7 citations · 16 across the 7 of their papers we have counts for
8 papers
Learning virulence-transmission relationships using causal inference
Sudam Surasinghe, C. Brandon Ogbunugafor
The relationship between traits that influence pathogen virulence and transmission is part of the central canon of the evolution and ecology of infectious disease. However, identif…
Optimal virulence strategies in epidemiological models with asymptomatic transmission
C. Brandon Ogbunugafor, Sudam Surasinghe
Asymptomatic infection has gained notoriety as an important feature of infectious disease dynamics. Despite increasing attention, there have been few rigorous examinations of how a…
Structural causal influence (SCI) captures the forces of social inequality in models of disease dynamics
Sudam Surasinghe, Swathi Nachiar Manivannan, Samuel V. Scarpino +2
Mathematical modeling has played a central role in understanding how infectious disease transmission manifests in populations. These models have demonstrated the importance of key…
The context-specificity of virulence evolution revealed through evolutionary invasion analysis
Sudam Surasinghe, Ketty Kabengele, Paul E. Turner +1
Models are often employed to integrate knowledge about epidemics across scales and simulate disease dynamics. While these approaches have played a central role in studying the mech…
Analysis of tidal flows through the Strait of Gibraltar using Dynamic Mode Decomposition
Sathsara Dias, Sudam Surasinghe, Kanaththa Priyankara +4
The Strait of Gibraltar is a region characterized by intricate oceanic sub-mesoscale features, influenced by topography, tidal forces, instabilities, and nonlinear hydraulic proces…
Learning Transfer Operators by Kernel Density Estimation
Sudam Surasinghe, Jeremie Fish, Erik M. Bollt
Inference of transfer operators from data is often formulated as a classical problem that hinges on the Ulam method. The conventional description, known as the Ulam-Galerkin method…
Randomized Projection Learning Method forDynamic Mode Decomposition
Sudam Surasinghe, Erik M. Bollt
A data-driven analysis method known as dynamic mode decomposition (DMD) approximates the linear Koopman operator on projected space. In the spirit of Johnson-Lindenstrauss Lemma, w…
On Geometry of Information Flow for Causal Inference
Sudam Surasinghe, Erik M. Bollt
Causal inference is perhaps one of the most fundamental concepts in science, beginning originally from the works of some of the ancient philosophers, through today, but also weaved…