Remodelling selection to optimise disease forecasts and policies

Funding Information: This paper benefited from supportive discussions with numerous colleagues, especially Mauricio Barreto, Maxine Caws, Andrea Doeschl-Wilson, Nicholas Feasey, Marcelo Ferreira, Philippe Glaziou, Stephen Gordon, Jessica King, James LaCourse, Christian Lienhardt, Paul McKeigue, Penelope Phillips-Howard, Lisa Reimer, Meta Roestenberg, Jamie Rylance, Bertel Squire, Russell Stothard, Miriam Taegtmeyer, Dianne Terlouw, Rachel Tolhurst, Tom Wingfield. This work is funded by national funds through the FCT – Fundação para a Ciência e a Tecnologia, I.P., under the scope of the projects UIDB/00297/2020 ( https://doi.org/10.54499/UIDB/00297/2020 ) and UIDP/00297/2020 ( https://doi.org/10.54499/UIDP/00297/2020 ) (Center for Mathematics and Applications) MGMG has received additional funding from the Innovative Medicines Initiative 2 Joint Undertaking under Grant Agreement No 101007799 (Inno4Vac). This Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation programme and EFPIA. This communication reflects the author’s view and that neither IMI nor the European Union, EFPIA, or any Associated Partners are responsible for any use that may be made of the information contained therein. Publisher Copyright: © 2024 The Author(s). Published by IOP Publishing Ltd.Mathematical models are increasingly adopted for setting disease prevention and control targets. As model-informed policies are implemented, however, the inaccuracies of some forecasts become apparent, for example overprediction of infection burdens and intervention impacts. Here, we attribute these discrepancies to methodological limitations in capturing the heterogeneities of real-world systems. The mechanisms underpinning risk factors of infection and their interactions determine individual propensities to acquire disease. These factors are potentially so numerous and complex that to attain a full mechanistic description is likely unfeasible. To contribute constructively to the development of health policies, model developers either leave factors out (reductionism) or adopt a broader but coarse description (holism). In our view, predictive capacity requires holistic descriptions of heterogeneity which are currently underutilised in infectious disease epidemiology, in comparison to other population disciplines, such as non-communicable disease epidemiology, demography, ecology and evolution.publishersversionpublishe

Remodelling selection to optimise disease forecasts and policies

Mathematical models are increasingly adopted for setting disease prevention and control targets. As model-informed policies are implemented, however, the inaccuracies of some forecasts become apparent, for example overprediction of infection burdens and intervention impacts. Here, we attribute these discrepancies to methodological limitations in capturing the heterogeneities of real-world systems. The mechanisms underpinning risk factors of infection and their interactions determine individual propensities to acquire disease. These factors are potentially so numerous and complex that to attain a full mechanistic description is likely unfeasible. To contribute constructively to the development of health policies, model developers either leave factors out (reductionism) or adopt a broader but coarse description (holism). In our view, predictive capacity requires holistic descriptions of heterogeneity which are currently underutilised in infectious disease epidemiology, in comparison to other population disciplines, such as non-communicable disease epidemiology, demography, ecology and evolution

Limited available evidence supports theoretical predictions of reduced vaccine efficacy at higher exposure dose

Understanding the causes of vaccine failure is important for predicting disease dynamics in vaccinated populations and planning disease interventions. Pathogen exposure dose and heterogeneity in host susceptibility have both been implicated as important factors that may reduce overall vaccine efficacy and cause vaccine failure. Here, we explore the effect of pathogen dose and heterogeneity in host susceptibility in reducing efficacy of vaccines. Using simulation-based methods, we find that increases in pathogen exposure dose decrease vaccine efficacy, but this effect is modified by heterogeneity in host susceptibility. In populations where the mode of vaccine action is highly polarized, vaccine efficacy decreases more slowly with exposure dose than in populations with less variable protection. We compared these theoretical results to empirical estimates from a systematic literature review of vaccines tested over multiple exposure doses. We found that few studies (nine of 5,389) tested vaccine protection against infection over multiple pathogen challenge doses, with seven studies demonstrating a decrease in vaccine efficacy with increasing exposure dose. Our research demonstrates that pathogen dose has potential to be an important determinant of vaccine failure, although the limited empirical data highlight a need for additional studies to test theoretical predictions on the plausibility of reduced host susceptibility and high pathogen dose as mechanisms responsible for reduced vaccine efficacy in high transmission settings

Integral Projection Models for host-parasite systems with an application to amphibian chytrid fungus

1. Host–parasite models are typically constructed under either a microparasite or macroparasite paradigm. However, this has long been recognized as a false dichotomy because many infectious disease agents, including most fungal pathogens, have attributes of both microparasites and macroparasites. 2. We illustrate how Integral Projection Models (IPMs) provide a novel modelling framework to represent both types of pathogens. We build a simple host–parasite IPM that tracks both the number of susceptible and infected hosts and the distribution of parasite burdens in infected hosts. 3. The vital rate functions necessary to build IPMs for disease dynamics share many commonalities with classic micro and macroparasite models and we discuss how these functions can be parameterized to build a host–parasite IPM. We illustrate the utility of this IPM approach by modelling the temperature-dependent epizootic dynamics of amphibian chytrid fungus in Mountain yellow-legged frogs (Rana muscosa). 4. The host–parasite IPM can be applied to other diseases such as facial tumour disease in Tasmanian devils and white-nose syndrome in bats. Moreover, the host–parasite IPM can be easily extended to capture more complex disease dynamics and provides an exciting new frontier in modelling wildlife disease.Full Tex

Aquaculture Reuse Water, Genetic Line, and Vaccination Affect Rainbow Trout (Oncorhynchus mykiss) Disease Susceptibility and Infection Dynamics

Infectious hematopoietic necrosis virus (IHNV) and Flavobacterium psychrophilum are major pathogens of farmed rainbow trout. Improved control strategies are desired but the influence of on-farm environmental factors that lead to disease outbreaks remain poorly understood. Water reuse is an important environmental factor affecting disease. Prior studies have established a replicated outdoor-tank system capable of varying the exposure to reuse water by controlling water flow from commercial trout production raceways. The goal of this research was to evaluate the effect of constant or pulsed reuse water exposure on survival, pathogen prevalence, and pathogen load. Herein, we compared two commercial lines of rainbow trout, Clear Springs Food (CSF) and Troutex (Tx) that were either vaccinated against IHNV with a DNA vaccine or sham vaccinated. Over a 27-day experimental period in constant reuse water, all fish from both lines and treatments, died while mortality in control fish in spring water was PPP ≤ 0.001), while risk of death did not differ in spring water (P=0.98). Sham-vaccinated fish had 2.1-fold greater risk of death compared to vaccinated fish (P=0.02). Both IHNV prevalence and load were lower in vaccinated fish compared to sham-vaccinated fish, and unexpectedly, F. psychrophilum load associated with fin/gill tissues from live-sampled fish was lower in vaccinated fish compared to sham-vaccinated fish. As a result, up to forty-five percent of unvaccinated fish were naturally co-infected with F. psychrophilum and IHNV and the coinfected fish exhibited the highest IHNV loads. Under laboratory challenge conditions, co-infection with F. psychrophilum and IHNV overwhelmed IHNV vaccine-induced protection. In summary, we demonstrate that exposure to reuse water or multi-pathogen challenge can initiate complex disease dynamics that can overwhelm both vaccination and host genetic resistance

Sociality, density-dependence and microclimates determine the persistence of populations uffereing from a novel fungal disease, white nose syndrome

Abstract Disease has caused striking declines in wildlife and threatens numerous species with extinction. Theory suggests that the ecology and density-dependence of transmission dynamics can determine the probability of disease-caused extinction, but few empirical studies have simultaneously examined multiple factors influencing disease impact. We show, in hibernating bats infected with Geomyces destructans, that impacts of disease on solitary species were lower in smaller populations, whereas in socially gregarious species declines were equally severe in populations spanning four orders of magnitude. However, as these gregarious species declined, we observed decreases in social group size that reduced the likelihood of extinction. In addition, disease impacts in these species increased with humidity and temperature such that the coldest and driest roosts provided initial refuge from disease. These results expand our theoretical framework and provide an empirical basis for determining which host species are likely to be driven extinct while management action is still possible

White-nose syndrome restructures bat skin microbiomes

ImportanceInherent complexities in the composition of microbiomes can often preclude investigations of microbe-associated diseases. Instead of single organisms being associated with disease, community characteristics may be more relevant. Longitudinal microbiome studies of the same individual bats as pathogens arrive and infect a population are the ideal experiment but remain logistically challenging; therefore, investigations like our approach that are able to correlate invasive pathogens to alterations within a microbiome may be the next best alternative. The results of this study potentially suggest that microbiome-host interactions may determine the likelihood of infection. However, the contrasting relationship between Pd and the bacterial microbiomes of Myotis lucifugus and Perimyotis subflavus indicate that we are just beginning to understand how the bat microbiome interacts with a fungal invader such as Pd

Context-dependent conservation responses to emerging wildlife diseases

Emerging infectious diseases pose an important threat to wildlife. While established protocols exist for combating outbreaks of human and agricultural pathogens, appropriate management actions before, during, and after the invasion of wildlife pathogens have not been developed. We describe stage-specific goals and management actions that minimize disease impacts on wildlife, and the research required to implement them. Before pathogen arrival, reducing the probability of introduction through quarantine and trade restrictions is key because prevention is more cost effective than subsequent responses. On the invasion front, the main goals are limiting pathogen spread and preventing establishment. In locations experiencing an epidemic, management should focus on reducing transmission and disease, and promoting the development of resistance or tolerance. Finally, if pathogen and host populations reach a stable stage, then recovery of host populations in the face of new threats is paramount. Successful management of wildlife disease requires risk-taking, rapid implementation, and an adaptive approach."Funding was provided by the US National Science Foundation (grants EF-0914866, DGE-0741448, DEB-1115069, DEB-1336290) and the National Institutes of Health (grant 1R010AI090159)."https://esajournals.onlinelibrary.wiley.com/doi/abs/10.1890/14024

Moving Beyond Too Little, Too Late: Managing Emerging Infectious Diseases in Wild Populations Requires International Policy and Partnerships

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