We fit a second-order polynomial for the temperature predictor because it captures the hypothesized unimodal shape of the temperature response curve over the range of measured temperatures in the garden experiment (Austin 2002).
Some efforts have been made to explore the best temperature predictor of mortality and an appropriate approach to develop a local definition of heatwave [ 15, 17- 19], but, few data are available on the use of health risk-based metrics in the development of a heatwave definition.
Consistent with remote sensing research that measures surface temperature, the predictors of elevated nighttime air temperatures were land cover variables.
In particular, we focused on dense time series of land surface temperature as predictors for urban and rural air temperature.
A univariate ANOVA of pain ratings (with task and temperature as predictors) was performed.
On other hand, the hypothesis that human monkeypox occurrences depend primarily on climatic variables is not supported by our data for Sankuru, to the extent that our best model did not include either precipitation or temperature as predictors of human monkeypox cases.
Additionally, the use of long-term climatic variables (i.e., mean monthly precipitation and mean monthly temperature) as predictors in the California RIVPACS O/E models may incorporate some long-term variability in their estimates of E (i.e., expected number of taxa), albeit in a non-dynamic way.
Loha and Lindtjorn [ 30] reported that monthly rainfall, minimum and maximum temperatures were predictors of P. falciparum malaria incidence in different localities in Ethiopia.
Thus, to find out which variable had the strongest association with laying date, we ran linear mixed models in Proc Mixed, SAS 9.3, with laying date in a given year as response variable and mean or maximum temperature as predictor (since including both will cause intercollinearity due to their significant correlation).
Since the goal is to predict the number of chest radiographs, for each city we fit a linear time series regression model, using the number of chest radiographs as the response, and the number of visits and temperature as predictor variables.
