Graduate student Daniel Parker along with co-author and graduate alumnus Darryl Holman (1996), is presenting a poster titled "Event history
analysis of dengue fever outbreaks in eight different endemic regions" at the AAPA meeting in April. Darryl Holman is an associate professor at the University
of Washington.
Abstract:
Dengue fever, a tropical illness resulting from infection by one of four dengue viruses, is a major public health concern. An estimated 50 million people become infected each year, making dengue the most common vector-borne infection in the world. Since there currently is no vaccine, public health measures are reliant on disease modeling, surveillance, and prevention. The aim of this project was to both determine significant factors in dengue fever epidemics and then to quantify the effects of those significant factors on the length of epidemics and on the time in between epidemics. Eight different endemic regions were chosen as study sites and region-specific data for all covariates were collected for all sites. Sites were selected according to the availability of historical data for all covariates. A piecewise logistic regression model with time-varying covariates was used to analyze the historical data. Mean monthly temperature was the most significant factor, increased temperature acted to hasten the onset of outbreaks during interepidemic spells and lengthen the duration of outbreaks during epidemic spells. Previous investigations have noted the influence of temperature on the geographic range and vector efficiency of dengue fever, this model describes the effects of temperature on epidemics in endemic regions.
Abstract:
Dengue fever, a tropical illness resulting from infection by one of four dengue viruses, is a major public health concern. An estimated 50 million people become infected each year, making dengue the most common vector-borne infection in the world. Since there currently is no vaccine, public health measures are reliant on disease modeling, surveillance, and prevention. The aim of this project was to both determine significant factors in dengue fever epidemics and then to quantify the effects of those significant factors on the length of epidemics and on the time in between epidemics. Eight different endemic regions were chosen as study sites and region-specific data for all covariates were collected for all sites. Sites were selected according to the availability of historical data for all covariates. A piecewise logistic regression model with time-varying covariates was used to analyze the historical data. Mean monthly temperature was the most significant factor, increased temperature acted to hasten the onset of outbreaks during interepidemic spells and lengthen the duration of outbreaks during epidemic spells. Previous investigations have noted the influence of temperature on the geographic range and vector efficiency of dengue fever, this model describes the effects of temperature on epidemics in endemic regions.
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