Abstract
The process of transmission of infection in epidemics is analyzed by studying a pair of random walkers, the motion of each of which in two dimensions is confined spatially by the action of a quadratic potential centered at different locations for the two walks. The walkers are animals such as rodents in considerations of the Hantavirus epidemic, infected or susceptible. In this reaction–diffusion study, the reaction is the transmission of infection, and the confining potential represents the tendency of the animals to stay in the neighborhood of their home range centers. Calculations are based on a recently developed formalism (Kenkre and Sugaya in Bull Math Biol 76:3016–3027, 2014) structured around analytic solutions of a Smoluchowski equation and one of its aims is the resolution of peculiar but well-known problems of reaction–diffusion theory in two dimensions. The resolution is essential to a realistic application to field observations because the terrain over which the rodents move is best represented as a 2-d landscape. In the present analysis, reaction occurs not at points but in spatial regions of dimensions larger than 0. The analysis uncovers interesting nonintuitive phenomena one of which is similar to that encountered in the one-dimensional analysis given in the quoted article, and another specific to the fact that the reaction region is spatially extended. The analysis additionally provides a realistic description of observations on animals transmitting infection while moving on what is effectively a two-dimensional landscape. Along with the general formalism and explicit one-dimensional analysis given in Kenkre and Sugaya (2014), the present work forms a model calculational tool for the analysis for the transmission of infection in dilute systems.
Similar content being viewed by others
Notes
It has also appeared earlier in the context of heat conduction (Carslaw and Jaeger 1959).
See Kenkre and Sugaya (2014) for a detailed discussion. Section 5 is devoted entirely to this exponential representation. See particularly Equations (15)–(17).
Note that when \((2m-\theta ) =0\) the \(\nu \)-function is denoted simply by \(\nu (t)\) (the original definition Kenkre 1982; Kenkre and Parris 1983). In a couple of recent publications (Spendier and Kenkre 2013; Kenkre and Sugaya 2014), the symbol \(\nu (t)\) has been applied to a function with varying dimensions according to the dimension of the motion and the trap.
We mention here in passing that if the motion of the rodents (in the absence of the transmission of infection) is not Gaussian as concluded in Hantavirus observations in Refs. Giuggioli et al. (2005), Abramson et al. (2006), and MacInnis et al. (2008) but rather anomalous in nature, the time dependence of infection curve \(\mathcal {I}(t)\), can be quite complex making it definition of the effective rate \(\alpha \) impossible.
References
Abramowitz M, Stegun IA (1970) Handbook of mathematical functions. Dover Publications, Toronto
Abramson G, Giuggioli L, Kenkre VM, Dragoo J, Parmenter R, Parmenter C, Yates TL (2006) Diffusion and home range parameters of rodents: peromyscus maniculatus in New Mexico. Ecol Complex 3:64
Abramson G, Kenkre VM (2002) Spatiotemporal patterns in the Hantavirus infection. Phys Rev E 66:011912
Abramson G, Wio HS (1995) Time behavior for diffusion in the presence of static imperfect traps. Chaos Solitons Fractals 6:1
Aguirre MA, Abramson G, Bishop AR, Kenkre VM (2002) Simulations in the mathematical modeling of the spread of the Hantavirus. Phys Rev E 66:041908
Anderson RM, May RM (1991) Infectious diseases of humans. Oxford University Press Inc., New York
Berg HC (1983) Random walks in biology. Princeton University Press, Princeton
Brauer F, Castillo-Chávez C (2001) Mathematical models in population biology and epidemiology. Springer, New York
Cantrell RS, Cosner C (2003) Spatial ecology via reaction–diffusion equations. Wiley, Hoboken
Carslaw HS, Jaeger CJ (1959) Condition of heats in solids. Oxford University Press, Oxford
Dickmann U, Law R, Metz JAJ (2000) The geometry of ecological interactions. Cambridge University Press, Cambridge
Giuggioli L, Abramson G, Kenkre VM, Parmenter C, Yates TL (2006) Theory of home range estimation from displacement measurements of animal populations. J Theor Biol 240:126
Giuggioli L, Abramson G, Kenkre VM, Suzán E, Marcé G, Yates TL (2005) Diffusion and home range parameters from rodent population measurements in Panama Bull. Math Biol 67(5):1135
Hemenger RP, Lakatos-Lindenberg K, Pearlstein RM (1974) Impurity quenching of molecular excitons. III. Partially coherent excitons in linear chains. J Chem Phys 60:3271
Hethcote HW (2000) The mathematics of infectious diseases. SIAM Rev 42:599
Kenkre VM (1980) Theory of exciton annihilation in molecular crystals. Phys Rev B 22:2089
Kenkre VM (1982) Exciton dynamics in molecular crystals and aggregates. In: Springer tracts in modern physics, vol 94, Springer, Berlin (and references therein)
Kenkre VM (1982) A theoretical approach to exciton trapping in systems with arbitrary trap concentration. Chem Phys Lett 93:260
Kenkre VM, Parris PE (1983) Exciton trapping and sensitized luminescence: a generalized theory for all trap concentrations. Phys Rev B 27:3221
Kenkre VM (2003) Memory formalism, nonlinear techniques, and kinetic equation approaches. In: Proceedings of the PASI on modern challenges in statistical mechanics: patterns, noise, and the interplay of nonlinearity and complexity, AIP
Kenkre VM, Giuggioli L, Abramson G, Camelo-Neto G (2007) Theory of hantavirus infection spread incorporating localized adult and itinerant juvenile mice. Eur Phys J B 55:461
Kenkre VM (2004) Results from variants of the fisher equation in the study of epidemics and bacteria. Physica A 342:242
Kenkre VM (2005) Statistical mechanical considerations in the theory of the spread of the Hantavirus. Physica A 356:121
Kenkre VM, Parmenter RR, Peixoto ID, Sadasiv L (2005) A theoretical framework for the analysis of the west nile virus epidemic. Math Comput Model 42:313
Kenkre VM, Sugaya S (2014) Theory of the transmission of infection in the spread of epidemics: interacting random walkers with and without confinement. Bull Math Biol 76:3016–3027
McKane AJ, Newman TJ (2004) Stochastic models in population biology and their deterministic analogs. Phys Rev E 70:041902
MacInnis D, Abramson G, Kenkre VM (2008) University of New Mexico preprint; see also D. MacInnis, Ph. D. thesis, unpublished, University of New Mexico
Montroll EW, West BJ (1979) On an enriched collection of stochastic process. Fluctuation phenomena, North-Holland, Amsterdam
Nasci RS, Savage HM, White DJ, Miller JR, Cropp BC, Godsey MS, Kerst AJ, Bennet P, Gottfried K, Lanciotti RS (2001) West Nile virus in overwintering Culex mosquitoes, New York City, 2000. Emerg Infect Dis 7:4
Okubo A, Levin SA (2001) Diffusion and ecological problems: modern perspectives. Springer, New York
Redner S (2001) A guide to first-passage processes. Cambridge University Press, Cambridge
Reichl LE (2009) A modern course in statistical physics. WILEY-VCH Verlag, Weinheim
Risken H (1989) The Fokker–Planck equation: Methods of solution and applications. Springer, Berlin
Roberts GE, Kaufman H (1966) Table of laplace transforms. W. B. Saunders Company, Philadelphia
Redner S, ben-Avraham D (1990) Nearest-neighbor distances of diffusing particles from a single trap. J Phys A: Math Gen 23:L1169
Szabo A, Lamm G, Weiss GH (1984) Localized partial traps in diffusion processes and random walks. J Stat Phys 34:225
Spendier K, Kenkre VM (2013) Solutions for some reaction–diffusion scenarios. J Phys Chem B 117:15639
Spendier K, Sugaya S, Kenkre VM (2013) Reaction–diffusion theory in the presence of an attractive harmonic potential. Phys Rev E 88:062142
Sugaya S (2016) Ph.D. Thesis, University of New Mexico (unpublished)
Strausbaugh LJ, Martin AA, Gubler DJ (2001) West Nile encephalitis: an emerging disease in the United States. Clin Infect Dis 33:1713–1719
Wax N (1954) Selected papers on noise and stochastic processes. Dover Publications INC., New York
Yates TL, Mills JN, Parmenter CA, Ksiazek TG, Parmenter RR, Vande Castle JR, Calisher CH, Nichol ST, Abbott KD, Young JC, Morrison ML, Beaty BJ, Dunnum JL, Baker RJ, Salazar-Bravo J, Peters CJ (2002) The ecology and evolutionary history of an emergent disease: hantavirus pulmonary syndrome. Bioscience 52:989
Author information
Authors and Affiliations
Corresponding author
Appendix: Analysis in One Dimension: Introduction of Infection Range
Appendix: Analysis in One Dimension: Introduction of Infection Range
We consider that the infection is transmitted at a range b, while the mice move in one dimension, and start the analysis in CM-relative coordinates. The equation of motion for \(P(x_{+},x_{-},t)\), the probability density to find the walkers at CM and relative coordinates \(x_+\) and \(x_-\), respectively, at time t is
The first three terms represent the Smoluchowski motion. The infection transmission is described in the last two terms; the arguments of the \(\delta \)-functions indicate that the infection is transmitted to the susceptible mouse from the infected one when they are at distance b apart, i.e., when \(x_{-}=\pm b\). The infection rate is given by \(\mathcal {C}_{1}\).
The symmetry and simplicity of this infection region allows for an exact calculation of \(\tilde{\nu }(\epsilon )\), without the approximation of the \(\nu \)-function method. The propagator for this problem is given by
In terms of this propagator, the solution to Eq. (A.1) in the Laplace domain as
From its definition given in Eq. (3), the infection probability in the Laplace domain is given in Eq. (A.3) by
The use of the defect technique, i.e., setting \(x_{-}=\pm b\) and integrating \(x_{+}\) over all space, after some algebra, yields
where
In the calculation of the \(\mu _{1}(t)\)’s, a \(\delta \)-function initial condition was assumed. The infection probability in the Laplace domain is then given exactly by
The non-monotonic effect is explained in detail in Ref. (Kenkre and Sugaya 2014) in the steady state and in the contact limit where \(1/\mathcal {C}_{1}\) is much greater than any of the \(\nu _{1}(t)\)’s and \(\mu _{1}(t)\)’s in its effect. In these limit, \(\mathcal {I}(t)\) approximately becomes
where we note that \(\nu ^{++}_{1}(\infty )=\nu ^{+-}_{1}(\infty )=\mu ^{+}_{1}(\infty )\) and \(\nu ^{--}_{1}(\infty )=\nu ^{-+}_{1}(\infty )=\mu ^{-}_{1}(\infty )\). The condition for the optimal value of \(\gamma \tau _H\) value found from this result yields the transcendental relation,
The value of \(\gamma \tau _H\) for given value of b / H found from this equation is plotted in Fig. 4.
Rights and permissions
About this article
Cite this article
Sugaya, S., Kenkre, V.M. Analysis of Transmission of Infection in Epidemics: Confined Random Walkers in Dimensions Higher Than One. Bull Math Biol 80, 3106–3126 (2018). https://doi.org/10.1007/s11538-018-0507-2
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11538-018-0507-2