Workbook 7 Naumova

Ross River virus (RRV), Barmah Forest virus (BFV), and dengue are three common mosquito-borne diseases in Australia that display notable seasonal patterns. Although all three diseases have been modeled on localized scales, no previous study has used harmonic models to compare seasonality of mosquito-borne diseases on a continent-wide scale.

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We fit Poisson harmonic regression models to surveillance data on RRV, BFV, and dengue (from 1993, 1995 and 1991, respectively, through 2015) incorporating seasonal, trend, and climate (temperature and rainfall) parameters. The models captured an average of 50–65% variability of the data. Disease incidence for all three diseases generally peaked in January or February, but peak timing was most variable for dengue. The most significant predictor parameters were trend and inter-annual periodicity for BFV, intra-annual periodicity for RRV, and trend for dengue.

We found that a Temperature Suitability Index (TSI), designed to reclassify climate data relative to optimal conditions for vector establishment, could be applied to this context. Finally, we extrapolated our models to estimate the impact of a false-positive BFV epidemic in 2013. Creating these models and comparing variations in periodicities may provide insight into historical outbreaks as well as future patterns of mosquito-borne diseases. Seasonal patterns of disease, understood as periods of high and low disease incidence that are consistent across years, were recognized as early as 380 BCE. Seasonality in vector-borne diseases (VBDs), in particular, is a well-known epidemiological phenomenon, in part because vectors are so highly influenced by meteorological and environmental conditions. Understanding the intricacies of seasonal fluctuations in VBDs is critical in the optimization of forecasting and control efforts. Temperature and rainfall are frequently used in models to predict incidence of VBDs.

In addition, a Temperature Suitability Index (TSI) has been used in previous studies to estimate mosquito abundance, another proxy for disease incidence. The TSI method involves reclassifying weather data based on its suitability relative to optimal climate conditions for vector establishment and may be useful in assessing the impact of weather on disease for macro-geographic scales. In Australia, Ross River virus (RRV), Barmah Forest virus (BFV), and dengue are three of the most common and clinically important VBDs. All display notable seasonal patterns and are transmitted by mosquitos. RRV and BFV contribute the largest annual disease burden and are endemic to Australia, whereas dengue exhibits periodic epidemic activity currently limited to the northeast corner of Australia.

RRV and BFV have several mosquito genera implicated in their transmission while dengue has only one local vector species, Aedes aegypti. Such distinctions may influence or explain underlying differences in the spatio-temporal patterns of these infections.

Ross River virus (RRV), Barmah Forest virus (BFV), and dengue are three common mosquito-borne diseases in Australia that display notable seasonal patterns. Although all three diseases have been modeled on localized scales, no previous study has used harmonic models to compare seasonality of mosquito-borne diseases on a continent-wide scale.

Pearson general studies paper 1,pearson international business 7th edition test bank. Education inc 3 answer key quiz,pearson education biology workbook. Navi In Bottiglia Book Mediafile Free File Sharing, Naumova T V Kuhnya.

We fit Poisson harmonic regression models to surveillance data on RRV, BFV, and dengue (from 1993, 1995 and 1991, respectively, through 2015) incorporating seasonal, trend, and climate (temperature and rainfall) parameters. The models captured an average of 50–65% variability of the data. Disease incidence for all three diseases generally peaked in January or February, but peak timing was most variable for dengue. The most significant predictor parameters were trend and inter-annual periodicity for BFV, intra-annual periodicity for RRV, and trend for dengue.

We found that a Temperature Suitability Index (TSI), designed to reclassify climate data relative to optimal conditions for vector establishment, could be applied to this context. Finally, we extrapolated our models to estimate the impact of a false-positive BFV epidemic in 2013. Creating these models and comparing variations in periodicities may provide insight into historical outbreaks as well as future patterns of mosquito-borne diseases. Seasonal patterns of disease, understood as periods of high and low disease incidence that are consistent across years, were recognized as early as 380 BCE. Seasonality in vector-borne diseases (VBDs), in particular, is a well-known epidemiological phenomenon, in part because vectors are so highly influenced by meteorological and environmental conditions. Understanding the intricacies of seasonal fluctuations in VBDs is critical in the optimization of forecasting and control efforts. Temperature and rainfall are frequently used in models to predict incidence of VBDs.

In addition, a Temperature Suitability Index (TSI) has been used in previous studies to estimate mosquito abundance, another proxy for disease incidence. The TSI method involves reclassifying weather data based on its suitability relative to optimal climate conditions for vector establishment and may be useful in assessing the impact of weather on disease for macro-geographic scales. In Australia, Ross River virus (RRV), Barmah Forest virus (BFV), and dengue are three of the most common and clinically important VBDs. All display notable seasonal patterns and are transmitted by mosquitos. RRV and BFV contribute the largest annual disease burden and are endemic to Australia, whereas dengue exhibits periodic epidemic activity currently limited to the northeast corner of Australia.

RRV and BFV have several mosquito genera implicated in their transmission while dengue has only one local vector species, Aedes aegypti. Such distinctions may influence or explain underlying differences in the spatio-temporal patterns of these infections.