Mathematical and Theoretical Epidemiology and Ecology Models

This paper optimizes the renewal model for real-time epidemic prediction by developing a principled method to select the optimal window length k using the accumulated prediction error metric.

This paper introduces a method to optimally select the number of parameters in epidemic renewal and phylogenetic skyline models using minimum description length to improve estimation accuracy.

This paper develops a model of adaptive social contact rates during epidemics, showing complex dynamics including oscillations and multiple peaks under parameter changes.

This paper investigates whether predator functional response follows mass action at macro-ecological scales and finds it follows a power-law with square root dependence on predator-prey densities.

This paper applies Pade-approximation based Differential Evolution to solve a nonlinear Hepatitis-B model, showing improved convergence and error performance compared to NSFD.

The paper introduces a sub-exponential branching process model to study early epidemic dynamics, applied to Ebola data showing sub-exponential transmission.

This study models ZIKV transmission through sexual contact networks with double asymmetric percolation, revealing a quadruple epidemic transition.

This study shows cooperative interactions enhance robustness of coexistence in spatially structured microbial consortia, while competition leads to spatial heterogeneity and fragile coexistence.

The paper develops a mathematical framework to model waterborne and indirectly transmitted infections and applies it to cholera and hepatitis C to show the importance of environmental reservoir dynamics.

The paper develops a mathematical model to quantify bacterial fitness dynamics in intracellular infection scenarios, identifying key parameters that influence infection outcomes.

The paper demonstrates that process noise can distinguish between indistinguishable stochastic population dynamics models using Bayesian methods and summary statistics.

This paper uses Approximate Bayesian Computation to improve parameter estimation in stochastic birth-death models for disease outbreaks, showing better identifiability of key parameters like R and infectious population size.

This paper develops a mathematical model to simulate infections in stratified epithelia, finding that infection outcomes depend on pathogen interactions with epithelial layers and cell responses.

The paper develops a general rule for predicting mutualistic system outcomes and introduces a calibration method to apply it without full mechanistic understanding.

The paper develops a mathematical model to study plant-insect interactions, focusing on the role of BIK-1 inhibition in enhancing plant immunity and biomass production.

FAVITES simulates epidemic datasets for fast-evolving pathogens like HIV, enabling study of transmission networks and intervention impacts.

The paper develops a mathematical model to predict HIV infection clearance with PrEP, showing that reducing infection frequency per cell is more effective than reducing viral burst size.

The paper introduces a multi-state birth-death model to detect HIV transmission clusters from phylogenetic trees, showing it reliably infers cluster structure and transmission parameters.

The paper studies noise-induced bistability in viral RNA replication modes using stochastic simulations and diffusion approximation, showing noise significantly impacts amplification and leads to bistability.

The paper models dynamic systems to analyze biodiversity's role in resource usage across different biological levels, showing that maximum resource usage regimes exhibit greater evenness in resource utilization strategies.

This paper describes a method for parameter estimation in viral infection models using R, applicable to influenza and other viruses.

The paper models how gene drives spread spatially and identifies conditions for controlled release and containment.

This study shows that domain growth mechanisms affect species dominance and spatial density variation in multispecies agent populations.

The paper analyzes an HIV-1 model with cancer cells, showing chaotic dynamics and using optimal homotopy analysis to improve solution efficiency.