Interactions and marginal effects of meteorological factors on haemorrhagic fever with renal syndrome in different climate zones: Evidence from 254 cities of China

Interactions and marginal effects of meteorological factors on haemorrhagic fever with renal syndrome in different climate zones: Evidence from 254 cities of China

Science of the Total Environment 721 (2020) 137564 Contents lists available at ScienceDirect Science of the Total Environment journal homepage: www...

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Science of the Total Environment 721 (2020) 137564

Contents lists available at ScienceDirect

Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv

Interactions and marginal effects of meteorological factors on haemorrhagic fever with renal syndrome in different climate zones: Evidence from 254 cities of China Lina Cao a,b, Xiyuan Huo c, Jianjun Xiang d, Liang Lu b, Xiaobo Liu b, Xiuping Song b, Chongqi Jia a,⁎, Qiyong Liu b,e,⁎⁎ a

Department of Epidemiology, School of Public Health, Shandong University, 44 Wenhuaxi Road, Lixia District, Jinan 250012, Shandong Province, PR China State Key Laboratory of Infectious Disease Prevention and Control, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, WHO Collaborating Centre for Vector Surveillance and Management, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, PR China c Weifang Center for Disease Control and Prevention, Weifang 261061, Shandong Province, PR China d School of Public Health, The University of Adelaide, Adelaide, South Australia 5005, Australia e Shandong University Climate Change and Health Centre, 44 Wenhuaxi Road, Lixia District, Jinan 250012, Shandong Province, PR China b

H I G H L I G H T S

G R A P H I C A L

A B S T R A C T

• Interaction effects of meteorological factors on HFRS were analysed by the division of climate zones based on a national wide database for 11 years in China. • Marginal effects of meteorological factors on HFRS were first estimated. • We found that cold weather, high precipitation may increase the incidence of HFRS in temperate zone.

a r t i c l e

i n f o

Article history: Received 17 December 2019 Received in revised form 17 February 2020 Accepted 24 February 2020 Available online 25 February 2020 Editor: Wei Huang Keywords: Haemorrhagic fever with renal syndrome Meteorological factors Interaction Marginal effects Climate zones

a b s t r a c t Background: Haemorrhagic fever with renal syndrome (HFRS) is climate sensitive. HFRS-weather associations have been investigated by previous studies, but few of them looked into the interaction of meteorological factors on HFRS in different climate zones. Objective: We aim to explore the interactions and marginal effects of meteorological factors on HFRS in China. Methods: HFRS surveillance data and meteorological data were collected from 254 cities during 2006–2016. A monthly time-series study design and generalized estimating equation models were adopted to estimate the interactions and marginal effects of meteorological factors on HFRS in different climate zones of China. Results: Monthly meteorological variables and the number of HFRS cases showed seasonal fluctuations and the patterns varied by climate zone. We found that maximum lagged effects of temperature on HFRS were 1month in temperate zone, 2-month in warm temperate zone, 3-month in subtropical zone, respectively. There is an interaction effect between mean temperature and precipitation in temperate zone, while in warm temperate zone the interaction effect was found between mean temperature and relative humidity. Conclusion: The interaction effects and marginal effects of meteorological factors on HFRS varied from region to region in China. Findings of this study may be helpful for better understanding the roles of meteorological variables in the transmission of HFRS in different climate zones, and provide implications for the development of weather-based HFRS early warning systems. © 2020 Elsevier B.V. All rights reserved.

⁎ Corresponding author. ⁎⁎ Correspondence to: Q. Liu, State Key Laboratory of Infectious Disease Prevention and Control, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, WHO Collaborating Centre for Vector Surveillance and Management, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, PR China. E-mail addresses: [email protected] (C. Jia), [email protected] (Q. Liu).

https://doi.org/10.1016/j.scitotenv.2020.137564 0048-9697/© 2020 Elsevier B.V. All rights reserved.

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1. Introduction Haemorrhagic fever with renal syndrome (HFRS) is a kind of rodent-borne zoonotic disease caused by different species of hantavirus transmitted to humans. Transmission of the viruses to humans is mainly due to inhalation of, and/or contact with, viruscontaminated rodent excreta (e.g. urine, faeces, and saliva). In China, up to 70% HFRS cases were caused by Hantaan virus (HTNV) and Seoul virus (SEOV) (Hansen et al., 2015), which were prone to develop into moderate or severe cases with a death rate of 1%–15% (Schmaljohn and Hjelle, 1997). Typical symptoms of HFRS include fever, haemorrhage, headache, back pain, abdominal pain, acute renal dysfunction, and hypotension. China has the largest number of HFRS cases worldwide with approximately 10,000 cases reported annually (Yan et al., 2007). As one of the 39 notifiable infectious diseases regulated using an ABC-classification system, HFRS is recognized as a B-category infectious disease given its prevalence and potential threats to public health in China (Zhang and Wilson, 2012). The incidence of HFRS can be affected by many factors: environmental factors, rodent population, human-rodent interaction, and hantavirus dynamics (Li et al., 2019). Of them, meteorological factors play very important roles in HFRS transmission (Tian et al., 2017a, 2017b), and can affect HFRS epidemics directly and indirectly (Tian et al., 2017a, 2017b). The spread of hantavirus transmission between rodents within the reservoir and from rodents to humans requires a high abundances of rodent hosts (Samia et al., 2011). Climate factors are important for the survival, reproduction, distribution, population change of rodents and then affect the transmission of hantavirus (Tian et al., 2015; Wu et al., 2016; Gubler et al., 2001). Temperature, humidity and precipitation affect vegetation growth/crop yields for the growth of juvenile and sub-adult rodents (Xiao et al., 2013). Stability and infectivity of hantavirus were sensitive to temperature and relative humidity in the ex vivo environment (Hardestam et al., 2007). Human activities

are constrained by weather conditions and regulated by seasonal changes. Epidemiological studies showed that the epidemics of HFRS exhibit a distinct seasonal pattern, which also clearly indicates weather sensitivity (Tian et al., 2017a, 2017b; Huang et al., 2012; Bi et al., 1998). HFRS is sensitive to climate. In recent years, there are growing concerns about the re-emergence of HFRS in HFRS-eliminated regions and emergence of the disease in previously unaffected regions due to possibly extended areas desirable for rodent breading and prolonged breeding seasons in a warming climate (Wu et al., 2016; Mills et al., 2010; Jonsson et al., 2010). A study included 19 cities of China indicated that HFRS- weather associations varied by regions which suggested that it may necessary to analysis in different areas due to geographic and ecological heterogeneity (Xiang et al., 2018). Although many studies have quantified HFRS-weather associations, few of them looked into the interaction of meteorological factors on HFRS in different regions of China (Hansen et al., 2015; Zhang et al., 2010). In this study, using a national HFRS surveillance database, we aim to explore the marginal effects of meteorological factors and their interactions on HFRS. Findings of this study may deepen the understanding of the associations between HFRS and meteorological factors, and provide further comprehensive evidence for policy-makers and public health practitioners to develop preventive measures to reduce the occurrence of meteorological factors attributed HFRS. 2. Materials and methods 2.1. Study area China has an area of 9.6 million square kilometres, with the boundary spanning 73°33′-135°05′E longitude and 3°51′-53°33.5′N latitude, having a population over 1383 million by the end of 2016. China can be divided into six climate zones as shown in Fig. 1 (Du, 1999; Wu et al., 2010). In this study, we focused on three climate zones:

Fig. 1. Division of climate zones in China.

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temperate, warm temperate, and subtropical zones. Cold temperate zone, plateau temperate zone and tropical zone were excluded due to too few cases (b15 cases per year on average). Finally 254 prefecturelevel cities were included in our study. For cities with boundaries spanning more than one climate zone, their climate zones were assigned according to where the city centre was located.

2.2. Data collection 2.2.1. HFRS cases data Surveillance data of HFRS cases between 1 January 2006 and 31 December 2016 were provided by Chinese centre for disease control and prevention. All clinical HFRS cases were confirmed by professional medical institutions according to the united diagnostic criteria issued by the Ministry of Health (Ministry of Health of China, 1998). The daily counts of HFRS clinical HFRS cases based on the date of symptom onset were then aggregated for each of the selected 254 prefecture-level cities (Xiao et al., 2017). Ethical approval was obtained from ethical review committee of the institute for environmental health and related product safety, Chinese centre for disease control and prevention (Approval No. 201606).

2.2.2. Meteorological data Daily meteorological data from 839 climate monitor stations during 2006–2016 across China were downloaded from the China Meteorological Data Sharing Service System (www.data.cma.cn). 839 climate monitor stations were assigned to cities with the nearest distance to city centre. Monthly meteorological variables for each city, including monthly mean temperature, accumulated monthly precipitation, monthly mean relative humidity were then calculated based on daily meteorological data. After the monthly onset number of HFRS cases were merged with monthly meteorological data from the same city, we got the serial HFRS-weather database.

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2.3. Statistical analyses Descriptive analysis was performed to describe seasonal distribution of HFRS cases and the city-level monthly mean temperature in the selected three climate zones during 2006–2016. Seasons were defined as: spring (March–May), summer. (June–August), autumn (September–November) and winter (December–February). Generalized estimating equations (GEE) have been a popular analytic tool for correlated longitudinal data since the concepts were proposed by Liang and Zeger (1986) (Liang and Zeger, 1986; Zeger and Liang, 1986). Generalized linear models and quasi-likelihood approach with specified “working” correlation matrix in which repeated observations for a subject assumed to be independent were applied in GEE model. The advantage of GEE model is that the model could give consistent estimators of regression coefficients/variances under weak assumptions of actual correlations among repeated observations of each subject (Zeger and Liang, 1986). GEE can be used to test main effects and interactions, and also can be applied to evaluate categorical and continuous independent variables (Ballinger, 2004). In this study, generalized estimating equation models (GEE) with Poisson distribution, scale parameter to the deviance divided by the residual degrees of freedom for continuous distributions and for overdispersed discrete distributions, and with a first order autocorrelation structure was developed to estimate the interaction effect of meteorological factors on HFRS. The meteorological factors were chosen based on literature reviews of the relationship between climatic variables and HFRS, which indicated that temperature, relative humidity, precipitation were associated with the transmission of hantavirus (Hansen et al., 2015; Bi et al., 1998; Jiang et al., 2017). Monthly HFRS cases counts worked as the dependent variable in the model. For each climate zone, separate GEE models were used to examine interaction effects of meteorological factors. Monthly mean Temperature, precipitation, and relative humidity entered model as continuous variables. Monthly accumulative precipitation was calculated and transformed into format

Fig. 2. Temporal variation of the monthly number of HFRS cases in different climate zones, 2006–2016.

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Table 1 Maximum coefficients of the association at optimal lag months between monthly mean temperature and counts of HFRS from GEE model in different climate zones, China, 2006–2016. Climate zone

Optimal Lag(month)

Maximum coefficient

SE

p value

95%CI

Temperate zone Warm temperate zone Subtropical zone

1 2 3

−0.032 −0.574 0.188

0.007 0.011 0.005

b0.001 b0.001 b0.001

−0.046 −0.079 0.008

−0.018 −0.036 0.029

Abbreviation: GEE, generalized estimating equation models; SE, standard error; CI, confidence interval.

ln(precipitation) for large range of the variable. Lagged effects were set to a maximum of 6 months based on previous studies (Hansen et al., 2015; Xiang et al., 2018). Optimal lag period was identified by

maximum coefficients from GEE model. The interaction of meteorological factors on HFRS was then explored at the optimal lag period of temperature with adjusting seasonality and long-time trend.

Fig. 3. Partial predictor of HFRS with 95% confidence interval from fractional polynomial model with covariates of mean temperature, relative humidity and precipitation at 1-month lag in the temperate zone, 2-month lag in the warm temperate zone, and 3-month lag in the subtropical zone.

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The model can be specified as: ln fEðY it Þg ¼ β0 þ β1 ðtemperatureÞ þ β2 ðprecipitationÞ þ β3 ðhumidityÞ þβ4 ðtemperature  Z Þ þ β5 ðtimeÞ þ ε; Y  Poisson Yit fits generalized estimating equation models with meteorological factors, and E(Yit) was the expected monthly counts of HFRS on day t of group identifier(city) i; βi (i = 0,1…5) were coefficients; Z was precipitation/humidity and temperature* Z was the interaction between temperature and precipitation/humidity; t = 1, …, ni, and ni were observations for each city i. Y was distributed Poisson and log linked. The model was assumed within-group correlation with a first order autocorrelation structure. Stata command “mvrs”-multivariable regression spline models-was applied to test linear or nonlinear relationship between HFRS and meteorological factors. The algorithm of “mvrs” starts from a most complex permitted regression spline model and attempts to simplify the model by removing spline terms according to their statistical significance. Margins are statistics calculated from predictions of a previously fit model at fixed values of some covariates and averaging or integrating over the remaining covariates. Marginal effects can summarize the average responsive change of dependent variable related to every one-unit increase of a covariate (Ballinger, 2004; Williams, 2012). Marginal effects of meteorological factors on HFRS were estimated ^ as: after the running of GEE model. And the marginal effects estimates p

^¼ p

M   1 X δ j ðiÞw j f z j ; ^θ w: j¼1

^ ^θ was parameter estimates-coefficient; zj was covariate values; f ðz j ; θÞ was the function of poisson distribution; M was the total number of observations for cities in each climate zone; wj was the weight for the jth

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observation; δj(i) indicated whether observation j was in city i: δj (i) = 1 if j ∈ i, δj(i) = 0 if j ∉ i. However, the marginal effects are nonlinear functions of the parameter estimates and the levels of the explanatory variables, so they cannot generally be inferred directly from the parameter estimates (Anderson and Newell, 2003). Graphs were made to illustrate predictions from GEE models that include interactions between continuous variables more effectively and clearly (Mitchell, 2012). A two-sided p b 0.05 was regarded as statistically significant. All analyses were performed using software Stata 16.0 (Stata Corporation, College Station, TX, USA) and R 3.4.3 (R Foundation for Statistical Computing, Vienna, Austria). 3. Results 3.1. Descriptive analysis A total of 98,952 HFRS cases were involved in our study. There were totally 35,373; 36,538 and 27,041 HFRS cases in temperate, warm temperate and subtropical zones during 2006–2016. The stacked bar charts showed the seasonal variation of HFRS cases for the three climate zones (Fig. 2). Peaks of HFRS cases occurred in autumn for the temperate zone; autumn, winter as well as spring for the warm temperate zone; winter and spring for the subtropical zone. The monthly mean temperature, averaged monthly relative humidity and accumulative monthly precipitation were 9.2 °C, 62.3%, 462 mm; 14.2 °C, 62.8%, 508 mm; 20 °C, 75%, 1165 mm respectively in the temperate, warm temperate and subtropical zones, which gradually increased from north to south China. 3.2. Regression analysis After controlling for precipitation, relative humidity, seasonality and long-term trends, the maximum coefficients of the association between monthly mean temperature and counts of HFRS from GEE model in

Fig. 4-6. Interactions of meteorological factors on HFRS at 1-month lag in temperate zone (Fig. 4), at 2-month lag in warm temperate zone (Fig. 5), and at 3-month lag in subtropical zone (Fig. 6).

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Fig. 4-6 (continued).

different climate zones of China were summarized in Table 1. Nonlinear relationships tests between climate variables and HFRS at optimal lags of temperature in different climate zones from GEE model were verified and shown in Fig. 3. We observed a non-linear relationship temperature-HFRS in the temperate and warm temperate zones. A non-linear humidity-HFRS relatively was observed in the warm temperate zone. In contrast, it is a positive linear relationship between precipitation and HFRS in the three climate zones. 3.3. Interaction analysis The interaction term in the model caused the curvature of contour lines in the graphs. Without interaction, the contour lines would be straight (Huber, n.d..). Fig. 4-6 showed the distribution of HFRS cases with different scenarios of mean temperature and relative humidity/ precipitation, and the curvature of contour lines in the graphs indicated possible interaction between mean temperature and relative humidity/ precipitation. For example, curvature in Fig. 4 showed that there were interaction between mean temperature and precipitation for temperate zone after adjusting relative humidity, seasonality and long-term trends at a 1-month lag. And interaction between mean temperature and relative humidity at a 2-month lag in warm temperate zone was observed (Fig. 5). However, the interactions between mean temperature and precipitation/relative humidity in subtropical zone was not observed with adjustment of confounders. 3.4. Marginal effects Fig. 7 showed marginal effects of relative humidity/precipitation at different levels of temperature after taking interactions into account. For temperate zone, marginal effects of relative humidity on HFRS increased negatively with the increase of temperature, and the results were statistically significant when temperature between −5 °C and

25 °C; while marginal effects of precipitation on HFRS decreased positively in cold days and then decreased negatively and significantly if temperature above 5 °C. On average each one unit increase of ln (precipitation) may lead to 19.8% (95%CI: 3.2%–36.4%) decrease in the number of HFRS cases at 1-month lag. For warm temperate zone, marginal effects of relative humidity on HFRS increased positively if temperature below 0 °C while decreased significantly afterwards with the increase of temperature. On average each 1% increase of humidity may lead to 8.8% (95%CI: 6.5%–11.0%) decrease in the number of HFRS cases at 2month lag. Marginal effects of precipitation decreased steeply and significantly when temperature below −5 °C, and levelled off afterwards with increased temperature. For subtropical zone, when temperature went up, marginal effects of relative humidity and precipitation on HFRS showed insignificant change.

4. Discussion Many studies have investigated the association between HFRS and meteorological factors at a single or multi-locations level (Xiang et al., 2018). However, few of them explored the interaction and marginal effects of meteorological factors on the incidence of HFRS. To our knowledge, this is the first study using a national data to explore the interactions and marginal effects of meteorological factors on HFRS in China. Previous studies found that HFRS cases caused by HTNV through Apodemus agrarius mainly peaked in autumn and winter, while Rattus norvegicus associated with HTNV infections mostly occurred in the spring (Bi et al., 1998; Chen et al., 1986). In this study, we observed an obvious seasonal pattern of HFRS cases in these three involved climate zones. Temperate zone peaks in autumn; warm temperate zone peaks in autumn and winter, as well as spring; subtropical zone peaks in winter and spring. So we may infer that SEOV is epidemic in temperate zone, while warm temperate zone and subtropical zone are mixed epidemic areas dominated by HTNV and SEOV. Prior studies investigated single

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Fig. 4-6 (continued).

locations and identified hantavirus like Huludao city in temperate zone, Qingdao city in warm temperate zone and Wuhan city in subtropical zone were consistent with our inference (Jiang et al., 2017; Guan et al., 2009; Kang et al., 2012). The association between meteorological factors and climatesensitive diseases is usually characterized as non-linear, most of which is a U-shaped curve relationship (Xiang et al., 2017). In our results, a non-linear relationship between temperature and HFRS was found in the temperate and warm temperate zones, while a linear relationship in subtropical zone. Northern China covers both temperate zone and warm temperate zone which are characterized with distinct four seasons and great temperature changes. Ambient temperature is associated with HFRS occurrence, therefore a non-linear relationship between temperature and HFRS was observed. In contrast, the range of temperature change is relatively small all the year in subtropical zone and there was a linear relationship between temperature and HFRS. Identification of the optimal lag time with maximum effects of temperature provides clues for early warnings of HFRS occurrence (Xiang et al., 2018). We found that maximum lagged effects of temperature were 1-month, 2-month, 3-month from the north to the south China (Table 1). However, inconsistent findings of the lag time with maximum effects have also been reported in single-site studies that locates in the same climate zones we studied (Hansen et al., 2015; Bai et al., 2019; Xiang et al., 2018). This discrepancy might be due to the differences of environmental characteristics among cities and our climate-zone level results are averaged estimates of each location within certain region (Lin et al., 2014). The heterogeneity across climate zones exists and it should be cautious to generalize their results. Results of the GEE model showed interaction between temperature and precipitation on HFRS in temperate zone, and the marginal effect of precipitation is positive in cold days while negative in warm days. The results indicated that cold weather, high precipitation may increase the incidence of HFRS in temperate zone. The increased HFRS cases may be due to recent adverse weather conditions (such as cold weather and plenty of rainfall), which not only drove rodents into the residential area

for shelter and food, but also keep individuals stay more time indoors (increased human-rodent exposure opportunities)(Schmaljohn and Hjelle, 1997; Tamerius et al., 2011). Interactions between temperature and relative humidity were observed in warm temperate zone, and the average marginal effect of relative humidity was positive on HFRS. High level of humidity means moisture environment which benefited the survival of rodents and also favourable for the stability of hantaviruses in the ex vivo environment (Hardestam et al., 2007). In subtropical zone, warm temperature and abundant precipitation were desirable for vegetation growth, shortened gestation period and sexual maturation of murine hosts, which resulted in increased rodent population, and boosted the risk of viral transmission among hosts and increased transmission to human beings (Hansen et al., 2015). Limitations of our study should be acknowledged. Firstly, in addition to meteorological factors many other factors may also affect the incidence of HFRS. However, these factors including vaccination, population intervention, economic level differences and hygiene conditions, and other potential influencing factors on HFRS were not considered here. Secondly, HFRS cases data came from a passive surveillance system and underreporting bias was inevitable (Zhang et al., 2010). For example, some patients with mild symptoms may treat themselves at home rather than seeking medical service. The underreporting bias may underestimate the marginal effects between meteorological factors and HFRS. Thirdly, the specific types of viral infection were not distinguished due to limited immunological testing equipment and techniques. Mixed viral infections may affect the accuracy of our analysis (Xiang et al., 2018). 5. Conclusions Using a national dataset, results of this study provide the first-hand evidence of the interaction and marginal effects of meteorological factors on HFRS in China. Moreover, the effects vary by climate zone. This may be helpful for better understanding the weather-HFRS association and the development of weather-based HFRS early warning systems.

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Fig. 7. Marginal effects of relative humidity and precipitation at different level of temperature on HFRS at 1-month lag in temperate zone, at 2-month lag in warm temperate zone, and at 3month lag in subtropical zone.

CRediT authorship contribution statement Lina Cao: Software, Conceptual, Methodology, Writing-original draf. Xiyuan Huo: Investigation, Software, Validation. Jianjun Xiang:

Writing - review & editing. Liang Lu: Writing - original draft. Xiaobo Liu: Writing - original draft. Xiuping Song: Writing - original draft. Chongqi Jia: Writing - review & editing, Supervision. Qiyong Liu: Writing - review & editing, Supervision.

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Acknowledgments We would like to thank Chinese centre for disease control and prevention, National Meteorological Information Centre of China for providing the data for our study. Funding This work was supported by the Special Foundation of Basic Science and Technology Resources Survey of Ministry of Science and Technology, China (Grant No. 2017FY101202) and the National Basic Research Program of China (973 Program) (Grant No. 2012CB955504). Declaration of competing interest The authors declare no competing interests. References Anderson, S., Newell, R.G., 2003. Simplified marginal effects in discrete choice models. Econ. Lett. 81 (3), 321–326. Bai, X.H., Peng, C., Jiang, T., Hu, Z.M., Huang, D.S., Guan, P., et al., 2019. Distribution of Geographical Scale, Data Aggregation Unit and Period in the Correlation Analysis between Temperature and Incidence of HFRS in Mainland China: A Systematic Review of 27 Ecological Studies. pp. 1935–2735 (Electronic). Ballinger, G.A., 2004. Using generalized estimating equations for longitudinal data analysis. Organ. Res. Methods 7 (2), 127–150. Bi, P., Wu, X., Zhang, F., Parton, K.A., Tong, S., et al., 1998. Seasonal rainfall variability, the incidence of haemorrhagic fever with renal syndrome, and prediction of the disease in low-lying areas of China. Am. J. Epidemiol. 148 (3), 276–281. Chen, H.X., Qiu, F.X., Dong, B.J., Ji, S.Z., Li, Y.T., Wang, Y., et al., 1986. Epidemiological studies on haemorrhagic fever with renal syndrome in China. J. Infect. Dis. 154 (3), 394–398. Du, Z., 1999. A study on the eco-geographic regional system of China Cambridge, UK. FAO FRA2000 Global Ecological Zoning Workshop. Guan, P., Huang, D., He, M., Shen, T., Guo, J., Zhou, B., et al., 2009. Investigating the effects of climatic variables and reservoir on the incidence of hemorrhagic fever with renal syndrome in Huludao City, China: a 17-year data analysis based on structure equation model. BMC Infect. Dis. 9, 109. Gubler, D.J., Reiter, P., Ebi, K.L., Yap, W., Nasci, R., Patz, J.A., et al., 2001. Climate variability and change in the United States: potential impacts on vector- and rodent-borne diseases. Environ. Health Perspect. 109 (Suppl. 2), 223–233. Hansen, A., Cameron, S., Liu, Q., Sun, Y., Weinstein, P., Williams, C., Han, G.S., Bi, P., et al., 2015. Transmission of haemorrhagic fever with renal syndrome in China and the role of climate factors: a review. Int. J. Infect. Dis. 33, 212–218. Hardestam, J., Simon, M., Hedlund, K.O., Vaheri, A., Klingström, J., Lundkvist, A., et al., 2007. Ex vivo stability of the rodent-borne Hantaan virus in comparison to that of arthropod-borne members of the Bunyaviridae family. Appl. Environ. Microbiol. 73 (8), 2547–2551. Huang, X., Yin, H., Yan, L., Wang, X., Wang, S., et al., 2012. Epidemiologic characteristics of haemorrhagic fever with renal syndrome in mainland China from 2006 to 2010. Western Pac Surveill Response J 3 (1), 12–18. Huber, Chuck, d. In the spotlight: visualizing continuous-by-continuous interactions with margins and twoway contour. Available from. https://www.stata.com/stata-news/ news32-1/spotlight/. Jiang, F., Wang, L., Wang, S., Zhu, L., Dong, L., Zhang, Z., Hao, B., Yang, F., et al., 2017. Meteorological factors affect the epidemiology of hemorrhagic fever with renal syndrome via altering the breeding and hantavirus-carrying states of rodents and mites: a 9 years' longitudinal study. Emerg Microbes Infect 6 (11), e104. Jonsson, C.B., Figueiredo, L.T., Vapalahti, O., 2010. A global perspective on hantavirus ecology, epidemiology, and disease. Clin. Microbiol. Rev. 23 (2), 412–441.

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