Mathematical and Theoretical Epidemiology and Ecology Models

This paper develops a stochastic model of VSV replication to study how mutations affect virion production and intercellular variability.

The paper develops transparent mathematical models using logical deterministic cellular automata to address issues in ecological modeling and scientific reasoning.

The paper models optimal vaccination coverage with low-efficacy vaccines during outbreaks, showing that high coverage may reduce severe outcomes even if it doesn't minimize total infections.

This study investigates parameter identifiability in within-host influenza models using structural and practical analyses to guide model development and experimental design.

The paper reviews multiscale modeling approaches for infectious diseases and introduces two tools for calibrating and resolving multiscale models, using tuberculosis as an example.

This study reformulates a rice-pest-control model to optimize pest management by combining cultural methods and pesticides, reducing environmental impact and increasing rice production.

This paper models how migration affects gene drive spread and displacement in populations.

The paper develops a computationally efficient framework for diagnosing, understanding, and predicting biphasic population growth using profile likelihood analysis.

The paper develops a model linking epidemiological and immunological dynamics to show sustained low viral load infections can trigger epidemics even with within-host reproduction numbers below one.

The paper models coffee rust infection dynamics by incorporating spatial factors like dispersal and planting patterns to explain infection timing and intensity.

This paper extends an ecological model to predict microbiome dynamics under environmental changes and shows how precise environmental perturbations can improve microbiome control.

The paper compares differential sensitivity analysis methods for biological models, finding forward-mode differentiation is fastest and complex-perturbation is simplest to implement.

The paper introduces a mathematical model to assess multi-strain pandemics' impact on mortality, reproduction number, and infection peaks, showing logarithmic growth in infected numbers and equivalence to single-strain reproduction numbers.

This paper estimates the basic reproduction number (R0) for rubella in 98 settings using seroprevalence data and evaluates indicator-based approaches, finding that regional averaging performs as well as machine learning methods.

This paper introduces a hierarchical mechanistic Bayesian model for epidemiological inference, showing it effectively identifies transmission patterns and hotspots in drug-resistant pathogens.

This study constructs a mathematical model to analyze how protection measures and treatment affect pneumonia transmission dynamics, finding that reducing the effective reproduction number below 1 is not always sufficient to eliminate the disease.

The paper introduces a computationally efficient birth-death model that integrates phylogenetic and epidemiological data to estimate transmission dynamics and unreported infections.

The paper estimates the probability of epidemic fade-out using Bayesian hierarchical modeling on synthetic outbreak data, showing improved accuracy compared to individual epidemic analysis.

The paper develops a model to study antimicrobial resistance dynamics and competition, finding that competitive release has limited impact once resistance emerges, and effective treatment is key to mitigating spread.

This paper introduces SSATAN-X, a stochastic simulation algorithm for pathogen spreading on adaptive networks, achieving up to 100x speed-up over existing methods.

This paper develops a distributed delay dynamic model to study Dengue fever transmission dynamics, incorporating cross immunity and antibody dependent enhancement.

The paper develops a mathematical model to study Sporotrichosis transmission dynamics in domestic cats, finding that mass treatment combined with contact reduction strategies significantly reduces disease prevalence.

The paper introduces a hybrid PDE-ABM model to simulate viral dynamics in cell populations, showing it can predict SARS-CoV-2 and influenza infections and replicate experimental results.

This study compares Lotka-Volterra and Multivariate Autoregressive models for ecological interaction systems, finding LV models generally superior for dynamic data with large deviations from steady states.

The paper investigates biophysical models to resolve the Paradox of the Plankton by combining resource competition, metabolic trade-offs, and stochastic assembly, showing simulations align with empirical observations.

The paper infers and validates interaction parameters in Lotka-Volterra models using two new strategies and assesses data compliance with LV structure.

This paper develops a mathematical framework to model how urban design influences the spread of influenza-like diseases using census and pandemic data from Guadalajara.

The paper develops a mathematical model showing that dividing cells and lytic viruses can coexist through viral load partitioning during cell division and growth.

The paper models socio-spatial networks with strong spatial embedding to show how they generate non-standard epidemic dynamics with sub-exponential growth and lower peak incidence.

The paper shows stochastic logistic models can reproduce stochastic properties of microbial time series from plankton and the human microbiome.