Haemorrhagic fever with renal syndrome (HFRS) is transmitted to humans mainly by rodents and this transmission could be easily influenced by meteorological factors. Given the long-term changes in climate associated with global climate change, it is important to better identify the effects of meteorological factors of HFRS in epidemic areas. Shandong province is one of the most seriously suffered provinces of HFRS in China. Daily HFRS data and meteorological data from 2007 to 2012 in Shandong province were applied. Quasi-Poisson regression with the distributed lag non-linear model was used to estimate the influences of mean temperature and Diurnal temperature range (DTR) on HFRS by sex, adjusting for the effects of relative humidity, precipitation, day-of-the-week, long-term trends and seasonality. A total of 6707 HFRS cases were reported in our study. The two peaks of HFRS were from March to June and from October to December, particularly, the latter peak in 2012. The estimated effects of mean temperature and DTR on HFRS were non-linear. The immediate and strong effect of low temperature and high DTR on HFRS was found. The lowest temperature −8.86°C at lag 0 days indicated the largest related relative risk (RRs) with the reference (14.85 °C), respectively, 1.46 (95% CI 1.11–1.90) for total cases, 1.33 (95% CI 1.00–1.78) for the males and 1.76 (95% CI 1.12–2.79) for the females. Highest DTR was associated with a higher risk on HFRS, the largest RRs (95% CI) were obtained when DTR = 15.97 °C with a reference at 8.62 °C, with 1.26 (0.96–1.64) for total cases and 1.52 (0.97–2.38) for the female at lag 0 days, 1.22 (1.05–1.41) for the male at lag 5 days. Non-linear lag effects of mean temperature and DTR on HFRS were identified and there were slight differences for different sexes.

Haemorrhagic fever with renal syndrome (HFRS) is a type of vector-borne zoonosis sensitive to climate change. To explore the short-term effect of air temperature and amount of precipitation on HFRS incidence, a total of 13 722 clinically confirmed HFRS cases from January 1977 to December 2001 in Junan County, China were included in this study. According to symmetric bidirectional case-crossover design, the hazard period (the three calendar months preceding the month when the case was diagnosed) and the control period (the same calendar month of the year before and the year after the hazard period) matched and conditional logistic regression was used to examine the effect of monthly mean temperature and precipitation on the risk of HFRS. The results showed the facilitating climatic conditions for HFRS included: condition with moderate mean air temperature (10–25 °C) and abundant precipitation (>120 mm) 3 months before [odds ratio (OR) 1·346, 95% confidence interval (CI) 1·191–1·522] and 2 months before (OR 1·193, 95% CI 1·063–1·339); and condition with temperature >25 °C and abundant precipitation (>120 mm) 3 months before (OR 1·17, 95% CI 1·004–1·363). Temperature of 10–25 °C and moderate precipitation (10–120 mm) in the current month was the most favourable condition for HFRS incidence.

The monthly and annual incidence of haemorrhagic fever with renal syndrome (HFRS) in China for 2004–2009 was analysed in conjunction with associated geographical and demographic data. We applied the seasonal autoregressive integrated moving average (SARIMA) model to fit and forecast monthly HFRS incidence in China. HFRS was endemic in most regions of China except Hainan Province. There was a high risk of infection for male farmers aged 30–50 years. The fitted SARIMA(0,1,1)(0,1,1)12 model had a root-mean-square-error criterion of 0·0133 that indicated accurate forecasts were possible. These findings have practical applications for more effective HFRS control and prevention. The conducted SARIMA model may have applications as a decision support tool in HFRS control and risk-management planning programmes.

In Baden-Wuerttemberg, a federal state in south-west Germany, a large outbreak of 1089 laboratory-confirmed human Puumala virus (PUUV) infections occurred in 2007. We conducted a survey to describe the disease burden and a case-control study to identify risk factors for acquiring PUUV. Case-patients were interviewed about clinical outcome and both case-patients and randomly recruited controls were interviewed about exposure. We calculated matched odds ratios (mOR) using a conditional logistic regression model. Multivariable analysis of 191 matched case-control pairs showed that case-patients were more likely than controls to have seen small rodents/their droppings (mOR 1·9, 95% CI 1·2–3·0), cleaned utility rooms (mOR 1·8, 95% CI 1·0–3·4) and visited forest shelters (mOR 3·9, 95% CI 1·1–14·3). Two thirds of case-patients required hospitalization. During PUUV epidemics rodent control measures and use of protective equipment should be considered in utility rooms and shelters.