We established a time series Poisson regression model that simultaneously included time factors such as time trend, lagged terms of weather predictors, lags of dengue cases as auto regressive terms and we accounted for changes in size of the population by offsetting midyear population.
Lagged effects of daily weather were studied using lag strata of average meteorology respectively for lag 0 1, lag 2 6, and lag 7 13 to avoid problems arising from using highly correlated lags of weather variables in the same model.
Finally, a full model (model-5) including interactions between weather factors of lag 1 3 together with main effects was fitted to study more complex associations.
This included models with a) different lags of PM10, b) various sets of degrees of freedom for time and weather, c) different lags of temperature and relative humidity, and d) penalized splines for time and weather in place of natural splines.
To account for overdispersion (deviance = 2.39 in the final model) in the norovirus report data, standard errors were scaled using square root of Pearson chi-squared goodness of fit statistic.[17] The model was built in a stepwise fashion by first constructing the confounder model, then adding the variables of interest (lagged weather variables, population immunity, new virus variants).
We included time factors and daily lagged weather predictors.
The correlation between lagged weather predictors was also considered.
12 Typically, the weather covariates were lagged, to account for the delayed effects of weather on malaria infections.
Time series Poisson regression with cubic spline functions was used, allowing for over-dispersion, including lagged weather parameters, and adjusting for time trends and seasonal patterns.
Additional sensitivity analyses indicated that the species results were insensitive to treatment of missing values, alternative df used for the smoothers of time and weather, and different lags for the weather terms in the model specifications (data not shown).
We survived the plane rides, jet lag, unpredictable weather, tiny hotel rooms and each other.
