myofilament force was measured as a function of -log [Ca2+ ] (pCa) in the range of 9.0-4.5 under five conditions: vehicle, oCOm-21, the oCOm-21 control BP-21, and levosimendan, (9 cells/group). Ca2+ sensitivity was assessed by the Ca2+ concentration at which 50% of maximal force is produced (pCa50 ). oCOm-21, but not BP-21 significantly increased pCa50 compared to vehicle, respectively (pCa50 5.52 vs. 5.47 vs. 5.44; p < 0.05). No change in myofilament phosphorylation was seen after oCOm-21 treatment. Pretreatment of cardiomyocytes with the heme scavenger hemopexin, abolished the Ca2+ sensitizing effect of oCOm-21. These results support the hypothesis that oCOm-21-derived CO increases myofilament Ca2+ sensitivity through a heme-dependent mechanism but not by phosphorylation. Further analyses will confirm if this Ca2+ sensitizing effect occurs in an intact heart.

Precision public health in schools enabled by wastewater surveillance: A case study of COVID-19 in an Upstate New York middle-high school campus during the 2021-2022 academic year.

Wastewater surveillance provides a cost-effective and non-invasive way to gain an understanding of infectious disease transmission including for COVID-19. We analyzed wastewater samples from one school site in Jefferson County, New York during the 2021-2022 school year. We tested for SARS-CoV-2 RNA once weekly and compared those results with the clinical COVID-19 cases in the school. The amount of SARS-CoV-2 RNA correlated with the number of incident COVID-19 cases, with the best correlation being one day lead time between the wastewater sample and the number of COVID-19 cases. The sensitivity and positive predictive value of wastewater surveillance to correctly identify any COVID-19 cases up to 7 days after a wastewater sample collection ranged from 82-100% and 59-78% respectively, depending upon the amount of SARS-CoV-2 RNA in the sample. The specificity and negative predictive value of wastewater surveillance to correctly identify when the school was without a case of COVID-19 ranged from 67-78% and 70-80%, respectively, depending upon the amount of SARS-CoV-2 RNA in the sample. The lead time observed in this study suggests that transmission might occur within a school before SARS-CoV-2 is identified in wastewater. However, wastewater surveillance should still be considered as a potential means of understanding school-level COVID-19 trends and is a way to enable precision public health approaches tailored to the epidemiologic situation in an individual school.

Wastewater surveillance provides 10-days forecasting of COVID-19 hospitalizations superior to cases and test positivity: A prediction study.

Background: The public health response to COVID-19 has shifted to reducing deaths and hospitalizations to prevent overwhelming health systems. The amount of SARS-CoV-2 RNA fragments in wastewater are known to correlate with clinical data including cases and hospital admissions for COVID-19. We developed and tested a predictive model for incident COVID-19 hospital admissions in New York State using wastewater data. Methods: Using county-level COVID-19 hospital admissions and wastewater surveillance covering 13.8 million people across 56 counties, we fit a generalized linear mixed model predicting new hospital admissions from wastewater concentrations of SARS-CoV-2 RNA from April 29, 2020 to June 30, 2022. We included covariates such as COVID-19 vaccine coverage in the county, comorbidities, demographic variables, and holiday gatherings. Findings: Wastewater concentrations of SARS-CoV-2 RNA correlated with new hospital admissions per 100,000 up to ten days prior to admission. Models that included wastewater had higher predictive power than models that included clinical cases only, increasing the accuracy of the model by 15%. Predicted hospital admissions correlated highly with observed admissions (r = 0.77) with an average difference of 0.013 hospitalizations per 100,000 (95% CI = [0.002, 0.025]). Interpretation: Using wastewater to predict future hospital admissions from COVID-19 is accurate and effective with superior results to using case data alone. The lead time of ten days could alert the public to take precautions and improve resource allocation for seasonal surges.

Deployment of Rotavirus Vaccine in Western Kenya Coincides with a Reduction in All-Cause Child Mortality: A Retrospective Cohort Study.

Rotavirus is an important cause of fatal pediatric diarrhea worldwide. Many national immunization programs began adding rotavirus vaccine following a 2009 World Health Organization recommendation. Kenya added rotavirus vaccine to their immunization program at the end of 2014. From a cohort of 38,463 children in the Kisumu health and demographic surveillance site in western Kenya, we assessed how the implementation of the rotavirus vaccine affected mortality in children under 3 years of age. Following its introduction in late 2014, the span of rotavirus vaccine coverage for children increased to 75% by 2017. Receiving the rotavirus vaccine was associated with a 44% reduction in all-cause child mortality (95% confidence interval = 28-68%, p < 0.0001), but not diarrhea-specific mortality (p = 0.401). All-cause child mortality declined 2% per month following the implementation of the rotavirus vaccine (p = 0.002) among both vaccinated and unvaccinated children, but diarrhea-specific mortality was not associated with the implementation of the rotavirus vaccine independent of individual vaccine status (p = 0.125). The incidence of acute diarrhea decreased over the study period, and the introduction of the rotavirus vaccine was not associated with population-wide trends (p = 0.452). The receipt of the rotavirus vaccine was associated with a 34% reduction in the incidence of diarrhea (95% confidence interval = 24-43% reduction). These results suggest that rotavirus vaccine may have had an impact on all-cause child mortality. The analyses of diarrhea-specific mortality were limited by relatively few deaths (n = 57), as others have found a strong reduction in diarrhea-specific mortality. Selection bias may have played a part in these results-children receiving rotavirus vaccine were more likely to be fully immunized than children not receiving the rotavirus vaccine.

Establishing a Statewide Wastewater Surveillance System in Response to the COVID-19 Pandemic: A Reliable Model for Continuous and Emerging Public Health Threats.

CONTEXT: The COVID-19 pandemic sparked efforts across the globe to implement wastewater surveillance for SARS-CoV-2. PROGRAM: New York State (NYS) established the NYS Wastewater Surveillance Network to estimate the levels of COVID-19 community risk and to provide an early indication of SARS-CoV-2 transmission trends. The network is designed to provide a better understanding of public health burdens and to assist health departments to respond effectively to public health threats. IMPLEMENTATION: Wastewater surveillance across NYS increased from sporadic and geographically spare in 2020 to routine and widespread in 2022, reaching all 62 counties in the state and covering 74% of New Yorkers. The network team focused on engaging local health departments and wastewater treatment plants to provide wastewater samples, which are then analyzed through a network-affiliated laboratory. Both participating local health departments and wastewater treatment plants receive weekly memos on current SARS-CoV-2 trends and levels. The data are also made publicly available at the state dashboard. EVALUATION: Using standard indicators to evaluate infectious disease surveillance systems, the NYS Wastewater Surveillance Network was assessed for accuracy, timeliness, and completeness during the first year of operations. We observed 96.5% sensitivity of wastewater to identify substantial/high COVID-19 transmission and 99% specificity to identify low COVID-19 transmission. In total, 80% of results were reported within 1 day of sample collection and were published on the public dashboard within 2 days of sample collection. Among participating wastewater treatment plants, 32.5% provided weekly samples with zero missing data, 31% missed 1 or 2 weeks, and 36.5% missed 3 or more weeks. DISCUSSION: The NYS Wastewater Surveillance Network continues to be a key component of the state and local health departments' pandemic response. The network fosters prompt public health actions through real-time data, enhancing the preparedness capability for both existing and emerging public health threats.

Operationalizing an open-source dashboard for communicating results of wastewater-based surveillance.

COVID-19 saw the expansion of public health tools to manage the pandemic. One tool that saw extensive use was the public health dashboard, web-based visualization tools that communicate information to users in easy-to-read graphics. Dashboards were widely used prior to the pandemic, but COVID-19 saw expanded use and development. To date, dashboards have become an important part of public health surveillance programs around the world helping decisionmakers use data to evaluate different public health metrics including caseloads, hospitalizations, and environmental surveillance results from testing wastewater. Wastewater surveillance provides community-based, spatially relevant data on disease trends within communities to assess the scale of infection in a region, which makes it an excellent candidate for dashboard development to improve public health. We developed a dashboard for New York State's wastewater surveillance program using open-source, reproducible web programming. The dashboard we developed has been used for the COVID-19 response in New York, and our methods can be adapted to other programs and pathogens. We provide:•descriptions of how the dashboard was developed and maintained•specific guidance for reproducing our dashboard in other areas and for other pathogens•fully reproducible code with step-by-step instructions for researchers and professionals to make their own data dashboards.

The prevalence of SARS-CoV-2 infection and other public health outcomes during the BA.2/BA.2.12.1 surge, New York City, April-May 2022.

BACKGROUND: Routine case surveillance data for SARS-CoV-2 are incomplete, unrepresentative, missing key variables of interest, and may be increasingly unreliable for timely surge detection and understanding the true burden of infection. METHODS: We conducted a cross-sectional survey of a representative sample of 1030 New York City (NYC) adult residents ≥18 years on May 7-8, 2022. We estimated the prevalence of SARS-CoV-2 infection during the preceding 14-day period. Respondents were asked about SARS-CoV-2 testing, testing outcomes, COVID-like symptoms, and contact with SARS-CoV-2 cases. SARS-CoV-2 prevalence estimates were age- and sex-adjusted to the 2020 U.S. POPULATION: We triangulated survey-based prevalence estimates with contemporaneous official SARS-CoV-2 counts of cases, hospitalizations, and deaths, as well as SARS-CoV-2 wastewater concentrations. RESULTS: We show that 22.1% (95% CI 17.9-26.2%) of respondents had SARS-CoV-2 infection during the two-week study period, corresponding to ~1.5 million adults (95% CI 1.3-1.8 million). The official SARS-CoV-2 case count during the study period is 51,218. Prevalence is estimated at 36.6% (95% CI 28.3-45.8%) among individuals with co-morbidities, 13.7% (95% CI 10.4-17.9%) among those 65+ years, and 15.3% (95% CI 9.6-23.5%) among unvaccinated persons. Among individuals with a SARS-CoV-2 infection, hybrid immunity (history of both vaccination and infection) is 66.2% (95% CI 55.7-76.7%), 44.1% (95% CI 33.0-55.1%) were aware of the antiviral nirmatrelvir/ritonavir, and 15.1% (95% CI 7.1-23.1%) reported receiving it. Hospitalizations, deaths and SARS-CoV-2 virus concentrations in wastewater remained well below that during the BA.1 surge. CONCLUSIONS: Our findings suggest that the true magnitude of NYC's BA.2/BA.2.12.1 surge may have been vastly underestimated by routine case counts and wastewater surveillance. Hybrid immunity, bolstered by the recent BA.1 surge, likely limited the severity of the BA.2/BA.2.12.1 surge.

It is difficult to assess the true prevalence of SARS-CoV-2, the virus that causes COVID-19, due to changes in testing practices and behaviors, including increasing at-home testing and decreasing healthcare provider-based testing. We conducted a population-representative survey in New York City to estimate the prevalence of SARS-CoV-2 during the second Omicron surge in spring 2022. We compared survey-based SARS-CoV-2 prevalence estimates with data on diagnosed cases, hospitalizations, deaths, and SARS-CoV-2 concentration in wastewater. Our survey-based estimates were nearly 30 times higher than official case counts and estimates of immunity among those with active infection were high. Taken together, our results suggest that the magnitude of the second Omicron surge was likely significantly underestimated, and high levels of immunity likely prevented a major surge in hospitalizations/deaths. Our findings might inform future work on COVID-19 surveillance and how to mitigate its spread.

Correction: A 10+10+30 radio campaign is associated with increased infant vaccination and decreased morbidity in Jimma Zone, Ethiopia: A prospective, quasi-experimental trial.

[This corrects the article DOI: 10.1371/journal.pgph.0001002.].

Using Google Trends to Estimate the Geographic Distribution of Soil-Transmitted Helminthiasis in the United States from 2016 to 2021.

Soil-transmitted helminth infections are assumed to be uncommon in the US, despite numerous studies in the past few decades showing high burdens in Appalachia and the southern states. We assessed trends of interest in the Google search engine to gauge spatiotemporal patterns of potential soil-transmitted helminth transmission. We conducted a further ecological study comparing Google search trends to risk factors for soil-transmitted helminth transmission. Google search trends for terms related to soil-transmitted helminths were clustered in Appalachia and the south, with seasonal surges suggestive of endemic transmission for hookworm, roundworm (Ascaris), and threadworm. Furthermore, lower access to plumbing, increased septic tank use, and more rural environments were associated with increased soil-transmitted helminth-related Google search terms. Together, these results suggest that soil-transmitted helminthiasis remains endemic in parts of Appalachia and the south.

Using geographic information systems to link population estimates to wastewater surveillance data in New York State, USA.

Sewer systems provide many services to communities that have access to them beyond removal of waste and wastewater. Understanding of these systems' geographic coverage is essential for wastewater-based epidemiology (WBE), which requires accurate estimates for the population contributing wastewater. Reliable estimates for the boundaries of a sewer service area or sewershed can be used to link upstream populations to wastewater samples taken at treatment plants or other locations within a sewer system. These geographic data are usually managed by public utilities, municipal offices, and some government agencies, however, there are no centralized databases for geographic information on sewer systems in New York State. We created a database for all municipal sewersheds in New York State for the purpose of supporting statewide wastewater surveillance efforts to support public health. We used a combination of public tax records with sewer access information, physical maps, and municipal records to organize and draw digital boundaries compatible with geographic information systems. The methods we employed to create these data will be useful to inform similar efforts in other jurisdictions and the data have many public health applications as well as being informative for water/environmental research and infrastructure projects.

Airborne levels of cadmium are correlated with urinary cadmium concentrations among young children living in the New York state city of Syracuse, USA.

Air pollution is a serious public health issue with early childhood exposure being of high concern because of the greater risk that children might experience negative health outcomes. Industrial sources in and near communities are one potential path of exposure that children might face with greater levels of air pollution correlating with higher levels of toxicants detected in children. We compare estimated ambient air concentrations of Cadmium (Cd) to a cohort (n = 281) of 9 to 11-year old children during their early childhood years (0-5 years of age) in a mid-size city in Upstate New York. Levels of Cd air pollution are compared to children's urine-Cd levels. Urine has been shown to be a superior biomarker to blood for Cd exposure particularly for longer-term exposures. We find that participants who reside in households that faced greater Cd air pollution during the child's early years have higher urine-Cd levels. This association is stable and stronger than previously presented associations for blood-Cd. Findings support expanded use of air modelling data for risk screening to reduce the potential health burden that industrial pollution can have.

Wastewater Surveillance for Infectious Disease: A Systematic Review.

Wastewater surveillance for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been shown to be a valuable source of information regarding SARS-CoV-2 transmission and coronavirus disease 2019 (COVID-19) cases. Although the method has been used for several decades to track other infectious diseases, there has not been a comprehensive review outlining all of the pathogens that have been surveilled through wastewater. Herein we identify the infectious diseases that have been previously studied via wastewater surveillance prior to the COVID-19 pandemic. Infectious diseases and pathogens were identified in 100 studies of wastewater surveillance across 38 countries, as were themes of how wastewater surveillance and other measures of disease transmission were linked. Twenty-five separate pathogen families were identified in the included studies, with the majority of studies examining pathogens from the family Picornaviridae, including polio and nonpolio enteroviruses. Most studies of wastewater surveillance did not link what was found in the wastewater to other measures of disease transmission. Among those studies that did, the value reported varied by study. Wastewater surveillance should be considered as a potential public health tool for many infectious diseases. Wastewater surveillance studies can be improved by incorporating other measures of disease transmission at the population-level including disease incidence and hospitalizations.

Utilising fluorescent reporters to probe the mode of action of norbornen-7-one CO releasing molecules.

The synthesis of the fluorescent organic carbon monoxide releasing molecules oCOm-57, oCOm-58, and oCOm-66 are reported. These oCOms are water soluble and exhibit a "turn-on" fluorescent behaviour when CO is released under physiological conditions. oCOm-66 also contains an additional nitro-naphthalimide moiety that functions as a fluorescent reporter. Delivery of CO released from these oCOms to the mitochondria of AC-16 cardiomyocytes was confirmed using confocal microscopy in conjuction with MitoTracker Red. While the neutral, PEGylated oCOm-57 was found to remain in the extracellular environment releasing CO to diffuse into the cellular compartments, the positively charged oCOm-58 and -66 are targeted to the mitochondria where they release CO. Notably, the use of the fluorescent oCOms in live cellular imaging, allows the intracellular CO delivery and oCOm localisation to be characterised. This cellular confocal study also shows that, subtoxic concentrations of CO released from these molecules preserved mitochondrial energetics as indicated by the membrane potential dependent MitoTracker Red.

Wastewater treatment plant operators report high capacity to support wastewater surveillance for COVID-19 across New York State, USA.

Wastewater surveillance for infectious disease expanded greatly during the COVID-19 pandemic. As a collaboration between sanitation engineers and scientists, the most cost-effective deployment of wastewater surveillance routinely tests wastewater samples from wastewater treatment plants. To evaluate the capacity of treatment plants of different sizes and characteristics to participate in surveillance efforts, we developed and distributed a survey to New York State municipal treatment plant supervisors in the summer and fall of 2021. The goal of the survey was to assess the knowledge, capacity, and attitudes toward wastewater surveillance as a public health tool. Our objectives were to: (1) determine what treatment plant operators know about wastewater surveillance for public health; (2) assess how plant operators feel about the affordability and benefits of wastewater surveillance; and (3) determine how frequently plant personnel can take and ship samples using existing resources. Results show that 62% of respondents report capacity to take grab samples twice weekly. Knowledge about wastewater surveillance was mixed with most supervisors knowing that COVID-19 can be tracked via wastewater but having less knowledge about surveillance for other public health issues such as opioids. We found that attitudes toward wastewater testing for public health were directly associated with differences in self-reported capacity of the plant to take samples. Further, findings suggest a diverse capacity for sampling across sewer systems with larger treatment plants reporting greater capacity for more frequent sampling. Findings provide guidance for outreach activities as well as important insight into treatment plant sampling capacity as it is connected to internal factors such as size and resource availability. These may help public health departments understand the limitations and ability of wastewater surveillance for public health benefit.

High Sensitivity and Specificity of Dormitory-Level Wastewater Surveillance for COVID-19 during Fall Semester 2020 at Syracuse University, New York.

A residential building's wastewater presents a potential non-invasive method of surveilling numerous infectious diseases, including SARS-CoV-2. We analyzed wastewater from 16 different residential locations at Syracuse University (Syracuse, NY, USA) during fall semester 2020, testing for SARS-CoV-2 RNA twice weekly and compared the presence of clinical COVID-19 cases to detection of the viral RNA in wastewater. The sensitivity of wastewater surveillance to correctly identify dormitories with a case of COVID-19 ranged from 95% (95% confidence interval [CI] = 76-100%) on the same day as the case was diagnosed to 73% (95% CI = 53-92%), with 7 days lead time of wastewater. The positive predictive value ranged from 20% (95% CI = 13-30%) on the same day as the case was diagnosed to 50% (95% CI = 40-60%) with 7 days lead time. The specificity of wastewater surveillance to correctly identify dormitories without a case of COVID-19 ranged from 60% (95% CI = 52-67%) on the day of the wastewater sample to 67% (95% CI = 58-74%) with 7 days lead time. The negative predictive value ranged from 99% (95% CI = 95-100%) on the day of the wastewater sample to 84% (95% CI = 77-91%) with 7 days lead time. Wastewater surveillance for SARS-CoV-2 at the building level is highly accurate in determining if residents have a COVID-19 infection. Particular benefit is derived from negative wastewater results that can confirm a building is COVID-19 free.

COVID-19 vaccination intention and behavior in a large, diverse, U.S. refugee population.

INTRODUCTION: Refugees often face increased risk of exposure to COVID-19 due to their disproportionate representation in the essential workforce and crowded household conditions. There is a paucity of data about risk factors for under-immunization for COVID-19 among refugees. METHODS: Refugees were surveyed in two phases that corresponded to before and after wide availability of COVID-19 vaccines. Participants were asked about their attitudes, and perceptions about COVID-19, previous acceptance of vaccines, sources utilized to obtain trusted health information, and intent to get vaccinated. The overall participant vulnerability was assessed using the social vulnerability index. In-depth semi-structured interviews were completed with key stakeholders through snowball sampling. RESULTS: Of 247 refugees, 244 agreed to participate in the initial survey. Among those, 140 (57.4%) intended to get vaccinated, 43 (17.6%) were unsure, and 61 (25%) did not intend to get vaccinated. In the follow up survey, all 215 who were reached, agreed to provide information about their vaccination status. Among those respondents, 141 (65.6%) were either vaccinated or expressed intent to do so, and 74 (34.4%) remained hesitant. We did not observe any significant correlation between socio-demographic variables, country of origin, and vaccination status/intent. Among those who initially intended to get vaccinated, nearly 1 in 5 changed their mind and decided to forego vaccination, and among those who initially did not plan getting vaccinated, 1 in 3 changed their mind and got vaccinated. Fears related to the vaccine, concerns that the vaccine is religiously prohibited, "wait and see" how others did with the vaccine, communication and transportation barriers were commonly cited as reason not to get vaccinated. CONCLUSIONS: Over a third of refugees in our study were hesitant to get vaccinated. Refugees desired additional education about the benefits and safety of vaccines along with easier access to vaccination clinics in their communities.