Stochasticity and Infectious Disease Dynamics: Density and Weather Effects on a Fungal Insect Pathogen

Kyle, Colin H.; Liu, Jiawei; Gallagher, Molly; Dukić, Vanja; Dwyer, Greg

doi:10.1086/707138

Cited by 11 publications

(18 citation statements)

References 67 publications

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“…The second explanation is that, even under density‐dependent transmission, stochasticity in infection dynamics and the environment can mask the effects of density (Briggs et al, 2010; Kyle et al, 2020; Lloyd‐Smith et al, 2005). For example, Briggs et al (2010) developed a stochastic model of this frog‐ Bd system and showed that given only density‐dependent infection dynamics one could obtain response trajectories consistent with enzootic or epizootic dynamics.…”

Section: Discussionmentioning

confidence: 99%

“…These differences in response trajectories were not necessarily mediated by differences in initial host density, but by within‐host infection processes such as the rate that Bd zoospores reinfect the same host (Briggs et al, 2010). Moreover, for density‐dependent host–pathogen systems, demographic and environmental stochasticity can significantly blur the effects of host density on disease invasion and persistence (Kyle et al, 2020; Lloyd‐Smith et al, 2005). The dataset we used here was unique in that it addressed many of the challenges identified when testing for density thresholds in wildlife pathogen systems (Lloyd‐Smith et al, 2005): it contained hundreds of replicate populations, host abundance spanned orders of magnitude (from 10s to 1000s of individuals), and the system was largely driven by a single host species (though see Reeder et al, 2012).…”

Section: Discussionmentioning

confidence: 99%

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Host density has limited effects on pathogen invasion, disease‐induced declines and within‐host infection dynamics across a landscape of disease

Wilber

Knapp

Smith

et al. 2022

Journal of Animal Ecology

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1. Host density is hypothesized to be a major driver of variability in the responses and outcomes of wildlife populations following pathogen invasion. While the effects of host density on pathogen transmission have been extensively studied, these studies are dominated by theoretical analyses and small‐scale experiments. This focus leads to an incomplete picture regarding how host density drives observed variability in disease outcomes in the field. 2. Here, we leveraged a dataset of hundreds of replicate amphibian populations that varied by orders of magnitude in host density. We used these data to test the effects of host density on three outcomes following the arrival of the amphibian‐killing fungal pathogen Batrachochytrium dendrobatidis (Bd): the probability that Bd successfully invaded a host population and led to a pathogen outbreak, the magnitude of the host population‐level decline following an outbreak and within‐host infection dynamics that drive population‐level outcomes in amphibian–pathogen systems. 3. Based on previous small‐scale transmission experiments, we expected that populations with higher densities would be more likely to experience Bd outbreaks and would suffer larger proportional declines following outbreaks. To test these predictions, we developed and fitted a Hidden Markov Model that accounted for imperfectly observed disease outbreak states in the amphibian populations we surveyed. 4. Contrary to our predictions, we found minimal effects of host density on the probability of successful Bd invasion, the magnitude of population decline following Bd invasion and the dynamics of within‐host infection intensity. Environmental conditions, such as summer temperature, winter severity and the presence of pathogen reservoirs, were more predictive of variability in disease outcomes. 5. Our results highlight the limitations of extrapolating findings from small‐scale transmission experiments to observed disease trajectories in the field and provide strong evidence that variability in host density does not necessarily drive variability in host population responses following pathogen arrival. In an applied context, we show that feedbacks between host density and disease will not necessarily affect the success of reintroduction efforts in amphibian‐Bd systems of conservation concern.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Host density has limited effects on pathogen invasion, disease‐induced declines and within‐host infection dynamics across a landscape of disease

Wilber

Knapp

Smith

et al. 2022

Journal of Animal Ecology

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“…Scientific organizations are currently involved in the development of possible vaccines to further stop the deadly spread of COVID-19 6 – 15 . Weather conditions always play important roles to the enhancement or eradication of health issues 16 – 19 . Thus, we can look for finding answer of the research question: whether weather has any correlation with COVID-19 20 .…”

Section: Introductionmentioning

confidence: 99%

A systematic review and meta-analysis on correlation of weather with COVID-19

Majumder

Ray

2021

Sci Rep

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This study presents a systematic review and meta-analysis over the findings of significance of correlations between weather parameters (temperature, humidity, rainfall, ultra violet radiation, wind speed) and COVID-19. The meta-analysis was performed by using ‘meta’ package in R studio. We found significant correlation between temperature (0.11 [95% CI 0.01–0.22], 0.22 [95% CI, 0.16–0.28] for fixed effect death rate and incidence, respectively), humidity (0.14 [95% CI 0.07–0.20] for fixed effect incidence) and wind speed (0.58 [95% CI 0.49–0.66] for fixed effect incidence) with the death rate and incidence of COVID-19 (p < 0.01). The study included 11 articles that carried extensive research work on more than 110 country-wise data set. Thus, we can show that weather can be considered as an important element regarding the correlation with COVID-19.

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“…The models presented here incorporate heterogeneity as a continuous Gamma distribution described by the mean transmissionν and the squared coefficient of variation of transmission C 2 (which is unitless). We term this heterogeneity in transmission rather than host heterogeneity to recognize that it is a trait of both pathogen and host and can arise from various sources, including within-host stochastic processes [28], host behavior [53], environmental factors [80], as well as both host and pathogen genetics and G × G interactions [15][16][17]63,79]. When heterogeneity C 2 equals 0, all individuals are identical in their susceptibility to disease and the infection process will be completely driven by the mean transmission rate.…”

Section: Model Description Force Of Infection Heterogeneity Tradeoffsmentioning

confidence: 99%

Understanding the Evolutionary Ecology of host–pathogen Interactions Provides Insights into the Outcomes of Insect Pest Biocontrol

Páez

Fleming-Davies

2020

Viruses

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The use of viral pathogens to control the population size of pest insects has produced both successful and unsuccessful outcomes. Here, we investigate whether those biocontrol successes and failures can be explained by key ecological and evolutionary processes between hosts and pathogens. Specifically, we examine how heterogeneity in pathogen transmission, ecological and evolutionary tradeoffs, and pathogen diversity affect insect population density and thus successful control. We first review the existing literature and then use numerical simulations of mathematical models to further explore these processes. Our results show that the control of insect densities using viruses depends strongly on the heterogeneity of virus transmission among insects. Overall, increased heterogeneity of transmission reduces the effect of viruses on insect densities and increases the long-term stability of insect populations. Lower equilibrium insect densities occur when transmission is heritable and when there is a tradeoff between mean transmission and insect fecundity compared to when the heterogeneity of transmission arises from non-genetic sources. Thus, the heterogeneity of transmission is a key parameter that regulates the long-term population dynamics of insects and their pathogens. We also show that both heterogeneity of transmission and life-history tradeoffs modulate characteristics of population dynamics such as the frequency and intensity of “boom–bust" population cycles. Furthermore, we show that because of life-history tradeoffs affecting the transmission rate, the use of multiple pathogen strains is more effective than the use of a single strain to control insect densities only when the pathogen strains differ considerably in their transmission characteristics. By quantifying the effects of ecology and evolution on population densities, we are able to offer recommendations to assess the long-term effects of classical biocontrol.

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Stochasticity and Infectious Disease Dynamics: Density and Weather Effects on a Fungal Insect Pathogen

Cited by 11 publications

References 67 publications

Host density has limited effects on pathogen invasion, disease‐induced declines and within‐host infection dynamics across a landscape of disease

Host density has limited effects on pathogen invasion, disease‐induced declines and within‐host infection dynamics across a landscape of disease

A systematic review and meta-analysis on correlation of weather with COVID-19

Understanding the Evolutionary Ecology of host–pathogen Interactions Provides Insights into the Outcomes of Insect Pest Biocontrol

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