Hostname: page-component-78c5997874-g7gxr Total loading time: 0 Render date: 2024-11-10T08:03:47.916Z Has data issue: false hasContentIssue false

VARIABILITY FOR CARRIER-BORNE EPIDEMICS AND REED–FROST MODELS INCORPORATING UNCERTAINTIES AND DEPENDENCIES FROM SUSCEPTIBLES AND INFECTIVES

Published online by Cambridge University Press:  18 March 2010

Eva María Ortega
Affiliation:
Dept. Estadística, Matemáticas e Informática, Centro de Investigación Operativa, Universidad Miguel Hernández, 03312 Orihuela (Alicante), Spain E-mail: evamaria@umh.es
Laureano F. Escudero
Affiliation:
Dept. Estadística, Matemáticas e Informática, Centro de Investigación Operativa, Universidad Miguel Hernández, 03312 Orihuela (Alicante), Spain E-mail: evamaria@umh.es

Abstract

This article provides analytical results on which are the implications of the statistical dependencies among certain random parameters on the variability of the number of susceptibles of the carrier-borne epidemic model with heterogeneous populations and of the number of infectives under the Reed–Frost model with random infection rates. We consider dependencies among the random infection rates, among the random infectious times, and among random initial susceptibles of several carrier-borne epidemic models. We obtain conditions for the variability ordering between the number of susceptibles for carrier-borne epidemics under two different random environments, at any time-scale value. These results are extended to multivariate comparisons of the random vectors of populations in the strata. We also obtain conditions for the increasing concave order between the number of infectives in the Reed–Frost model under two different random environments, for any generation. Variability bounds are obtained for different epidemic models from modeling dependencies for a range of special cases that are useful for risk assessment of disease propagation.

Type
Research Article
Copyright
Copyright © Cambridge University Press 2010

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1.Abbey, H. (1952). An examination of the Reed–Frost theory of epidemics. Human Biology 24: 201.Google ScholarPubMed
2.Bailey, N.T.J. (1975). The mathematical theory of infectious diseases and its applications. London: Griffin.Google Scholar
3.Ball, F. & Clancy, D. (1995). The final outcome and temporal solution of carrier-borne epidemic model. Journal of Applied Probability 32: 304315.Google Scholar
4.Ball, F. & Clancy, D. (1995). The final outcome of an epidemic model with several different types of infective in a large population. Journal of Applied Probability 32: 579590.Google Scholar
5.Becker, N.G. & Dietz, K. (1995). The effect of the household distribution on transmission and control of highly infectious diseases. Mathematical Biosciences 127: 207219.Google Scholar
6.Becker, N.G. & Utev, S. (2002). Protective vaccine efficacy when vaccine response is random. Biometrical Journal 44: 2942.Google Scholar
7.Chang, C.-S., Shanthikumar, J.G. & Yao, D.D. (1994). Stochastic convexity and stochastic Majorization. In Yao, D.D. (ed.), Stochastic modeling and analysis of manufacturing systems. New York: Springer-Verlag.Google Scholar
8.Denuit, M., Lefèvre, C. & Utev, S. (1999). Generalized stochastic convexity and stochastic orderings of mixtures. Probability in the Engineering and Informational Sciences 13: 275291.Google Scholar
9.Diekmann, O. & Heesterbeek, J.A.P. (2000). Mathematical epidemiology of infectious diseases. Chichester, UK: Wiley.Google Scholar
10.Donnelly, P. (1993). The correlation structure of epidemic models. Mathematical Biosciences 117: 4975.Google Scholar
11.Escudero, L.F. & Ortega, E.M. (2008). Actuarial comparisons of aggregate claims with randomly right truncated claims. Insurance: Mathematics and Economics 43: 255262.Google Scholar
12.Escudero, L.F., Ortega, E.M. & Alonso, J. (2009). Variability comparisons for some mixture models with stochastic environments in biosciences and engineering. Stochastic Environmental Research and Risk Assessment, onlinefirst, doi:10.1007/s00477-009-0310-6.Google Scholar
13.Fernández-Ponce, J.M., Ortega, E.M. & Pellerey, F. (2008). Convex comparisons for random sums in random environments and applications. Probability in the Engineering and Informational Sciences 22: 389413.Google Scholar
14.Greenwood, M. (1931). On the statistical measure of infectiousness, Journal of Hygene Cambridge 31: 336.Google Scholar
15.Haas, C.N. (2002). Conditional dose-response relationships for microorganisms: development and application. Risk Analysis 22: 455463.CrossRefGoogle ScholarPubMed
16.Helander, M.E. & Batta, R. (1994). A discrete transmission model for HIV. In Kaplan, E.H. & Brandeu, M.I. (eds.), Modeling the AIDS epidemic: Planning, policy and prediction. New York: Raven Press, pp. 585611.Google Scholar
17.Isham, V. (2005). Stochastic models for epidemics: current issues and developments. In: Celebrating Statistics: Papers in honor of Sir David Cox on his 80th Birthday. Oxford: Oxford University Press.Google Scholar
18.Joe, H. (1997). Multivariate models and dependence concepts. London: Chapman and Hall.Google Scholar
19.Kegan, B. & West, R.W. (2005). Modeling the simple epidemics with deterministic differential equations and random initial conditions. Mathematical Biosciences 195: 179193.CrossRefGoogle ScholarPubMed
20.Lefèvre, C. (2005). SIR epidemic models. In: Armitage, P. & Colton, T. (eds.), Encyclopedia of biostatistics. vol. 7, New York: Wiley, pp. 49604966.Google Scholar
21.Lefèvre, C. & Malice, M.P. (1988). Comparisons for carrier-borne epidemics in heterogeneous and homogeneous populations. Journal of Applied Probability 25: 663674.Google Scholar
22.Lefèvre, C. & Picard, P. (1990). A non-standard family of polynomials and the final size distribution of Reed–Frost epidemic processes. Advances Applied Probability 22: 2548.Google Scholar
23.Lefèvre, C. & Picard, P. (1996). Collective epidemic models. Mathematical Biosciences 134: 5170.Google Scholar
24.Lefèvre, C. & Picard, P. (2005). Nonstationarity and randomization in the Reed–Frost epidemic model. Journal of Applied Probability 42: 950963.Google Scholar
25.Lefèvre, C. & Utev, S. (1996). Comparing sums of exchangeable Bernoulli random variables. Journal of Applied Probability 33: 285310.Google Scholar
26.Lefèvre, C. & Utev, S. (1998). On order-preserving properties of probability metrics. Journal of Theoretical Probability 11: 907920.Google Scholar
27.Lloyd-Smith, J.O., Schreiber, S.J., Kopp, P.E. & Getz, W.M. (2005). Superspreading and the effect of individual variation disease emergence. Nature 438: 355359.Google Scholar
28.Malice, M.P. & Lefèvre, C. (1988). On some effects of variability in the Weiss epidemic model. Communications in Statistics- Theory and Methods 17: 37233731.Google Scholar
29.Marinacci, M. & Montrucchio, L. (2005). Ultramodular functions. Mathematics of Operations Research 30: 311332.Google Scholar
30.Marshall, A.W. & Olkin, I. (1979). Inequalities: Theory of Majorization and Its Applications. New York: Academic Press.Google Scholar
31.Meester, L.E. & Shanthikumar, J.G. (1993). Regularity of stochastic processes. A theory based on directional convexity. Probability in the Engineering and Informational Sciences 7: 343360.Google Scholar
32.Meester, L.E. & Shanthikumar, J.G. (1999). Stochastic convexity on general space. Mathematics of Operations Research 24: 472494.Google Scholar
33.Menezes, R.X., Ortega, N.R.S. & Massad, E. (2004). A Reed–Frost model taking into account uncertainties in the diagnosis of the infection. Bulletin of Mathematical Biology 66: 689706.CrossRefGoogle ScholarPubMed
34.Müller, A. & Scarsini, M. (2000). Some remarks on the supermodular order. Journal of Multivariate Analysis 73: 107119.Google Scholar
35.Müller, A. & Scarsini, M. (2001). Stochastic comparisons of random vectors with a common copula. Mathematics of Operations Research 26: 723740.Google Scholar
36.Nelsen, R.B. (1999). An Introduction to Copulas. Springer: New York.Google Scholar
37.O'Neill, P.D. & Becker, N.G. (2001). Inference for an epidemic when susceptibility varies. Biostatistics 2: 99108.Google Scholar
38.Ortega, N.R.S., Santos, F.S., Zanetta, D.M.T. & Massad, E. (2008). A fuzzy Reed–Frost Model for epidemic spreading. Bulletin of Mathematical Biology 70: 19251936.Google Scholar
39.Picard, P. & Lefévre, C. (1991). The dimension of the Reed–Frost epidemic models with randomized susceptibility levels. Mathematical Biosciences 107: 225233.Google Scholar
40.Ross, S.M. (1996). Stochastic processes, 2nd ed.New York: Wiley.Google Scholar
41.Rüschendorf, L. (2004). Comparison of multivariate risks and positive dependence. Advances in Applied Probability 41: 391406.Google Scholar
42.Shaked, M. & Shanthikumar, J.G. (1990). Parametric stochastic convexity and concavity of stochastic processes. Annals of the Institute of Statistical Mathematics 42: 509531.Google Scholar
43.Shaked, M. & Shanthikumar, G.J. (2007). Stochastic orders New York: Springer.Google Scholar
44.Stoyan, D. (1983). Comparisons methods for queues and other stochastic models. New York: Wiley.Google Scholar
45.Tong, Y.L. (1997). Some majorization orderings of heterogeneity in a class of epidemics. Journal of Applied Probability 34: 8493.Google Scholar
46.Tuckwell, H.C. & Williams, R.J. (2007). Some properties of a simple stochastic epidemic model of SIR type. Mathematical Biosciences 208: 7697.Google Scholar
47.Von Bahr, B. & Martin-Löf, A. (1980). Threshold limit theorems for some epidemic processes. Advances in Applied Probability 12: 319349.Google Scholar
48.Weiss, G.H. (1965). On the spread of epidemics by carriers. Biometrics 21: 481491.Google Scholar
49.Wright, E.M. (1954). An inequality for convex functions. American Mathematical Monthly 61: 620622.Google Scholar
50.Yi, Z. & Weng, C. (2006). On the correlation order. Statistics and Probability Letters 76: 14101416.Google Scholar