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1. Wastewater surveillance for bacterial targets: current challenges and future goals

   https://doi.org/10.1128/aem.01428-23  

   Philo, Sarah E; De_León, Kara B; Noble, Rachel T; Zhou, Nicolette A; Alghafri, Rashed; Bar-Or, Itay; Darling, Amanda; D'Souza, Nishita; Hachimi, Oumaima; Kaya, Devrim; et al (January 2024, Applied and Environmental Microbiology)

   Elkins, Christopher A (Ed.)

   ABSTRACT Wastewater-based epidemiology (WBE) expanded rapidly in response to the COVID-19 pandemic. As the public health emergency has ended, researchers and practitioners are looking to shift the focus of existing wastewater surveillance programs to other targets, including bacteria. Bacterial targets may pose some unique challenges for WBE applications. To explore the current state of the field, the National Science Foundation-funded Research Coordination Network (RCN) on Wastewater Based Epidemiology for SARS-CoV-2 and Emerging Public Health Threats held a workshop in April 2023 to discuss the challenges and needs for wastewater bacterial surveillance. The targets and methods used in existing programs were diverse, with twelve different targets and nine different methods listed. Discussions during the workshop highlighted the challenges in adapting existing programs and identified research gaps in four key areas: choosing new targets, relating bacterial wastewater data to human disease incidence and prevalence, developing methods, and normalizing results. To help with these challenges and research gaps, the authors identified steps the larger community can take to improve bacteria wastewater surveillance. This includes developing data reporting standards and method optimization and validation for bacterial programs. Additionally, more work is needed to understand shedding patterns for potential bacterial targets to better relate wastewater data to human infections. Wastewater surveillance for bacteria can help provide insight into the underlying prevalence in communities, but much work is needed to establish these methods. IMPORTANCEWastewater surveillance was a useful tool to elucidate the burden and spread of SARS-CoV-2 during the pandemic. Public health officials and researchers are interested in expanding these surveillance programs to include bacterial targets, but many questions remain. The NSF-funded Research Coordination Network for Wastewater Surveillance of SARS-CoV-2 and Emerging Public Health Threats held a workshop to identify barriers and research gaps to implementing bacterial wastewater surveillance programs. 

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2. Social Media based Simulation Models for Understanding Disease Dynamics

   https://doi.org/10.24963/ijcai.2018/528  

   Hua, Ting; Reddy, Chandan K; Zhang, Lei; Wang, Lijing; Zhao, Liang; Lu, Chang-Tien; Ramakrishnan, Naren (July 2018, Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence)

   In this modern era, infectious diseases, such as H1N1, SARS, and Ebola, are spreading much faster than any time in history. Efficient approaches are therefore desired to monitor and track the diffusion of these deadly epidemics. Traditional computational epidemiology models are able to capture the disease spreading trends through contact network, however, one unable to provide timely updates via real-world data. In contrast, techniques focusing on emerging social media platforms can collect and monitor real-time disease data, but do not provide an understanding of the underlying dynamics of ailment propagation. To achieve efficient and accurate real-time disease prediction, the framework proposed in this paper combines the strength of social media mining and computational epidemiology. Specifically, individual health status is first learned from user's online posts through Bayesian inference, disease parameters are then extracted for the computational models at population-level, and the outputs of computational epidemiology model are inversely fed into social media data based models for further performance improvement. In various experiments, our proposed model outperforms current disease forecasting approaches with better accuracy and more stability. 

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3. Where ethics is taught: an institutional epidemiology

   https://doi.org/10.1007/s40889-021-00121-7  

   Beever, Jonathan; Kuebler, Stephen M.; Collins, Jordan (March 2021, International Journal of Ethics Education)

   null (Ed.)

   The goal of this project is to argue for ethics as a necessary component of the institutional health. The authors offer an epidemiology of ethics for a large, metropolitan, very-high-research-activity (R1) university in the U.S. Where epidemiology of a pandemic looks at quantifiable data on infection and exposure rates, control, and broad implications for public health, an epidemiology of ethics looks to parallel data on those same themes. Their hypothesis is that knowing more about how undergraduates are exposed to ethics will help us understand to what extent they are infected with interest in ethics literacy, and potentially what immunity they develop against unethical and unprofessional conduct. These data also tell a story about the ethical health of institutions: to what extent its members are empowered to cultivate a culture of ethics and inoculated against ethical missteps. The authors argue that pro-ethics inoculation at research institutions is shaped by issues of complexity (space given to “hard” vs. “soft” skills within curricula), connotation (differences in meaning of “ethics” among and within disciplines), and collaboration (tensions between Ethics-Across-the-Curriculum and Ethics-In-the-Disciplines approaches to ethics). These issues make assessment of where ethics is taught all the more difficult. The methodology used in this project can readily be taken up by other institutions, with much to be learned from inter-institutional comparisons about the distribution of ethics across the curriculum and within the disciplines. 

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4. Investigating and forecasting infectious disease dynamics using epidemiological and molecular surveillance data

   https://doi.org/10.1016/j.plrev.2024.10.011  

   Chowell, Gerardo; Skums, Pavel (December 2024, Physics of Life Reviews)

   The integration of viral genomic data into public health surveillance has revolutionized our ability to track and forecast infectious disease dynamics. This review addresses two critical aspects of infectious disease forecasting and monitoring: the methodological workflow for epidemic forecasting and the transformative role of molecular surveillance. We first present a detailed approach for validating epidemic models, emphasizing an iterative workflow that utilizes ordinary differential equation (ODE)-based models to investigate and forecast disease dynamics. We recommend a more structured approach to model validation, systematically addressing key stages such as model calibration, assessment of structural and practical parameter identifiability, and effective uncertainty propagation in forecasts. Furthermore, we underscore the importance of incorporating multiple data streams by applying both simulated and real epidemiological data from the COVID-19 pandemic to produce more reliable forecasts with quantified uncertainty. Additionally, we emphasize the pivotal role of viral genomic data in tracking transmission dynamics and pathogen evolution. By leveraging advanced computational tools such as Bayesian phylogenetics and phylodynamics, researchers can more accurately estimate transmission clusters and reconstruct outbreak histories, thereby improving data-driven modeling and forecasting and informing targeted public health interventions. Finally, we discuss the transformative potential of integrating molecular epidemiology with mathematical modeling to complement and enhance epidemic forecasting and optimize public health strategies. 

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5. Coupling wastewater‐based epidemiology with data‐driven machine learning for managing public health risks

   https://doi.org/10.1111/risa.70075  

   Pagsuyoin, Sheree; Ng, Calvin; Molejon, Nerissa; Luo, Yan (July 2025, Risk Analysis)

   Abstract Traditional health surveillance methods play a critical role in public health safety but are limited by the data collection speed, coverage, and resource requirements. Wastewater‐based epidemiology (WBE) has emerged as a cost‐effective and rapid tool for detecting infectious diseases through sewage analysis of disease biomarkers. Recent advances in big data analytics have enhanced public health monitoring by enabling predictive modeling and early risk detection. This paper explores the application of machine learning (ML) in WBE data analytics, with a focus on infectious disease surveillance and forecasting. We highlight the advantages of ML‐driven WBE prediction models, including their ability to process multimodal data, predict disease trends, and evaluate policy impacts through scenario simulations. We also examine challenges such as data quality, model interpretability, and integration with existing public health infrastructure. The integration of ML WBE data analytics enables rapid health data collection, analysis, and interpretation that are not feasible in current surveillance approaches. By leveraging ML and WBE, decision makers can reduce cognitive biases and enhance data‐driven responses to public health threats. As global health risks evolve, the synergy between WBE, ML, and data‐driven decision‐making holds significant potential for improving public health outcomes. 

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