Journal of The Royal Society Interface最新文献

Hand, foot and mouth disease (HFMD) remains a major public health challenge in China, exhibiting distinct seasonal patterns. This study integrates meteorological, behavioural and social determinants to elucidate the transmission dynamics of HFMD in Guangzhou. Utilizing surveillance data from 2012 to 2022, we employed regression analysis and developed a mechanistic transmission model incorporating absolute humidity (AH), the Baidu search index (BDI) as a proxy for health-seeking behaviour and holiday effects. The model, calibrated via Markov chain Monte Carlo methods, explained 91.4% of the case variance and estimated a mean time-varying reproduction number of 2.29. Our findings demonstrate that AH and BDI act as significant nonlinear drivers of transmission, while holidays reduced incidence by an average of 21.3%. The implementation of non-pharmaceutical interventions during the COVID-19 pandemic was associated with a substantial reduction in HFMD incidence, with cases declining by 88.1% in 2020, 36.6% in 2021 and 72.2% in 2022. This integrative modelling framework effectively captures the multifactorial drivers of HFMD seasonality and provides a robust tool for forecasting outbreaks and informing targeted public health interventions.

Climate change can impact the persistence of native and invasive parasites and their effects on hosts. Given the complexity of interactions in natural systems, models based on parasite-host systems can be helpful to explore long-term impacts. We investigate how two intestinal parasitic copepods impact host populations, and how the predicted temperature increase by year [Formula: see text] may affect the persistence and impacts of the parasites. We study Mytilicola intestinalis (a specialist established in blue mussels, Mytilus edulis) and Mytilicola orientalis (a recent invader infecting mussels and Pacific oysters, Magallana gigas) in the Wadden Sea. The parasites are non-lethal but can influence host maturation and fecundity. Using a mathematical model parametrized with empirical, field and literature data, we explore how temperature increase affects parasite basic reproduction numbers and the long-term population trends of parasites and mussels. Temperature increase reduces mussel populations below the critical community size for M. intestinalis persistence, while allowing M. orientalis to persist without oysters. M. orientalis does not have a negative effect on the host population in additional to that of M. intestinalis when both are present. We show that environmental change can have qualitatively different effects on related parasites by changing the role of the shared host as a maintenance population.

At its core, the physics paradigm adopts a reductionist approach, aiming to understand fundamental phenomena by decomposing them into simpler, elementary processes. While this strategy has been tremendously successful in physics, it has often fallen short in addressing fundamental questions in the biological sciences. This arises from the inherent complexity of biological systems, characterized by heterogeneity, polyfunctionality and interactions across spatio-temporal scales. Nevertheless, the traditional framework of complex systems modelling falls short, as its emphasis on broad theoretical principles has often failed to produce predictive, empirically grounded insights. To advance towards actionable mathematical models in biology, we argue, using neuroscience as a case study, that it is necessary to move beyond reductionist approaches and instead embrace the complexity of biological systems-leveraging the growing availability of high-resolution data and advances in high-performance computing. We advocate for a holistic mathematical modelling paradigm that harnesses rich representational structures such as annotated and multilayer networks, employs agent-based models and simulation-based approaches and focuses on the inverse problem of inferring system dynamics from observations. We emphasize that this approach is fully compatible with the search for fundamental biophysical principles and highlight the potential it holds to drive progress in mathematical biology over the next two decades.

The rise of artificial intelligence enables new ways to influence human cooperation with high controllability and broad scalability. While prior theoretical studies in symmetric snowdrift games suggest that cooperative bots can reduce cooperation and defective bots can enhance it, these conclusions rely on the assumption of equal payoffs across agents. Here, we extend this analysis to asymmetric human-machine hybrid populations, where normal players may receive higher or lower payoffs than bots depending on their relative power. We find that power asymmetry fundamentally reshapes the role of simple bots: when normal players are advantaged, cooperative bots suppress cooperation and defective bots enhance it; however, this effect reverses when normal players are disadvantaged, with cooperative bots promoting cooperation and defective bots undermining it. These findings hold across both structured and unstructured populations. Our results advance the understanding of how simple bots can be strategically used to influence cooperation and underscore the critical role of power asymmetry in hybrid systems.

Small-scale structures on biological surfaces can profoundly impact how animals move, appear and interact with their environments. Such textures may be especially important for limbless reptiles, such as snakes and legless lizards, because their skin serves as the primary interface with the world around them. Here, we examine ventral microstructures of several limbless reptiles, which are hypothesized to be highly specialized to aid locomotion via frictional interactions. Inspired by prior studies that investigated potential links between microtextures, phylogeny, habitat and locomotion-but that were limited by their reliance on shed skins-we characterized the structures present on preserved museum specimens and found that they are quantitatively similar to those found on shed skins. Using this result, we confirmed a previously hypothesized-but untested due to the lack of shed skins-third independent evolution of sidewinding-specific isotropic microtexture. Specifically, we examined a museum-preserved Bitis peringueyi specimen and identified a new instance of convergent evolution in sidewinding viper microstructures: the loss of micro-spikes (present on many snake species) and the appearance of micro-pits with a characteristic spacing. Our results reveal that museum-preserved specimens retain intact microtextures, greatly expanding the availability of samples for evolutionary studies.

Accurate forecasting of contagious illnesses has become increasingly important to public health policymaking and better prediction could prevent the loss of millions of lives. To better prepare for future pandemics, it is essential to improve forecasting methods and capabilities. In this work, we implement physics-informed neural networks (PINNs), a popular tool in the area of scientific machine learning, to perform infectious disease forecasting. The used PINNs model incorporates dynamical systems representations of disease transmission into the loss function, thereby assimilating epidemiological theory and data using neural networks. Our approach is designed to prevent model overfitting, which often occurs when training deep-learning models with observation data alone. In addition, we use an additional sub-network to account for mobility, cumulative vaccine doses and other covariates that influence the transmission rate, a key parameter in the compartmental model. To demonstrate the capability of the proposed model, we examine the performance of the model using state-level COVID-19 data in California. Our simulation results show that predictions of the PINNs model on the number of cases, deaths and hospitalizations are consistent with existing benchmarks. In particular, the PINNs model outperforms naive baseline forecasts and various sequence deep-learning models, such as recurrent neural networks, long short-term memory networks, gated recurrent units and transformer models. We also show that the performance of the PINNs model is comparable with that of a sophisticated Gaussian infection state forecasting model that combines the compartmental model, a data observation model and a regression model for inferring parameters in the compartmental model. Nonetheless, the PINNs model offers a simpler structure and is easier to implement. In summary, we perform a systematic study of the predictive capability of the PINNs model in forecasting the dynamics of infectious diseases and our results showcase the potential of the proposed model as an efficient computational tool to enhance the current capacity of infectious disease forecasting.

Human walking contains variability due to small intrinsic perturbations arising from sensory or motor noise, or to promote motor learning. We hypothesize that such stride-to-stride variability may increase the metabolic cost of walking over and above a perfectly periodic motion, and that neglecting such variability in simulations may mis-estimate the metabolic cost. Here, we quantify the metabolic estimation errors accrued by neglecting the stride-to-stride variability using human data and a musculoskeletal model by comparing the cost of multiple strides of walking and the cost of a perfectly periodic stride with averaged kinematics and kinetics. We find that using an averaged stride underestimates the cost by approximately 2.5%, whereas using a random stride may mis-estimate the cost positively or negatively by up to 15%, ignoring the contribution of measurement errors to the observed stride-to-stride variability. As a further illustration of the cost increase in a simpler dynamical context, we use a feedback-controlled inverted pendulum walking model to show that increasing the sensory or motor noise increases the overall metabolic cost, as well as the variability of stride-to-stride metabolic costs, as seen with the musculoskeletal simulations. Our work establishes the importance of accounting for stride-to-stride variability when estimating metabolic costs from motion.

No two microbial communities share the same species richness and abundance profiles. Experiments have shown that the assembly of new microbial communities from the same environmental pool is sufficient to generate diversity within and between communities: when microbial dispersal is slower than division, communities exhibit low richness but high between-community dissimilarity; when dispersal is faster, richness increases while dissimilarity decreases. Here, we study a minimal stochastic model that recovers these empirically observed assembly regimes. Our mathematical framework yields explicit expressions for the abundance fluctuation distributions across low-, intermediate- and high-dispersal regimes, providing a quantitative lens on microbiome assembly. We derive analytical predictions for the bimodality coefficient that quantifies the transition between assembly regimes, which appears as a robust metric to predict community richness and dissimilarity. Additionally, we highlight the mean relative abundance as a complementary metric sensitive to differences in microbial traits (e.g. dispersal or division rates). Applying these metrics to experimental data indicates their practical value for the rapid identification of assembly regimes and trait asymmetries. Overall, our study provides general predictions about how stochasticity, timescales and microbial traits influence both within-community diversity (richness) and between-community diversity (dissimilarity) during the assembly of new microbial communities. Our work thus contributes to a better understanding of the factors driving variation in microbiome formation.

Feral pigeons in Seoul, South Korea, pose a significant public health risk due to their potential to spread zoonotic pathogens. However, the diversity of their eukaryotic microbiota, particularly parasitic organisms, remains underexplored with urban environmental factors. We aimed to characterize the eukaryotic microbiota in pigeon faeces and assess how urban factors such as housing density, proximity to water sources and parks and the presence of homeless individuals influence parasite diversity. Faecal samples were collected from pigeons in various regions of Seoul and adjacent cities. Metabarcoding identified Eimeria (86.58%), Isospora (40.94%) and Tetrameres (20.81%) as the most prevalent pathogens. Regions with homeless populations exhibited significantly lower eukaryotic diversity (p < 0.001), while areas with higher housing density and parks showed increased Eimeria prevalence (odds ratio (OR) = 1.0005, p = 0.0251 and OR = 5.3015, p = 0.0251, respectively). Water sources were positively associated with Isospora prevalence (OR = 2.5340, p = 0.0268). This study represents the first empirical investigation into the influence of urban environments on parasite diversity in feral pigeons in one of the world's most densely populated cities. The findings underscore the need for targeted public health interventions and urban planning strategies to mitigate zoonotic disease transmission from urban wildlife.