A comprehensive spatial-temporal infection model

Motivated by analogies between the spread of infections and of chemical processes, we develop a model that accounts for infection and transport where infected populations correspond to chemical species. Areal densities emerge as the key variables, thus capturing the effect of spatial density. We derive expressions for the kinetics of the infection rates, and for the important parameter R 0 , that include areal density and its spatial distribution. We present results for a batch reactor, the chemical process equivalent of the SIR model, where we examine how the dependence of R 0 on process extent, the initial density of infected individuals, and fluctuations in population densities effect the progression of the disease. We then consider spatially distributed systems. Diffusion generates traveling waves that propagate at a constant speed, proportional to the square root of the diffusivity and R 0 . Preliminary analysis shows a similar behavior for the effect of stochastic advection.

[1]  C. F. Curtiss,et al.  Molecular Theory Of Gases And Liquids , 1954 .

[2]  W. F. Wells,et al.  On Air-borne Infection. Study II. Droplets and Droplet Nuclei. , 1934 .

[3]  Qun Liu,et al.  Dynamical behavior of a stochastic multigroup SIR epidemic model , 2019, Physica A: Statistical Mechanics and its Applications.

[4]  Fabrizio Croccolo,et al.  Spreading of infections on random graphs: A percolation-type model for COVID-19 , 2020, Chaos, Solitons & Fractals.

[5]  A. Chaves,et al.  A fractional diffusion equation to describe Lévy flights , 1998 .

[6]  Stéphane Lafon,et al.  Diffusion maps , 2006 .

[7]  Noble Jv,et al.  Geographic and temporal development of plagues , 1974 .

[8]  Cécile Viboud,et al.  Spatial Transmission of 2009 Pandemic Influenza in the US , 2014, PLoS Comput. Biol..

[9]  Anders Logg,et al.  The FEniCS Project Version 1.5 , 2015 .

[10]  Felix Sharipov,et al.  Ab initio simulation of transport phenomena in rarefied gases. , 2012, Physical review. E, Statistical, nonlinear, and soft matter physics.

[11]  Vasilis Z. Marmarelis,et al.  Predictive Modeling of Covid-19 Data in the US: Adaptive Phase-Space Approach , 2020, IEEE Open Journal of Engineering in Medicine and Biology.

[12]  K. Sreenivasan Turbulent mixing: A perspective , 2018, Proceedings of the National Academy of Sciences.

[13]  N. Rashevsky Looking at History Through Mathematics. , 1968 .

[14]  M. Holmes Introduction to Perturbation Methods , 1995 .

[15]  A. Fokas,et al.  Two alternative scenarios for easing COVID-19 lockdown measures: one reasonable and one catastrophic , 2020, medRxiv.

[16]  Y. Li,et al.  How far droplets can move in indoor environments--revisiting the Wells evaporation-falling curve. , 2007, Indoor air.

[17]  Thomas E. Yankeelov,et al.  Diffusion–reaction compartmental models formulated in a continuum mechanics framework: application to COVID-19, mathematical analysis, and numerical study , 2020, Computational mechanics.

[18]  Benoit B. Mandelbrot,et al.  Fractal Geometry of Nature , 1984 .

[19]  Kristina Lerman,et al.  Unequal impact and spatial aggregation distort COVID-19 growth rates , 2020, Philosophical Transactions of the Royal Society A.

[20]  A. Fokas,et al.  COVID-19: Predictive Mathematical Models for the Number of Deaths in South Korea, Italy, Spain, France, UK, Germany, and USA , 2020, medRxiv.

[21]  H. J.,et al.  Hydrodynamics , 1924, Nature.

[22]  A. Bensoussan,et al.  Asymptotic analysis for periodic structures , 1979 .

[23]  A. Bahrampour,et al.  Comparison of methods to Estimate Basic Reproduction Number (R0) of influenza, Using Canada 2009 and 2017-18 A (H1N1) Data , 2019, Journal of research in medical sciences : the official journal of Isfahan University of Medical Sciences.

[24]  R. May,et al.  Population Biology of Infectious Diseases , 1982, Dahlem Workshop Reports.

[25]  C. Axness,et al.  Three‐dimensional stochastic analysis of macrodispersion in aquifers , 1983 .

[26]  Thomas E. Yankeelov,et al.  Simulating the spread of COVID-19 via a spatially-resolved susceptible–exposed–infected–recovered–deceased (SEIRD) model with heterogeneous diffusion , 2020, Applied Mathematics Letters.

[27]  Rajesh Sharma,et al.  Asymptotic analysis , 1986 .

[28]  R. Scott Molecular theory of gases and liquids , 1955 .

[29]  Péter Érdi,et al.  Mathematical Models of Chemical Reactions: Theory and Applications of Deterministic and Stochastic Models , 1989 .

[30]  Andrew M. Stuart,et al.  Spectral analysis of weighted Laplacians arising in data clustering , 2022, Applied and Computational Harmonic Analysis.

[31]  R. Horrox,et al.  Black Death. , 1994, Trends in molecular medicine.

[32]  M. K. Mak,et al.  Exact analytical solutions of the Susceptible-Infected-Recovered (SIR) epidemic model and of the SIR model with equal death and birth rates , 2014, Appl. Math. Comput..

[33]  John W. M. Bush,et al.  Violent expiratory events: on coughing and sneezing , 2014, Journal of Fluid Mechanics.

[34]  Mohamed A. Helal,et al.  Solitons, Introduction to , 2009, Encyclopedia of Complexity and Systems Science.

[35]  R. May,et al.  Population biology of infectious diseases: Part I , 1979, Nature.

[36]  W. O. Kermack,et al.  A contribution to the mathematical theory of epidemics , 1927 .

[37]  V E Lynch,et al.  Front dynamics in reaction-diffusion systems with Levy flights: a fractional diffusion approach. , 2002, Physical review letters.

[38]  HighWire Press Proceedings of the Royal Society of London. Series A, Containing papers of a mathematical and physical character , 1934 .

[39]  Rajamani Krishna,et al.  Mass Transfer in Multicomponent Mixtures , 2006 .

[40]  Angelika Bayer,et al.  A First Course In Probability , 2016 .

[41]  V. S. Vaidhyanathan,et al.  Transport phenomena , 2005, Experientia.

[42]  L. Goddard First Course , 1969, Nature.

[43]  R. May,et al.  Population biology of infectious diseases: Part II , 1979, Nature.