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1. Zootherapy as a potential pathway for zoonotic spillover: a mixed-methods study of the use of animal products in medicinal and cultural practices in Nigeria

   https://doi.org/10.1186/s42522-022-00060-3  

   Friant, Sagan ; Bonwitt, Jesse ; Ayambem, Wilfred A. ; Ifebueme, Nzube M. ; Alobi, Alobi O. ; Otukpa, Oshama M. ; Bennett, Andrew J. ; Shea, Corrigan ; Rothman, Jessica M. ; Goldberg, Tony L. ; et al ( February 2022 , One Health Outlook)

   Understanding how and why people interact with animals is important for the prevention and control of zoonoses. To date, studies have primarily focused on the most visible forms of human-animal contact (e.g., hunting and consumption), thereby blinding One Health researchers and practitioners to the broader range of human-animal interactions that can serve as cryptic sources of zoonotic diseases. Zootherapy, the use of animal products for traditional medicine and cultural practices, is widespread and can generate opportunities for human exposure to zoonoses. Existing research examining zootherapies omits details necessary to adequately assess potential zoonotic risks.

   We used a mixed-methods approach, combining quantitative and qualitative data from questionnaires, key informant interviews, and field notes to examine the use of zootherapy in nine villages engaged in wildlife hunting, consumption, and trade in Cross River State, Nigeria. We analyzed medicinal and cultural practices involving animals from a zoonotic disease perspective, by including details of animal use that may generate pathways for zoonotic transmission. We also examined the sociodemographic, cultural, and environmental contexts of zootherapeutic practices that can further shape the nature and frequency of human-animal interactions.

   Within our study population, people reported using 44 different animal species for zootherapeutic practices, including taxonomic groups considered to be “high risk” for zoonoses and threatened with extinction. Variation in use of animal parts, preparation norms, and administration practices generated a highly diverse set of zootherapeutic practices (n = 292) and potential zoonotic exposure risks. Use of zootherapy was patterned by demographic and environmental contexts, with zootherapy more commonly practiced by hunting households (OR = 2.47,p < 0.01), and prescriptions that were gender and age specific (e.g., maternal and pediatric care) or highly seasonal (e.g., associated with annual festivals and seasonal illnesses). Specific practices were informed by species availability and theories of healing (i.e., “like cures like” and sympathetic healing and magic) that further shaped the nature of human-animal interactions via zootherapy.

   Epidemiological investigations of zoonoses and public health interventions that aim to reduce zoonotic exposures should explicitly consider zootherapy as a potential pathway for disease transmission and consider the sociocultural and environmental contexts of their use in health messaging and interventions.

    

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2. Pruning digital contact networks for meso-scale epidemic surveillance using foursquare data

   https://doi.org/10.1145/3487351.3490971  

   Hurtado, Sofia ; Marculescu, Radu ; Drake, Justin ; Srinivasan, Ravi ( November 2021 , IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM '21))

   With the recent advances in human sensing, the push to integrate human mobility tracking with epidemic modeling highlights the lack of groundwork at the mesoscale (e.g., city-level) for both contact tracing and transmission dynamics. Although GPS data has been used to study city-level outbreaks in the past, existing approaches fail to capture the path of infection at the individual level. Consequently, in this paper, we extend epidemics prediction from estimating the size of an outbreak at the population level to estimating the individuals who may likely get infected within a finite period of time. To this end, we propose a network science based method to first build and then prune the dynamic contact networks for recurring interactions; these networks can serve as the backbone topology for mechanistic epidemics modeling. We test our method using Foursquare’s Points of Interest (POI) smart phone geolocation data from over 1.3 million devices to better approximate the COVID-19 infection curves for two major (yet very different) US cities, (i.e., Austin and New York City), while maintaining the granularity of individual transmissions and reducing model uncertainty. Our method provides a foundation for building a disease prediction framework at the mesoscale that can help both policy makers and individuals better understand their estimated state of health and help the pandemic mitigation efforts. 

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3. Multi-Scale Drivers of Immunological Variation and Consequences for Infectious Disease Dynamics

   https://doi.org/10.1093/icb/icz138  

   Becker, Daniel J. ; Downs, Cynthia J. ; Martin, Lynn B. ( July 2019 , Integrative and Comparative Biology)

   The immune system is the primary barrier to parasite infection, replication, and transmission following exposure, and variation in immunity can accordingly manifest in heterogeneity in traits that govern population-level infectious disease dynamics. While much work in ecoimmunology has focused on individual-level determinants of host immune defense (e.g., reproductive status and body condition), an ongoing challenge remains to understand the broader evolutionary and ecological contexts of this variation (e.g., phylogenetic relatedness and landscape heterogeneity) and to connect these differences into epidemiological frameworks. Ultimately, such efforts could illuminate general principles about the drivers of host defense and improve predictions and control of infectious disease. Here, we highlight recent work that synthesizes the complex drivers of immunological variation across biological scales of organization and scales these within-host differences to population-level infection outcomes. Such studies note the limitations involved in making species-level comparisons of immune phenotypes, stress the importance of spatial scale for immunology research, showcase several statistical tools for translating within-host data into epidemiological parameters, and provide theoretical frameworks for linking within- and between-host scales of infection processes. Building from these studies, we highlight several promising avenues for continued work, including the application of machine learning tools and phylogenetically controlled meta-analyses to immunology data and quantifying the joint spatial and temporal dependencies in immune defense using range expansions as model systems. We also emphasize the use of organismal traits (e.g., host tolerance, competence, and resistance) as a way to interlink various scales of analysis. Such continued collaboration and disciplinary cross-talk among ecoimmunology, disease ecology, and mathematical modeling will facilitate an improved understanding of the multi-scale drivers and consequences of variation in host defense.

    

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4. Top-Down Genomic Surveillance Approach To Investigate the Genomic Epidemiology and Antibiotic Resistance Patterns of Enterococcus faecium Detected in Cancer Patients in Arkansas

   Se-Ran Jun ( March 2023 , Microbiology spectrum)

   Control of hospital-associated Enterococcus faecium infection is a strenuous task due to the difficulty of identifying transmission routes and the persistence of this nosocomial pathogen despite the implementation of infection control measures that have been successful with other important nosocomial pathogens. This study provides a comprehensive analysis of over 100 E. faecium isolates collected from 66 cancer patients at the University of Arkansas for Medical Sciences (UAMS) between June 2018 and May 2019. In the top-down approach used in this study, we employed, in addition to the 106 E. faecium UAMS isolates, a filtered set of 2,167 E. faecium strains from the GenBank database to assess the current population structure of E. faecium species and, consequently, to identify the lineages associated with our clinical isolates. We then evaluated the antibiotic resistance and virulence profiles of hospital-associated strains from the species pool, focusing on antibiotics of last resort, to establish an updated classification of high-risk and multidrug-resistant nosocomial clones. Further investigation of the clinical isolates collected from UAMS patients using whole-genome sequencing analytical methodologies (core genome multilocus sequence typing [cgMLST], core single nucleotide polymorphism [coreSNP] analysis, and phylogenomics), with the addition of patient epidemiological data, revealed a polyclonal outbreak of three sequence types occurring simultaneously in different patient wards. The integration of genomic and epidemiological data collected from the patients increased our understanding of the relationships and transmission dynamics of the E. faecium isolates. Our study provides new insights into genomic surveillance of E. faecium to assist in monitoring and further limiting the spread of multidrug-resistant E. faecium. 

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5. Network Thermodynamics-Based Scalable Compartmental Model for Multi-Strain Epidemics

   https://doi.org/10.3390/math10193513  

   Pateras, J. ; Vaidya, A. ; Ghosh, P. ( September 2022 , Mathematics)

   SARS-CoV-2 continues to upend human life by posing novel threats related to disease spread and mutations. Current models for the disease burden of SARS-CoV-2 consider the aggregate nature of the virus without differentiating between the potency of its multiple strains. Hence, there is a need to create a fundamental modeling framework for multi-strain viruses that considers the competing viral pathogenic pathways. Alongside the consideration that other viral pathogens may coexist, there is also a need for a generalizable modeling framework to account for multiple epidemics (i.e., multi-demics) scenarios, such as influenza and COVID-19 occurring simultaneously. We present a fundamental network thermodynamics approach for assessing, determining, and predicting viral outbreak severity, which extends well-known standard epidemiological models. In particular, we use historical data from New York City’s 2011–2019 influenza seasons and SARS-CoV-2 spread to identify the model parameters. In our model-based analysis, we employ a standard susceptible–infected–recovered (SIR) model with pertinent generalizations to account for multi-strain and multi-demics scenarios. We show that the reaction affinities underpinning the formation processes of our model can be used to categorize the severity of infectious or deceased populations. The spontaneity of occurrence captured by the change in Gibbs free energy of reaction (ΔG) in the system suggests the stability of forward occurring population transfers. The magnitude of ΔG is used to examine past influenza outbreaks and infer epidemiological factors, such as mortality and case burden. This method can be extrapolated for wide-ranging utility in computational epidemiology. The risk of overlapping multi-demics seasons between influenza and SARS-CoV-2 will persist as a significant threat in forthcoming years. Further, the possibility of mutating strains requires novel ways of analyzing the network of competing infection pathways. The approach outlined in this study allows for the identification of new stable strains and the potential increase in disease burden from a complex systems perspective, thereby allowing for a potential response to the significant question: are the effects of a multi-demic greater than the sum of its individual viral epidemics? 

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