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Kenny U, Finn S, Barrett D. Private veterinarians' views of the Irish bovine TB eradication programme. Res Vet Sci 2024; 173:105246. [PMID: 38677074 DOI: 10.1016/j.rvsc.2024.105246] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Revised: 01/31/2024] [Accepted: 03/24/2024] [Indexed: 04/29/2024]
Abstract
There is limited understanding of Private Veterinary Practitioners' (PVPs) perceptions of, opinions about and attitudes towards the Irish Bovine Tuberculosis (bTB) eradication programme. Understanding their attitudes and behaviors towards the bTB eradication programme is both timely and crucial as their actions have a great influence on the effectiveness and sustainability of the programme itself. To date, PVPs have been consulted about how they view their role in the programme, however, less is known about the challenges they face in carrying out good quality bTB testing, and how likely they feel the programme will succeed to eradicate bTB in the future. The results from this study represent a good part of the probable sphere of perceptions, behaviors, attitudes and knowledge of the respective study population and several key critical points that are believed to have hindered the success of the bTB eradication programme in Ireland to date. This study progressed our understanding of the reasons for why farmers are demotivated by and disconnected with the Irish bTB eradication programme according to PVPs, how PVPs feel challenged in their role carrying out bTB testing, and their views on how, if possible, bTB can be eradicated in the future. Their insights will influence how the Department of Agriculture, Food and the Marine (DAFM) interacts with PVPs and farmers in the future with respect to the bTB and the eradication programme.
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Affiliation(s)
- Ursula Kenny
- Department of Agriculture, Food, and the Marine, Ireland.
| | - Siobhan Finn
- Department of Agriculture, Food, and the Marine, Ireland
| | - Damien Barrett
- Department of Agriculture, Food, and the Marine, Ireland
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2
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Mihaljevic JR, Chief C, Malik M, Oshinubi K, Doerry E, Gel E, Hepp C, Lant T, Mehrotra S, Sabo S. An inaugural forum on epidemiological modeling for public health stakeholders in Arizona. Front Public Health 2024; 12:1357908. [PMID: 38883190 PMCID: PMC11176426 DOI: 10.3389/fpubh.2024.1357908] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Accepted: 05/13/2024] [Indexed: 06/18/2024] Open
Abstract
Epidemiological models-which help us understand and forecast the spread of infectious disease-can be valuable tools for public health. However, barriers exist that can make it difficult to employ epidemiological models routinely within the repertoire of public health planning. These barriers include technical challenges associated with constructing the models, challenges in obtaining appropriate data for model parameterization, and problems with clear communication of modeling outputs and uncertainty. To learn about the unique barriers and opportunities within the state of Arizona, we gathered a diverse set of 48 public health stakeholders for a day-and-a-half forum. Our research group was motivated specifically by our work building software for public health-relevant modeling and by our earnest desire to collaborate closely with stakeholders to ensure that our software tools are practical and useful in the face of evolving public health needs. Here we outline the planning and structure of the forum, and we highlight as a case study some of the lessons learned from breakout discussions. While unique barriers exist for implementing modeling for public health, there is also keen interest in doing so across diverse sectors of State and Local government, although issues of equal and fair access to modeling knowledge and technologies remain key issues for future development. We found this forum to be useful for building relationships and informing our software development, and we plan to continue such meetings annually to create a continual feedback loop between academic molders and public health practitioners.
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Affiliation(s)
- Joseph R Mihaljevic
- School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, AZ, United States
| | - Carmenlita Chief
- Center for Health Equity Research, College of Health and Human Services, Northern Arizona University, Flagstaff, AZ, United States
| | - Mehreen Malik
- Interdisciplinary Health Program, College of Health and Human Services, Northern Arizona University, Flagstaff, AZ, United States
| | - Kayode Oshinubi
- School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, AZ, United States
| | - Eck Doerry
- School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, AZ, United States
| | - Esma Gel
- Department of Supply Chain Management and Analytics, University of Nebraska-Lincoln, Lincoln, NE, United States
| | - Crystal Hepp
- School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, AZ, United States
- Pathogen and Microbiome Division, Translational Genomics Research Institute, Flagstaff, AZ, United States
| | - Tim Lant
- Office of the Vice President for Research, Knowledge Enterprise, Arizona State University, Tempe, AZ, United States
| | - Sanjay Mehrotra
- Department of Industrial Engineering and Management Sciences, Northwestern University, Evanston, IL, United States
- Center for Engineering and Health, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
| | - Samantha Sabo
- Center for Health Equity Research, College of Health and Human Services, Northern Arizona University, Flagstaff, AZ, United States
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3
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Waring TM, Niles MT, Kling MM, Miller SN, Hébert-Dufresne L, Sabzian H, Gotelli N, McGill BJ. Operationalizing cultural adaptation to climate change: contemporary examples from United States agriculture. Philos Trans R Soc Lond B Biol Sci 2023; 378:20220397. [PMID: 37718600 PMCID: PMC10505858 DOI: 10.1098/rstb.2022.0397] [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: 02/17/2023] [Accepted: 06/24/2023] [Indexed: 09/19/2023] Open
Abstract
It has been proposed that climate adaptation research can benefit from an evolutionary approach. But related empirical research is lacking. We advance the evolutionary study of climate adaptation with two case studies from contemporary United States agriculture. First, we define 'cultural adaptation to climate change' as a mechanistic process of population-level cultural change. We argue this definition enables rigorous comparisons, yields testable hypotheses from mathematical theory and distinguishes adaptive change, non-adaptive change and desirable policy outcomes. Next, we develop an operational approach to identify 'cultural adaptation to climate change' based on established empirical criteria. We apply this approach to data on crop choices and the use of cover crops between 2008 and 2021 from the United States. We find evidence that crop choices are adapting to local trends in two separate climate variables in some regions of the USA. But evidence suggests that cover cropping may be adapting more to the economic environment than climatic conditions. Further research is needed to characterize the process of cultural adaptation, particularly the routes and mechanisms of cultural transmission. Furthermore, climate adaptation policy could benefit from research on factors that differentiate regions exhibiting adaptive trends in crop choice from those that do not. This article is part of the theme issue 'Climate change adaptation needs a science of culture'.
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Affiliation(s)
- Timothy M. Waring
- School of Economics, University of Maine, Orono 04473, ME, USA
- Mitchell Center for Sustainability Solutions, University of Maine, Orono 04473, ME, USA
| | - Meredith T. Niles
- Department of Nutrition and Food Sciences, University of Vermont, Burlington 05405-0160, VT, USA
- Gund Institute for Environment, University of Vermont, Burlington 05405-0160, VT, USA
| | - Matthew M. Kling
- Department of Nutrition and Food Sciences, University of Vermont, Burlington 05405-0160, VT, USA
- Department of Biology, University of Vermont, Burlington 05405-0160, VT, USA
| | - Stephanie N. Miller
- Mitchell Center for Sustainability Solutions, University of Maine, Orono 04473, ME, USA
- School of Biology and Ecology, University of Maine, Orono 04473, ME, USA
| | - Laurent Hébert-Dufresne
- Department of Computer Science, University of Vermont, Burlington 05405-0160, VT, USA
- Vermont Complex Systems Center, University of Vermont, Burlington 05405-0160, VT, USA
| | - Hossein Sabzian
- Mitchell Center for Sustainability Solutions, University of Maine, Orono 04473, ME, USA
| | - Nicholas Gotelli
- Department of Biology, University of Vermont, Burlington 05405-0160, VT, USA
| | - Brian J. McGill
- Mitchell Center for Sustainability Solutions, University of Maine, Orono 04473, ME, USA
- School of Biology and Ecology, University of Maine, Orono 04473, ME, USA
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4
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Engelmann L, Montgomery CM, Sturdy S, Moreno Lozano C. Domesticating models: On the contingency of Covid-19 modelling in UK media and policy. SOCIAL STUDIES OF SCIENCE 2023; 53:121-145. [PMID: 36227023 PMCID: PMC9892880 DOI: 10.1177/03063127221126166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Our article traces the representation of pandemic modelling in UK print media from the emergence of Covid-19 to the early stages of implementing the first UK-wide lockdown in late March 2020. Covid modelling, it is widely assumed, has shaped policy decisions and public responses to the pandemic in unprecedented ways. We analyse how the UK print media has configured modelling as a significant evidence tool in the representation of the pandemic. Interrogating assumptions about infectious disease modelling, we ask why models became the trusted tool of choice for knowing and responding to the Covid pandemic in the UK. Our analysis has yielded four different periods in the evolution of intersecting policy and media frames. Initially, modellers, policymakers and media alike emphasized uncertainty about available data, and hence the speculative character of modelled projections, thus justifying a 'wait and see' approach to government intervention. With growing public pressure for government action, policy and media frames were adjusted to emphasize the importance of timing interventions for best effect, with modelling evidence mobilized to justify inaction. This gave way to a period of crisis, as the press increasingly questioned the reliability of the existing models and policies, leading modellers and policy makers to dramatically revise their projections. Finally, with the imposition of the first UK lockdown, policy and media frames were brought back into alignment with one another, in a process of domestication through which the language of modelling became a basic resource for the discussion of the epidemic. Our epistemological microhistory thus challenges general accounts of the impacts of pandemic modelling and instead emphasizes contingency and interpretative flexibility.
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5
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Dial NJ, Croft SL, Chapman LAC, Terris-Prestholt F, Medley GF. Challenges of using modelling evidence in the visceral leishmaniasis elimination programme in India. PLOS GLOBAL PUBLIC HEALTH 2022; 2:e0001049. [PMID: 36962829 PMCID: PMC10021829 DOI: 10.1371/journal.pgph.0001049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Accepted: 10/25/2022] [Indexed: 06/18/2023]
Abstract
As India comes closer to the elimination of visceral leishmaniasis (VL) as a public health problem, surveillance efforts and elimination targets must be continuously revised and strengthened. Mathematical modelling is a compelling research discipline for informing policy and programme design in its capacity to project incidence across space and time, the likelihood of achieving benchmarks, and the impact of different interventions. To gauge the extent to which modelling informs policy in India, this qualitative analysis explores how and whether policy makers understand, value, and reference recently produced VL modelling research. Sixteen semi-structured interviews were carried out with both users- and producers- of VL modelling research, guided by a knowledge utilisation framework grounded in knowledge translation theory. Participants reported that barriers to knowledge utilisation include 1) scepticism that models accurately reflect transmission dynamics, 2) failure of modellers to apply their analyses to specific programme operations, and 3) lack of accountability in the process of translating knowledge to policy. Political trust and support are needed to translate knowledge into programme activities, and employment of a communication intermediary may be a necessary approach to improve this process.
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Affiliation(s)
- Natalie J. Dial
- Department of Global Health and Development, Faculty of Public Health and Policy, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Simon L. Croft
- Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Lloyd A. C. Chapman
- Department of Global Health and Development, Faculty of Public Health and Policy, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Fern Terris-Prestholt
- Department of Global Health and Development, Faculty of Public Health and Policy, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Graham F. Medley
- Department of Global Health and Development, Faculty of Public Health and Policy, London School of Hygiene and Tropical Medicine, London, United Kingdom
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6
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Rhodes T, Lancaster K. Uncomfortable science: How mathematical models, and consensus, come to be in public policy. SOCIOLOGY OF HEALTH & ILLNESS 2022; 44:1461-1480. [PMID: 36127860 PMCID: PMC9826476 DOI: 10.1111/1467-9566.13535] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/15/2022] [Accepted: 06/30/2022] [Indexed: 05/31/2023]
Abstract
We explore messy translations of evidence in policy as a site of 'uncomfortable science'. Drawing on the work of John Law, we follow evidence as a 'fluid object' of its situation, also enacted in relation to a hinterland of practices. Working with the qualitative interview accounts of mathematical modellers and other scientists engaged in the UK COVID-19 response, we trace how models perform as evidence. Our point of departure is a moment of controversy in the public announcement of second national lockdown in the UK, and specifically, the projected daily deaths from COVID-19 presented in support of this policy decision. We reflect on this event to trace the messy translations of "scientific consensus" in the face of uncertainty. Efforts among scientists to realise evidence-based expectation and to manage the troubled translations of models in policy, including via "scientific consensus", can extend the dis-ease of uncomfortable science rather than clean it up or close it down. We argue that the project of evidence-based policy is not so much in need of technical management or repair, but that we need to be thinking altogether differently.
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Affiliation(s)
- Tim Rhodes
- London School of Hygiene and Tropical MedicineLondonUK
- University of New South WalesSydneyAustralia
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7
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Rhodes T, Lancaster K. Making pandemics big: On the situational performance of Covid-19 mathematical models. Soc Sci Med 2022; 301:114907. [PMID: 35303668 PMCID: PMC8917648 DOI: 10.1016/j.socscimed.2022.114907] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Revised: 02/07/2022] [Accepted: 03/11/2022] [Indexed: 11/03/2022]
Abstract
In this paper, we trace how mathematical models are made 'evidence enough' and 'useful for policy'. Working with the interview accounts of mathematical modellers and other scientists engaged in the UK Covid-19 response, we focus on two weeks in March 2020 prior to the announcement of an unprecedented national lockdown. A key thread in our analysis is how pandemics are made 'big'. We follow the work of one particular device, that of modelled 'doubling-time'. By following how modelled doubling-time entangles in its assemblage of evidence-making, we draw attention to multiple actors, including beyond models and metrics, which affect how evidence is performed in relation to the scale of epidemic and its policy response. We draw attention to: policy; Government scientific advice infrastructure; time; uncertainty; and leaps of faith. The 'bigness' of the pandemic, and its evidencing, is situated in social and affective practices, in which uncertainty and dis-ease are inseparable from calculus. This materialises modelling in policy as an 'uncomfortable science'. We argue that situational fit in-the-moment is at least as important as empirical fit when attending to what models perform in policy.
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Affiliation(s)
- Tim Rhodes
- London School of Hygiene and Tropical Medicine, London, UK; University of New South Wales, Sydney, Australia.
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8
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On-farm evaluation of a predictive model for Australian beef and sheep producers’ vulnerability to an outbreak of foot and mouth disease. Prev Vet Med 2022; 204:105656. [DOI: 10.1016/j.prevetmed.2022.105656] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Revised: 12/05/2021] [Accepted: 04/19/2022] [Indexed: 11/19/2022]
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9
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Aylett-Bullock J, Gilman RT, Hall I, Kennedy D, Evers ES, Katta A, Ahmed H, Fong K, Adib K, Al Ariqi L, Ardalan A, Nabeth P, von Harbou K, Hoffmann Pham K, Cuesta-Lazaro C, Quera-Bofarull A, Gidraf Kahindo Maina A, Valentijn T, Harlass S, Krauss F, Huang C, Moreno Jimenez R, Comes T, Gaanderse M, Milano L, Luengo-Oroz M. Epidemiological modelling in refugee and internally displaced people settlements: challenges and ways forward. BMJ Glob Health 2022; 7:bmjgh-2021-007822. [PMID: 35264317 PMCID: PMC8915287 DOI: 10.1136/bmjgh-2021-007822] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Accepted: 01/23/2022] [Indexed: 11/06/2022] Open
Abstract
The spread of infectious diseases such as COVID-19 presents many challenges to healthcare systems and infrastructures across the world, exacerbating inequalities and leaving the world’s most vulnerable populations at risk. Epidemiological modelling is vital to guiding evidence-informed or data-driven decision making. In forced displacement contexts, and in particular refugee and internally displaced people (IDP) settlements, it meets several challenges including data availability and quality, the applicability of existing models to those contexts, the accurate modelling of cultural differences or specificities of those operational settings, the communication of results and uncertainties, as well as the alignment of strategic goals between diverse partners in complex situations. In this paper, we systematically review the limited epidemiological modelling work applied to refugee and IDP settlements so far, and discuss challenges and identify lessons learnt from the process. With the likelihood of disease outbreaks expected to increase in the future as more people are displaced due to conflict and climate change, we call for the development of more approaches and models specifically designed to include the unique features and populations of refugee and IDP settlements. To strengthen collaboration between the modelling and the humanitarian public health communities, we propose a roadmap to encourage the development of systems and frameworks to share needs, build tools and coordinate responses in an efficient and scalable manner, both for this pandemic and for future outbreaks.
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Affiliation(s)
- Joseph Aylett-Bullock
- UN Global Pulse, United Nations, New York, New York, USA .,Institute for Data Science, Durham University, Durham, UK
| | - Robert Tucker Gilman
- Centre for Crisis Studies and Mitigation, The University of Manchester, Manchester, UK.,Department of Earth and Environmental Sciences, The University of Manchester, Manchester, UK
| | - Ian Hall
- Centre for Crisis Studies and Mitigation, The University of Manchester, Manchester, UK.,Division of Informatics, Imaging and Data Sciences, The University of Manchester, Manchester, UK.,Department of Mathematics, The University of Manchester, Manchester, UK
| | - David Kennedy
- UK Public Health Rapid Support Team, London School of Hygiene & Tropical Medicine/Public Health England, London, UK
| | - Egmond Samir Evers
- WHO Cox's Bazar Emergency Sub-Office, United Nations, Cox's Bazar, Bangladesh
| | - Anjali Katta
- UN Global Pulse, United Nations, New York, New York, USA
| | - Hussien Ahmed
- UNHCR Cox's Bazar Sub-Office, United Nations, Cox's Bazar, Bangladesh
| | - Kevin Fong
- Department of Science, Technology, Engineering and Public Policy, University College London, London, UK
| | - Keyrellous Adib
- WHO Eastern Mediterranean Regional Office, United Nations, Cairo, Egypt
| | - Lubna Al Ariqi
- WHO Eastern Mediterranean Regional Office, United Nations, Cairo, Egypt
| | - Ali Ardalan
- WHO Eastern Mediterranean Regional Office, United Nations, Cairo, Egypt
| | - Pierre Nabeth
- WHO Eastern Mediterranean Regional Office, United Nations, Cairo, Egypt
| | - Kai von Harbou
- WHO Cox's Bazar Emergency Sub-Office, United Nations, Cox's Bazar, Bangladesh
| | - Katherine Hoffmann Pham
- UN Global Pulse, United Nations, New York, New York, USA.,Stern School of Business, New York University, New York City, New York, USA
| | | | | | | | - Tinka Valentijn
- OCHA Centre for Humanitarian Data, United Nations, The Hague, The Netherlands
| | - Sandra Harlass
- UNHCR Public Health Unit, United Nations, Geneva, Switzerland
| | - Frank Krauss
- Institute for Data Science, Durham University, Durham, UK
| | - Chao Huang
- UNHCR Global Data Service, United Nations, Copenhagen, New York, USA
| | | | - Tina Comes
- Faculty of Technology, Policy, and Management, Department of Engineering Systems and Services, Delft University of Technology, Delft, The Netherlands
| | - Mariken Gaanderse
- Faculty of Technology, Policy, and Management, Department of Engineering Systems and Services, Delft University of Technology, Delft, The Netherlands
| | - Leonardo Milano
- OCHA Centre for Humanitarian Data, United Nations, The Hague, The Netherlands
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D’Agostino McGowan L, Grantz KH, Murray E. Quantifying Uncertainty in Mechanistic Models of Infectious Disease. Am J Epidemiol 2021; 190:1377-1385. [PMID: 33475686 PMCID: PMC7929394 DOI: 10.1093/aje/kwab013] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2020] [Revised: 01/12/2021] [Accepted: 01/14/2021] [Indexed: 12/23/2022] Open
Abstract
This primer describes the statistical uncertainty in mechanistic models and provides R code to quantify it. We begin with an overview of mechanistic models for infectious disease, and then describe the sources of statistical uncertainty in the context of a case study on SARS-CoV-2. We describe the statistical uncertainty as belonging to three categories: data uncertainty, stochastic uncertainty, and structural uncertainty. We demonstrate how to account for each of these via statistical uncertainty measures and sensitivity analyses broadly, as well as in a specific case study on estimating the basic reproductive number, \documentclass[12pt]{minimal}
\usepackage{amsmath}
\usepackage{wasysym}
\usepackage{amsfonts}
\usepackage{amssymb}
\usepackage{amsbsy}
\usepackage{upgreek}
\usepackage{mathrsfs}
\setlength{\oddsidemargin}{-69pt}
\begin{document}
}{}${R}_0$\end{document}, for SARS-CoV-2.
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Affiliation(s)
- Lucy D’Agostino McGowan
- Department of Mathematics and Statistics, Wake Forest University, Winston-Salem, NC, United States
- Correspondence to Dr. Lucy D’Agostino McGowan, Department of Mathematics and Statistics, Wake Forest University, 127 Manchester Hall Box 7388, Winston-Salem, NC 27109, (e-mail: )
| | - Kyra H Grantz
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States
| | - Eleanor Murray
- Department of Epidemiology, Boston University, Boston MA, United States
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Heriot GS, Jamrozik E. Imagination and remembrance: what role should historical epidemiology play in a world bewitched by mathematical modelling of COVID-19 and other epidemics? HISTORY AND PHILOSOPHY OF THE LIFE SCIENCES 2021; 43:81. [PMID: 34100155 PMCID: PMC8183318 DOI: 10.1007/s40656-021-00422-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/12/2020] [Accepted: 04/21/2021] [Indexed: 06/12/2023]
Abstract
Although every emerging infectious disease occurs in a unique context, the behaviour of previous pandemics offers an insight into the medium- and long-term outcomes of the current threat. Where an informative historical analogue exists, epidemiologists and policymakers should consider how the insights of the past can inform current forecasts and responses.
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Affiliation(s)
- George S Heriot
- School of Public Health and Preventive Medicine, Monash University, 553 St Kilda Road, Melbourne VIC 3004, Clayton, VIC, Australia.
- Royal Melbourne Hospital Department of Medicine, University of Melbourne, Parkville, VIC, Australia.
| | - Euzebiusz Jamrozik
- The Ethox Centre & Wellcome Centre for Ethics and the Humanities, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Monash Bioethics Centre, Monash University, Clayton, VIC, Australia
- Royal Melbourne Hospital Department of Medicine, University of Melbourne, Parkville, VIC, Australia
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12
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Gesser-Edelsburg A. Using Narrative Evidence to Convey Health Information on Social Media: The Case of COVID-19. J Med Internet Res 2021; 23:e24948. [PMID: 33674257 PMCID: PMC7962859 DOI: 10.2196/24948] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2020] [Revised: 11/09/2020] [Accepted: 03/05/2021] [Indexed: 01/14/2023] Open
Abstract
During disease outbreaks or pandemics, policy makers must convey information to the public for informative purposes (eg, morbidity or mortality rates). They must also motivate members of the public to cooperate with the guidelines, specifically by changing their usual behavior. Policy makers have traditionally adopted a didactic and formalistic stance by conveying dry, statistics-based health information to the public. They have not yet considered the alternative of providing health information in the form of narrative evidence, using stories that address both cognitive and emotional aspects. The aim of this viewpoint paper is to introduce policy makers to the advantages of using narrative evidence to provide health information during a disease outbreak or pandemic such as COVID-19. Throughout human history, authorities have tended to employ apocalyptic narratives during disease outbreaks or pandemics. This viewpoint paper proposes an alternative coping narrative that includes the following components: segmentation; barrier reduction; role models; empathy and support; strengthening self-efficacy, community efficacy, and coping tools; preventing stigmatization of at-risk populations; and communicating uncertainty. It also discusses five conditions for using narrative evidence to produce an effective communication campaign on social media: (1) identifying narratives that reveal the needs, personal experiences, and questions of different subgroups to tailor messaging to produce targeted behavioral change; (2) providing separate and distinct treatment of each information unit or theory that arises on social networks; (3) identifying positive deviants who found creative solutions for stress during the COVID-19 crisis not found by other members of the community; (4) creating different stories of coping; and (5) maintaining a dialogue with population subgroups (eg, skeptical and hesitant groups). The paper concludes by proposing criteria for evaluating the effectiveness of a narrative.
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Affiliation(s)
- Anat Gesser-Edelsburg
- School of Public Health, University of Haifa, Haifa, Israel.,Health and Risk Communication Research Center, University of Haifa, Haifa, Israel
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13
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Ding C, Liu X, Yang S. The value of infectious disease modeling and trend assessment: a public health perspective. Expert Rev Anti Infect Ther 2021; 19:1135-1145. [PMID: 33522327 DOI: 10.1080/14787210.2021.1882850] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
INTRODUCTION Disease outbreaks of acquired immunodeficiency syndrome, severe acute respiratory syndrome, pandemic H1N1, H7N9, H5N1, Ebola, Zika, Middle East respiratory syndrome, and recently COVID-19 have raised the attention of the public over the past half-century. Revealing the characteristics and epidemic trends are important parts of disease control. The biological scenarios including transmission characteristics can be constructed and translated into mathematical models, which can help to predict and gain a deeper understanding of diseases. AREAS COVERED This review discusses the models for infectious diseases and highlights their values in the field of public health. This information will be of interest to mathematicians and clinicians, and make a significant contribution toward the development of more specific and effective models. Literature searches were performed using the online database of PubMed (inception to August 2020). EXPERT OPINION Modeling could contribute to infectious disease control by means of predicting the scales of disease epidemics, indicating the characteristics of disease transmission, evaluating the effectiveness of interventions or policies, and warning or forecasting during the pre-outbreak of diseases. With the development of theories and the ability of calculations, infectious disease modeling would play a much more important role in disease prevention and control of public health.
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Affiliation(s)
- Cheng Ding
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases,National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiaoxiao Liu
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases,National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Shigui Yang
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases,National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.,Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, USA
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Killeen GF, Kiware SS. Why lockdown? Why national unity? Why global solidarity? Simplified arithmetic tools for decision-makers, health professionals, journalists and the general public to explore containment options for the 2019 novel coronavirus. Infect Dis Model 2020; 5:442-458. [PMID: 32691016 PMCID: PMC7342051 DOI: 10.1016/j.idm.2020.06.006] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Revised: 06/20/2020] [Accepted: 06/28/2020] [Indexed: 01/08/2023] Open
Abstract
As every country in the world struggles with the ongoing COVID-19 pandemic, it is essential that as many people as possible understand the epidemic containment, elimination and exclusion strategies required to tackle it. Simplified arithmetic models of COVID-19 transmission, control and elimination are presented in user-friendly Shiny and Excel formats that allow non-specialists to explore, query, critique and understand the containment decisions facing their country and the world at large. Although the predictive model is broadly applicable, the simulations presented are based on parameter values representative of the United Republic of Tanzania, which is still early enough in its epidemic cycle and response to avert a national catastrophe. The predictions of these models illustrate (1) why ambitious lock-down interventions to crush the curve represent the only realistic way for individual countries to contain their national-level epidemics before they turn into outright catastrophes, (2) why these need to be implemented so early, so stringently and for such extended periods, (3) why high prevalence of other pathogens causing similar symptoms to mild COVID-19 precludes the use of contact tracing as a substitute for lock down interventions to contain and eliminate epidemics, (4) why partial containment strategies intended to merely flatten the curve, by maintaining epidemics at manageably low levels, are grossly unrealistic, and (5) why local elimination may only be sustained after lock down ends if imported cases are comprehensively excluded, so international co-operation to conditionally re-open trade and travel between countries certified as free of COVID-19 represents the best strategy for motivating progress towards pandemic eradication at global level. The three sequential goals that every country needs to emphatically embrace are contain, eliminate and exclude. As recently emphasized by the World Health Organization, success will require widespread genuine national unity and unprecedented global solidarity.
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Affiliation(s)
- Gerry F Killeen
- Environmental Health and Ecological Sciences Department, Ifakara Health Institute, Ifakara, Morogoro, United Republic of Tanzania
- School of Biological, Earth & Environmental Sciences and Environmental Research Institute, University College Cork, Ireland
| | - Samson S Kiware
- Environmental Health and Ecological Sciences Department, Ifakara Health Institute, Ifakara, Morogoro, United Republic of Tanzania
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15
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Abdulkareem SA, Augustijn EW, Filatova T, Musial K, Mustafa YT. Risk perception and behavioral change during epidemics: Comparing models of individual and collective learning. PLoS One 2020; 15:e0226483. [PMID: 31905206 PMCID: PMC6944362 DOI: 10.1371/journal.pone.0226483] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2018] [Accepted: 11/26/2019] [Indexed: 11/21/2022] Open
Abstract
Modern societies are exposed to a myriad of risks ranging from disease to natural hazards and technological disruptions. Exploring how the awareness of risk spreads and how it triggers a diffusion of coping strategies is prominent in the research agenda of various domains. It requires a deep understanding of how individuals perceive risks and communicate about the effectiveness of protective measures, highlighting learning and social interaction as the core mechanisms driving such processes. Methodological approaches that range from purely physics-based diffusion models to data-driven environmental methods rely on agent-based modeling to accommodate context-dependent learning and social interactions in a diffusion process. Mixing agent-based modeling with data-driven machine learning has become popularity. However, little attention has been paid to the role of intelligent learning in risk appraisal and protective decisions, whether used in an individual or a collective process. The differences between collective learning and individual learning have not been sufficiently explored in diffusion modeling in general and in agent-based models of socio-environmental systems in particular. To address this research gap, we explored the implications of intelligent learning on the gradient from individual to collective learning, using an agent-based model enhanced by machine learning. Our simulation experiments showed that individual intelligent judgement about risks and the selection of coping strategies by groups with majority votes were outperformed by leader-based groups and even individuals deciding alone. Social interactions appeared essential for both individual learning and group learning. The choice of how to represent social learning in an agent-based model could be driven by existing cultural and social norms prevalent in a modeled society.
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Affiliation(s)
- Shaheen A Abdulkareem
- Center of Studies of Technology and Sustainability Development (CSTM), Faculty of Behavioral, Management, and Social sciences (BMS), University of Twente, Enschede, The Netherlands.,Department of Computer Science, College of Science, University of Duhok (UoD), Kurdistan region, Iraq
| | - Ellen-Wien Augustijn
- Department of Geo-Information Processing (GIP), Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Enschede, The Netherlands
| | - Tatiana Filatova
- Center of Studies of Technology and Sustainability Development (CSTM), Faculty of Behavioral, Management, and Social sciences (BMS), University of Twente, Enschede, The Netherlands.,School of Information, Systems and Modeling, Faculty of Engineering and Information Technology, University of Technology Sydney (UTS), Sydney, Australia
| | - Katarzyna Musial
- Advanced Analytics Institute, School of Software, Faculty of Engineering and IT, University of Technology Sydney (UTS), Sydney, Australia
| | - Yaseen T Mustafa
- Faculty of Science, University of Zakho (UoZ), Kurdistan region, Iraq
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16
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Adam KE, Baillie S, Rushton J. ‘Clients. Outdoors. Animals.
’: retaining vets in UK farm animal practice-thematic analysis of free-text survey responses. Vet Rec 2019; 184:121. [DOI: 10.1136/vr.105066] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2018] [Revised: 10/12/2018] [Accepted: 10/19/2018] [Indexed: 11/04/2022]
Affiliation(s)
- Katherine E Adam
- Innogen Institute, Science Technology and Innovation Studies, School of Social and Political Science, University of Edinburgh; Edinburgh UK
| | - Sarah Baillie
- Bristol Veterinary School, University of Bristol; Bristol UK
| | - Jonathan Rushton
- Epidemiology and Population Health; Institute of Infection and Global Health, University of Liverpool; Liverpool UK
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17
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Abstract
During emerging disease outbreaks, public health, emergency management officials and decision-makers increasingly rely on epidemiological models to forecast outbreak progression and determine the best response to health crisis needs. Outbreak response strategies derived from such modelling may include pharmaceutical distribution, immunisation campaigns, social distancing, prophylactic pharmaceuticals, medical care, bed surge, security and other requirements. Infectious disease modelling estimates are unavoidably subject to multiple interpretations, and full understanding of a model's limitations may be lost when provided from the disease modeller to public health practitioner to government policymaker. We review epidemiological models created for diseases which are of greatest concern for public health protection. Such diseases, whether transmitted from person-to-person (Ebola, influenza, smallpox), via direct exposure (anthrax), or food and waterborne exposure (cholera, typhoid) may cause severe illness and death in a large population. We examine disease-specific models to determine best practices characterising infectious disease outbreaks and facilitating emergency response and implementation of public health policy and disease control measures.
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18
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MacGregor H, Waldman L. Views from many worlds: unsettling categories in interdisciplinary research on endemic zoonotic diseases. Philos Trans R Soc Lond B Biol Sci 2018; 372:rstb.2016.0170. [PMID: 28584178 PMCID: PMC5468695 DOI: 10.1098/rstb.2016.0170] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/27/2017] [Indexed: 01/08/2023] Open
Abstract
Interdisciplinary research on zoonotic disease has tended to focus on ‘risk’ of disease transmission as a conceptual common denominator. With reference to endemic zoonoses at the livestock–human interface, we argue for considering a broader sweep of disciplinary insights from anthropology and other social sciences in interdisciplinary dialogue, in particular cross-cultural perspectives on human–animal engagement. We consider diverse worldviews where human–animal encounters are perceived of in terms of the kinds of social relations they generate, and the notion of culture is extended to the ‘natural’ world. This has implications for how animals are valued, treated and prioritized. Thinking differently with and about animals and about species' boundaries could enable ways of addressing zoonotic diseases which have closer integration with people's own cultural norms. If we can bring this kind of knowledge into One Health debates, we find ourselves with a multiplicity of worldviews, where bounded categories such as human:animal and nature:culture cannot be assumed. This might in turn influence our scientific ways of seeing our own disciplinary cultures, and generate novel ways of understanding zoonoses and constructing solutions. This article is part of the themed issue ‘One Health for a changing world: zoonoses, ecosystems and human well-being’.
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Affiliation(s)
- Hayley MacGregor
- STEPS Centre, Institute for Development Studies, University of Sussex, Brighton BN1 9RE, UK
| | - Linda Waldman
- STEPS Centre, Institute for Development Studies, University of Sussex, Brighton BN1 9RE, UK
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19
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Bardosh KL, Scoones JC, Grace D, Kalema-Zikusoka G, Jones KE, de Balogh K, Waltner-Toews D, Bett B, Welburn SC, Mumford E, Dzingirai V. Engaging research with policy and action: what are the challenges of responding to zoonotic disease in Africa? Philos Trans R Soc Lond B Biol Sci 2018; 372:rstb.2016.0172. [PMID: 28584180 PMCID: PMC5468697 DOI: 10.1098/rstb.2016.0172] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/22/2017] [Indexed: 01/09/2023] Open
Abstract
Zoonotic diseases will maintain a high level of public policy attention in the coming decades. From the spectre of a global pandemic to anxieties over agricultural change, urbanization, social inequality and threats to natural ecosystems, effectively preparing and responding to endemic and emerging diseases will require technological, institutional and social innovation. Much current discussion emphasizes the need for a 'One Health' approach: bridging disciplines and sectors to tackle these complex dynamics. However, as attention has increased, so too has an appreciation of the practical challenges in linking multi-disciplinary, multi-sectoral research with policy, action and impact. In this commentary paper, we reflect on these issues with particular reference to the African sub-continent. We structure the themes of our analysis on the existing literature, expert opinion and 11 interviews with leading One Health scholars and practitioners, conducted at an international symposium in 2016. We highlight a variety of challenges in research and knowledge production, in the difficult terrain of implementation and outreach, and in the politicized nature of decision-making and priority setting. We then turn our attention to a number of strategies that might help reconfigure current pathways and accepted norms of practice. These include: (i) challenging scientific expertise; (ii) strengthening national multi-sectoral coordination; (iii) building on what works; and (iv) re-framing policy narratives. We argue that bridging the research-policy-action interface in Africa, and better connecting zoonoses, ecosystems and well-being in the twenty-first century, will ultimately require greater attention to the democratization of science and public policy.This article is part of the themed issue 'One Health for a changing world: zoonoses, ecosystems and human well-being'.
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Affiliation(s)
- Kevin Louis Bardosh
- Department of Anthropology and Emerging Pathogens Institute, University of Florida, 2055 Mowry Road, Gainesville, FL 32610, USA
| | | | - Delia Grace
- International Livestock Research Institute, PO Box 30709, Nairobi, Kenya
| | - Gladys Kalema-Zikusoka
- Conservation Through Public Health, Plot 3 Mapeera Lane, Entebbe PO Box 75298 Clock Towers, Kampala, Uganda
| | - Kate E Jones
- Centre for Biodiversity and Environment Research, Department of Genetics, Evolution and Environment, University College London, Gower Street, London WC1E 6BT, UK.,Institute of Zoology, Zoological Society of London, Regent's Park, London NW1 4RY, UK
| | - Katinka de Balogh
- Regional Office for Asia and the Pacific, Food and Agriculture Organization of the United Nations (FAO), 39 Phra Atit Road, Phranakon, Bangkok 10200, Thailand
| | - David Waltner-Toews
- Department of Population Medicine, Ontario Veterinary College, University of Guelph, Guelph, Ontario, Canada N1G 2W1
| | - Bernard Bett
- International Livestock Research Institute, PO Box 30709, Nairobi, Kenya
| | - Susan C Welburn
- Division of Pathway Medicine and Centre for Infectious Diseases, School of Biomedical Sciences, College of Medicine and Veterinary Medicine, University of Edinburgh, Edinburgh, UK
| | - Elizabeth Mumford
- Department of Country Health Emergency Preparedness and IHR, World Health Organization, 1211 Geneva 27, Switzerland
| | - Vupenyu Dzingirai
- Centre for Applied Social Science, University of Zimbabwe, MP167 Mt Pleasant, Harare, Zimbabwe
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20
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Scoones I, Jones K, Lo Iacono G, Redding DW, Wilkinson A, Wood JLN. Integrative modelling for One Health: pattern, process and participation. Philos Trans R Soc Lond B Biol Sci 2017; 372:20160164. [PMID: 28584172 PMCID: PMC5468689 DOI: 10.1098/rstb.2016.0164] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/30/2017] [Indexed: 12/23/2022] Open
Abstract
This paper argues for an integrative modelling approach for understanding zoonoses disease dynamics, combining process, pattern and participatory models. Each type of modelling provides important insights, but all are limited. Combining these in a '3P' approach offers the opportunity for a productive conversation between modelling efforts, contributing to a 'One Health' agenda. The aim is not to come up with a composite model, but seek synergies between perspectives, encouraging cross-disciplinary interactions. We illustrate our argument with cases from Africa, and in particular from our work on Ebola virus and Lassa fever virus. Combining process-based compartmental models with macroecological data offers a spatial perspective on potential disease impacts. However, without insights from the ground, the 'black box' of transmission dynamics, so crucial to model assumptions, may not be fully understood. We show how participatory modelling and ethnographic research of Ebola and Lassa fever can reveal social roles, unsafe practices, mobility and movement and temporal changes in livelihoods. Together with longer-term dynamics of change in societies and ecologies, all can be important in explaining disease transmission, and provide important complementary insights to other modelling efforts. An integrative modelling approach therefore can offer help to improve disease control efforts and public health responses.This article is part of the themed issue 'One Health for a changing world: zoonoses, ecosystems and human well-being'.
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Affiliation(s)
- I Scoones
- STEPS Centre, Institute of Development Studies, University of Sussex, Brighton BN1 9RE, UK
| | - K Jones
- Centre for Biodiversity and Environment Research, Department of Genetics, Evolution and Environment, University College London, Gower Street, London WC1E 6BT, UK
- Institute of Zoology, Zoological Society of London, Regent's Park, London NW1 4RY, UK
| | - G Lo Iacono
- Department of Veterinary Medicine, Disease Dynamics Unit, University of Cambridge, Madingley Road, Cambridge CB3 0ES, UK
- Environmental Change, Public Health England, Didcot OX11 0RQ, UK
| | - D W Redding
- Centre for Biodiversity and Environment Research, Department of Genetics, Evolution and Environment, University College London, Gower Street, London WC1E 6BT, UK
| | - A Wilkinson
- STEPS Centre, Institute of Development Studies, University of Sussex, Brighton BN1 9RE, UK
| | - J L N Wood
- Department of Veterinary Medicine, Disease Dynamics Unit, University of Cambridge, Madingley Road, Cambridge CB3 0ES, UK
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21
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Boden LA, McKendrick IJ. Model-Based Policymaking: A Framework to Promote Ethical "Good Practice" in Mathematical Modeling for Public Health Policymaking. Front Public Health 2017; 5:68. [PMID: 28424768 PMCID: PMC5380671 DOI: 10.3389/fpubh.2017.00068] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2017] [Accepted: 03/20/2017] [Indexed: 11/25/2022] Open
Abstract
Mathematical models are increasingly relied upon as decision support tools, which estimate risks and generate recommendations to underpin public health policies. However, there are no formal agreements about what constitutes professional competencies or duties in mathematical modeling for public health. In this article, we propose a framework to evaluate whether mathematical models that assess human and animal disease risks and control strategies meet standards consistent with ethical "good practice" and are thus "fit for purpose" as evidence in support of policy. This framework is derived from principles of biomedical ethics: independence, transparency (autonomy), beneficence/non-maleficence, and justice. We identify ethical risks associated with model development and implementation and consider the extent to which scientists are accountable for the translation and communication of model results to policymakers so that the strengths and weaknesses of the scientific evidence base and any socioeconomic and ethical impacts of biased or uncertain predictions are clearly understood. We propose principles to operationalize a framework for ethically sound model development and risk communication between scientists and policymakers. These include the creation of science-policy partnerships to mutually define policy questions and communicate results; development of harmonized international standards for model development; and data stewardship and improvement of the traceability and transparency of models via a searchable archive of policy-relevant models. Finally, we suggest that bespoke ethical advisory groups, with relevant expertise and access to these resources, would be beneficial as a bridge between science and policy, advising modelers of potential ethical risks and providing overview of the translation of modeling advice into policy.
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Affiliation(s)
- Lisa A. Boden
- School of Veterinary Medicine, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, UK
| | - Iain J. McKendrick
- Biomathematics and Statistics Scotland, JCMB, The King’s Buildings, Edinburgh, UK
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22
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Moss R, Hickson RI, McVernon J, McCaw JM, Hort K, Black J, Madden JR, Tran NH, McBryde ES, Geard N. Model-Informed Risk Assessment and Decision Making for an Emerging Infectious Disease in the Asia-Pacific Region. PLoS Negl Trop Dis 2016; 10:e0005018. [PMID: 27661978 PMCID: PMC5035030 DOI: 10.1371/journal.pntd.0005018] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2016] [Accepted: 09/01/2016] [Indexed: 01/01/2023] Open
Abstract
BACKGROUND Effective response to emerging infectious disease (EID) threats relies on health care systems that can detect and contain localised outbreaks before they reach a national or international scale. The Asia-Pacific region contains low and middle income countries in which the risk of EID outbreaks is elevated and whose health care systems may require international support to effectively detect and respond to such events. The absence of comprehensive data on populations, health care systems and disease characteristics in this region makes risk assessment and decisions about the provision of such support challenging. METHODOLOGY/PRINCIPAL FINDINGS We describe a mathematical modelling framework that can inform this process by integrating available data sources, systematically explore the effects of uncertainty, and provide estimates of outbreak risk under a range of intervention scenarios. We illustrate the use of this framework in the context of a potential importation of Ebola Virus Disease into the Asia-Pacific region. Results suggest that, across a wide range of plausible scenarios, preemptive interventions supporting the timely detection of early cases provide substantially greater reductions in the probability of large outbreaks than interventions that support health care system capacity after an outbreak has commenced. CONCLUSIONS/SIGNIFICANCE Our study demonstrates how, in the presence of substantial uncertainty about health care system infrastructure and other relevant aspects of disease control, mathematical models can be used to assess the constraints that limited resources place upon the ability of local health care systems to detect and respond to EID outbreaks in a timely and effective fashion. Our framework can help evaluate the relative impact of these constraints to identify resourcing priorities for health care system support, in order to inform principled and quantifiable decision making.
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Affiliation(s)
- Robert Moss
- Centre for Epidemiology and Biostatistics, Melbourne School of Population Health, The University of Melbourne, Melbourne, Australia
| | | | - Jodie McVernon
- Centre for Epidemiology and Biostatistics, Melbourne School of Population Health, The University of Melbourne, Melbourne, Australia
- Murdoch Childrens Research Institute, Royal Children’s Hospital, Melbourne, Australia
| | - James M. McCaw
- Centre for Epidemiology and Biostatistics, Melbourne School of Population Health, The University of Melbourne, Melbourne, Australia
- Murdoch Childrens Research Institute, Royal Children’s Hospital, Melbourne, Australia
- School of Mathematics and Statistics, The University of Melbourne, Melbourne, Australia
| | - Krishna Hort
- Nossal Institute, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia
| | - Jim Black
- Nossal Institute, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia
| | - John R. Madden
- Centre of Policy Studies, Victoria University, Melbourne, Australia
| | - Nhi H. Tran
- Centre of Policy Studies, Victoria University, Melbourne, Australia
| | - Emma S. McBryde
- Department of Medicine, The University of Melbourne, Melbourne, Australia
- Australian Institute of Tropical Health and Medicine, James Cook University, Townsville, Australia
| | - Nicholas Geard
- Centre for Epidemiology and Biostatistics, Melbourne School of Population Health, The University of Melbourne, Melbourne, Australia
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23
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Ganusov VV. Strong Inference in Mathematical Modeling: A Method for Robust Science in the Twenty-First Century. Front Microbiol 2016; 7:1131. [PMID: 27499750 PMCID: PMC4956646 DOI: 10.3389/fmicb.2016.01131] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2016] [Accepted: 07/07/2016] [Indexed: 12/30/2022] Open
Abstract
While there are many opinions on what mathematical modeling in biology is, in essence, modeling is a mathematical tool, like a microscope, which allows consequences to logically follow from a set of assumptions. Only when this tool is applied appropriately, as microscope is used to look at small items, it may allow to understand importance of specific mechanisms/assumptions in biological processes. Mathematical modeling can be less useful or even misleading if used inappropriately, for example, when a microscope is used to study stars. According to some philosophers (Oreskes et al., 1994), the best use of mathematical models is not when a model is used to confirm a hypothesis but rather when a model shows inconsistency of the model (defined by a specific set of assumptions) and data. Following the principle of strong inference for experimental sciences proposed by Platt (1964), I suggest “strong inference in mathematical modeling” as an effective and robust way of using mathematical modeling to understand mechanisms driving dynamics of biological systems. The major steps of strong inference in mathematical modeling are (1) to develop multiple alternative models for the phenomenon in question; (2) to compare the models with available experimental data and to determine which of the models are not consistent with the data; (3) to determine reasons why rejected models failed to explain the data, and (4) to suggest experiments which would allow to discriminate between remaining alternative models. The use of strong inference is likely to provide better robustness of predictions of mathematical models and it should be strongly encouraged in mathematical modeling-based publications in the Twenty-First century.
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Affiliation(s)
- Vitaly V Ganusov
- Department of Microbiology, University of TennesseeKnoxville, TN, USA; Department of Mathematics, University of TennesseeKnoxville, TN, USA; National Institute for Mathematical and Biological Synthesis, University of TennesseeKnoxville, TN, USA
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24
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Boden L, Auty H, Goddard P, Stott A, Ball N, Mellor D. Working at the science-policy interface. Vet Rec 2014; 174:165-7. [PMID: 24526537 DOI: 10.1136/vr.g1430] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Affiliation(s)
- Lisa Boden
- School of Veterinary Medicine, University of Glasgow, 464 Bearsden Road, Glasgow G61 1QH, UK
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