Epidemics and Information Diffusion (Simons Institute, 2022)
2022-10-24..2022-10-28
Berkeley, United States
who it's for
computational epidemiologists, statistical modelers, network scientists, public health researchers, data scientists, reinforcement learning researchers, policy makers
topics
contact tracing models; K-neighborhood local estimators; graphon-coupled spatial SIR; simplicial complex random walks; reinforcement learning border testing; within-host to between-host coupling; dynamical survival analysis; Watts threshold cascades; data-augmented MCMC for SIR; contact process with avoidance; pool-testing badge frameworks; transmission neural networks
| title | author | length |
|---|---|---|
| Epidemic Models with Manual and Digital Contact Tracing | Tom Britton | 01h 14m |
A Markovian stochastic SIR on a large, homogeneously mixing population uses infection rate β, recovery rate γ and diagnosis rate δ; manual tracing reaches each… | ||
| Algorithms Using Local Graph Features to Predict Epidemics | Yeganeh Alimohammadi | 52m |
Claims a nonparametric local estimator that uses repeated K‑neighborhood contact tracing queries (constant K and constant number of queries) to estimate… | ||
| Functional Law of Large Numbers and PDEs for Spatial Epidemic Models with... | Etienne Pardoux | 01h 10m |
Proves a functional law of large numbers for a non‑Markovian SIR model as both number of patches and individuals per patch → ∞, giving local‑uniform… | ||
| Random Walks on Simplicial Complexes for Exploring Networks | Viet Chi Tran | 01h 05m |
Proposes a concrete chain-valued random walk on k‑simplices whose transitions use adjacent (k+1)-simplices so the generator = upward combinatorial Laplacian… | ||
| Efficient and Targeted COVID-19 Border Testing via Reinforcement Learning | Hamsa Bastani | 01h 01m |
Reinforcement learning system Eva deploys at 40 Greek entry points in summer 2020, combining passenger origin and two‑week rolling PCR results to allocate… | ||
| Mathematics of the COVID-19 Pandemics: Lessons Learned and How to Mitigate the Next One | Abba Gumel | 01h 18m |
Simple compartmental SEIR-style models with added pre-symptomatic and asymptomatic classes are fit to mortality data (not case counts) and show… | ||
| The Effect of Restrictive Interactions between Susceptible and Infected Individuals... | Shirshendu Chatterjee | 01h 06m |
Rigorous analysis introduces a contact process with avoidance with infection rate λ and avoidance/deactivation rate α, using Harris-style graphical… | ||
| Between-host, within-host Interactions in Simple Epidemiological Models | Jorge Velasco-Hernández | 57m |
Links a Perelson-style within-host viral model to a simple SI between-host model through a logistic environmental reservoir, uses fast-slow separation and… | ||
| Dynamical Survival Analysis: Survival Models for Epidemic | Grzegorz Rempala | 01h 08m |
Introduces dynamical survival analysis that replaces the random infectious pressure in a stochastic SIR with its law-of-large-numbers limit, using the Selke… | ||
| Complex Contagions and Hybrid Phase Transitions | Joel Miller | 01h 08m |
Adapts infectious-disease methods to produce closed-form dynamical equations for the Watts threshold model on configuration-model and triangle-based clustered… | ||
| Testing, Voluntary Social Distancing, and the Spread of an Infection | Ali Makhdoumi | 01h 04m |
A game-theory model links individual social activity xi∈[0,1] to a random contact network (edge prob ∝ xi xj) and a discrete-time independent-cascade infection… | ||
| Likelihood-based Inference for Stochastic Epidemic Models | Jason Xu | 01h 03m |
Claims an exact likelihood route for continuous‑time stochastic SIR by treating missing event times as latent variables and exploring them with data-augmented… | ||
| Bayesian Learning in Social Networks | Ilan Lobel | 57m |
Revisits the 2011 Review of Economic Studies model (with Acemoglu, Dahleh, Ozdaglar) where agents observe past actions through a stochastic network; proves a… | ||
| Just a Few Seeds More: The Inflated Value of Network Data for Diffusion... | Mohammad Akbarpour | 01h 04m |
Shows that under the classic SIR/independent cascade model, seeding s + x random people often outperforms optimally chosen s seeds: simulations on 75 Indian… | ||
| Learning through the Grapevine and the Impact of the Breadth and Depth of Social Networks | Suraj Malladi | 01h 07m |
Model of noisy relay on directed trees with mutation rates mu01 and mu10 and dropout probability shows that message fidelity falls with distance and with… | ||
| Diversity and Inequality in Information Diffusion on Social Networks | Ana-Andreea Stoica | 54m |
Uses a preferential attachment with unequal communities model plus estimated homophily to show when seeding for proportional representation also improves… | ||
| A Global Comparison of COVID-19 Variant Waves and Relationships with Clinical and... | Sara del Valle | 56m |
Global analysis of 11.5 million sequences from GISAID using PANGO lineages fits a flexible multinomial model with cubic splines (better than linear fits) to… | ||
| Spatial Spread of Dengue Virus: Appropriate Spatial Scales for Transmission | Alun L. Lloyd | 01h 13m |
City-level dengue spread hinges on the household scale: Aedes aegypti disperses <100 m so human movement drives city spread; GPS traces from Iquitos (≈300,000… | ||
| Transmission Neural Networks: From Virus Spread Models to Neural Networks | Shuang Gao | 52m |
A discrete-time virusspread model is rewritten by taking states S = −log(1−P) and becomes a linear summation through linkwise nonlinearities, leading to a new… | ||
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