Modelling livestock infectious disease control policy under differing social perspectives on vaccination behaviour.

2021 ◽  
Author(s):  
Edward M. Hill ◽  
Naomi S. Prosser ◽  
Eamonn Ferguson ◽  
Jasmeet Kaler ◽  
Martin J. Green ◽  
...  
Keyword(s):  
Infectious Disease ◽  
Disease Control ◽  
Animal Health ◽  
Disease Outbreaks ◽  
Control Policy ◽  
Population Level ◽  
Control Measures ◽  
Individual Level ◽  
Social Perspectives ◽  
Behavioural Characteristics

Abstract Background: The spread of infection amongst livestock depends not only on the traits of the pathogen and the livestock themselves, but also on the behavioural characteristics of farmers and how that impacts the implementation of livestock disease control measures. Livestock owners may change their disease management behaviours in response to complex factors such as increased awareness of disease risks, pressure to conform with social expectations and the direct imposition of animal health regulations. Controls that are costly may make it beneficial for individuals to rely on the protection offered by others, though that may be sub-optimal for the population. Thus, failing to account for behavioural dynamics may produce a substantial layer of bias in infectious disease models. Methods: We investigated the role of vaccine behaviour across a population of farmers on epidemic outbreaks amongst livestock, caused by pathogens with differential speed of spread over spatial landscapes of farms for two counties in England (Cumbria and Devon). Under different compositions of three vaccine behaviour groups (precautionary, reactionary, non-vaccination), we evaluated from population- and individual-level perspectives the optimum threshold distance to premises with notified infection that would trigger responsive vaccination by the reactionary vaccination group. Results: On our data-informed livestock systems, we demonstrate a divergence between population and individual perspectives in the optimal scale of reactive voluntary vaccination response. In general, minimising the population-level cost requires a broader reactive uptake of the intervention, with individualistic behaviours increasing the likelihood of larger scale disease outbreaks. When the relative cost of vaccination was low and the majority of premises had undergone precautionary vaccination, then an individual perspective gave a broader spatial extent of reactive response compared to the population perspective. Conclusions: Mathematical models integrating epidemiological and socio-behavioural properties, and the feedback between them, can identify instances of strong disagreement between the intervention stringency that is best for a sole individual compared to the overall population. These modelling insights can aid our understanding of how stakeholders may react to veterinary health interventions.

scholarly journals Brucellosis of Ruminants in Bosnia and Herzegovina: Disease Status, Past Experiences and Initiation of a New Surveillance Strategy

2008 ◽  
Vol 8 (1) ◽  
pp. 27-33 ◽  
Author(s):  
Sabina Šerić Haračić ◽  
Mo Salman ◽  
Nihad Fejzić ◽  
Semra Čavaljuga
Keyword(s):  
Infectious Disease ◽  
Bosnia And Herzegovina ◽  
Detection System ◽  
Animal Health ◽  
Control Policy ◽  
Control Program ◽  
Disease Status ◽  
Control Measures ◽  
Surveillance Strategy ◽  
The Government

The current animal health situation in Bosnia and Herzegovina requires the prioritization of diseases for the application of control measures. One of the diseases requiring high priority is brucellosis of ruminants. Brucellosis is a zoonotic infectious disease and one of the most important zoonoses in the world. Brucellosis has been recognized during the past five decades as an important infectious disease in ruminants in Bosnia and Herzegovina. Control and eradication of brucellosis in animals is based on test and slaughter control policy. When the existing brucellosis control program was instituted, the veterinary and animal production sector was almost exclusively owned by the government, an arrangement that promoted compliance with the program and resulted in the successful control of the disease.This paper provides an overview of the current institutional and legislative framework for brucellosis control including the laboratory detection system and the epidemiological status of brucellosis in ruminants in Bosnia and Herzegovina. Relevant data were collected during the period spanning from the beginning of 2001 until the middle of 2007.Data we collected reveal an increase in the number of reported outbreaks in ruminants as well as a related increase in the number of human cases.This has brought serious consequences to public health, animal health and production and international trade.

scholarly journals Key data for outbreak evaluation: building on the Ebola experience

2017 ◽  
Vol 372 (1721) ◽  
pp. 20160371 ◽  
Author(s):  
Anne Cori ◽  
Christl A. Donnelly ◽  
Ilaria Dorigatti ◽  
Neil M. Ferguson ◽  
Christophe Fraser ◽  
...  
Keyword(s):  
Infectious Disease ◽  
Disease Outbreaks ◽  
Population Level ◽  
West African ◽  
Public Health Response ◽  
The West ◽  
Individual Level ◽  
Infectious Disease Outbreaks ◽  
Exposure Data ◽  
Level Data

Following the detection of an infectious disease outbreak, rapid epidemiological assessment is critical for guiding an effective public health response. To understand the transmission dynamics and potential impact of an outbreak, several types of data are necessary. Here we build on experience gained in the West African Ebola epidemic and prior emerging infectious disease outbreaks to set out a checklist of data needed to: (1) quantify severity and transmissibility; (2) characterize heterogeneities in transmission and their determinants; and (3) assess the effectiveness of different interventions. We differentiate data needs into individual-level data (e.g. a detailed list of reported cases), exposure data (e.g. identifying where/how cases may have been infected) and population-level data (e.g. size/demographics of the population(s) affected and when/where interventions were implemented). A remarkable amount of individual-level and exposure data was collected during the West African Ebola epidemic, which allowed the assessment of (1) and (2). However, gaps in population-level data (particularly around which interventions were applied when and where) posed challenges to the assessment of (3). Here we highlight recurrent data issues, give practical suggestions for addressing these issues and discuss priorities for improvements in data collection in future outbreaks. This article is part of the themed issue ‘The 2013–2016 West African Ebola epidemic: data, decision-making and disease control’.

scholarly journals Geographically dependent individual-level models for infectious diseases transmission

Biostatistics ◽  
2020 ◽  
Author(s):  
M D Mahsin ◽  
Rob Deardon ◽  
Patrick Brown
Keyword(s):  
Infectious Disease ◽  
Infectious Diseases ◽  
Social Deprivation ◽  
Spatial Location ◽  
Control Measures ◽  
Disease Models ◽  
Individual Level ◽  
New Class ◽  
Geographical Regions ◽  
Infectious Disease Models

Summary Infectious disease models can be of great use for understanding the underlying mechanisms that influence the spread of diseases and predicting future disease progression. Modeling has been increasingly used to evaluate the potential impact of different control measures and to guide public health policy decisions. In recent years, there has been rapid progress in developing spatio-temporal modeling of infectious diseases and an example of such recent developments is the discrete-time individual-level models (ILMs). These models are well developed and provide a common framework for modeling many disease systems; however, they assume the probability of disease transmission between two individuals depends only on their spatial separation and not on their spatial locations. In cases where spatial location itself is important for understanding the spread of emerging infectious diseases and identifying their causes, it would be beneficial to incorporate the effect of spatial location in the model. In this study, we thus generalize the ILMs to a new class of geographically dependent ILMs, to allow for the evaluation of the effect of spatially varying risk factors (e.g., education, social deprivation, environmental), as well as unobserved spatial structure, upon the transmission of infectious disease. Specifically, we consider a conditional autoregressive (CAR) model to capture the effects of unobserved spatially structured latent covariates or measurement error. This results in flexible infectious disease models that can be used for formulating etiological hypotheses and identifying geographical regions of unusually high risk to formulate preventive action. The reliability of these models is investigated on a combination of simulated epidemic data and Alberta seasonal influenza outbreak data ($2009$). This new class of models is fitted to data within a Bayesian statistical framework using Markov chain Monte Carlo methods.

scholarly journals A HYBRID MODEL FOR STUDYING SPATIAL ASPECTS OF INFECTIOUS DISEASES

2012 ◽  
Vol 54 (1-2) ◽  
pp. 37-49 ◽  
Author(s):  
BENJAMIN J. BINDER ◽  
JOSHUA V. ROSS ◽  
MATTHEW J. SIMPSON
Keyword(s):  
Infectious Disease ◽  
Spatial Analysis ◽  
Infectious Diseases ◽  
Hybrid Model ◽  
Population Level ◽  
Infectious Agents ◽  
Agent Based ◽  
Individual Level ◽  
The Individual ◽  
Spread Of Infection

AbstractWe consider a hybrid model, created by coupling a continuum and an agent-based model of infectious disease. The framework of the hybrid model provides a mechanism to study the spread of infection at both the individual and population levels. This approach captures the stochastic spatial heterogeneity at the individual level, which is directly related to deterministic population level properties. This facilitates the study of spatial aspects of the epidemic process. A spatial analysis, involving counting the number of infectious agents in equally sized bins, reveals when the spatial domain is nonhomogeneous.

scholarly journals Mainstreaming of Health Equity in Infectious Disease Control Policy During the COVID-19 Pandemic Era

2021 ◽  
Vol 54 (1) ◽  
pp. 1-7
Author(s):  
Hongjo Choi ◽  
Seong-Yi Kim ◽  
Jung-Woo Kim ◽  
Yukyung Park ◽  
Myoung-Hee Kim ◽  
...  
Keyword(s):  
Infectious Disease ◽  
Disease Control ◽  
Health Equity ◽  
Control Policy ◽  
Infectious Disease Control

scholarly journals Perfect counterfactuals for epidemic simulations

2019 ◽  
Vol 374 (1776) ◽  
pp. 20180279 ◽  
Author(s):  
Joshua Kaminsky ◽  
Lindsay T. Keegan ◽  
C. Jessica E. Metcalf ◽  
Justin Lessler
Keyword(s):  
Infectious Disease ◽  
Disease Outbreaks ◽  
Computation Time ◽  
Control Measures ◽  
Mathematical Representation ◽  
Theme Issue ◽  
Stochastic Variation ◽  
Infectious Disease Outbreaks ◽  
Negative Effect ◽  
And Control

Simulation studies are often used to predict the expected impact of control measures in infectious disease outbreaks. Typically, two independent sets of simulations are conducted, one with the intervention, and one without, and epidemic sizes (or some related metric) are compared to estimate the effect of the intervention. Since it is possible that controlled epidemics are larger than uncontrolled ones if there is substantial stochastic variation between epidemics, uncertainty intervals from this approach can include a negative effect even for an effective intervention. To more precisely estimate the number of cases an intervention will prevent within a single epidemic, here we develop a ‘single-world’ approach to matching simulations of controlled epidemics to their exact uncontrolled counterfactual. Our method borrows concepts from percolation approaches, prunes out possible epidemic histories and creates potential epidemic graphs (i.e. a mathematical representation of all consistent epidemics) that can be ‘realized’ to create perfectly matched controlled and uncontrolled epidemics. We present an implementation of this method for a common class of compartmental models (e.g. SIR models), and its application in a simple SIR model. Results illustrate how, at the cost of some computation time, this method substantially narrows confidence intervals and avoids nonsensical inferences. This article is part of the theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control’. This theme issue is linked with the earlier issue ‘Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes’.

1. Introduction

2015 ◽  
pp. 1-10
Author(s):  
Marta L. Wayne ◽  
Benjamin M. Bolker
Keyword(s):  
Infectious Disease ◽  
Population Level ◽  
Evolutionary Change ◽  
Control Individual ◽  
Moving Target ◽  
Human History ◽  
Level Control ◽  
Infected People ◽  
Individual Level ◽  
Living Organisms

The ‘Introduction’ explains what infectious diseases are: diseases that are transmitted from one person to another. For most of human history, diseases could only be controlled at the population level, using quarantines to separate uninfected from infected people. The discovery of immunization, and later the invention of disease treatments like antibiotics, allowed individual-level infectious disease control. Individual-level control can filter up to the population level: if enough of the population are vaccinated, we can reduce transmission enough to stamp out a local epidemic, or even to wipe a disease out globally. But both humans and infectious disease agents are living organisms that undergo ecological and evolutionary change, making infectious disease a moving target.

scholarly journals Development of a curriculum for training in One Health analytical epidemiology at the University of Zambia

2012 ◽  
Vol 79 (2) ◽  
Author(s):  
J. Muma ◽  
Martin Simuunza ◽  
K. Mwachalimba ◽  
M. Munyeme ◽  
B. Namangala ◽  
...  
Keyword(s):  
Disease Control ◽  
One Health ◽  
Animal Health ◽  
Control Policy ◽  
Antibiotic Use ◽  
Hiv And Aids ◽  
London School ◽  
Human Interface ◽  
The One ◽  
The University

Recently, the world has witnessed emergence of novel diseases such as avian influenza, HIV and AIDS, West Nile Virus and Ebola. The evolution of these pathogens has been facilitated mainly by a constantly evolving animal-human interface. Whilst infectious disease control was previously conceptualised as either public health or animal health related issues, the distinction between disciplinary foci have been blurred by multiple causal factors that clearly traverse traditional disciplinary divides. These multiple evolutionary pressures have included changes in land use, ecosystems, human-livestock-wildlife interactions and antibiotic use, representing novel routes for pathogen emergence. With the growing realisation that pathogens do not respect traditional epistemological divides, the ‘One Health’ initiative has emerged to advocate for closer collaboration across the health disciplines and has provided a new agenda for health education. Against this background, the One Health Analytical Epidemiology course was developed under the auspices of the Southern African Centre for Infectious Diseases Surveillance by staff from the University of Zambia with collaborators from the London School of Hygiene and Tropical Medicine and the Royal Veterinary College in London. The course is aimed at equipping scientists with multidisciplinary skill sets to match the contemporary challenges of human, animal and zoonotic disease prevention and control. Epidemiology is an important discipline for both public and animal health. Therefore, this two-year programme has been developed to generate a cadre of epidemiologists with a broad understanding of disease control and prevention and will be able to conceptualise and design holistic programs for informing health and disease control policy decisions.

scholarly journals Making the Case for Centralized Dementia Care Through Adaptive Reuse in the Time of COVID-19

2020 ◽  
Vol 57 ◽  
pp. 004695802096930
Author(s):  
Emily Roberts ◽  
Heather Carlile Carter
Keyword(s):  
Infectious Disease ◽  
Disease Control ◽  
Disease Outbreaks ◽  
Dementia Care ◽  
Infectious Disease Control ◽  
Community Revitalization ◽  
Existing Structures ◽  
Infectious Disease Outbreaks ◽  
Memory Care

It is estimated that 5.4 million Americans have some form of dementia and these numbers are expected to rise in the coming decades, leading to an unprecedented demand for memory care housing and services. At the same time, infectious disease outbreaks like the COVID-19 pandemic have raised great concerns for the future of care settings for people living with dementia. In searching for innovative options to create more autonomy and better quality of life in dementia care settings, while at the same time improving infectious disease control, repurposing existing structures, in particular vacant urban malls, may be one option for the large sites needed for the European model of dementia villages. This editorial paper makes the case for the Dementia Friendly City Center model for centralized dementia programs, medical services and housing. By working across multiple disciplines, this research team has simultaneously addressed numerous issues, including community revitalization, building sustainability, and the strengthening of infectious disease control in care sites which are inclusive, progressive and convergent with the needs of an aging population.

scholarly journals Societal Implications of the Internet of Pathogens

2019 ◽  
Vol 57 (6) ◽  
Author(s):  
Alexander L. Greninger
Keyword(s):  
Infectious Disease ◽  
Disease Transmission ◽  
New Technology ◽  
Disease Outbreaks ◽  
Societal Implications ◽  
Patient Privacy ◽  
Infectious Disease Transmission ◽  
Genomic Databases ◽  
Individual Level ◽  
Exponential Bounds

ABSTRACT The growth of pathogen genomics shows no signs of abating. Whole-genome sequencing of clinical viral and bacterial isolates continues to grow in nearly exponential bounds. Reductions in cost driven by new technology have created a seamless environment for generating, sharing, and analyzing pathogen genomes. The high-resolution view of infectious disease transmission dynamics offered by analyzing whole genomes from pathogens, coupled with the genomicist ethic of widespread data sharing, has created a veritable Internet of pathogens, which inadvertently produces new threats to patient privacy and protected heath information. The health care system, and society more generally, have yet to explore the far-reaching privacy concerns raised by readily accessible pathogen genomic data. The recent use of human genomic databases, the existence of freely available alternative data and metadata sources, and lax regulation of collecting publicly available genomes to identify individuals in a criminal context raise concerning parallels about what is possible with pathogen genomics. The growing ability to ascertain culpability for infectious disease transmission at a nearly individual level could change our perspective on disease outbreaks from one based on public health to one based on individual liability. These technological breakthroughs in the absence of an understanding of potential privacy and liability issues lead to questions about the dominant paradigm of better living through pathogen genomics.

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