62 research outputs found

    An Introduction to Systems Modeling

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    Municipality Level Simulations of Dengue Fever Incidence in Puerto Rico Using Ground Based and Remotely Sensed Climate Data

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    Dengue fever (DF) is caused by a virus transmitted between humans and Aedes genus mosquitoes through blood feeding. In recent decades incidence of the disease has drastically increased in the tropical Americas, culminating with the Pan American outbreak in 2010 which resulted in 1.7 million reported cases. In Puerto Rico dengue is endemic, however, there is significant interannual, intraannual, and spatial variability in case loads. Variability in climate and the environment, herd immunity and virus genetics, and demographic characteristics may all contribute to differing patterns of transmission both spatially and temporally. Knowledge of climate influences on dengue incidence could facilitate development of early warning systems allowing public health workers to implement appropriate transmission intervention strategies. In this study, we simulate dengue incidence in several municipalities in Puerto Rico using population and meteorological data derived from ground based stations and remote sensing instruments. This data was used to drive a process based model of vector population development and virus transmission. Model parameter values for container composition, vector characteristics, and incubation period were chosen by employing a Monte Carlo approach. Multiple simulations were performed for each municipality and the results were compared with reported dengue cases. The best performing simulations were retained and their parameter values and meteorological input were compared between years and municipalities. Parameter values varied by municipality and year illustrating the complexity and sensitivity of the disease system. Local characteristics including the natural and built environment impact transmission dynamics and produce varying responses to meteorological conditions

    Weather/Climate Sensitive Infectious Diseases

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    Climate-Driven Mosquito Population Modeling

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    Modeled Forecasts of Dengue Fever in San Juan, PR Using NASA Satellite Enhanced Weather Forecasts

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    Dengue virus is transmitted between humans and mosquitoes of the genus Aedes and causes approximately 96 million cases of disease (dengue fever) each year (Bhatet al. 2013). Symptoms of dengue fever include fever, headache, nausea, vomiting, and eye, muscle and joint pain (CDC). More sever manifestations such as abdominal pain, bleeding from nose and gums, vomiting of blood, and clammy skin occur in rare cases of dengue hemorrhagic fever (CDC). Dengue fever occurs throughout tropical and sub-tropical regions worldwide, however, the geographical range and size of epidemics is increasing. Weather and climate are drivers of dengue virus transmission dynamics (Morin et al. 2013) by affecting mosquito proliferation and the virus extrinsic incubation period (i.e. required time for the virus to replicate and disseminate within the mosquito before it can retransmit the virus)

    Variations in Modeled Dengue Transmission over Puerto Rico Using a Climate Driven Dynamic Model

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    Dengue fever is a mosquitoborne viral disease reemerging throughout much of the tropical Americas. Dengue virus transmission is explicitly influenced by climate and the environment through its primary vector, Aedes aegypti. Temperature regulates Ae. aegypti development, survival, and replication rates as well as the incubation period of the virus within the mosquito. Precipitation provides water for many of the preferred breeding habitats of the mosquito, including buckets, old tires, and other places water can collect. Because of variations in topography, ocean influences and atmospheric processes, temperature and rainfall patterns vary across Puerto Rico and so do dengue virus transmission rates. Using NASA's TRMM (Tropical Rainfall Measuring Mission) satellite for precipitation input, groundbased observations for temperature input, and laboratory confirmed dengue cases reported by the Centers for Disease Control and Prevention for parameter calibration, we modeled dengue transmission at the county level across Puerto Rico from 20102013 using a dynamic dengue transmission model that includes interacting vector ecology and epidemiological components. Employing a Monte Carlo approach, we performed ensembles of several thousands of model simulations for each county in order to resolve the model uncertainty arising from using different combinations of parameter values that are not well known. The top 1% of model simulations that best reproduced the reported dengue case data were then analyzed to determine the most important parameters for dengue virus transmission in each county, as well as the relative influence of climate variability on transmission. These results can be used by public health workers to implement dengue control methods that are targeted for specific locations and climate conditions
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