Modeling dengue outbreaks.

We introduce a dengue model (SEIR) where the human individuals are treated on an individual basis (IBM) while the mosquito population, produced by an independent model, is treated by compartments (SEI). We study the spread of epidemics by the sole action of the mosquito. Exponential, deterministic and experimental distributions for the (human) exposed period are considered in two weather scenarios, one corresponding to temperate climate and the other to tropical climate. Virus circulation, final epidemic size and duration of outbreaks are considered showing that the results present little sensitivity to the statistics followed by the exposed period provided the median of the distributions are in coincidence. Only the time between an introduced (imported) case and the appearance of the first symptomatic secondary case is sensitive to this distribution. We finally show that the IBM model introduced is precisely a realization of a compartmental model, and that at least in this case, the choice between compartmental models or IBM is only a matter of convenience.

[1]  D. Kendall An Artificial Realization of a Simple “Birth-And-Death” Process , 1950 .

[2]  P. Pongsumpun,et al.  Transmission of dengue hemorrhagic fever in an age structured population , 2003 .

[3]  E. Galun,et al.  A Method for determining the Flight Range of Aedes aegypti (Linn.). , 1953 .

[4]  J S Koopman,et al.  Individual causal models and population system models in epidemiology. , 1999, American journal of public health.

[5]  Ling Bian,et al.  A Conceptual Framework for an Individual-Based Spatially Explicit Epidemiological Model , 2004 .

[6]  M. Espinosa,et al.  Brote de dengue autóctono en el área metropolitana Buenos Aires: Experiencia del Hospital de Enfermedades Infecciosas F. J. Muñiz , 2009 .

[7]  L. Esteva,et al.  Influence of vertical and mechanical transmission on the dynamics of dengue disease. , 2000, Mathematical biosciences.

[8]  T. Scott,et al.  Aedes aegypti (Diptera: Culicidae) movement influenced by availability of oviposition sites. , 1998, Journal of medical entomology.

[9]  P Reiter,et al.  Short report: dispersal of Aedes aegypti in an urban area after blood feeding as demonstrated by rubidium-marked eggs. , 1995, The American journal of tropical medicine and hygiene.

[10]  Alun L Lloyd,et al.  Stochasticity and heterogeneity in host–vector models , 2007, Journal of The Royal Society Interface.

[11]  M. Natiello,et al.  Stochastic population dynamics: the Poisson approximation. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[12]  H. Solari,et al.  A Stochastic Population Dynamics Model for Aedes Aegypti: Formulation and Application to a City with Temperate Climate , 2006, Bulletin of mathematical biology.

[13]  P. Fine The interval between successive cases of an infectious disease. , 2003, American journal of epidemiology.

[14]  M. Varkey,et al.  Dengue outbreak in Vellore, southern India, in 1968, with isolation of four dengue types from man and mosquitoes. , 1970, The Indian journal of medical research.

[15]  M. Varkey,et al.  The 1968 outbreak of dengue in Vellore, Southern India. , 1971, American journal of public health.

[16]  Ả. Svensson A note on generation times in epidemic models. , 2007, Mathematical Biosciences.

[17]  Haiyun Zhao,et al.  Epidemiological Models with Non-Exponentially Distributed Disease Stages and Applications to Disease Control , 2007, Bulletin of mathematical biology.

[18]  D. Focks,et al.  A simulation model of the epidemiology of urban dengue fever: literature analysis, model development, preliminary validation, and samples of simulation results. , 1995, The American journal of tropical medicine and hygiene.

[19]  Hiroshi Nishiura,et al.  Natural history of dengue virus (DENV)-1 and DENV-4 infections: reanalysis of classic studies. , 2007, The Journal of infectious diseases.

[20]  B. Kay,et al.  Aedes aegypti survival and dispersal estimated by mark-release-recapture in northern Australia. , 1998, The American journal of tropical medicine and hygiene.

[21]  T. Scott,et al.  Skeeter Buster: A Stochastic, Spatially Explicit Modeling Tool for Studying Aedes aegypti Population Replacement and Population Suppression Strategies , 2009, PLoS neglected tropical diseases.

[22]  H G Solari,et al.  Stochastic eco-epidemiological model of dengue disease transmission by Aedes aegypti mosquito. , 2010, Mathematical biosciences.

[23]  Hernán G. Solari,et al.  A Stochastic Spatial Dynamical Model for Aedes Aegypti , 2008, Bulletin of mathematical biology.

[24]  J. Hyman,et al.  Estimation of the reproduction number of dengue fever from spatial epidemic data. , 2007, Mathematical biosciences.

[25]  A L Lloyd,et al.  Realistic distributions of infectious periods in epidemic models: changing patterns of persistence and dynamics. , 2001, Theoretical population biology.

[26]  D H Barmak,et al.  Dengue epidemics and human mobility. , 2011, Physical review. E, Statistical, nonlinear, and soft matter physics.

[27]  L. Esteva,et al.  Analysis of a dengue disease transmission model. , 1998, Mathematical biosciences.

[28]  D. Focks,et al.  Dynamic life table model for Aedes aegypti (diptera: Culicidae): simulation results and validation. , 1993, Journal of medical entomology.

[29]  P. Reiter,et al.  A model of the transmission of dengue fever with an evaluation of the impact of ultra-low volume (ULV) insecticide applications on dengue epidemics. , 1992, The American journal of tropical medicine and hygiene.

[30]  V. Grimm Ten years of individual-based modelling in ecology: what have we learned and what could we learn in the future? , 1999 .

[31]  D. Gubler,et al.  Dengue and dengue hemorrhagic fever. , 2014 .

[32]  Lourdes Esteva,et al.  A model for dengue disease with variable human population , 1999, Journal of mathematical biology.

[33]  Miss A.O. Penney (b) , 1974, The New Yale Book of Quotations.

[34]  William Feller,et al.  On the integro-differential equations of purely discontinuous Markoff processes , 1940 .

[35]  D. Focks,et al.  Dynamic life table model for Aedes aegypti (Diptera: Culicidae): analysis of the literature and model development. , 1993, Journal of medical entomology.

[36]  A L Lloyd,et al.  Destabilization of epidemic models with the inclusion of realistic distributions of infectious periods , 2001, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[37]  Charly Favier,et al.  Influence of spatial heterogeneity on an emerging infectious disease: the case of dengue epidemics , 2005, Proceedings of the Royal Society B: Biological Sciences.

[38]  C. Donnelly,et al.  The seasonal pattern of dengue in endemic areas: mathematical models of mechanisms. , 2002, Transactions of the Royal Society of Tropical Medicine and Hygiene.