The contact process is a stochastic process used to model population growth on the set of sites S of a graph in which occupied sites become vacant at a constant rate, while vacant sites become occupied at a rate proportional to the number of occupied neighboring sites. Therefore, if we denote by \( \lambda \) the proportionality constant, each site remains occupied for a random time period which is exponentially distributed parameter 1 and places descendants at every vacant neighboring site at times of events of a Poisson process parameter \( \lambda \) during this period. All processes are independent of one another and of the random period of time sites remains occupied. The contact process can also be interpreted as a model for the spread of an infection by thinking of particles as a bacterium spreading over individuals that are positioned at the sites of S, occupied sites correspond to infected individuals, whereas vacant correspond to healthy ones.
The contact process (on a 1-D lattice): Active sites are indicated by grey circles and inactive sites by dotted circles. Active sites can activate inactive sites to either side of them at a rate r/2 or become inactive at rate 1.
The main quantity of interest is the number of particles in the process, say \( {\displaystyle N_{t}} \), in the first interpretation, which corresponds to the number of infected sites in the second one. Therefore, the process survives whenever the number of particles is positive for all times, which corresponds to the case that there are always infected individuals in the second one. For any infinite graph S there exists a positive and finite critical value \( \lambda _{c} \) so that if \( \lambda>\lambda_c \) then survival of the process starting from a finite number of particles occurs with positive probability, while if \( \lambda<\lambda_c \) their extinction is almost certain. Note that by reductio ad absurdum and the infinite monkey theorem, survival of the process is equivalent to \( {\displaystyle N_{t}\to \infty } \), as \( t\to \infty \), whereas extinction is equivalent to \( {\displaystyle N_{t}\to 0} \), as \( t\to \infty \) , and therefore, it is natural to ask about the rate at which \( {\displaystyle N_{t}\to \infty } \) when the process survives.
Mathematical Definition
If the state of the process at time t is \( {\displaystyle \xi _{t}} \), then a site x in S is occupied, say by a particle, if \( {\displaystyle \xi _{t}(x)=1} \) and vacant if \( {\displaystyle \xi _{t}(x)=0} \). The contact process is a continuous-time Markov process with state space \( \{0,1\}^S \), where S is a finite or countable graph, usually \( \mathbb {Z} ^{d} \), and a special case of an interacting particle system. More specifically, the dynamics of the basic contact process is defined by the following transition rates: at site x,
\( {\displaystyle 1\rightarrow 0\quad {\text{at rate }}1,} \)
\( {\displaystyle 0\rightarrow 1\quad {\text{at rate }}\lambda \sum _{y\,:\,y\,\sim \,x}\xi _{t}(y),} \)
where the sum is over all the neighbors y of x in S. This means that each site waits an exponential time with the corresponding rate, and then flips (so 0 becomes 1 and vice versa).
Connection to Percolation
The contact process is a stochastic process that is closely connected to percolation theory. Ted Harris (1974) noted that the contact process on ℤd when infections and recoveries can occur only in discrete times \( {\displaystyle \{1,2,\ldots ,\}} \) corresponds to one-step-at-a-time bond percolation on the graph obtained by orienting each edge of ℤd + 1 in the direction of increasing coordinate-value.
The Law of large numbers on the integers
A law of large numbers for the number of particles in the process on the integers informally means that for all large t, \( {\displaystyle N_{t}} \) is approximately equal to \( {\displaystyle ct} \) for some positive constant \( {\displaystyle c=c(\lambda )} \). Ted Harris (1974) proved that, if the process survives, then the rate of growth of \( {\displaystyle N_{t}} \) is at most and at least linear in time. A weak law of large numbers (that the process converges in probability) was shown by Durrett (1980). A few years later, Durrett and Griffeath (1983) improved this to a strong law of large numbers, giving almost sure convergence of the process.
Die out at criticality
For contact process on all integer lattices, a major breakthrough came in 1990 when Bezuidenhout and Grimmett showed that the contact process also dies out almost surely at the critical value.
Durrett's conjecture and the central limit theorem
Durrett conjectured in survey papers and lecture notes during the 80s and early 90s regarding the central limit theorem for the Harris' contact process, viz. that, if the process survives, then for all large t, \( {\displaystyle N_{t}} \) equals ct and the error equals \( {\displaystyle \sigma {\sqrt {t}}} \) multiplied by a (random) error distributed according to a standard Gaussian distribution.[1][2][3]
Durrett's conjecture turned out to be correct for a different value of \( \sigma \) as proved in 2018.[4]
References
Durrett, Richard (1984). "Oriented Percolation in Two Dimensions Number". The Annals of Probability. 12 (4): 999–1040. doi:10.1214/aop/1176993140.
Durrett, Richard. "Lecture Notes on Particle Systems and Percolation". Wadsworth.
.Durrett, Richard. "The contact process, 1974–1989". Cornell University, Mathematical Sciences Institute.
Tzioufas, Achillefs (2018). "The Central Limit Theorem for Supercritical Oriented Percolation in Two Dimensions". Journal of Statistical Physics. 171 (5): 802–821. arXiv:1411.4543. doi:10.1007/s10955-018-2040-y.
C. Bezuidenhout and G. R. Grimmett, The critical contact process dies out, Ann. Probab. 18 (1990), 1462–1482.
Durrett, Richard (1980). "On the Growth of One Dimensional Contact Processes". The Annals of Probability. 8 (5): 890–907. doi:10.1214/aop/1176994619.
Durrett, Richard (1988). "Lecture Notes on Particle Systems and Percolation", Wadsworth.
Durrett, Richard (1991). "The contact process, 1974–1989." Cornell University, Mathematical Sciences Institute.
Durrett, Richard (1984). "Oriented Percolation in Two Dimensions Number". The Annals of Probability. 12 (4): 999–1040. doi:10.1214/aop/1176993140.
Durrett, Richard; David Griffeath (1983). "Supercritical Contact Processes on Z". The Annals of Probability. 11 (1): 1–15. doi:10.1214/aop/1176993655.
Grimmett, Geoffrey (1999), Percolation, Springer
Liggett, Thomas M. (1985). Interacting Particle Systems. New York: Springer Verlag. ISBN 978-0-387-96069-2.
Thomas M. Liggett, "Stochastic Interacting Systems: Contact, Voter and Exclusion Processes", Springer-Verlag, 1999.
Stochastic processes
Discrete time
Bernoulli process Branching process Chinese restaurant process Galton–Watson process Independent and identically distributed random variables Markov chain Moran process Random walk
Loop-erased Self-avoiding Biased Maximal entropy
Continuous time
Additive process Bessel process Birth–death process
pure birth Brownian motion
Bridge Excursion Fractional Geometric Meander Cauchy process Contact process Continuous-time random walk Cox process Diffusion process Empirical process Feller process Fleming–Viot process Gamma process Geometric process Hunt process Interacting particle systems Itô diffusion Itô process Jump diffusion Jump process Lévy process Local time Markov additive process McKean–Vlasov process Ornstein–Uhlenbeck process Poisson process
Compound Non-homogeneous Schramm–Loewner evolution Semimartingale Sigma-martingale Stable process Superprocess Telegraph process Variance gamma process Wiener process Wiener sausage
Both
Branching process Galves–Löcherbach model Gaussian process Hidden Markov model (HMM) Markov process Martingale
Differences Local Sub- Super- Random dynamical system Regenerative process Renewal process Stochastic chains with memory of variable length White noise
Fields and other
Dirichlet process Gaussian random field Gibbs measure Hopfield model Ising model
Potts model Boolean network Markov random field Percolation Pitman–Yor process Point process
Cox Poisson Random field Random graph
Time series models
Autoregressive conditional heteroskedasticity (ARCH) model Autoregressive integrated moving average (ARIMA) model Autoregressive (AR) model Autoregressive–moving-average (ARMA) model Generalized autoregressive conditional heteroskedasticity (GARCH) model Moving-average (MA) model
Financial models
Black–Derman–Toy Black–Karasinski Black–Scholes Chen Constant elasticity of variance (CEV) Cox–Ingersoll–Ross (CIR) Garman–Kohlhagen Heath–Jarrow–Morton (HJM) Heston Ho–Lee Hull–White LIBOR market Rendleman–Bartter SABR volatility Vašíček Wilkie
Actuarial models
Bühlmann Cramér–Lundberg Risk process Sparre–Anderson
Queueing models
Bulk Fluid Generalized queueing network M/G/1 M/M/1 M/M/c
Properties
Càdlàg paths Continuous Continuous paths Ergodic Exchangeable Feller-continuous Gauss–Markov Markov Mixing Piecewise deterministic Predictable Progressively measurable Self-similar Stationary Time-reversible
Limit theorems
Central limit theorem Donsker's theorem Doob's martingale convergence theorems Ergodic theorem Fisher–Tippett–Gnedenko theorem Large deviation principle Law of large numbers (weak/strong) Law of the iterated logarithm Maximal ergodic theorem Sanov's theorem
Inequalities
Burkholder–Davis–Gundy Doob's martingale Kunita–Watanabe
Tools
Cameron–Martin formula Convergence of random variables Doléans-Dade exponential Doob decomposition theorem Doob–Meyer decomposition theorem Doob's optional stopping theorem Dynkin's formula Feynman–Kac formula Filtration Girsanov theorem Infinitesimal generator Itô integral Itô's lemma Karhunen–Loève_theorem Kolmogorov continuity theorem Kolmogorov extension theorem Lévy–Prokhorov metric Malliavin calculus Martingale representation theorem Optional stopping theorem Prokhorov's theorem Quadratic variation Reflection principle Skorokhod integral Skorokhod's representation theorem Skorokhod space Snell envelope Stochastic differential equation
Tanaka Stopping time Stratonovich integral Uniform integrability Usual hypotheses Wiener space
Classical Abstract
Disciplines
Actuarial mathematics Control theory Econometrics Ergodic theory Extreme value theory (EVT) Large deviations theory Mathematical finance Mathematical statistics Probability theory Queueing theory Renewal theory Ruin theory Signal processing Statistics System on Chip design Stochastic analysis Time series analysis Machine learning
Undergraduate Texts in Mathematics
Graduate Studies in Mathematics
Hellenica World - Scientific Library
Retrieved from "http://en.wikipedia.org/"
All text is available under the terms of the GNU Free Documentation License