scholarly journals Multi-Scale Drivers of Immunological Variation and Consequences for Infectious Disease Dynamics

2019 ◽  
Vol 59 (5) ◽  
pp. 1129-1137 ◽  
Author(s):  
Daniel J Becker ◽  
Cynthia J Downs ◽  
Lynn B Martin
Keyword(s):  
Infectious Disease ◽  
Host Defense ◽  
Disease Ecology ◽  
Landscape Heterogeneity ◽  
Population Level ◽  
Immune Defense ◽  
Model Systems ◽  
Disease Dynamics ◽  
Multi Scale ◽  
Infectious Disease Dynamics

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

scholarly journals Contact structure, mobility, environmental impact and behaviour: the importance of social forces to infectious disease dynamics and disease ecology

2017 ◽  
Vol 372 (1719) ◽  
pp. 20160454 ◽  
Author(s):  
Ronan F. Arthur ◽  
Emily S. Gurley ◽  
Henrik Salje ◽  
Laura S. P. Bloomfield ◽  
James H. Jones
Keyword(s):  
Infectious Disease ◽  
Infectious Diseases ◽  
Social Factors ◽  
Disease Ecology ◽  
Contact Structure ◽  
Human Populations ◽  
Disease Dynamics ◽  
Social Ecological ◽  
The Social ◽  
Infectious Disease Dynamics

Human factors, including contact structure, movement, impact on the environment and patterns of behaviour, can have significant influence on the emergence of novel infectious diseases and the transmission and amplification of established ones. As anthropogenic climate change alters natural systems and global economic forces drive land-use and land-cover change, it becomes increasingly important to understand both the ecological and social factors that impact infectious disease outcomes for human populations. While the field of disease ecology explicitly studies the ecological aspects of infectious disease transmission, the effects of the social context on zoonotic pathogen spillover and subsequent human-to-human transmission are comparatively neglected in the literature. The social sciences encompass a variety of disciplines and frameworks for understanding infectious diseases; however, here we focus on four primary areas of social systems that quantitatively and qualitatively contribute to infectious diseases as social–ecological systems. These areas are social mixing and structure, space and mobility, geography and environmental impact, and behaviour and behaviour change. Incorporation of these social factors requires empirical studies for parametrization, phenomena characterization and integrated theoretical modelling of social–ecological interactions. The social–ecological system that dictates infectious disease dynamics is a complex system rich in interacting variables with dynamically significant heterogeneous properties. Future discussions about infectious disease spillover and transmission in human populations need to address the social context that affects particular disease systems by identifying and measuring qualitatively important drivers. This article is part of the themed issue ‘Opening the black box: re-examining the ecology and evolution of parasite transmission’.

scholarly journals The use and underuse of model systems in infectious disease ecology & evolution.

2020 ◽  
Author(s):  
Nina Wale ◽  
Meghan A Duffy
Keyword(s):  
Infectious Disease ◽  
Infectious Diseases ◽  
Disease Ecology ◽  
Model Systems ◽  
Disease Dynamics ◽  
Biological Organization ◽  
Systems Research ◽  
Holistic Understanding ◽  
Testing Theory ◽  
Definition Of

Ever since biologists began studying the ecology and evolution of infectious diseases (EEID), laboratory-based ‘model systems’ have been important for developing and testing theory. Yet what EEID researchers mean by ‘model systems’ and what they want from them remains to be clearly delineated. This uncertainty holds back our ability to maximally exploit these systems, identify knowledge gaps, and establish effective new model systems. Here, we borrow a definition of model systems from the biomolecular sciences to assess how EEID researchers are (and are not) using ten key model systems. According to this definition, model systems in EEID are not being used to their fullest and, in fact, cannot even be considered to be model systems. Research using these systems consistently addresses only two of the three fundamental processes that underlie disease dynamics-transmission and disease, but not recovery. Further, studies tend to focus on only a few of the scales of biological organization that matter for disease ecology and evolution. Moreover, the field lacks an infrastructure to perform comparative analyses. We aim to begin a discussion of what we want from model systems, which would further progress toward a thorough, holistic understanding of EEID.

scholarly journals Real-time forecasting of infectious disease dynamics with a stochastic semi-mechanistic model

Epidemics ◽  
2018 ◽  
Vol 22 ◽  
pp. 56-61 ◽  
Author(s):  
Sebastian Funk ◽  
Anton Camacho ◽  
Adam J. Kucharski ◽  
Rosalind M. Eggo ◽  
W. John Edmunds
Keyword(s):  
Infectious Disease ◽  
Real Time ◽  
Mechanistic Model ◽  
Disease Dynamics ◽  
Infectious Disease Dynamics

scholarly journals Risk Causal Analysis of Traffic-Intensive Waters Based on Infectious Disease Dynamics

2019 ◽  
Vol 7 (8) ◽  
pp. 277
Author(s):  
Yong-jun Chen ◽  
Qing Liu ◽  
Cheng-peng Wan
Keyword(s):  
Infectious Disease ◽  
Risk Factors ◽  
Causal Model ◽  
Cloud Model ◽  
Rapid Development ◽  
River Estuary ◽  
Yangtze River Estuary ◽  
Disease Dynamics ◽  
Transmission Process ◽  
Infectious Disease Dynamics

Accidents occur frequently in traffic-intensive waters, which restrict the safe and rapid development of the shipping industry. Due to the suddenness, randomness, and uncertainty of accidents in traffic-intensive waters, the probability of the risk factors causing traffic accidents is usually high. Thus, properly analyzing those key risk factors is of great significance to improve the safety of shipping. Based on the analysis of influencing factors of ship navigational risks in traffic-intensive waters, this paper proposes a cloud model to excavate the factors affecting navigational risk, which could accurately screen out the key risk factors. Furthermore, the risk causal model of ship navigation in traffic-intensive waters is constructed by using the infectious disease dynamics method in order to model the key risk causal transmission process. Moreover, an empirical study of the Yangtze River estuary is conducted to illustrate the feasibility of the proposed models. The research results show that the cloud model is useful in screening the key risk factors, and the constructed causal model of ship navigational risks in traffic-intensive waters is able to provide accurate analysis of the transmission process of key risk factors, which can be used to reduce the navigational risk of ships in traffic-intensive waters. This research provides both theoretical basis and practical reference for regulators in the risk management and control of ships in traffic-intensive waters.

scholarly journals Game theoretic modelling of infectious disease dynamics and intervention methods: a review

2020 ◽  
Vol 14 (1) ◽  
pp. 57-89 ◽  
Author(s):  
Sheryl L. Chang ◽  
Mahendra Piraveenan ◽  
Philippa Pattison ◽  
Mikhail Prokopenko
Keyword(s):  
Infectious Disease ◽  
Disease Dynamics ◽  
Game Theoretic ◽  
Infectious Disease Dynamics

scholarly journals Incorporating genomic methods into contact networks to reveal new insights into animal behaviour and infectious disease dynamics

Behaviour ◽  
2018 ◽  
Vol 155 (7-9) ◽  
pp. 759-791 ◽  
Author(s):  
Marie L.J. Gilbertson ◽  
Nicholas M. Fountain-Jones ◽  
Meggan E. Craft
Keyword(s):  
Infectious Disease ◽  
Disease Ecology ◽  
Pathogen Transmission ◽  
Contact Network ◽  
Contact Networks ◽  
Host Behaviour ◽  
Infectious Disease Dynamics ◽  
Insight Into ◽  
Dynamic Interplay ◽  
Broad Overview

Abstract Utilization of contact networks has provided opportunities for assessing the dynamic interplay between pathogen transmission and host behaviour. Genomic techniques have, in their own right, provided new insight into complex questions in disease ecology, and the increasing accessibility of genomic approaches means more researchers may seek out these tools. The integration of network and genomic approaches provides opportunities to examine the interaction between behaviour and pathogen transmission in new ways and with greater resolution. While a number of studies have begun to incorporate both contact network and genomic approaches, a great deal of work has yet to be done to better integrate these techniques. In this review, we give a broad overview of how network and genomic approaches have each been used to address questions regarding the interaction of social behaviour and infectious disease, and then discuss current work and future horizons for the merging of these techniques.

scholarly journals Using GPS Technology to Quantify Human Mobility, Dynamic Contacts and Infectious Disease Dynamics in a Resource-Poor Urban Environment

PLoS ONE ◽  
2013 ◽  
Vol 8 (4) ◽  
pp. e58802 ◽  
Author(s):  
Gonzalo M. Vazquez-Prokopec ◽  
Donal Bisanzio ◽  
Steven T. Stoddard ◽  
Valerie Paz-Soldan ◽  
Amy C. Morrison ◽  
...  
Keyword(s):  
Infectious Disease ◽  
Urban Environment ◽  
Human Mobility ◽  
Disease Dynamics ◽  
Gps Technology ◽  
Resource Poor ◽  
Infectious Disease Dynamics
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