Bulletin of the American Physical Society
APS March Meeting 2022
Volume 67, Number 3
Monday–Friday, March 14–18, 2022; Chicago
Session T08: Statistical Physics of Disease PropagationFocus Recordings Available
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Sponsoring Units: GSNP DBIO Chair: Saul Huitzil, Northwestern Institute on Complex Systems Room: McCormick Place W-179B |
Thursday, March 17, 2022 11:30AM - 11:42AM |
T08.00001: A Tutorial Model for the Role of Superspreaders in Pandemic Outbreaks Phil Nelson Popular media have spread widely the concepts behind SIR and related differential-equation models for pandemic spread, and in particular the role of the basic reproduction number R0. However, deterministic models of this sort tend to underestimate the variability of outbreaks, in part because stochastic effects cannot be ignored. Specifically, a huge variability of the infectivity of COVID-infected individuals has been documented, yet the "overdispersion parameter" k quantifying it is hardly ever mentioned even in semipopular accounts. I will give a simple implementation of this idea suitable for an undergraduate-level project. A straightforward Gillespie simulation of the model showcases the dramatic effects of different k values in models with the same R0, and motivates some public-policy conclusions. This material is publicly available as a module that can be introduced into any biological physics course. |
Thursday, March 17, 2022 11:42AM - 11:54AM |
T08.00002: Modeling the Impact of Social Distancing and Targeted Vaccination on the Spread of COVID-19 through a Real City-Scale Contact Network Gavin S Hartnett We use mobile device data to construct empirical interpersonal physical contact networks in the city of Portland, Oregon, both before and after social distancing measures were enacted during the COVID-19 pandemic. These networks reveal how social distancing measures and the public's reaction to the incipient pandemic affected the connectivity patterns within the city. We find that as the pandemic developed there was a substantial decrease in the number of individuals with many contacts. We further study the impact of these different network topologies on the spread of COVID-19 by simulating an SEIR epidemic model over these networks, and find that the reduced connectivity greatly suppressed the epidemic. We then investigate how the epidemic responds when part of the population is vaccinated, and we compare two vaccination distribution strategies, both with and without social distancing. Our main result is that the heavy-tailed degree distribution of the contact networks causes a targeted vaccination strategy that prioritizes high-contact individuals to reduce the number of cases far more effectively than a strategy that vaccinates individuals at random. Combining both targeted vaccination and social distancing leads to the greatest reduction in cases, and we also find that the marginal benefit of a targeted strategy as compared to a random strategy exceeds the marginal benefit of social distancing for reducing the number of cases. These results have important implications for ongoing vaccine distribution efforts worldwide. |
Thursday, March 17, 2022 11:54AM - 12:06PM |
T08.00003: A Continuous Commuter Model for the Spread of Infectious Disease Aaron C Winn, Eleni Katifori, Adam Konkol Once an infectious disease has entered a population, the dynamics are dominated by local interactions among citizens. Infection spread is often modeled by adding diffusion terms to a compartmental model, suggesting that individuals diffuse in their environment, but these models do not conserve local population. Our proposed model gives individuals a well-defined home but allows them to make contact with others and spread disease at destinations not too far from home according to a commuting probability function. The localized nature of daily commutes allows us to derive a continuous, spatially resolved SIR model with diffusion that is independent of the exact form of the commuting probabilities. The infected diffusion coefficient is positive and proportional to the characteristic commuting length while the susceptible diffusion coefficient is negative, emphasizing the fact that the disease diffuses rather than the individuals. Fisher waves are observed, but unlike in the simplest reaction-diffusion epidemic models, there is an additional drift term that increases the rate at which disease spreads from populated cities. Our commuter model can provide intuition about expected patterns of disease transmission in more complicated and noisy real-world epidemics. |
Thursday, March 17, 2022 12:06PM - 12:18PM |
T08.00004: S-I-R and Disease Spreading in Active Matter Models Cynthia Reichhardt, Peter Forgacs, Andras Libal, Charles M Reichhardt The term active matter describes systems involving self propulsion, such as particles obeying driven diffusive or run-and-tumble dynamics. Active matter has been extensively studied in biological systems and for artificial swimmers made from active colloidal particles. Here we examine S-I-R (Susceptible-Infected-Recovered) dynamics on an active matter assembly of run-and-tumble disks. In the absence of the S-I-R dynamics, the system undergoes a motility-induced phase separation as a function of density and running time. We find that when we introduce S-I-R dynamics in which the mobility of infected particles is reduced, there can be an enhancement of motility-induced clustering followed by the disintegration of the clusters. We also find that the survival of susceptible particles to the time at which the infection is extinguished depends strongly on whether the particles are part of a cluster or not. In some cases, the formation of clusters can enhance the number of surviving susceptible particles due to the heterogeneities induced by the activity. We discuss the relevance of active matter models to epidemic modeling in systems with spatially heterogeneous transport. |
Thursday, March 17, 2022 12:18PM - 12:30PM |
T08.00005: Front propagation in a system of weakly connected networks Madhavan Iyengar, Evgeniy Khain A metapopulation consists of a group of spatially distanced subpopulations, each occupying a separate patch. It is usually assumed that each localized patch is well-mixed. In this talk, we will discuss a model for the spread of an epidemic in a system of weakly connected patches, where the disease dynamics of each patch occurs on a network. The SIR dynamics in a single patch is governed by the rate of disease transmission, the disease duration, and the node degree distribution of a network. Monte-Carlo simulations of the model reveal the phenomenon of spatial disease propagation. The speed of front propagation and its dependence on the single patch parameters and on the strength of interaction between the patches was determined analytically, and a good agreement with simulation results was observed. |
Thursday, March 17, 2022 12:30PM - 12:42PM |
T08.00006: Spreading dynamics of an infection in a growing population Rory Claydon
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Thursday, March 17, 2022 12:42PM - 12:54PM |
T08.00007: Recovery of resonant stochastic fluctuations in an interacting-particle system-based contagion model coupled with social mimicry: comparative analysis of the effect of event ordering in their corresponding agent based models Santiago Núñez-Corrales Invariance to the ordering of events appears to be a feature of compartmental contagion models endowed with a spatial component, which are derived from interacting particle systems (IPS). Overlaying networks on interacting particle systems appears to break that invariance by introducing path dependencies leading to different ensemble outcomes as a result of modulating the coupling between local spatial interactions and non-local information exchange. When enacted using agent-based modeling frameworks, however, the ordering of events tends to be thought of as a feature of the computational machinery used to compute model consequences rather than a choice reflecting an aspect of the physical system itself. |
Thursday, March 17, 2022 12:54PM - 1:06PM |
T08.00008: A Seascape Origin of Richards Growth Bertrand J Ottino-Loffler, Mehran Kardar, Daniel W Swartz First proposed as an empirical rule over half a century ago, the Richards growth equation has been frequently invoked in population modeling and pandemic forecasting. Central to this model is the advent of a fractional exponent $\gamma$, typically fitted to the data. While various motivations for this non-analytical form have been proposed, it is still considered foremost an empirical fitting procedure. Here, we find that Richards-like growth laws emerge naturally from generic analytical growth rules in a distributed population, upon inclusion of {\bf (i)} migration (spatial diffusion) amongst different locales, and {\bf (ii)} stochasticity in the growth rate, also known as ``seascape noise.'' The latter leads to a wide (power-law) distribution in local population number that, while smoothened through the former, can still result in a fractional growth law for the overall population. This justification of the Richards growth law thus provides a testable connection to the distribution of constituents of the population. |
Thursday, March 17, 2022 1:06PM - 1:18PM |
T08.00009: Structure and Information Dynamics of Pro- and Anti-Vaccine Interaction Networks in Twitter Cristian L Huepe The spread of information and misinformation regarding the Covid-19 vaccines has greatly affected immunization efforts during the current pandemic. A rigorous quantitative analysis of online social network data can reveal the structures of pro- and anti-vaccine communities and how they propagate their messages, which can help develop strategies that promote vaccination. |
Thursday, March 17, 2022 1:18PM - 1:54PM |
T08.00010: COVID Variants Invited Speaker: Bette Korber
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Thursday, March 17, 2022 1:54PM - 2:06PM Withdrawn |
T08.00011: Theoretical and Numerical Analysis of a Locally Nonchaotic Energy Barrier Yu Qiao, Zhaoru Shang In the current research, we investigate a billiard-type model system, in which elastic particles randomly move across a locally nonchaotic energy barrier between an upper plateau and a lower plain. The energy barrier is a type of spontaneously nonequilibrium dimension. Its size is much smaller than the mean free path of the particles; it leads to a non-Boltzmann steady-state particle distribution, without any specific knowledge of the system microstate. |
Thursday, March 17, 2022 2:06PM - 2:18PM |
T08.00012: Choosing Optimal Reservoir Computers Thomas L Carroll A reservoir computer is a high dimensional dynamical system used for computation. Typically a reservoir computer is created by connecting a large number of nonlinear nodes in a network. There can be hundreds to thousands of nodes, so optimizing the structure of the reservoir computer is difficult. There are a number of conventional rules for optimizing a reservoir computer based on experience with simulations, but these rules are based on observations of a limited number of node nonlinearities. One feature of reservoir computers is that they may be built from connecting together analog nodes such as lasers, quantum dots, memristors, or other devices. There is a great range of possible nonlinear functions describing these nodes, so design rules beyond the conventional wisdom are required. |
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