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Preface

Published online by Cambridge University Press:  24 April 2014

J.M. Hyman,
F. Milner and
J. Saldaña
J.M. Hyman
Affiliation:
Department of Mathematics, Tulane University, New Orleans, LA, USA
F. Milner
Affiliation:
School of Mathematical and Statistical Sciences, Arizona State University, Tempe, Arizona, USA
J. Saldaña
Affiliation:
Departament d’IMAE, Universitat de Girona, Girona, Catalonia, Spain

Abstract

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References

Anderson, R.M.. The epidemiology if HIV infection: Variable incubation plus infectious periods and heterogeity in sexual activity. J. R. Statist. Soc. A, 151 (1988), 66–93. CrossRefGoogle Scholar
R.M. Anderson, R.M. May. Infectious diseases of humans; dynamics and control, Oxford Univ. Press, Oxford, 1991.
O. Diekmann, J.A.P. Heesterbeek, T. Britton, Mathematical tools for understanding infectious diseases dynamics, Princeton University Press, Princeton, 2013.
House, T.. Non-Markovian stochastic epidemics in extremely heterogeneous populations. Math. Model. Nat. Phenom., 9 (2014), no. 2, 153–160. CrossRefGoogle Scholar
Jolly, A.M., Wylie, J. L.. Gonorrhoea and chlamydia core groups and sexual networks in Manitoba. Sex. Transm. Infect. 78 (2002), i145–i151. CrossRefGoogle Scholar
Juher, D., Mañosa, V.. Spectral properties of the connectivity matrix and the SIS-epidemic threshold for mid-size metapopulations. Math. Model. Nat. Phenom., 9 (2014), no. 2, 108–120. CrossRefGoogle Scholar
Meyers, L.A., Pourbohloul, B., Newman, M.E.J., Skowronski, D.M., Brunham, R.C.. Network theory and SARS: predicting outbreak diversity. J. Theor. Biol. 232 (2005), 71–81. CrossRefGoogle ScholarPubMed
McMahon, B.H., Manore, C.A., Hyman, J.M., LaBute, M.X., Fair, J.M.. Coupling vector-host dynamics with weather geography and mitigation measures to model Rift Valley fever in Africa. Math. Model. Nat. Phenom., 9 (2014), no. 2, 161–177. CrossRefGoogle ScholarPubMed
Miller, J.C., Kiss, I.Z.. Epidemic spread in networks: existing methods and current challenges. Math. Model. Nat. Phenom., 9 (2014), no. 2, 4–42. CrossRefGoogle ScholarPubMed
Nagy, N., Kiss, I.Z., Simon, P.L.. Approximate master equations for dynamical processes on graphs. Math. Model. Nat. Phenom., 9 (2014), no. 2, 43–57. CrossRefGoogle Scholar
Rattana, P., Miller, J.C., Kiss, I.Z.. Pairwise and edge-based models of epidemic dynamics on correlated weighted networks. Math. Model. Nat. Phenom., 9 (2014), no. 2, 58–81. CrossRefGoogle ScholarPubMed
Bradonjic, M.. Outbreak of infectious diseases through the weighted random connection model. Math. Model. Nat. Phenom., 9 (2014), no. 2, 82–88. CrossRefGoogle Scholar
Riley, S. et al. Transmission dynamics of the etiological agent of SARS in Hong Kong: Impact of public health interventions. Science 300 (2003), 1961–1966. CrossRefGoogle Scholar
Romero-Severson, E.O., Meadors, G.D., Volz, E.M.. A generating function approach to HIV transmission with dynamic contact rates. Math. Model. Nat. Phenom., 9 (2014), no. 2, 121–135. CrossRefGoogle ScholarPubMed
Sahneh, F.D., Chowdhury, F.N., Brase, G., Scoglio, C.M.. Individual-based information dissemination in multilayer epidemic modeling. Math. Model. Nat. Phenom., 9 (2014), no. 2, 136–152. CrossRefGoogle Scholar
Szabó-Solticzky, A., Simon, P.L.. The effect of graph structure on epidemic spread in a class of modified cycle graphs. Math. Model. Nat. Phenom., 9 (2014), no. 2, 89–107. CrossRefGoogle Scholar
Yorke, J.A., Hethcote, H.W., Nold, A.. Dynamics and control of the transmission of gonorrhea. Sex. Transm. Dis. 5 (1978), 51–56. CrossRefGoogle ScholarPubMed