A simulation model of the epidemiology of urban dengue fever: literature analysis, model development, preliminary validation, and samples of simulation results.
We have developed a pair of stochastic simulation models that describe the daily dynamics of dengue virus transmission in the urban environment. Our goal has been to construct comprehensive models that take into account the majority of factors known to influence dengue epidemiology. The models have an orientation toward site-specific data and are designed to be used by operational programs as well as researchers. The first model, the container-inhabiting mosquito simulation model (CIMSiM), a weather-driven dynamic life-table model of container-inhabiting mosquitoes such as Aedes aegypti, provides inputs to the tranmission model, the dengue simulation model (DENSiM); a description and validation of the entomology model was published previously. The basis of the transmission model is the simulation of a human population growing in response to country- and age-specific birth and death rates. An accounting of individual serologies is maintained by type of dengue virus, reflecting infection and birth to seropositive mothers. Daily estimates of adult mosquito survival, gonotrophic development, and the weight and number of emerging females from the CIMSiM are used to create the biting mosquito population in the DENSiM. The survival and emergence values determine the size of the population while the rate of gonotrophic development and female weight estimates influence biting frequency. Temperature and titer of virus in the human influences the extrinsic incubation period; titer may also influence the probability of transfer of virus from human to mosquito. The infection model within the DENSiM accounts for the development of virus within individuals and its passage between both populations. As in the case of the CIMSiM, the specific values used for any particular phenomenon are on menus where they can be readily changed. It is possible to simulate concurrent epidemics involving different serotypes. To provide a modicum of validation and to demonstrate the parameterization process for a specific location, we compare simulation results with reports on the nature of epidemics and seroprevalence of antibody in Honduras in low-lying coastal urbanizations and Tegucigalpa following the initial introduction of dengue-1 in 1978 into Central America. We conclude with some additional examples of simulation results to give an indication of the types of questions that can be investigated with the models.
D000293:Adolescent; D000328:Adult; D000330:Aedes / Q000502:physiology; D017677:Age Distribution; D000368:Aged; D000369:Aged, 80 and over; D000818:Animals; D002648:Child; D002675:Child, Preschool; D002980:Climate; D003198:Computer Simulation; D003715:Dengue / Q000453:epidemiology* / Q000635:transmission; D004196:Disease Outbreaks / Q000706:statistics & numerical data; D005260:Female; D006721:Honduras / Q000453:epidemiology; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D007303:Insect Vectors / Q000502:physiology; D008875:Middle Aged; D008954:Models, Biological; D015995:Prevalence; D013269:Stochastic Processes; D014504:Urban Health; D014766:Viremia / Q000453:epidemiology