Summary of the model obtained for Changsha.^a

<p>IRR, incidence rate ratio.</p>a<p>Dummy variables for month were included in the final model.</p

Annual HFRS cases and annual moisture condition, 1991–2010.

<p>(<i>A</i>) Temporal dynamics of annual precipitation and HFRS cases. (<i>B</i>) Scatterplot of annual precipitation and HFRS cases. (<i>C</i>) Temporal dynamics of annual mean AH and HFRS cases. (<i>D</i>) Scatterplot of annual mean AH and HFRS cases. The thick solid straight lines are linear regressions of annual HFRS cases and moisture condition.</p

Geographic location of Changsha, China.

<p>Geographic location of Changsha, China.</p

Observed versus predicted HFRS cases in Changsha.

<p>(<i>A</i>) Temporal dynamics, and (<i>B</i>) scatterplot.</p

Wavelet power spectrum of HFRS incidence in Changsha.

<p>(<i>A</i>) Temporal variation in climatic variables and the number of hemorrhagic fever with renal syndrome (HFRS) cases in Changsha, 1991–2010. (<i>B</i>) The wavelet power spectrum of monthly number of HFRS cases by date of symptoms onset reported through the surveillance system in Changsha during the period 1991–2010 (square root transformed). The left panel illustrates the wavelet power spectrum for the different series (x-axia: time in year; y-axis: period in year). The power is coded from low values, in dark blue, to high values, in dark red. Statistically significant areas (threshold of 5% confidence interval) in wavelet power spectrum (left panels) are highlighted with dashed line; the cone of influence (region not influenced by edge effects) is also indicated. Finally, the right panels show the mean spectrm (solid line) with its significant threshold value of 5% (dashed line).</p

Maximum cross-correlation coefficients of monthly environmental variables and notifications of HFRS: Changsha, China, 1991–2010

*<p><i>p</i><0.01.</p>a<p>Chi-square test for cross-correlations.</p

Spearman correlation coefficient (95% confidence intervals) between the monthly incidence of malaria and climate variables in Anhui Province, 1990–2009.

<p>Lx: the lagged months. MaxT: average maximum temperature; T: average temperature; MinT: average minimum temperature; RH: relative humidity; MEI: multivariate El Niño/Southern Oscillation index.</p

Validations of polynomial distributed lag models of malaria incidence in three regions, Anhui Province, China.

<p>The data from January 1990 to December 2007 were used to construct the models, and the data from January 2008 to December 2009 were used for the validation of the models. (A) In northern Anhui Province; (B) In mid Anhui Province; (C) In southern Anhui Province.</p

Polynomial distributed lag time-series regression coefficients of the monthly rainfall associated with malaria incidence in mid and southern Anhui Province, 1990–2009^a.

a<p>Adjusting for auto-correlation; no significant association was found with temperature, humidity and MEI (p>0.10).</p>**<p>p<0.01.</p>*<p>p<0.05.</p

Lag distribution coefficients of the monthly rainfall (100 mm) with malaria incidence in the three regions of Anhui Province, China during 1990–2009.

**<p>p<0.01.</p>*<p>p<0.05.</p