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1. Meteorological data for public health surveillance

   https://doi.org/10.56367/OAG-042-10923  

   Wimberly, Michael (January 2024, Open Access Government)

   none (Ed.)

   Meteorological data for public health surveillanceMichael Wimberly, Professor from the University of Oklahoma, walks us through integrating meteorological data for public health surveillance and disease forecasting. Public health surveillance involves the collection, analysis, interpretation, and dissemination of health-related data to plan, implement, and evaluate public health practices. The resulting information supports the detection of emerging health threats, planning interventions, and evaluating policies and programs to protect and improve population health. 

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2. Wastewater surveillance for infectious disease preparedness

   https://doi.org/10.56367/OAG-040-10923  

   Gaardbo_Kuhn, Katrin (January 2023, Open Access Government)

   Wastewater surveillance for infectious disease preparednessThe University of Oklahoma Wastewater Based Epidemiology (OU WBE) team highlights successes from their three years of wastewater surveillance in Oklahoma & how this surveillance approach can be used as next-level monitoring for infectious disease preparedness. The OU WBE team, founded by Bradley Stevenson, Jason Vogel, and Katrin Gaardbo Kuhn in response to the COVID-19 pandemic in Summer 2020, has expanded to one of the most extensive wastewater monitoring networks in the world with a team that has included over 50 faculty, students and staff. In a paper published in 1942, Drs. James Trask and John Paul described a study to detect poliovirus in wastewater samples collected in New York and New Haven. They concluded, “It is likely that the periodic sampling of sewage for pathogenic viruses or bacteria may be a method of epidemiological value”. (1) Since then, wastewater surveillance has been used to detect sporadic outbreaks or clusters of various infectious pathogens, reaching new levels of routine utilization during the COVID-19 pandemic.(2) 

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3. Expanding National‐Scale Wildlife Disease Surveillance Systems With Research Networks

   https://doi.org/10.1002/ece3.71492  

   Pepin, Kim_M; Combs, Matthew_A; Bastille‐Rousseau, Guillaume; Craft, Meggan_E; Cross, Paul; Diuk‐Wasser, Maria_A; Gagne, Roderick_B; Gallo, Travis; Garwood, Tyler; Heale, Jonathon_D; et al (June 2025, Ecology and Evolution)

   ABSTRACT Efficient learning about disease dynamics in free‐ranging wildlife systems can benefit from active surveillance that is standardized across different ecological contexts. For example, active surveillance that targets specific individuals and populations with standardized sampling across ecological contexts (landscape‐scale targeted surveillance) is important for developing a mechanistic understanding of disease emergence, which is the foundation for improving risk assessment of zoonotic or wildlife‐livestock disease outbreaks and predicting hotspots of disease emergence. However, landscape‐scale targeted surveillance systems are rare and challenging to implement. Increasing experience and infrastructure for landscape‐scale targeted surveillance will improve readiness for rapid deployment of this type of surveillance in response to new disease emergence events. Here, we describe our experience developing and rapidly deploying a landscape‐scale targeted surveillance system for severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) in two free‐ranging deer species across their ranges in the United States. Our surveillance system was designed to collect data across individual, population, and landscape scales for future analyses aimed at understanding mechanisms and risk factors of SARS‐CoV‐2 transmission, evolution, and persistence. Our approach leveraged partnerships between state and federal public service sectors and academic researchers in a landscape‐scale targeted surveillance research network. Methods describe our approach to developing the surveillance network and sampling design. Results report challenges with implementing our intended sampling design, specifically how the design was adapted as different challenges arose and summarize the sampling design that has been implemented thus far. In the discussion, we describe strategies that were important for the successful deployment of landscape‐scale targeted surveillance, development and operation of the research network, construction of similar networks in the future, and analytical approaches for the data based on the sampling design. 

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4. From mixed effects modeling to spike and slab variable selection: A Bayesian regression model for group testing data

   https://doi.org/10.1111/biom.13176  

   Joyner, Chase N.; McMahan, Christopher S.; Tebbs, Joshua M.; Bilder, Christopher R. (December 2019, Biometrics)

   Abstract Due to reductions in both time and cost, group testing is a popular alternative to individual‐level testing for disease screening. These reductions are obtained by testing pooled biospecimens (eg, blood, urine, swabs, etc.) for the presence of an infectious agent. However, these reductions come at the expense of data complexity, making the task of conducting disease surveillance more tenuous when compared to using individual‐level data. This is because an individual's disease status may be obscured by a group testing protocol and the effect of imperfect testing. Furthermore, unlike individual‐level testing, a given participant could be involved in multiple testing outcomes and/or may never be tested individually. To circumvent these complexities and to incorporate all available information, we propose a Bayesian generalized linear mixed model that accommodates data arising from any group testing protocol, estimates unknown assay accuracy probabilities and accounts for potential heterogeneity in the covariate effects across population subgroups (eg, clinic sites, etc.); this latter feature is of key interest to practitioners tasked with conducting disease surveillance. To achieve model selection, our proposal uses spike and slab priors for both fixed and random effects. The methodology is illustrated through numerical studies and is applied to chlamydia surveillance data collected in Iowa. 

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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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