The threshold behaviour of epidemic models

1983 ◽  
Vol 20 (2) ◽  
pp. 227-241 ◽  
Author(s):  
Frank Ball
Keyword(s):  
Almost Sure Convergence ◽  
Death Process ◽  
Time Interval ◽  
Birth And Death Process ◽  
Initial Number ◽  
Finite Time Interval ◽  
Threshold Behaviour ◽  
Total Size ◽  
Stochastic Epidemic ◽  
General Stochastic

We provide a method of constructing a sequence of general stochastic epidemics, indexed by the initial number of susceptibles N, from a time-homogeneous birth-and-death process. The construction is used to show strong convergence of the general stochastic epidemic to a birth-and-death process, over any finite time interval [0, t], and almost sure convergence of the total size of the general stochastic epidemic to that of a birth-and-death process. The latter result furnishes us with a new proof of the threshold theorem of Williams (1971). These methods are quite general and in the remainder of the paper we develop similar results for a wide variety of epidemics, including chain-binomial, host-vector and geographical spread models.

The threshold behaviour of epidemic models

1983 ◽  
Vol 20 (02) ◽  
pp. 227-241 ◽  
Author(s):  
Frank Ball
Keyword(s):  
Almost Sure Convergence ◽  
Death Process ◽  
Time Interval ◽  
Birth And Death Process ◽  
Initial Number ◽  
Finite Time Interval ◽  
Threshold Behaviour ◽  
Total Size ◽  
Stochastic Epidemic ◽  
General Stochastic

We provide a method of constructing a sequence of general stochastic epidemics, indexed by the initial number of susceptibles N, from a time-homogeneous birth-and-death process. The construction is used to show strong convergence of the general stochastic epidemic to a birth-and-death process, over any finite time interval [0, t], and almost sure convergence of the total size of the general stochastic epidemic to that of a birth-and-death process. The latter result furnishes us with a new proof of the threshold theorem of Williams (1971). These methods are quite general and in the remainder of the paper we develop similar results for a wide variety of epidemics, including chain-binomial, host-vector and geographical spread models.

The Early and Final States of an Epidemic in a Large Heterogeneous Population by a Small Initial Number of Infectives

1994 ◽  
Vol 26 (03) ◽  
pp. 671-689
Author(s):  
Steven M. Butler
Keyword(s):  
Random Number ◽  
Death Process ◽  
Time Interval ◽  
Final States ◽  
Birth And Death Process ◽  
Initial Number ◽  
Threshold Behavior ◽  
Final State ◽  
Epidemic Process ◽  
Susceptibility To Infection

This paper describes the early and final properties of a general S–I–R epidemic process in which the infectives behave independently, each infective has a random number of contacts with the others in the population, and individuals vary in their susceptibility to infection. For the case of a large initial number of susceptibles and a small (finite) initial number of infectives, we derive the threshold behavior and the limiting distribution for the final state of the epidemic. Also, we show strong convergence of the epidemic process over any finite time interval to a birth and death process, extending the results of Ball (1983). These complement some results due to Butler (1994), who considers the case of a large initial number of infectives.

The Early and Final States of an Epidemic in a Large Heterogeneous Population by a Small Initial Number of Infectives

1994 ◽  
Vol 26 (3) ◽  
pp. 671-689 ◽  
Author(s):  
Steven M. Butler
Keyword(s):  
Random Number ◽  
Death Process ◽  
Time Interval ◽  
Final States ◽  
Birth And Death Process ◽  
Initial Number ◽  
Threshold Behavior ◽  
Final State ◽  
Epidemic Process ◽  
Susceptibility To Infection

This paper describes the early and final properties of a general S–I–R epidemic process in which the infectives behave independently, each infective has a random number of contacts with the others in the population, and individuals vary in their susceptibility to infection. For the case of a large initial number of susceptibles and a small (finite) initial number of infectives, we derive the threshold behavior and the limiting distribution for the final state of the epidemic. Also, we show strong convergence of the epidemic process over any finite time interval to a birth and death process, extending the results of Ball (1983). These complement some results due to Butler (1994), who considers the case of a large initial number of infectives.

Asymptotic analysis of the general stochastic epidemic with variable infectious periods

2001 ◽  
Vol 38 (01) ◽  
pp. 18-35 ◽  
Author(s):  
A. N. Startsev
Keyword(s):  
Asymptotic Analysis ◽  
Asymptotic Behaviour ◽  
Size Distribution ◽  
Random Variables ◽  
Independent Random Variables ◽  
Boundary Crossing ◽  
Total Size ◽  
Stochastic Epidemic ◽  
General Stochastic

A generalisation of the classical general stochastic epidemic within a closed, homogeneously mixing population is considered, in which the infectious periods of infectives follow i.i.d. random variables having an arbitrary but specified distribution. The asymptotic behaviour of the total size distribution for the epidemic as the initial numbers of susceptibles and infectives tend to infinity is investigated by generalising the construction of Sellke and reducing the problem to a boundary crossing problem for sums of independent random variables.

THE TOTAL SIZE OF A GENERAL STOCHASTIC EPIDEMIC

Biometrika ◽  
1953 ◽  
Vol 40 (1-2) ◽  
pp. 177-185 ◽  
Author(s):  
NORMAN T. J. BAILEY
Keyword(s):  
Total Size ◽  
Stochastic Epidemic ◽  
General Stochastic

scholarly journals PREDIKSI PELUANG KEMATIAN DAN KELAHIRAN MURNI PENDUDUK KABUPATEN MERAUKE MENGGUNAKAN METODE BIRTH AND DEATH PROCESS

2021 ◽  
Vol 15 (1) ◽  
pp. 095-102
Author(s):  
Minuk Riyana ◽  
Marius Agustinus Welliken K.
Keyword(s):  
Population Data ◽  
The Elderly ◽  
Death Process ◽  
Initial Time ◽  
Time Interval ◽  
Birth And Death Process ◽  
Female Sex ◽  
A Value ◽  
Calculation Results ◽  
Process Method

This study aims to estimate the probability of birth and death purely based on gender and population data of Merauke City. The chance of birth and death will be used to estimate the life table of the elderly in a population of the City of Merauke. The method used in this research is the birth and process method. The Birth and death process method which is a Poisson distribution is used to predict the chances of birth and death at time t. If the birth and death process fulfills the linearity requirements, then the processes are called the Yule-Furry process. This research discusses the stochastic process of pure birth-death with two sexes in the Yule-Furry Process. From the data on the population of Merauke district which is divided based on the sex of men and women using the pure birth and death model, the calculation results show that the probability value at the time interval 0 ≤ t <1 hour, at the initial time t = 0, the chance of individual birth at female sex is stationary at a value of 0.1762, while the chance of individual death for female sex is stationary at a value of 0.00154. The odds of birth and death in male individuals are stationary at a value of 0.305034 and 0, 059487.

A note on the total size distribution of epidemic models

1986 ◽  
Vol 23 (03) ◽  
pp. 832-836 ◽  
Author(s):  
Frank Ball
Keyword(s):  
Size Distribution ◽  
Epidemic Models ◽  
Total Size ◽  
Stochastic Epidemic ◽  
General Stochastic

A simple coupling argument is used to obtain a new proof of a result of Daniels (1967) concerning the total size distribution of the general stochastic epidemic. The proof admits a straightforward generalisation to multipopulation epidemics and indicates that similar results are unlikely to be available for epidemics with non-exponential infectious periods.

A note on the total size distribution of epidemic models

1986 ◽  
Vol 23 (3) ◽  
pp. 832-836 ◽  
Author(s):  
Frank Ball
Keyword(s):  
Size Distribution ◽  
Epidemic Models ◽  
Total Size ◽  
Stochastic Epidemic ◽  
General Stochastic

A simple coupling argument is used to obtain a new proof of a result of Daniels (1967) concerning the total size distribution of the general stochastic epidemic. The proof admits a straightforward generalisation to multipopulation epidemics and indicates that similar results are unlikely to be available for epidemics with non-exponential infectious periods.

The Total Size of a General Stochastic Epidemic

Biometrika ◽  
1953 ◽  
Vol 40 (1/2) ◽  
pp. 177 ◽  
Author(s):  
Norman T. J. Bailey
Keyword(s):  
Total Size ◽  
Stochastic Epidemic ◽  
General Stochastic

On the asymptotic distribution of the maximum number of infectives in epidemic models by immigration

1994 ◽  
Vol 31 (3) ◽  
pp. 606-613 ◽  
Author(s):  
V. M. Abramov
Keyword(s):  
Asymptotic Distribution ◽  
Epidemic Model ◽  
Epidemic Models ◽  
Death Process ◽  
Birth And Death Process ◽  
Initial Number ◽  
Martingale Approach ◽  
Distribution Of The Maximum ◽  
Non Linear

This paper considers the asymptotic distribution of the maximum number of infectives in an epidemic model by showing that, as the initial number of susceptibles converges to infinity, the process of infectives converges almost surely to a birth and death process. The model studied here is more general than usual (see e.g. Bailey (1975), Bharucha-Reid (1960), Keilson (1979)) in that it incorporates immigration and the limiting birth and death process is non-linear. The main novelty of the present paper is the martingale approach used to prove the above-mentioned convergence.

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