Plastics are widely recognized as a pervasive marine pollutant. Microplastics have been garnering increasing attention due to reports documenting their ingestion by animals, including those intended for human consumption. Their accumulation in the marine food chain may also pose a threat to wildlife that consume species that can accumulate microplastic particles. Microplastic contamination in marine ecosystems has thus raised concerns for both human and wildlife health. Our study addresses an unexplored area of research targeting the interaction between plastic and pathogen pollution of coastal waters. We investigated the association of the zoonotic protozoan parasites Toxoplasma gondii, Cryptosporidium parvum, and Giardia enterica with polyethylene microbeads and polyester microfibers. These pathogens were chosen because they have been recognized by the World Health Organization as underestimated causes of illness from shellfish consumption, and due to their persistence in the marine environment. We show that pathogens are capable of associating with microplastics in contaminated seawater, with more parasites adhering to microfiber surfaces as compared with microbeads. Given the global presence of microplastics in fish and shellfish, this study demonstrates a novel pathway by which anthropogenic pollutants may be mediating pathogen transmission in the marine environment, with important ramifications for wildlife and human health.
Plastics are widely recognized as a pervasive marine pollutant. Microplastics have been garnering increasing attention due to reports documenting their ingestion by animals, including those intended for human consumption. Their accumulation in the marine food chain may also pose a threat to wildlife that consume species that can accumulate microplastic particles. Microplastic contamination in marine ecosystems has thus raised concerns for both human and wildlife health. Our study addresses an unexplored area of research targeting the interaction between plastic and pathogen pollution of coastal waters. We investigated the association of the zoonotic protozoan parasites Toxoplasma gondii, Cryptosporidium parvum, and Giardia enterica with polyethylene microbeads and polyester microfibers. These pathogens were chosen because they have been recognized by the World Health Organization as underestimated causes of illness from shellfish consumption, and due to their persistence in the marine environment. We show that pathogens are capable of associating with microplastics in contaminated seawater, with more parasites adhering to microfiber surfaces as compared with microbeads. Given the global presence of microplastics in fish and shellfish, this study demonstrates a novel pathway by which anthropogenic pollutants may be mediating pathogen transmission in the marine environment, with important ramifications for wildlife and human health.
Background: Wastewater-based epidemiology (WBE) has been deployed broadly as an early warning tool for emerging COVID-19 outbreaks. WBE can inform targeted interventions and identify communities with high transmission, enabling quick and effective response. As wastewater becomes an increasingly important indicator for COVID-19 transmission, more robust methods and metrics are needed to guide public health decision making. Objectives: The aim of this research was to develop and implement a mathematical framework to infer incident cases of COVID-19 from SARS-CoV-2 levels measured in wastewater. We propose a classification scheme to assess the adequacy of model training periods based on clinical testing and assess the sensitivity of model predictions to training periods. Methods: We present a Bayesian deconvolution method and linear regression to estimate COVID-19 cases from wastewater data. We described an approach to characterize adequacy in testing during specific time periods and provided evidence to highlight the importance of model training periods on the projection of cases. We estimated the effective reproductive number (Re) directly from observed cases and from the reconstructed incidence of cases from wastewater. The proposed modeling framework was applied to three Northern California communities served by distinct wastewater treatment plants. Results: Both deconvolution and linear regression models consistently projected robust estimates of prevalent cases and Re from wastewater influent samples when assuming training periods with adequate testing. Case estimates from models that used poorer-quality training periods consistently underestimated observed cases. Discussion: Wastewater surveillance data requires robust statistical modeling methods to provide actionable insight for public health decision-making. We propose and validate a modeling framework that can provide estimates of prevalent COVID-19 cases and Re from wastewater data that can be used as tool for disease surveillance including quality assessment for potential training data.
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