Bulletin of the American Physical Society
APS March Meeting 2021
Volume 66, Number 1
Monday–Friday, March 15–19, 2021; Virtual; Time Zone: Central Daylight Time, USA
Session P17: Statistical Mechanics of Disease Propagation IFocus Live

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Sponsoring Units: GSNP Chair: Cynthia Reichhardt, Los Alamos Natl Lab 
Wednesday, March 17, 2021 3:00PM  3:12PM Live 
P17.00001: ReducedOrder Modeling Of Compartmental Metapopulation Models in Epidemiology Cordelia Carlisle, Michael Sean Murillo, Liam G. Stanton The theoretical modeling of an epidemic poses serious challenges due to the vast complexities of the diseasehostpopulation system. While certain qualitative behavior can be inferred from SIRtype models, they can often be unrealistic in application owing to limiting factors such as homogeneity and geospatial isolation of the modeled population. A common remedy is to spatially decompose a population into locally homogeneous metapopulations (MPs); however, without allowing a disease to spread between MPs, the true dynamics can never be recovered, and no single MP can be modeled without simulating all of them. Furthermore, if the host mobility is highly nonlocal (e.g., as with humans), the parameter space scales as O(N^{2}) with the number of MPs, and there is typically a dearth of data available to construct reasonable estimates for these parameters. We present a reducedorder model for a single MP to act as a framework for capturing the contributions from exterior MPs through an effective “force of infection”, which encapsulates the necessary boundary conditions to accurately model a given MP while obviating a full simulation of the system. Simulation results are compared to synthetic data, and applications to modeling the current COVID19 pandemic are discussed. 
Wednesday, March 17, 2021 3:12PM  3:24PM Live 
P17.00002: Epidemiological model for the inhomogeneous spatial spreading of COVID19 and other diseases Yoav Tsori, Rony Granek

Wednesday, March 17, 2021 3:24PM  3:36PM Live 
P17.00003: Effects of social distancing and isolation modeled via dynamical density functional theory Michael te Vrugt, Jens Bickmann, Raphael Wittkowski For preventing the spread of epidemics such as the coronavirus disease COVID19, social distancing and the isolation of infected persons are crucial. However, existing reactiondiffusion equations for epidemic spreading are incapable of describing these effects. In this talk, we present an extended model for disease spread based on combining a susceptibleinfectedrecovered model with a dynamical density functional theory where social distancing and isolation of infected persons are explicitly taken into account [1]. We show that the model exhibits interesting transient phase separation associated with a reduction of the number of infections, and provides new insights into the control of pandemics. An extension of the model [2] allows for an investigation of adaptive containment strategies. Here, a variety of phases with different numbers of shutdowns and deaths are found, an effect that is of crucial importance for public health policy. 
Wednesday, March 17, 2021 3:36PM  3:48PM Live 
P17.00004: Discontinuous transitions of social distancing. Alexander Feigel, Roy Arazi The 1st wave of COVID19 changed social distancing around the globe: severe lockdowns to stop pandemics at the cost of state 
Wednesday, March 17, 2021 3:48PM  4:00PM Live 
P17.00005: Pathdependent course of epidemic: are two phases of quarantine better than one? Evgeniy Khain, Oleg Kogan, Varun Nimmagadda

Wednesday, March 17, 2021 4:00PM  4:12PM Live 
P17.00006: Vector difference equations, substochastic matrices, and design of multinetworks to reduce spread of epidemics Harold Hastings, Tai YoungTaft We start with the SIR model (susceptible, infected, removed) on a network. See, e.g., Cooper, Mondal & Antonopoulos, in Chaos, Solitons & Fractals 2020. Since the goal is to make I = 0 a (Lyapunov) stable equilibrium, we linearize the discretetime SIR model to obtain difference equations of the form I_{new} = I(1 + aS  b) at each node before including infections derived from other nodes. We assume S equal to its initial value at that node. Here a depends upon the infectivity and contact rate, b = 1/(duration of infectivity) and the traditional R_{t} = aS/b (R_{t} < 1 ↔ aS < b). This yields a vector difference equation I_{new} = MI. The entries of M may vary in time, even discontinuously as flows between nodes are turned on and off. The column sums of M may be interpreted as generalizations of (scalar) growth rates. Theorem. If the matrices M are columnsubstochastic, then the I=0 equilibrium is stable. This may yield useful design constraints for a multinetwork composed of weak and strong interactions between pairs of nodes representing interactions within and among cities. 
Wednesday, March 17, 2021 4:12PM  4:24PM Live 
P17.00007: Holographic Immunoassays: Battling COVID19 with Soft Matter Physics Kaitlynn Snyder, Rushna Quddus, Andrew David Hollingsworth, Kent Kirshenbaum, David G Grier Holographic immunoassays use holographic particle characterization to measure growth in the mean diameter of thousands of colloidal beads due to the binding of antibodies to the probe bead surface. This measurement has the advantage of being labelfree; binding can be detected without fluorescent labeling of the biomolecules. This reduces the cost and complexity of the technique compared with traditional assays like ELISA. Additionally, the probe beads can be batch synthesized and functionalized without the need for microfabrication. Our work on this topic measured the kinetics of irreversible binding of immunoglobulin G (IgG) and immunoglobulin M (IgM) to protein A coated probe beads. These measurements were used to determine the antibody binding rates and can be inverted to determine the concentration of antibodies in solution. This technique shows promise for detecting Antibody Deficiency Disorders and for the detection of antibodies for SARS COV2 and other infections. 
Wednesday, March 17, 2021 4:24PM  4:36PM Not Participating 
P17.00008: Photothermal killing of mamillian cancer cells via nonionizing radiation Victoria Gabriele, Purna Mukherjee, Thomas Seyfried, Michael J Naughton, Krzysztof Kempa In this work, we demonstrate methods for targeting and killing mammalian cancer cells with visible nonionizing radiation. We find that photothermal efficiency is massively enhanced when cells are sensitized with synthetic melanin nanoparticles, known to be excellent absorbers of light in the ultraviolet (UV), visible (VIS) and near infrared (NIR) frequency ranges. Nanoparticle uptake is highly efficent for malignant cancer cell lines, and this uptake is further enhanced by coating the melanin nanoparticles with glucose. Death of nanoparticlefilled cells occurs primarily by heating. We show that this process of cell elimination is highly targetselective in the presence of nonsensitized cells. 
Wednesday, March 17, 2021 4:36PM  5:12PM Live 
P17.00009: Largescale agentbased epidemiological modeling Invited Speaker: Timothy C Germann The collective behavior that results from large numbers of atoms interacting in a material (mainly each atom only with its immediate neighbors for metals) determines that material's response to impulsive loading, such as a shock wave passing through a solid. Similarly, a pandemic can spread throughout a country or even worldwide as the result of a series of individual humantohuman contacts. By combining a stochastic agentbased model of disease spread among individuals at the local community level with detailed U.S. Census and Department of Transportation data on population demographics and mobility, we have extended our “SPaSM” (Scalable Parallel Shortrange Molecular dynamics) code into a powerful epidemiological modeling tool for studying the spatiotemporal dynamics of regional to nationalscale outbreaks. This simulation model, developed in the early 2000s, has been used to assess potential mitigation strategies – school closures, vaccination or antiviral prophylaxis campaigns, restricted air or highway travel, quarantines, ..., and various combinations thereof – in the event of an influenza pandemic in the United States. The arrival of COVID19 presented additional challenges, in turning a simulation platform designed for “whatif” scenario exploration into one used for realtime response to an emerging pandemic outbreak. 
Wednesday, March 17, 2021 5:12PM  5:24PM Live 
P17.00010: Using Particle Diffusion to Study the Spread of Viral Infection Paulo Acioli The appearance of the coronavirus (COVID19) in late 2019 has dominated the news in the last few months as it developed into a pandemic. In many mathematics and physics classrooms, instructors are using the time series of the number of cases to show exponential growth of the 
Wednesday, March 17, 2021 5:24PM  5:36PM Live 
P17.00011: A random walk model of social distancing to mitigate COVID19 spread Ronit Agarwala, Aniket Bhattacharya We introduce a modified SIRD (SusceptibleInfectedRecoveredDeceased) model and study the impact of social distancing for interacting random walkers in two dimensional continuum using Monte Carlo methods. We use both fixed step length walkers as well as variable step length walkers drawn from a Gaussian distribution for different vulnerability factors, population density, intercity/country traffic, and a variety of possible scenario based on COVID19 data at various locations, and study to what extent social distancing would mitigate the spread of the disease. Our model can be used to understand how COVID19 would have impacted at a reduced degree if social distancing were maintained more rigidly, and suggests measures to be taken to resists spread of such disease before it becomes a pandemic. 
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