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Hamilton KA, Ciol Harrison J, Mitchell J, Weir M, Verhougstraete M, Haas CN, Nejadhashemi AP, Libarkin J, Gim Aw T, Bibby K, Bivins A, Brown J, Dean K, Dunbar G, Eisenberg JNS, Emelko M, Gerrity D, Gurian PL, Hartnett E, Jahne M, Jones RM, Julian TR, Li H, Li Y, Gibson JM, Medema G, Meschke JS, Mraz A, Murphy H, Oryang D, Owusu-Ansah EDGJ, Pasek E, Pradhan AK, Razzolini MTP, Ryan MO, Schoen M, Smeets PWMH, Soller J, Solo-Gabriele H, Williams C, Wilson AM, Zimmer-Faust A, Alja'fari J, Rose JB. Research gaps and priorities for quantitative microbial risk assessment (QMRA). RISK ANALYSIS : AN OFFICIAL PUBLICATION OF THE SOCIETY FOR RISK ANALYSIS 2024. [PMID: 38772724 DOI: 10.1111/risa.14318] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Revised: 03/12/2024] [Accepted: 04/28/2024] [Indexed: 05/23/2024]
Abstract
The coronavirus disease 2019 pandemic highlighted the need for more rapid and routine application of modeling approaches such as quantitative microbial risk assessment (QMRA) for protecting public health. QMRA is a transdisciplinary science dedicated to understanding, predicting, and mitigating infectious disease risks. To better equip QMRA researchers to inform policy and public health management, an Advances in Research for QMRA workshop was held to synthesize a path forward for QMRA research. We summarize insights from 41 QMRA researchers and experts to clarify the role of QMRA in risk analysis by (1) identifying key research needs, (2) highlighting emerging applications of QMRA; and (3) describing data needs and key scientific efforts to improve the science of QMRA. Key identified research priorities included using molecular tools in QMRA, advancing dose-response methodology, addressing needed exposure assessments, harmonizing environmental monitoring for QMRA, unifying a divide between disease transmission and QMRA models, calibrating and/or validating QMRA models, modeling co-exposures and mixtures, and standardizing practices for incorporating variability and uncertainty throughout the source-to-outcome continuum. Cross-cutting needs identified were to: develop a community of research and practice, integrate QMRA with other scientific approaches, increase QMRA translation and impacts, build communication strategies, and encourage sustainable funding mechanisms. Ultimately, a vision for advancing the science of QMRA is outlined for informing national to global health assessments, controls, and policies.
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Affiliation(s)
- Kerry A Hamilton
- The Biodesign Institute Center for Environmental Health Engineering, Arizona State University, Tempe, Arizona, USA
- School of Sustainable Engineering and the Built Environment, Arizona State University, Tempe, Arizona, USA
| | - Joanna Ciol Harrison
- The Biodesign Institute Center for Environmental Health Engineering, Arizona State University, Tempe, Arizona, USA
- School of Sustainable Engineering and the Built Environment, Arizona State University, Tempe, Arizona, USA
| | - Jade Mitchell
- Department of Biosystems and Agricultural Engineering, Michigan State University, East Lansing, Michigan, USA
| | - Mark Weir
- Division of Environmental Health Sciences and Sustainability Institute, The Ohio State University, Columbus, Ohio, USA
| | - Marc Verhougstraete
- Mel and Enid Zuckerman College of Public Health, The University of Arizona, Tucson, Arizona, USA
| | - Charles N Haas
- Department of Civil, Architectural, and Environmental Engineering, Drexel University, Philadelphia, Pennsylvania, USA
| | - A Pouyan Nejadhashemi
- Department of Biosystems and Agricultural Engineering, Michigan State University, East Lansing, Michigan, USA
| | - Julie Libarkin
- Department of Earth and Environmental Sciences, Michigan State University, East Lansing, Michigan, USA
| | - Tiong Gim Aw
- Department of Environmental Health Sciences, School of Public Health and Tropical Medicine, Tulane University, New Orleans, Louisiana, USA
| | - Kyle Bibby
- Department of Civil and Environmental Engineering and Earth Sciences, University of Notre Dame, Notre Dame, Indiana, USA
| | - Aaron Bivins
- Department of Civil & Environmental Engineering, Louisiana State University, Baton Rouge, Louisiana, USA
| | - Joe Brown
- Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Kara Dean
- Department of Biosystems and Agricultural Engineering, Michigan State University, East Lansing, Michigan, USA
| | - Gwyneth Dunbar
- Department of Biosystems and Agricultural Engineering, Michigan State University, East Lansing, Michigan, USA
| | - Joseph N S Eisenberg
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan, USA
| | - Monica Emelko
- Department of Civil and Environmental Engineering, University of Waterloo, Waterloo, Ontario, Canada
| | - Daniel Gerrity
- Applied Research and Development Center, Southern Nevada Water Authority, Las Vegas, Nevada, USA
| | - Patrick L Gurian
- Department of Civil, Architectural, and Environmental Engineering, Drexel University, Philadelphia, Pennsylvania, USA
| | | | - Michael Jahne
- Office of Research and Development, United States Environmental Protection Agency, Cincinnati, Ohio, USA
| | - Rachael M Jones
- Department of Environmental Health Sciences, Fielding School of Public Health, University of California, Los Angeles, California, USA
| | - Timothy R Julian
- Eawag, Swiss Federal Institute of Aquatic Science and Technology, Duebendorf, Switzerland
| | - Hongwan Li
- Department of Biosystems and Agricultural Engineering, Michigan State University, East Lansing, Michigan, USA
| | - Yanbin Li
- Department of Biological and Agricultural Engineering, The University of Arkansas, Fayetteville, Arkansas, USA
| | - Jacqueline MacDonald Gibson
- Department of Civil, Construction, and Environmental Engineering, North Carolina State University, Raleigh, North Carolina, USA
| | - Gertjan Medema
- KWR Water Research Institute, Nieuwegein, The Netherlands
- TU Delft, Delft, The Netherlands
| | - J Scott Meschke
- Department of Environmental and Occupational Health Sciences, School of Public Health, University of Washington, Seattle, Washington, USA
| | - Alexis Mraz
- Department of Public Health, School of Nursing, Health and Exercise Science, The College of New Jersey, Ewing, New Jersey, USA
| | - Heather Murphy
- Ontario Veterinary College Department of Pathobiology, University of Guelph, Ontario, Canada
| | - David Oryang
- Food and Drug Administration (FDA), US Department of Health and Human Services (DHHS), Center for Food Safety and Applied Nutrition (CFSAN), College Park, United States
| | | | - Emily Pasek
- Department of Earth and Environmental Sciences, Michigan State University, East Lansing, Michigan, USA
| | - Abani K Pradhan
- Department of Nutrition and Food Science & Center for Food Safety and Security Systems, University of Maryland, College Park, Maryland, USA
| | | | - Michael O Ryan
- Department of Civil, Architectural, and Environmental Engineering, Drexel University, Philadelphia, Pennsylvania, USA
| | - Mary Schoen
- Soller Environmental, Berkeley, California, USA
| | - Patrick W M H Smeets
- KWR Water Research Institute, Nieuwegein, The Netherlands
- TU Delft, Delft, The Netherlands
| | | | - Helena Solo-Gabriele
- Department of Chemical, Environmental, and Materials Engineering, College of Engineering, University of Miami, Coral Gables, Florida, USA
| | - Clinton Williams
- US Arid Land Agricultural Research Center, Maricopa, Arizona, USA
| | - Amanda M Wilson
- Community, Environment & Policy Department, Mel and Enid Zuckerman College of Public Health, The University of Arizona, Tucson, Arizona, USA
| | | | - Jumana Alja'fari
- National Institute of Standards and Technology (NIST), Gaithersburg, Maryland, USA
| | - Joan B Rose
- Department of Biosystems and Agricultural Engineering, Michigan State University, East Lansing, Michigan, USA
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Pizzitutti F, Bonnet G, Gonzales-Gustavson E, Gabriël S, Pan WK, Gonzalez AE, Garcia HH, O'Neal SE. Spatial transferability of an agent-based model to simulate Taenia solium control interventions. Parasit Vectors 2023; 16:410. [PMID: 37941062 PMCID: PMC10634186 DOI: 10.1186/s13071-023-06003-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Accepted: 10/06/2023] [Indexed: 11/10/2023] Open
Abstract
BACKGROUND Models can be used to study and predict the impact of interventions aimed at controlling the spread of infectious agents, such as Taenia solium, a zoonotic parasite whose larval stage causes epilepsy and economic loss in many rural areas of the developing nations. To enhance the credibility of model estimates, calibration against observed data is necessary. However, this process may lead to a paradoxical dependence of model parameters on location-specific data, thus limiting the model's geographic transferability. METHODS In this study, we adopted a non-local model calibration approach to assess whether it can improve the spatial transferability of CystiAgent, our agent-based model of local-scale T. solium transmission. The calibration dataset for CystiAgent consisted of cross-sectional data on human taeniasis, pig cysticercosis and pig serology collected in eight villages in Northwest Peru. After calibration, the model was transferred to a second group of 21 destination villages in the same area without recalibrating its parameters. Model outputs were compared to pig serology data collected over a period of 2 years in the destination villages during a trial of T. solium control interventions, based on mass and spatially targeted human and pig treatments. RESULTS Considering the uncertainties associated with empirical data, the model produced simulated pre-intervention pig seroprevalences that were successfully validated against data collected in 81% of destination villages. Furthermore, the model outputs were able to reproduce validated pig seroincidence values in 76% of destination villages when compared to the data obtained after the interventions. The results demonstrate that the CystiAgent model, when calibrated using a non-local approach, can be successfully transferred without requiring additional calibration. CONCLUSIONS This feature allows the model to simulate both baseline pre-intervention transmission conditions and the outcomes of control interventions across villages that form geographically homogeneous regions, providing a basis for developing large-scale models representing T. solium transmission at a regional level.
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Affiliation(s)
| | - Gabrielle Bonnet
- Centre for Mathematical Modelling of Infectious Disease (CMMID), Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Eloy Gonzales-Gustavson
- Tropical and Highlands Veterinary Research Institute, Universidad Nacional Mayor de San Marcos, Lima, Peru
| | - Sarah Gabriël
- Department of Veterinary Public Health and Food Safety, Ghent University, Ghent, Belgium
| | - William K Pan
- Nicholas School of Environment and Duke Global Health Institute, Duke University, Durham, USA
| | - Armando E Gonzalez
- School of Veterinary Medicine, Universidad Nacional Mayor de San Marcos, Lima, Peru
| | - Hector H Garcia
- Center for Global Health, Universidad Peruana Cayetano Heredia, Lima, Peru
- Cysticercosis Unit, National Institute of Neurological Sciences, Lima, Peru
| | - Seth E O'Neal
- Center for Global Health, Universidad Peruana Cayetano Heredia, Lima, Peru
- School of Public Health, Oregon Health & Science University and Portland State University, Portland, USA
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Borlase A, Prada JM, Crellen T. Modelling morbidity for neglected tropical diseases: the long and winding road from cumulative exposure to long-term pathology. Philos Trans R Soc Lond B Biol Sci 2023; 378:20220279. [PMID: 37598702 PMCID: PMC10440174 DOI: 10.1098/rstb.2022.0279] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2023] [Accepted: 07/13/2023] [Indexed: 08/22/2023] Open
Abstract
Reducing the morbidities caused by neglected tropical diseases (NTDs) is a central aim of ongoing disease control programmes. The broad spectrum of pathogens under the umbrella of NTDs lead to a range of negative health outcomes, from malnutrition and anaemia to organ failure, blindness and carcinogenesis. For some NTDs, the most severe clinical manifestations develop over many years of chronic or repeated infection. For these diseases, the association between infection and risk of long-term pathology is generally complex, and the impact of multiple interacting factors, such as age, co-morbidities and host immune response, is often poorly quantified. Mathematical modelling has been used for many years to gain insights into the complex processes underlying the transmission dynamics of infectious diseases; however, long-term morbidities associated with chronic or cumulative exposure are generally not incorporated into dynamic models for NTDs. Here we consider the complexities and challenges for determining the relationship between cumulative pathogen exposure and morbidity at the individual and population levels, drawing on case studies for trachoma, schistosomiasis and foodborne trematodiasis. We explore potential frameworks for explicitly incorporating long-term morbidity into NTD transmission models, and consider the insights such frameworks may bring in terms of policy-relevant projections for the elimination era. This article is part of the theme issue 'Challenges and opportunities in the fight against neglected tropical diseases: a decade from the London Declaration on NTDs'.
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Affiliation(s)
- Anna Borlase
- Department of Biology, University of Oxford, Oxford OX1 3SZ, UK
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, UK
| | - Joaquin M. Prada
- Faculty of Health and Medical Sciences, University of Surrey, Guildford GU2 7XH, UK
| | - Thomas Crellen
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, UK
- School of Biodiversity, One Health & Veterinary Medicine, Graham Kerr Building, University of Glasgow, Glasgow G12 8QQ, UK
- Wellcome Centre for Integrative Parasitology, Sir Graeme Davies Building, University of Glasgow, Glasgow G12 8TA, UK
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Prabhakar H, Kerr WL, Bock CH, Kong F. Effect of relative humidity, storage days, and packaging on pecan kernel texture: Analyses and modeling. J Texture Stud 2023; 54:115-126. [PMID: 36146907 PMCID: PMC10092868 DOI: 10.1111/jtxs.12723] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Revised: 09/08/2022] [Accepted: 09/18/2022] [Indexed: 11/27/2022]
Abstract
The studies expounding on the effects of storage conditions on texture changes are limited. The researchers have been proposing methods to measure pecan texture instrumentally. But current protocols and/or attributes fail to address huge variability during experimentation. Additionally, there are no predictive models to estimate changes in pecan texture during storage. This study addresses all the above concerns and investigates the effects of different relative humidity (RH, 30-90%) and packaging material (Polyethylene-Nylon [PEN], polypropylene [PP], low density polyethylene [LDPE], and metallic laminates [ML]) on pecan texture, introducing a rift ratio (F/H or fracturability to hardness ratio) to address variability in the data and predictive model to estimate changes in the textural attribute of pecans during storage. The textural analysis was conducted on pecan cores and intact pecans to measure the area under curve, fracturability, hardness, cohesiveness, chewiness, springiness, and rift ratio. It was observed that values for the rift ratio obtained using the intact pecan method had high R2 (0.72) as compared to the rest of the textural attributes. A three-parameter logistic model was employed to predict pecan texture during storage. The pecans stored at 75, 80, and 90% reached the rift ratio (F/H) of 0.5 at approx. 115, 3, and 0.15 days (~ 4 hr), respectively. Similarly, pecans stored in LDPE, PP, and PEN packs at 80% reached rift ratio (F/H) of 0.5 at approx. 26, 57, and 78 days, respectively. The presence of any kind of package delayed fracturability loss by at least eight folds at 80% RH. The pecans stored in ML did not experience a significant change in textural attributes.
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Affiliation(s)
- Himanshu Prabhakar
- Department of Food Science & Technology, University of Georgia, Athens, Georgia, USA
| | - William L Kerr
- Department of Food Science & Technology, University of Georgia, Athens, Georgia, USA
| | - Clive H Bock
- Fruit and Tree Nut Research, USDA-ARS-SEFNTRL, Byron, Georgia, USA
| | - Fanbin Kong
- Department of Food Science & Technology, University of Georgia, Athens, Georgia, USA
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Eaton SL, Murdoch F, Rzechorzek NM, Thompson G, Hartley C, Blacklock BT, Proudfoot C, Lillico SG, Tennant P, Ritchie A, Nixon J, Brennan PM, Guido S, Mitchell NL, Palmer DN, Whitelaw CBA, Cooper JD, Wishart TM. Modelling Neurological Diseases in Large Animals: Criteria for Model Selection and Clinical Assessment. Cells 2022; 11:cells11172641. [PMID: 36078049 PMCID: PMC9454934 DOI: 10.3390/cells11172641] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Revised: 08/19/2022] [Accepted: 08/22/2022] [Indexed: 11/16/2022] Open
Abstract
Issue: The impact of neurological disorders is recognised globally, with one in six people affected in their lifetime and few treatments to slow or halt disease progression. This is due in part to the increasing ageing population, and is confounded by the high failure rate of translation from rodent-derived therapeutics to clinically effective human neurological interventions. Improved translation is demonstrated using higher order mammals with more complex/comparable neuroanatomy. These animals effectually span this translational disparity and increase confidence in factors including routes of administration/dosing and ability to scale, such that potential therapeutics will have successful outcomes when moving to patients. Coupled with advancements in genetic engineering to produce genetically tailored models, livestock are increasingly being used to bridge this translational gap. Approach: In order to aid in standardising characterisation of such models, we provide comprehensive neurological assessment protocols designed to inform on neuroanatomical dysfunction and/or lesion(s) for large animal species. We also describe the applicability of these exams in different large animals to help provide a better understanding of the practicalities of cross species neurological disease modelling. Recommendation: We would encourage the use of these assessments as a reference framework to help standardise neurological clinical scoring of large animal models.
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Affiliation(s)
- Samantha L. Eaton
- Royal (Dick) School of Veterinary Studies and Roslin Institute, University of Edinburgh, Easter Bush Campus, Roslin, Midlothian EH25 9RG, UK
- Correspondence: (S.L.E.); (T.M.W.); Tel.: +44-(0)-131-651-9125 (S.L.E.); +44-(0)-131-651-9233 (T.M.W.)
| | - Fraser Murdoch
- Royal (Dick) School of Veterinary Studies and Roslin Institute, University of Edinburgh, Easter Bush Campus, Roslin, Midlothian EH25 9RG, UK
| | - Nina M. Rzechorzek
- Medical Research Council Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge CB2 0QH, UK
| | - Gerard Thompson
- Centre for Clinical Brain Sciences, University of Edinburgh, Chancellor’s Building, 49 Little France Crescent, Edinburgh EH16 4SB, UK
- Department of Clinical Neurosciences, NHS Lothian, 50 Little France Crescent, Edinburgh EH16 4TJ, UK
| | - Claudia Hartley
- Royal (Dick) School of Veterinary Studies and Roslin Institute, University of Edinburgh, Easter Bush Campus, Roslin, Midlothian EH25 9RG, UK
| | - Benjamin Thomas Blacklock
- Royal (Dick) School of Veterinary Studies and Roslin Institute, University of Edinburgh, Easter Bush Campus, Roslin, Midlothian EH25 9RG, UK
| | - Chris Proudfoot
- The Large Animal Research & Imaging Facility, Royal (Dick) School of Veterinary Studies and Roslin Institute, University of Edinburgh, Easter Bush Campus, Roslin, Midlothian EH25 9RG, UK
| | - Simon G. Lillico
- Royal (Dick) School of Veterinary Studies and Roslin Institute, University of Edinburgh, Easter Bush Campus, Roslin, Midlothian EH25 9RG, UK
| | - Peter Tennant
- The Large Animal Research & Imaging Facility, Royal (Dick) School of Veterinary Studies and Roslin Institute, University of Edinburgh, Easter Bush Campus, Roslin, Midlothian EH25 9RG, UK
| | - Adrian Ritchie
- The Large Animal Research & Imaging Facility, Royal (Dick) School of Veterinary Studies and Roslin Institute, University of Edinburgh, Easter Bush Campus, Roslin, Midlothian EH25 9RG, UK
| | - James Nixon
- The Large Animal Research & Imaging Facility, Royal (Dick) School of Veterinary Studies and Roslin Institute, University of Edinburgh, Easter Bush Campus, Roslin, Midlothian EH25 9RG, UK
| | - Paul M. Brennan
- Translational Neurosurgery, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh EH16 4SB, UK
| | - Stefano Guido
- Royal (Dick) School of Veterinary Studies and Roslin Institute, University of Edinburgh, Easter Bush Campus, Roslin, Midlothian EH25 9RG, UK
- Bioresearch & Veterinary Services, University of Edinburgh, Chancellor’s Building, 49 Little France Crescent, Edinburgh EH16 4SB, UK
| | - Nadia L. Mitchell
- Faculty of Agriculture and Life Sciences, Lincoln University, P.O. Box 85084, Lincoln 7647, New Zealand
| | - David N. Palmer
- Faculty of Agriculture and Life Sciences, Lincoln University, P.O. Box 85084, Lincoln 7647, New Zealand
| | - C. Bruce A. Whitelaw
- Royal (Dick) School of Veterinary Studies and Roslin Institute, University of Edinburgh, Easter Bush Campus, Roslin, Midlothian EH25 9RG, UK
| | - Jonathan D. Cooper
- Departments of Pediatrics, Genetics, and Neurology, Washington University School of Medicine in St. Louis, 660 S Euclid Ave, St. Louis, MO 63110, USA
| | - Thomas M. Wishart
- Royal (Dick) School of Veterinary Studies and Roslin Institute, University of Edinburgh, Easter Bush Campus, Roslin, Midlothian EH25 9RG, UK
- Correspondence: (S.L.E.); (T.M.W.); Tel.: +44-(0)-131-651-9125 (S.L.E.); +44-(0)-131-651-9233 (T.M.W.)
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