The cumulative relative risks of extreme DTR categorized by minimum and maximum temperature on disease-specific death effects. along the 27 lag days.

<p>Note:</p>*<p>Extreme low DTR – low maximum temperature group was defined as daily maximum temperature was less than median value in the chosen extreme low DTR group (16.2°C); Extreme low DTR –high minimum temperature group was defined as daily maximum temperature was higher than median value in the chosen extreme low DTR group (15.5°C); Extreme high DTR – high maximum temperature group was defined as daily maximum temperature was higher than median value in the chosen extreme high DTR group (23.7°C); Extreme high DTR – low minimum temperature group was defined as daily minimum temperature was less than median value in the chosen extreme low DTR group (7.6°C).</p

Three-Dimension plots for the effects (RR) of DTR on mortality in hot and cold season.

<p>Three-Dimension plots for the effects (RR) of DTR on mortality in hot and cold season.</p

District map of Guangzhou, China, with monitoring station locations.

<p>Two districts (the district names) were included in the present study.</p

The CERs of different DTRs on mortality at lag0–27 in the full year.

*<p>1.7°C, 5.5°C, 7.6°C, 9.2°C and 14.5°C represent the 1^st percentile, 25^th percentile, 50^th percentile, 75^th percentile and 99^th percentile of DTR in Guangzhou, respectively. The 8°C of DTR was selected as the reference.</p

CERs of extreme low and high DTR on disease-specific mortality in hot and cold seasons at different lag days.

<p>Note: The 8°C of DTR was selected as the reference, which was regarded as minimal mortality.</p

Summary statistics of daily weather, mortality and air pollution in Guangzhou, China (2006–2008).

<p>SD = standard deviation.</p

Dengue Baidu Search Index data can improve the prediction of local dengue epidemic: A case study in Guangzhou, China

<div><p>Background</p><p>Dengue fever (DF) in Guangzhou, Guangdong province in China is an important public health issue. The problem was highlighted in 2014 by a large, unprecedented outbreak. In order to respond in a more timely manner and hence better control such potential outbreaks in the future, this study develops an early warning model that integrates internet-based query data into traditional surveillance data.</p><p>Methodology and principal findings</p><p>A Dengue Baidu Search Index (DBSI) was collected from the Baidu website for developing a predictive model of dengue fever in combination with meteorological and demographic factors. Generalized additive models (GAM) with or without DBSI were established. The generalized cross validation (GCV) score and deviance explained indexes, intraclass correlation coefficient (ICC) and root mean squared error (RMSE), were respectively applied to measure the fitness and the prediction capability of the models. Our results show that the DBSI with one-week lag has a positive linear relationship with the local DF occurrence, and the model with DBSI (ICC:0.94 and RMSE:59.86) has a better prediction capability than the model without DBSI (ICC:0.72 and RMSE:203.29).</p><p>Conclusions</p><p>Our study suggests that a DSBI combined with traditional disease surveillance and meteorological data can improve the dengue early warning system in Guangzhou.</p></div

Weekly meteorological parameters, DBSI, and local and imported DF cases in Guangzhou from 2011–2014.

<p>Weekly meteorological parameters, DBSI, and local and imported DF cases in Guangzhou from 2011–2014.</p

Weekly observed and fitted local DF cases using two different models from 1^st week of 2011 to the 44^th week of 2014.

<p>Note: The dotted line represents the reported dengue cases and the solid lines represent the cases fitted by the respective models.</p

The time series of weekly rainfall, minimum temperature, DBSI, local dengue and imported dengue counts from 2011–2014 in Guangzhou.

<p>The time series of weekly rainfall, minimum temperature, DBSI, local dengue and imported dengue counts from 2011–2014 in Guangzhou.</p