×

Epidemiological models for heterogeneous populations: Proportionate mixing, parameter estimation, and immunization programs. (English) Zbl 0619.92006

In this paper, deterministic models are presented for epidemics which occur quickly and for long-term endemic diseases where birth and death must be considered. Initially the models have assumed that the population being considered is uniform and homogeneously mixing. But it is appropriate to consider a population divided into subpopulations which differ from each other. Subpopulations can be determined also on the basis of social, cultural, economic, demographic, and geographic factors.
In the model here, each subpopulation in the heterogeneous population is further subdivided into susceptible, infective, and removed classes. The model is called a SIRS model, since susceptible individuals become infectious, then removed with immunity, and then susceptible again if the immunity is temporary.
Complete assumptions and several formulations are given in Section 2, and the equilibria, thresholds, and asymptotic behavior are also described for the model. Methods are presented in Sections 4 and 5 for estimating the contact number for diseases in one homogeneous population from either epidemic or endemic data. Fractional activity levels and contact numbers are defined for the subpopulations in Section 6, and it is shown that the threshold is an average contact number under the proportionate-mixing assumption.
Parameter estimations are given for an endemic disease in a heterogeneous population (Section 7), and for an epidemic in such a population (Section 8).
Methods which have been used by other authors to formulate contact-rate matrices in heterogeneous population models are briefly described in Section 9. R. M. May and R. M. Anderson’s paper [ibid. 72, 83-111 (1984; Zbl 0564.92016)] on spatial heterogeneity is presented in Section 10. In Section 11, a model to compare the three immunization programs in a spatially heterogeneous population is developed, and the ”city and villages” example ends this interesting paper.
Reviewer: T.Postelnicu

MSC:

92D25 Population dynamics (general)

Citations:

Zbl 0564.92016
Full Text: DOI

References:

[1] Anderson, R. M., Transmission dynamics and control of infectious disease agents, (Anderson, R. M.; May, R. M., Population Biology of Infectious Diseases (1982), Springer: Springer New York), 149-176
[2] Anderson, R. M., Directly transmitted viral and bacterial infections of man, (Anderson, R. M., The Population Dynamics of Infectious Diseases: Theory and Applications (1982), Chapman and Hall: Chapman and Hall London), 1-37
[3] Anderson, R. M.; May, R. M., Directly transmitted diseases: Control by vaccination, Science, 215, 131-138 (1979)
[4] Anderson, R. M.; May, R. M., Vaccination and herd immunity to infectious diseases, Nature, 318, 323-329 (1985)
[5] Bailey, N. T.J., The Mathematical Theory of Infectious Diseases (1975), Hafner: Hafner New York · Zbl 0115.37202
[6] Becker, N.; Angulo, J., On estimating the contagiousness of a disease transmitted from person to person, Math. Biosci., 54, 137-154 (1981) · Zbl 0455.92017
[7] Berman, A.; Plemmons, R. J., Nonnegative Matrices in the Mathematical Sciences (1979), Academic: Academic New York · Zbl 0484.15016
[8] Dietz, K., Transmission and control of arbovirus diseases, (Ludwig, D.; Cooke, K. L., Epidemiology (1975), Soc. Indust. Appl. Math: Soc. Indust. Appl. Math Philadelphia), 104-121 · Zbl 0322.92023
[9] Dietz, K.; Schenzle, D., Mathematical models for infectious disease statistics, (Atkinson, A. C.; Fienberg, S. E., A Celebration of Statistics (1985), Springer: Springer New York), 167-204 · Zbl 0586.92017
[10] Enderle, J. D., A Stochastic Communicable Disease Model with Age Specific States and Applications to Measles, (Ph.D. Thesis (1980), Rensselaer Polytechnic Inst)
[11] Evans, A. S., Viral Infections of Humans (1982), Plenum Medical Book Co: Plenum Medical Book Co New York
[12] Hethcote, H. W., Qualitative analysis for communicable disease models, Math. Biosci., 28, 335-356 (1976) · Zbl 0326.92017
[13] Hethcote, H. W., An immunization model for a heterogeneous population, Theoret. Population Biol., 14, 338-349 (1978) · Zbl 0392.92009
[14] Hethcote, H. W., Measles and rubella in the United States, Amer. J. Epidemiol., 117, 2-13 (1983)
[15] Hethcote, H. W.; Stech, H. W.; van den Driessche, P., Periodicity and stability in epidemic models: A survey, (Busenberg, S.; Cooke, K. L., Differential Equations and Applications in Ecology, Epidemics and Population Problems (1981), Academic: Academic New York), 65-82 · Zbl 0477.92014
[16] Hethcote, H. W.; Thieme, H. R., Stability of the endemic equilibrium in epidemic models with subpopulations, Math. Biosci., 75, 205-227 (1985) · Zbl 0582.92024
[17] Hethcote, H. W.; Yorke, J. A., Gonorrhea Transmission Dynamics and Control, (Lecture Notes in Biomathematics, 56 (1984), Springer: Springer Heidelberg) · Zbl 0542.92026
[18] Kemper, J. T., On the identification of superspreaders for infectious diseases, Math. Biosci., 48, 111-127 (1980) · Zbl 0442.92024
[19] Lajmanovich, A.; Yorke, J. A., A deterministic model for gonorrhea in a nonhomogeneous population, Math. Biosci., 28, 221-236 (1976) · Zbl 0344.92016
[20] Longini, I. M., The generalized discrete-time epidemic model with immunity: A synthesis, Math. Biosci, 82, 19-41 (1986) · Zbl 0613.92021
[21] Longini, I. M.; Ackerman, E.; Elveback, L. R., An optimization model for influenza A epidemics, Math. Biosci., 38, 141-157 (1978)
[22] Longini, I. M.; Koopman, J. S., Household and community transmission parameters from final distributions of infections in households, Biometrics, 38, 114-126 (1982) · Zbl 0482.92016
[23] Longini, I. M.; Koopman, J. S.; Monto, A. S.; Fox, J. P., Estimating household and community transmission parameters for influenza, Amer. J. Epidemiology, 115, 736-751 (1982)
[24] May, R. M.; Anderson, R. M., Spatial heterogeneity and the design of immunization programs, Math. Biosci., 72, 83-111 (1984) · Zbl 0564.92016
[25] May, R. M.; Anderson, R. M., Spatial, temporal, and genetic heterogeneity in host populations and the design of immunization programmes, IMA J. Math. Appl. Med. Biol., 1, 233-266 (1984) · Zbl 0611.92020
[26] Mollison, D., Spatial contact models for ecological and epidemic spread, J. Roy. Statist. Soc. Ser. B, 39, 283-326 (1977) · Zbl 0374.60110
[27] Nold, A., Heterogeneity in disease-transmission modeling, Math. Biosci., 52, 227-240 (1980) · Zbl 0454.92020
[28] Post, W. M.; DeAngelis, D. L.; Travis, C. C., Endemic disease in environments with spatially heterogeneous host populations, Math. Biosci., 63, 289-302 (1983) · Zbl 0528.92018
[29] Rvachev, L. A.; Longini, I. M., A mathematical model for the global spread of influenza, Math. Biosci., 75, 3-22 (1985) · Zbl 0567.92017
[30] Schenzle, D., An age-structured model of pre- and postvaccination measles transmission, IMA J. Math. Appl. Med. Biol., 1, 169-191 (1984) · Zbl 0611.92021
[31] Schenzle, D., personal communication (1984)
[32] Schenzle, D.; Dietz, K., Critical population sizes for endemic virus transmission, (Fricke, W.; Hinz, E., Räumliche Persistenz und Diffusion von Krankheiten, 83 (1986), Heidelberger Geographische Arbeiten) · Zbl 0586.92017
[33] Travis, C. C.; Lenhart, S. M., Eradication of infectious diseases in heterogeneous populations, Math. Biosci., 83, 191-198 (1987) · Zbl 0613.92022
[34] Tudor, D. W., An age dependent epidemic model with application to measles, Math. Biosci., 73, 131-147 (1985) · Zbl 0572.92023
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.