Hantavirus, a rodent-borne zoonotic pathogen, has a global distribution with 200,000 human infections diagnosed annually. In recent decades, repeated outbreaks of hantavirus infections have been reported in Eurasia and America. These outbreaks have led to public concern and an interest in understanding the underlying biological mechanisms. Here, we propose a climate–animal–Hantaan virus (HTNV) infection model to address this issue, using a unique dataset spanning a 54-y period (1960–2013). This dataset comes from Central China, a focal point for natural HTNV infection, and includes both field surveillance and an epidemiological record. We reveal that the 8-y cycle of HTNV outbreaks is driven by the confluence of the cyclic dynamics of striped field mouse (Apodemus agrarius) populations and climate variability, at both seasonal and interannual cycles. Two climatic variables play key roles in the ecology of the HTNV system: temperature and rainfall. These variables account for the dynamics in the host reservoir system and markedly affect both the rate of transmission and the potential risk of outbreaks. Our results suggest that outbreaks of HTNV infection occur only when climatic conditions are favorable for both rodent population growth and virus transmission. These findings improve our understanding of how climate drives the periodic reemergence of zoonotic disease outbreaks over long timescales. Significance Interannual cycles of many zoonotic diseases are considered to be driven by climate variability. However, the role of climate forcing in the modulation of zoonotic dynamics has been highly controversial, chiefly due to the difficulty in quantifying the links between climate forcing, animal population dynamics, and disease dynamics. Here, we address this issue by using a unique field surveillance dataset from Central China, covering one-half century. Our results shed light on the drivers behind interannual variability and the dynamic patterns of disease ecology, and the links between interannual climate variability and the human–animal interface, adding up to 3-mo lead time over outbreak warnings.

Increased risks for hemorrhagic fever with renal syndrome (HFRS) caused by Hantaan virus have been observed since 2005, in Xi’an, China. Despite increased vigilance and preparedness, HFRS outbreaks in 2010, 2011, and 2012 were larger than ever, with a total of 3,938 confirmed HFRS cases and 88 deaths in 2010 and 2011. Data on HFRS cases and weather were collected monthly from 2005 to 2012, along with active rodent monitoring. Wavelet analyses were performed to assess the temporal relationship between HFRS incidence, rodent density and climatic factors over the study period. Results showed that HFRS cases correlated to rodent density, rainfall, and temperature with 2, 3 and 4-month lags, respectively. Using a Bayesian time-series Poisson adjusted model, we fitted the HFRS outbreaks among humans for risk assessment in Xi’an. The best models included seasonality, autocorrelation, rodent density 2 months previously, and rainfall 2 to 3 months previously. Our models well reflected the epidemic characteristics by one step ahead prediction, out-of-sample. In addition to a strong seasonal pattern, HFRS incidence was correlated with rodent density and rainfall, indicating that they potentially drive the HFRS outbreaks. Future work should aim to determine the mechanism underlying the seasonal pattern and autocorrelation. However, this model can be useful in risk management to provide early warning of potential outbreaks of this disease. Hemorrhagic fever with renal syndrome (HFRS, caused by hantavirus) is a zoonotic infectious disease reservoired in rodent populations worldwide, but with 90% of the total cases occurring in China. Xi’an is one of the most endemic areas in China, with a total of 7,748 confirmed HFRS cases from 2005 to 2012. HFRS came to the attention of the public when two larger outbreaks occurred in Xi’an in 2010 and 2011, with 1,366 and 1,067 cases being reported, respectively. By using 8 years of surveillance data (2005–2012) on HFRS dynamics, including data on the main rodent host reservoir, human cases, and weather conditions, we show how the epidemic dynamics of HFRS were associated with seasonality, rodent abundance, rainfall, and temperature. We find that the two larger HFRS outbreaks coincided with the abrupt increase of rodent abundance and/or rainfall. We present a statistical model revealing strong effects of seasonality and autocorrelation and additional effects of rodent density and rainfall on HFRS incidence that gives robust prediction; this approach could be a very practical tool in Xi’an.

Zoonoses are increasingly recognized as an important burden on global public health in the 21^st century. High-resolution, long-term field studies are critical for assessing both the baseline and future risk scenarios in a world of rapid changes. We have used a three-decade-long field study on hantavirus, a rodent-borne zoonotic pathogen distributed worldwide, coupled with epidemiological data from an endemic area of China, and show that the shift in the ecological dynamics of Hantaan virus was closely linked to environmental fluctuations at the human-wildlife interface. We reveal that environmental forcing, especially rainfall and resource availability, exert important cascading effects on intra-annual variability in the wildlife reservoir dynamics, leading to epidemics that shift between stable and chaotic regimes. Our models demonstrate that bimodal seasonal epidemics result from a powerful seasonality in transmission, generated from interlocking cycles of agricultural phenology and rodent behavior driven by the rainy seasons. Pathogens shared with wildlife cause more than 60% of human infectious diseases. However, there is a scarcity of comprehensive modeling of zoonotic disease dynamics at the wildlife-human interface. Here, we use 30 years of monthly rodent-hantavirus monitoring to show that the complex seasonality in human spillover results from the interplay between an ecological cascade that shapes reservoir infection dynamics and seasonal agricultural cycles that in turn determine human-wildlife contact patterns.

The first human infection with severe fever with thrombocytopenia syndrome virus (SFTSV) was detected in Shaanxi Province, China, in 2013, although the virus had been reported in 13 other provinces of China since 2010. We collected and analyzed a total of 4,011 samples, including 936 human serum samples, 155 animal serum samples, 895 ticks, 1,950 mosquitoes, 30 midges, and 20 sandflies. SFTSV antibodies were found in 44 human samples (4.7%) with no significant differences between males and females or across counties. The incidence rate of severe fever with thrombocytopenia syndrome was significantly higher among individuals 20–60 years of age. Moreover, SFTSV-specific antibodies were detected in goats (66.7%), cattle (13.2%), and dogs (15.0%), but not in pigs (0%). We detected the virus in Haemaphysalis concinna ticks with a prevalence rate of 21.3% (17/80 pools). All mosquito, midge, and sandfly samples were negative for SFTSV. These results support wide circulation of the virus in western China. Haemaphysalis concinna ticks may serve as a novel SFTSV vector, and the role of these ticks requires further investigation.

SUMMARY: The transmission of haemorrhagic fever with renal syndrome (HFRS) is deeply influenced by the reservoir and hantavirus prevalence rate. In this study, a surveillance on human HFRS cases, relative rodent abundance, and hantavirus infection prevalence was conducted in Shaanxi province, China, during 1984–2012. A generalized linear model with Poisson-distributed residuals and a log link was used to quantify the relationship between reservoir, virus and HFRS cases. The result indicated that there was a significant association of HFRS incidence with relative rodent density and the prevalence rate. This research provides evidence that the changes of infection prevalence in the reservoir could lead directly to the emergence of a new epidemic. It was concluded that the measurement of a number of these variables could be used in disease surveillance to give useful advance warning of potential disease epidemics.

Severe fever with thrombocytopenia syndrome (SFTS) is an emerging infectious disease discovered in China in 2009. In July 2013, the first human infection with SFTS virus (SFTSV) was detected in Shaanxi Province, Western China. A seroprevalence study among humans was carried out in an SFTS endemic village; specifically, serum samples were collected from 363 farmers in an SFTS endemic village in Shaanxi Province. The presence of SFTSV antibodies in serum was determined using an ELISA. SFTSV antibodies were found in a total of 20 people (5.51%), with no significant difference between males and females (6.93% and 4.42%, respectively; Chi-square=1.29, p=0.25). Moreover, the SFTSV antibody positive rate was not significantly different across different age groups (Chi-square=2.23, p=0.69). SFTSV readily infects humans with outdoor exposure. The results of the serological study indicate that the virus circulates widely in Shaanxi Province. SFTSV represents a public health threat in China.