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All Outputs (8)

Likelihood-Free Dynamical Survival Analysis applied to the COVID-19 epidemic in Ohio (2022)
Journal Article
Klaus, C., Wascher, M., KhudaBukhsh, W. R., & Rempała, G. A. (2023). Likelihood-Free Dynamical Survival Analysis applied to the COVID-19 epidemic in Ohio. Mathematical Biosciences and Engineering, 20(2), 4103-4127. https://doi.org/10.3934/mbe.2023192

The Dynamical Survival Analysis (DSA) is a framework for modeling epidemics based on mean field dynamics applied to individual (agent) level history of infection and recovery. Recently, the DSA method has been shown to be an effective tool in analyzi... Read More about Likelihood-Free Dynamical Survival Analysis applied to the COVID-19 epidemic in Ohio.

Hypergraphon mean field games (2022)
Journal Article
Cui, K., KhudaBukhsh, W. R., & Koeppl, H. (2022). Hypergraphon mean field games. Chaos, 32(11), Article 113129. https://doi.org/10.1063/5.0093758

We propose an approach to modeling large-scale multi-agent dynamical systems allowing interactions among more than just pairs of agents using the theory of mean field games and the notion of hypergraphons, which are obtained as limits of large hyperg... Read More about Hypergraphon mean field games.

A functional central limit theorem for SI processes on configuration model graphs (2022)
Journal Article
KhudaBukhsh, W. R., Woroszylo, C., Rempała, G. A., & Koeppl, H. (2022). A functional central limit theorem for SI processes on configuration model graphs. Advances in Applied Probability, 54(3), 880-912. https://doi.org/10.1017/apr.2022.52

We study a stochastic compartmental susceptible–infected (SI) epidemic process on a configuration model random graph with a given degree distribution over a finite time interval. We split the population of graph vertices into two compartments, namely... Read More about A functional central limit theorem for SI processes on configuration model graphs.

Dynamic survival analysis for non-Markovian epidemic models (2022)
Journal Article
Di Lauro, F., KhudaBukhsh, W. R., Kiss, I. Z., Kenah, E., Jensen, M., & Rempała, G. A. (2022). Dynamic survival analysis for non-Markovian epidemic models. Journal of the Royal Society. Interface, 19(191), Article 20220124. https://doi.org/10.1098/rsif.2022.0124

We present a new method for analysing stochastic epidemic models under minimal assumptions. The method, dubbed dynamic survival analysis (DSA), is based on a simple yet powerful observation, namely that population-level mean-field trajectories descri... Read More about Dynamic survival analysis for non-Markovian epidemic models.

Analysis of individual-level data from 2018–2020 Ebola outbreak in Democratic Republic of the Congo (2022)
Journal Article
Vossler, H., Akilimali, P., Pan, Y., KhudaBukhsh, W. R., Kenah, E., & Rempała, G. A. (2022). Analysis of individual-level data from 2018–2020 Ebola outbreak in Democratic Republic of the Congo. Scientific Reports, 12, Article 5534. https://doi.org/10.1038/s41598-022-09564-4

The 2018–2020 Ebola virus disease epidemic in Democratic Republic of the Congo (DRC) resulted in 3481 cases (probable and confirmed) and 2299 deaths. In this paper, we use a novel statistical method to analyze the individual-level incidence and hospi... Read More about Analysis of individual-level data from 2018–2020 Ebola outbreak in Democratic Republic of the Congo.

Motif-based mean-field approximation of interacting particles on clustered networks (2022)
Journal Article
Cui, K., Khudabukhsh, W. R., & Koeppl, H. (2022). Motif-based mean-field approximation of interacting particles on clustered networks. Physical Review E, 105(4), Article L042301. https://doi.org/10.1103/PhysRevE.105.L042301

Interacting particles on graphs are routinely used to study magnetic behavior in physics, disease spread in epidemiology, and opinion dynamics in social sciences. The literature on mean-field approximations of such systems for large graphs typically... Read More about Motif-based mean-field approximation of interacting particles on clustered networks.

Quantifying the Population-Level Effect of the COVID-19 Mass Vaccination Campaign in Israel: A Modeling Study (2022)
Journal Article
Somekh, I., Khudabukhsh, W. R., Root, E. D., Boker, L. K., Rempala, G., Simões, E. A., & Somekh, E. (2022). Quantifying the Population-Level Effect of the COVID-19 Mass Vaccination Campaign in Israel: A Modeling Study. Open Forum Infectious Diseases, 9(5), Article ofac087. https://doi.org/10.1093/ofid/ofac087

Background: Estimating real-world vaccine effectiveness is challenging as a variety of population factors can impact vaccine effectiveness. We aimed to assess the population-level reduction in cumulative severe acute respiratory syndrome coronavirus... Read More about Quantifying the Population-Level Effect of the COVID-19 Mass Vaccination Campaign in Israel: A Modeling Study.