Demographic stochasticity drives epidemiological patterns in wildlife with implications for diseases and population management

An early warning indicator trained on stochastic disease-spreading models with different noises

The timely detection of disease outbreaks through reliable early warning signals (EWSs) is indispensable for effective public health mitigation strategies. Nevertheless, the intricate dynamics of real-world disease spread, often influenced by diverse sources of noise and limited data in the early stages of outbreaks, pose a significant challenge in developing reliable EWSs, as the performance of existing indicators varies with extrinsic and intrinsic noises. Here, we address the challenge of modelling disease when the measurements are corrupted by additive white noise, multiplicative environmental noise and demographic noise into a standard epidemic mathematical model. To navigate the complexities introduced by these noise sources, we employ a deep learning algorithm that provides EWS in infectious disease outbreaks by training on noise-induced disease-spreading models. The indicator's effectiveness is demonstrated through its application to real-world COVID-19 cases in Edmonton and simulated time series derived from diverse disease spread models affected by noise. Notably, the indicator captures an impending transition in a time series of disease outbreaks and outperforms existing indicators. This study contributes to advancing early warning capabilities by addressing the intricate dynamics inherent in real-world disease spread, presenting a promising avenue for enhancing public health preparedness and response efforts.

SPARSEMODr: Rapidly simulate spatially explicit and stochastic models of COVID-19 and other infectious diseases

Building realistically complex models of infectious disease transmission that are relevant for informing public health is conceptually challenging and requires knowledge of coding architecture that can implement key modeling conventions. For example, many of the models built to understand COVID-19 dynamics have included stochasticity, transmission dynamics that change throughout the epidemic due to changes in host behavior or public health interventions, and spatial structures that account for important spatio-temporal heterogeneities. Here we introduce an R package, SPARSEMODr, that allows users to simulate disease models that are stochastic and spatially explicit, including a model for COVID-19 that was useful in the early phases of the epidemic. SPARSEMOD stands for SPAtial Resolution-SEnsitive Models of Outbreak Dynamics, and our goal is to demonstrate particular conventions for rapidly simulating the dynamics of more complex, spatial models of infectious disease. In this report, we outline the features and workflows of our software package that allow for user-customized simulations. We believe the example models provided in our package will be useful in educational settings, as the coding conventions are adaptable, and will help new modelers to better understand important assumptions that were built into sophisticated COVID-19 models.

Effects of sample preservation methods and duration of storage on the performance of mid-infrared spectroscopy for predicting the age of malaria vectors

BACKGROUND

Monitoring the biological attributes of mosquitoes is critical for understanding pathogen transmission and estimating the impacts of vector control interventions on the survival of vector species. Infrared spectroscopy and machine learning techniques are increasingly being tested for this purpose and have been proven to accurately predict the age, species, blood-meal sources, and pathogen infections in Anopheles and Aedes mosquitoes. However, as these techniques are still in early-stage implementation, there are no standardized procedures for handling samples prior to the infrared scanning. This study investigated the effects of different preservation methods and storage duration on the performance of mid-infrared spectroscopy for age-grading females of the malaria vector, Anopheles arabiensis.

METHODS

Laboratory-reared An. arabiensis (N = 3681) were collected at 5 and 17 days post-emergence, killed with ethanol, and then preserved using silica desiccant at 5 °C, freezing at - 20 °C, or absolute ethanol at room temperature. For each preservation method, the mosquitoes were divided into three groups, stored for 1, 4, or 8 weeks, and then scanned using a mid-infrared spectrometer. Supervised machine learning classifiers were trained with the infrared spectra, and the support vector machine (SVM) emerged as the best model for predicting the mosquito ages.

RESULTS

The model trained using silica-preserved mosquitoes achieved 95% accuracy when predicting the ages of other silica-preserved mosquitoes, but declined to 72% and 66% when age-classifying mosquitoes preserved using ethanol and freezing, respectively. Prediction accuracies of models trained on samples preserved in ethanol and freezing also reduced when these models were applied to samples preserved by other methods. Similarly, models trained on 1-week stored samples had declining accuracies of 97%, 83%, and 72% when predicting the ages of mosquitoes stored for 1, 4, or 8 weeks respectively.

CONCLUSIONS

When using mid-infrared spectroscopy and supervised machine learning to age-grade mosquitoes, the highest accuracies are achieved when the training and test samples are preserved in the same way and stored for similar durations. However, when the test and training samples were handled differently, the classification accuracies declined significantly. Protocols for infrared-based entomological studies should therefore emphasize standardized sample-handling procedures and possibly additional statistical procedures such as transfer learning for greater accuracy.

Positive effect of spring advance on the diet quality of an alpine herbivore

Changes in vegetation phenology related to global warming are having alarming effects on the life history traits of many herbivore species. Such changes are particularly critical in alpine ecosystems, where strong climate limitations on plant growth make seasonal synchronization imperative for the growth, reproduction and survival of herbivores. However, despite the pivotal role of resource-use strategies on the performances of such species, few studies have explicitly assessed the mechanistic impact of climate change on their diets. We aimed to fill this gap by studying the effect of spring onset on the dietary composition and quality of a medium-size alpine herbivore while considering density-dependent processes and age- and sex-specific differences in foraging behavior. Using an exceptional, long-term (24 years) direct individual-based dietary monitoring of a Pyrenean chamois population (Rupicapra pyrenaica pyrenaica), we showed that ongoing earlier onsets of spring are leading to an earlier access to high-quality forage and therefore a higher diet quality at a fixed date, without apparent changes in diet composition. We also showed that at high densities, intraspecific competition reduced diet quality by driving animals to feed more on woody plants and less on nutritious forbs and graminoids. By assessing the mechanistic effects of global warming on the dietary patterns of species at the center of trophic networks, this study is an essential step for predictive models aiming at understanding the ongoing ecosystem consequences of the global climatic crisis.

Characterizing the demographic history and prion protein variation to infer susceptibility to chronic wasting disease in a naïve population of white-tailed deer (Odocoileus virginianus)

Assessments of the adaptive potential in natural populations are essential for understanding and predicting responses to environmental stressors like climate change and infectious disease. Species face a range of stressors in human-dominated landscapes, often with contrasting effects. White-tailed deer (Odocoileus virginianus; deer) are expanding in the northern part of their range following decreasing winter severity and increasing forage availability. Chronic wasting disease (CWD), a prion disease affecting deer, is likewise expanding and represents a major threat to deer and other cervids. We obtained tissue samples from free-ranging deer across their native range in Ontario, Canada, which has yet to detect CWD in wild populations. We used high-throughput sequencing to assess neutral genomic variation and variation in the prion protein gene (PRNP) that is partly responsible for the protein misfolding when deer contract CWD. Neutral variation revealed a high number of rare alleles and no population structure, and demographic models suggested a rapid historical population expansion. Allele frequencies of PRNP variants associated with CWD susceptibility and disease progression were evenly distributed across the landscape and consistent with deer populations not infected with CWD. We estimated the selection coefficient of CWD, with simulations showing an observable and rapid shift in PRNP allele frequencies that coincides with the start of a novel CWD outbreak. Sustained surveillance of genomic and PRNP variation can be a useful tool for guiding management practices, which is especially important for CWD-free regions where deer are managed for ecological and economic benefits.

Significant reductions of host abundance weakly impact infection intensity of Batrachochytrium dendrobatidis

Infectious diseases are considered major threats to biodiversity, however strategies to mitigate their impacts in the natural world are scarce and largely unsuccessful. Chytridiomycosis is responsible for the decline of hundreds of amphibian species worldwide, but an effective disease management strategy that could be applied across natural habitats is still lacking. In general amphibian larvae can be easily captured, offering opportunities to ascertain the impact of altering the abundance of hosts, considered to be a key parameter affecting the severity of the disease. Here, we report the results of two experiments to investigate how altering host abundance affects infection intensity in amphibian populations of a montane area of Central Spain suffering from lethal amphibian chytridiomycosis. Our laboratory-based experiment supported the conclusion that varying density had a significant effect on infection intensity when salamander larvae were housed at low densities. Our field experiment showed that reducing the abundance of salamander larvae in the field also had a significant, but weak, impact on infection the following year, but only when removals were extreme. While this suggests adjusting host abundance as a mitigation strategy to reduce infection intensity could be useful, our evidence suggests only heavy culling efforts will succeed, which may run contrary to objectives for conservation.

How mechanistic modelling supports decision making for the control of enzootic infectious diseases

Controlling enzootic diseases, which generate a large cumulative burden and are often unregulated, is needed for sustainable farming, competitive agri-food chains, and veterinary public health. We discuss the benefits and challenges of mechanistic epidemiological modelling for livestock enzootics, with particular emphasis on the need for interdisciplinary approaches. We focus on issues arising when modelling pathogen spread at various scales (from farm to the region) to better assess disease control and propose targeted options. We discuss in particular the inclusion of farmers' strategic decision-making, the integration of within-host scale to refine intervention targeting, and the need to ground models on data.