Empirical Trials Research Articles

Population-level health impact of hypothetical waning 1-dose human papillomavirus vaccination and 2-dose mitigation strategies in a high cervical cancer burden setting.

We simulated the impact of hypothetical waning scenarios of a 1-dose human papillomavirus (HPV) vaccination paired with switching to 2-dose mitigation strategies guided by empirical vaccine trial reporting timelines. Using 2 independent mathematical models fitted to a high-burden setting, we projected the cumulative cervical cancer cases averted over 85 years for alternative HPV vaccination scenarios under 2 program adoption timelines: 1) de novo introduction of a 1-dose HPV vaccination and 2) a switch from an existing 2-dose HPV vaccination program to a 1-dose vaccination. We assumed 80% vaccination coverage with the bivalent vaccine and an average duration of a 1-dose HPV vaccine protection of either 30 or 25 years with 100% efficacy. We varied the eligible age group(s) at program introduction and the 2-dose mitigation (single-age cohort or multi-age cohort). If needed for mitigation, reintroduction of 2-dose vaccination was assumed to occur in 2036 (ie, 30 years after initiation of the Costa Rica Vaccine Trial). Under both vaccine adoption timelines, the models projected that countries could achieve the same level of health benefits by switching to 2 doses in 2036 using a multi-age cohort approach as with initiating a 2-dose or 1-dose vaccination program with no waning. With only a single-age cohort 2-dose mitigation approach, 98%-99% of cases would be prevented compared with the health benefits of 2 doses or a noninferior, durable 1 dose. Countries hesitant to adopt a 1-dose HPV vaccination program may have opportunities to leverage the benefits and efficiency of a 1-dose schedule while awaiting longer-term reporting from 1-dose durability studies, including Costa Rica Vaccine Trial.

A review on recent applications of machine learning in mechanical properties of composites

AbstractComposites are undergoing extensive research and utilization due to their excellent mechanical properties, driven by human needs. Traditionally, the research methods in materials science predominantly rely on empirical theory or experimental trial and error approaches. However, the increased complexity of composite materials results in a greater intricacy in their mechanical behavior. Consequently, the utilization of traditional research methods may not achieve sufficient efficiency. Materials science is rapidly transitioning into a data‐driven era, with machine learning (ML) emerging as a potent tool to expedite materials development and enhance properties prediction. Significant advancements have been achieved in the application of ML to the study of composite mechanics. In this review article, we elucidate various ML methods employed in the construction of constitutive models for isotropic and anisotropic composites, and delve into the research on construction ML models that leverage input data derived from composite processes, structures, and environmental conditions to predict material mechanical properties. Additionally, we summarize recent noteworthy ML applications in composite design and optimization. Finally, possible prospective viewpoints are proposed for future development, with the aim of providing essential scientific guidance for advancing material science and technology through ML.Highlights Machine learning can address complexity in constitutive model of the anisotropic composites. Machine learning predicts mechanical properties of composites well by process and structure. Machine learning enhances efficiency in inverse design to optimize composites. Limitations, challenges, development trends of ML in composites.

Clinician’s Perspectives on the Management of Gastroesophageal Reflux Disease and Prescription Practice of Proton Pump Inhibitors in Indian Settings

Objective: To assess the clinicians’ perspectives on GERD management and the prescription practices of proton pump inhibitors (PPIs) in Indian settings. Methods: This cross-sectional study was conducted among clinicians specialized in GERD management. Participants completed a 24-question questionnaire distributed via email or online platforms. The survey explored prescription practices, clinical observations, and preferences regarding PPIs and GERD management. Data analysis involved descriptive statistics, with responses presented as frequencies and percentages. Results: Out of 123 survey participants, 57% identified diet as the most common predisposing factor for GERD. Nighttime heartburn and disturbed sleep were recognized as key alarm symptoms by 57% of participants. Epigastric tenderness was the most prevalent physical finding, reported by 68% of clinicians. PPIs were favored as the first-line treatment for GERD by 78.86% of respondents. Domperidone was preferred as an adjunctive treatment for functional dyspepsia and gastroparesis. An empirical PPI trial was widely supported for its simplicity and cost-effectiveness. Rabeprazole emerged as the preferred PPI for nighttime heartburn relief due to its rapid onset and sustained efficacy. Conclusion: This study highlighted a consensus among Indian clinicians on the use of PPIs as a primary treatment for GERD, with a preference for rabeprazole. Domperidone is favored as an adjunctive treatment for functional dyspepsia and gastroparesis. Rabeprazole emerged as the preferred option for nighttime heartburn relief, owing to its rapid onset and long-lasting effectiveness.

Empirical Trials on Unmanned Agriculture in Open-Field Farming: Ridge Forming

Empirical Trials on Unmanned Agriculture in Open-Field Farming: Ridge Forming

Intermittent lysis on a single paper-based device to extract exosomal nucleic acid biomarkers from biological samples for downstream analysis.

As the role of exosomes in physiological and pathological processes has been properly perceived, harvesting them and their internal components is critical for subsequent applications. This study is a debut of intermittent lysis, which has been integrated into a simple and easy-to-operate procedure on a single paper-based device to extract exosomal nucleic acid biomarkers for downstream analysis. Exosomes from biological samples were captured by anti-CD63-modified papers before being intermittently lysed by high-temperature, short-time treatment with double-distilled water to release their internal components. Exosomal nucleic acids were finally adsorbed by sol-gel silica for downstream analysis. Empirical trials not only revealed that sporadically dropping 95°C ddH2O onto the anti-CD63-modified papers every 5min for 6 times optimized the exosomal nucleic acids extracted by the anti-CD63 paper but also verified that the whole deployed procedure is applicable for point-of-care testing (POCT) in low-resource areas and for both in vitro (culture media) and in vivo (plasma and chronic lesion) samples. Importantly, downstream analysis of exosomal miR-21 extracted by the paper-based procedure integrated with this novel technique discovered that the content of exosomal miR-21 in chronic lesions related to their stages and the levels of exosomal carcinoembryonic antigen originated from colorectal cancer cells correlated to their exosomal miR-21.

Augmentation-Free Graph Contrastive Learning of Invariant-Discriminative Representations.

Graph contrastive learning (GCL) is a promising direction toward alleviating the label dependence, poor generalization and weak robustness of graph neural networks, learning representations with invariance, and discriminability by solving pretasks. The pretasks are mainly built on mutual information estimation, which requires data augmentation to construct positive samples with similar semantics to learn invariant signals and negative samples with dissimilar semantics to empower representation discriminability. However, an appropriate data augmentation configuration depends heavily on lots of empirical trials such as choosing the compositions of data augmentation techniques and the corresponding hyperparameter settings. We propose an augmentation-free GCL method, invariant-discriminative GCL (iGCL), that does not intrinsically require negative samples. iGCL designs the invariant-discriminative loss (ID loss) to learn invariant and discriminative representations. On the one hand, ID loss learns invariant signals by directly minimizing the mean square error (MSE) between the target samples and positive samples in the representation space. On the other hand, ID loss ensures that the representations are discriminative by an orthonormal constraint forcing the different dimensions of representations to be independent of each other. This prevents representations from collapsing to a point or subspace. Our theoretical analysis explains the effectiveness of ID loss from the perspectives of the redundancy reduction criterion, canonical correlation analysis (CCA), and information bottleneck (IB) principle. The experimental results demonstrate that iGCL outperforms all baselines on five node classification benchmark datasets. iGCL also shows superior performance for different label ratios and is capable of resisting graph attacks, which indicates that iGCL has excellent generalization and robustness. The source code is available at https://github.com/lehaifeng/ T-GCN/tree/master/iGCL.

Unmasking Subacute Thyroiditis in a Young Male with PUO: A Case Report

Pyrexia of unknown origin (PUO) poses a diagnostic challenge, defined as persistent fever exceeding 38.3°C (100°F) without an identified cause after extensive investigation. Subacute thyroiditis (SAT), an uncommon inflammatory condition of the thyroid gland, typically presents with neck pain, thyrotoxic symptoms, and elevated inflammatory markers. However, it rarely manifests as PUO without typical symptoms. We report a case of a 32-year-old Indian male presenting with a 20-day history of persistent fever without localized symptoms, initially treated empirically with antibiotics without improvement. Clinical examination revealed a firm, non-tender goiter, and laboratory investigations showed elevated erythrocyte sedimentation rate (ESR) and C-reactive protein (CRP). Subsequent thyroid function tests indicated elevated free T4 levels with low TSH, suggestive of thyroiditis, after negative thyroid autoantibodies ruling out Graves' disease. Further investigations including imaging and tests for PUO were negative, prompting an empirical trial of steroid therapy which resulted in rapid clinical improvement. Subsequent thyroid function tests revealed transient hypothyroidism, resolving without intervention. Retrospectively, a diagnosis of subacute thyroiditis was established, highlighting its atypical presentation as PUO. Our case sheds light on the importance of considering uncommon thyroid pathologies in the differential diagnosis of PUO, especially when typical symptoms are absent.

How to distinguish PPI-refractory from PPI-responsive in gastroesophageal reflux-induced chronic cough: post reflux swallow induced peristaltic wave index and mean nocturnal baseline impedance provide new predictive factors

The results of empirical trials with proton pump inhibitors (PPIs) for management of gastroesophageal reflux-induced chronic cough (GERC) have resulted in considerable controversy and the mechanism of PPI refractoriness remain unclear. In total, 128 GERC patients were enrolled between March 2018 and October 2022. The post-reflux swallow-induced peristaltic wave index (PSPWI) was lower in the PPI-refractory group than the PPI-responsive group (33.89±7.38versus39.45±9.47, respectively, p<0.001), as were the mean nocturnal baseline impedance (MNBI) and proximal MNBI [2092.11 (IQR: 652.23)]versus2426.52 (IQR: 917.39) Ω, respectively, p=0.012; 1599.50 (IQR: 1206.63)versus2274.50 (IQR: 1775.29) Ω, respectively, p=0.001). Multivariate logistic regression analysis identified the PSPWI (odds ratio=0.919, p=0.001) as an independent predictor of PPI-refractory GERC. The diagnostic value of both proximal MNBI≤39.90% and MNBI≤2233.58 Ω had moderate sensitivity (71.67%) and specificity (75.00%) to identify PPI-refractory GERC.

Tolerance to waterlogging determines the effectiveness of biodrainage in mitigating runoff in hillslope-scale simulations

Reversal of land degradation in agricultural systems through a wide range of natural resources management tailored to the specific climate, soil, and topography is crucial. This is especially critical for subsistence farming systems such as the Ethiopian Highlands, subject to some of the highest soil erosion rates in the world. Although biodrainage is widely and successfully used for waterlogging and erosion control in arid regions, it is not widely applied in sub-humid places like the Ethiopian Highlands. Beginning with field observations of the complex vertical soil profiles and variations in lithology and soil hydraulic properties along a hillslope ridge-channel transect in the Debre Mawi watershed of the Abay (Upper Blue Nile) Basin in the Ethiopian Highlands, we construct a synthetic model hillslope in HYRDUS-2D. For the growing season coinciding with the rainy season, known as Kiremt, we calibrated the hillslope water storage using observations of water table elevation. The synthetic hillslope is then used to test the feasibility of biodrainage to mitigate saturation, a major driver of gullying and soil loss in the area, and to identify plant traits to which such mitigation is sensitive. The simulation results using bare soil, eucalyptus and maize land covers revealed that in spite of low potential evapotranspiration during the Kiremt season, biodrainage with Eucalyptus can reduce the occurrence and duration of saturation of the top 0.6 m surface soil profile of the hillslope. Of the different plant traits explored, the maximum soil water potential at which the crop can transpire at potential rates, and the LAI, which controls potential evaporation rates via the crop coefficient, provide the most important controls on the effectiveness of biodrainage in desaturating the hillslope. We conclude that biodrainage strategies to mitigate soil erosion in sub humid locales merits further investigation through empirical trials. However, implementation of biodrainage strategies should be subject to thoughtful consideration of the implications for socio-economic outcomes and the local environment.

Multi-objective optimization of PI controller for BLDC motor speed control and energy saving in Electric Vehicles: A constrained swarm-based approach

Proportional Integral Derivative (PID) controllers are widely employed in industrial applications due to their effectiveness, simplicity, and versatility. Within the automotive sector, one notable application of the Proportional Integral (PI) controller is in the realm of electric motors, particularly the Brushless Direct Current (BLDC) motors powering Electric Vehicles (EVs). The precision of speed control over BLDC motors, renowned for their efficiency and reliability, is paramount for optimizing vehicle performance and ensuring energy efficiency. PIDs must be tuned each time they are used in an application to optimize performance. A well-known empirical hit and trial tuning strategy is the Ziegler–Nichols method, which results in sub-optimal performance in the presence of locally optimal solutions of large-dimensional search spaces. Existing Artificial Intelligence methods tailored for PID controller tuning often focus on singular optimization aspects, neglecting potential performance gains achievable through multi-objective considerations. Furthermore, prevalent swarm-based PID optimization methods typically initialize the population randomly without imposing any bounds on input parameters, thus rendering them susceptible to inconsistent, unreliable, sub-optimal solutions in unconstrained search environments. In our research endeavors, we mathematically model the multi-objective optimization of BLDC motor speed control, energy usage, and efficiency using constrained Differential Evolution and Particle Swarm Optimized PID controllers. The Ziegler–Nichols tuning method is used to ascertain the initial PI parameters and define bounds within the input space. Our methodology addresses a critical gap by integrating real-time road gradient data into the EV control system, enhancing precision and minimizing energy consumption, especially during challenging road conditions. The proposed approach significantly improves motor speed control and energy efficiency in EVs. With our proposed swam-based optimization over 30 iterations, MSE decreased by approximately 95.4% (from 3.8834 to 0.1809), while energy consumption reduced by about 3.1% (from 1.015 kWh to 0.984 kWh). These results highlight the effectiveness of our method in enhancing both speed control precision and energy efficiency. In addition, code related data is available at Github.

On the Effect of Donor Strength on the Photoluminescence Performance in Mono‐Substituted N‐Donor Triarylmethyl Radicals

AbstractThe functionalization of light‐emitting triarylmethyl radicals with electron‐donating moieties can significantly increase their photoluminescence quantum yield ϕ. As luminophores in light‐emitting diodes, such open‐shell radicals can be used to overcome the problem of spin‐statistics inherent to conventional closed‐shell emitters. However, so far the functionalization of triarylmethyl radicals with donors of varying strength is limited by the restricted reactivity of the triarylmethyl radical, constraining optimization of performance to empirical trial and error approaches. Here, the reliable reactivity of N‐heterocyclic donors is used in radical‐mediated aromatic substitutions, allowing to systematically investigate the effect of donor strength on the emission characteristics of triarylmethyl radicals. As a single descriptor proxy to the donor strength, the ionization energy IE of the donor moiety is employed, which is determined by density functional theory calculations. A systematic bathochromic shift of the emission wavelength λmax is observed for increasing donor strength, while maximum ϕ values are obtained for medium‐strength donors. These effects are rationalized with a simple model based on Marcus theory supported by quantum chemical calculations and electron paramagnetic resonance. This allows to understand the effect of the donor strength on both λmax and ϕ, enabling the design of improved light‐emitting radicals in the future.

Development of Test Instruments Utilizing Multiple Representations for Enhancing Understanding of Motion Kinematics

Problem-solving in physics remains low due to the limited use of varied or multi-representation test instruments. This study aims to develop a multi-representation-based test instrument on kinematic motion and describe the results of the logical validity and empirical trials of the developed multi-representation-based test instrument for kinematic motion. This research follows a Research and Development (R&D) approach, using stages synthesized from various expert opinions, ranging from needs analysis to test assembly. The research instrument consists of a multi-representation-based test consisting of 30 questions. The data were analyzed using ANATES after being reviewed by three experts. The results indicate that 30 test items were successfully developed, with an average logical validity score of 0.63, demonstrating that the test instrument is valid and feasible for use. Based on statistical analysis, including item validity, difficulty level, discrimination index, and reliability, the empirical validity results show that 11 test items are suitable for use.

Coverage Planning for UVC Irradiation: Robot Surface Disinfection Based on Swarm Intelligence Algorithm.

Ultraviolet (UV) radiation has been widely utilized as a disinfection strategy to effectively eliminate various pathogens. The disinfection task achieves complete coverage of object surfaces by planning the motion trajectory of autonomous mobile robots and the UVC irradiation strategy. This introduces an additional layer of complexity to path planning, as every point on the surface of the object must receive a certain dose of irradiation. Nevertheless, the considerable dosage required for virus inactivation often leads to substantial energy consumption and dose redundancy in disinfection tasks, presenting challenges for the implementation of robots in large-scale environments. Optimizing energy consumption of light sources has become a primary concern in disinfection planning, particularly in large-scale settings. Addressing the inefficiencies associated with dosage redundancy, this study proposes a dose coverage planning framework, utilizing MOPSO to solve the multi-objective optimization model for planning UVC dose coverage. Diverging from conventional path planning methodologies, our approach prioritizes the intrinsic characteristics of dose accumulation, integrating a UVC light efficiency factor to mitigate dose redundancy with the aim of reducing energy expenditure and enhancing the efficiency of robotic disinfection. Empirical trials conducted with autonomous disinfecting robots in real-world settings have corroborated the efficacy of this model in deactivating viruses.

Prediction of machining performances in powder mixed electro-discharge machining to process skd61 steel by response surface methodology

In electro-discharge machining (EDM) with mixing powder, it is called powder mixed electro-discharge machining (PMEDM), then machining performances- i.e. material removal rate(MRR) and tool wear rate (TWR) has great significance in evaluating the effectiveness and machining accuracy of the machining method. Therefore, in this study, response surface methodology (RSM) was utilized for estimating functions of process variables {comprising peak current (Ip), pulse on time (Ton), and powder concentration (Cp)} for the machining performances for processing SKD61 steel during EDM process with tungsten compound powder. Box-Behnken matrix was utilized for designing and conducting a series of empirical trials. Analysis of variance (ANOVA) was applied to evaluate the adequate of predictive models. The outcomes reveal that the predicted models of MRR and TWR have a high precision with R2 values of MRR and TWR being 99.2% and 99.11%, respectively. The error comparison of the predictive and empirical values for the confirmed experiments is less than 5%, this once again consolidates that the developed models' accuracy. These development models can efficiently prognosticate the desired machining performances of the PMEDM method for processing SKD61 steel

Sinefungin analogs targeting VP39 methyltransferase as potential anti-monkeypox therapeutics: a multi-step computational approach.

The increasing spread of the Monkeypox virus (MPXV) presents a significant public health challenge, emphasising the urgent requirement for effective treatments. Our study focuses on the VP39 Methyltransferase enzyme of MPXV as a critical target for therapy. By utilising virtual screening, we investigated natural compounds with structural similarities to sinefungin, a broad-acting MTase inhibitor. From an initial set of 177 compounds, we identified three promising compounds-CNP0346326, CNP0343532, and CNP008361, whose binding scores were notably close to that of sinefungin. These candidates bonded strongly to the VP39 enzyme, hinting at a notable potential to impede the virus. Our rigorous computational assays, including re-docking, extended molecular dynamics simulations, and energetics analyses, validate the robustness of these interactions. The data paint a promising picture of these natural compounds as front-runners in the ongoing race to develop MPXV therapeutics and set the stage for subsequent empirical trials to refine these discoveries into actionable medical interventions.

Empirical Insights into 5G Deployments in Highway Operational Environments and Comparative Performance with 4G

Despite the promising benefits, the integration of 5G, particularly through Non-Standalone (NSA) architectures that rely on existing 4G infrastructures, introduces challenges in maintaining optimal radio network performance and service quality. This study evaluates 4G and 5G radio network performance through empirical field trials across highway operational scenarios, a domain less scrutinized compared with urban environments. By conducting simultaneous measurements and comparing all available networks, this research focuses on dissecting the performance of critical quality indicators to gauge the intricacies of radio network behavior in high-speed travel conditions. Our findings illuminate distinct behavioral patterns, highlighting the unique challenges and optimization opportunities in these scenarios. Variability in signal strength across the highway routes underscores the influence of geography and infrastructure on coverage, while the relative stability in signal quality suggests the networks’ capability to maintain signal quality amid fluctuating strength. Interference results indicate effective management of signal interference, crucial for high-quality links, whereas latency and throughput metrics highlight a lag behind anticipated goals for reduced latency but promising data rates. This study not only showcases the variances in network quality and performance, thus pinpointing areas for operator-specific enhancements, but also emphasizes the comparison between the robustness of 4G infrastructures and the challenges in optimizing 5G networks.

Strategies for the fast optimization of the glass transition temperature of sustainable epoxy resin systems via machine learning

AbstractAligned with the prevailing sustainability paradigm, the imperative adoption of bio‐based substitutes for constituents within petroleum‐derived epoxy resin becomes evident. Blending bio‐based and petroleum‐based epoxy resins and curing agents, establishes a synergistic compromise addressing both sustainability imperatives and the mechanical efficacy of thermosets. The conventional approach to discovering optimal compositions for multi‐component mixtures under specific boundary conditions includes empirical trial and error and is seen as a protracted and inefficient endeavor. Conversely, leveraging machine learning might afford a streamlined and confident resolution to this challenge. This investigation elucidates the requisite strategies for maximizing the efficiency of material property optimization through the application of Bayesian optimization and active learning. Illustratively, the study demonstrates the proficient optimization of the glass transition temperature within a four‐component epoxy resin system. This optimization is conducted across varying ranges of bio‐content and cost considerations. The study underscores the utility of machine learning in achieving this task with notable efficiency. The efficacy of least squares, kernel ridge regression, Gaussian process regression, and artificial neural networks, is meticulously evaluated through comprehensive seven‐fold cross‐validation and validated against experimental data.

Practical strategies to achieve resilient health systems: results from a scoping review

BackgroundThis paper presents the results of a systematic review to identify practical strategies to create the institutions, skills, values, and norms that will improve health systems resilience.MethodsA PRISMA 2020 compliant systematic review identified peer-reviewed and gray literature on practical strategies to make health systems more resilient. Investigators screened 970 papers to identify 65 English language papers published since 2015.ResultsPractical strategies focus efforts on system changes to improve a health system’s resilience components of collective knowing, collective thinking, and collaborative doing. The most helpful studies identified potential lead organizations to serve as the stewards of resilience improvement, and these were commonly in national and local departments of public health. Papers on practical strategies suggested possible measurement tools to benchmark resilience components in efforts to focus on performance improvement and ways to sustain their use. Essential Public Health Function (EPHF) measurement and improvement tools are well-aligned to the resilience agenda. The field of health systems resilience lacks empirical trials linking resilience improvement interventions to outcomes.ConclusionsThe rigorous assessment of practical strategies to improve resilience based on cycles of measurement should be a high priority.

Predictive reinforcement learning in non-stationary environments using weighted mixture policy

Reinforcement Learning (RL) within non-stationary environments presents a formidable challenge. In some applications, anticipating abrupt alterations in the environment model might be possible. The existing literature lacks a framework that proactively harnesses such predictions to enhance reward optimization. This paper introduces an innovative methodology designed to preemptively leverage these predictions, thereby maximizing the overall achieved performance. This is executed by formulating a novel approach that generates a weighted mixture policy from both the optimal policies of the prevailing and forthcoming models. To ensure safe learning, an adaptive learning rate is derived to facilitate training of the weighted mixture policy. This theoretically guarantees monotonic performance improvement at each update during training. Empirical trials focus on a model-free predictive reference tracking scenario involving piecewise constant references. Through the utilization of the cart–pole position control problem, it is demonstrated that the proposed algorithm surpasses prior techniques such as context Q-learning and RL with context detection algorithms in non-stationary environments. Moreover, the algorithm outperforms the application of individual optimal policies derived from each observed environment model (i.e., policies not utilizing predictions).

Modelling the displacement and coexistence of clonal lineages of Phytophthora infestans through revisiting past outbreaks

AbstractThe continuous changes in the lineage proportions of populations in the clonal plant pathogen Phytophthora infestans on potato and tomato crops have been perplexing to researchers and disease managers. Sudden outbreaks of newly emergent genotypes are often associated with these rapid composition changes. Modelling can predict the persistence and displacement of pathogen genotypes with differential fitness among hosts. Building upon previous models, we combined analytical and simulation methods to model the outcome of interactions between competing lineages on different hosts. Model inputs include pathogenesis parameters, and the outputs are fitness and lineage proportions within each host. Analytical solutions yielding complete displacement, partial coexistence‐displacement and complete coexistence were described. In a retrospective study, the lesion growth rate and sporulation density of P. infestans lineages on potato and tomato from pathogenicity trials were used as inputs. Output lineage frequencies were compared with historical epidemiological situations to check model accuracy. The results showed that pathogenesis traits measured from empirical trials could simulate lineage constituents on potato and tomato and estimate genotypic fitness with reasonable accuracy. The model also showed promise in predicting ongoing lineage displacements in the subsequent year or few years, even when the displaced lineage was still highly prevalent during the time of isolation. However, large uncertainties remain at temporal–spatial scales owing to complex meta‐population dynamics in some regions and adaptation to local environmental factors. This simulation model provides a new tool for forecasting pathogen compositions and can be used to identify potentially problematic genotypes based on pathogen life‐history traits.