Predicting The Transmission Risk Of Hemorrhagic Fever With Renal Syndrome And Malaria Based On Environmental Factors

Background: Hemorrhagic fever with renal syndrome (HFRS) is a zoonosis caused by different species of Hantavirus (HV). China is one of the most severe endemic countries, where there are 90% of the total reported HFRS cases in the world. The causative agents of HFRS in China are predominately Hantaan virus (HTNV) and Seoul virus (SEOV), which cause case fatality rates around 10% and 1%, respectively. HFRS has become a significant public health problem in mainland China because it not only affects the people's health and safety, but also impacts on the socio-economic development. In recent years, the prevalence of HFRS has shown some new features: on the one hand, the scope of HFRS endemic area is expanding and HFRS has spread to major cities, and on the other hand, HFRS incidence still maintains high in the HTNV-type natural foci. However, the environmental factors facilitating the spread and expansion of the virus in a newly-identified focus remain unclear, and the quantitative relationship between climate variation and the transmission of HFRS remains to be determined in HTNV-type foci.Malaria is a parasitic disease caused by the bite of Anopheles. Since 2000, malaria resurgence has occurred in China. And Anhui Province is the most seriously affected area with the highest number of malaria cases after 2005. The incidence of malaria shows high variability at the county level in Anhui Province. What are the characteristics of temporal and spatial distribution of malaria in this province, and where are the hot spots? This is the urgent scientific questions addressed in the prevention and control of malaria. Meanwhile, the prevalence of malaria has a certain cyclical characteristics, and the incidence time series are typically noisy, complex and strongly non-stationary. However, previous studies have rarely considered these features of the incidence time series, so it is necessary to characterize the seasonality of the malaria in the typical endemic areas in China and also to identify the association between climatic factors and malaria incidences.Objectives:①T o understand the spatial distribution of HV infection in rodent hosts in Beijing, and to identify environmental factors contributing to the presence of HV in rodent population, and also to predict spatial distribution of HFRS for possible preemptive public health warnings.②To evaluate the quantitative relationship between climate variation and the transmission of HFRS in northeastern China.③To characterize the temporal and spatial distribution patterns of malaria in Anhui Province, and to identify the distribution of the hot spots at the county level.④To characterize the periodicity of the malaria in the typical endemic areas in China (Anhui Province, Hainan Province, Yunnan Province) and also to identify the association between climatic factors and malaria incidences.Methods: The spatial distribution of HV infections in host rodents from Beijing were predicted by using Logistic regression and spatial statistical analysis in combination with field investigation and laboratory testing. The cross correlation analysis and time-series Poisson regression model were used to evaluate the quantitative relationship between climate variation and the transmission of HFRS in HTNV-type foci in northeastern China. Spatial autocorrelation analysis and spatial statistics were used to characterize the temporal and spatial distribution patterns of malaria in Anhui Province, and space-time scanning cluster analysis was used to determine the distribution of the hot spots at the county level. Cross wavelet transform (XWT) and wavelet coherence (WTC) techniques were employed to characterize the periodicity of the malaria in the typical endemic areas in China (Anhui Province, Hainan Province, Yunnan Province) and also to assess and compare the associations between climatic factors and malaria incidences.Results:①A total of 1,639 rodents were at 86 sites during HFRS epidemic seasons from 2005 to 2007 in Beijing. 117 rodents were positive for SEOV by RT-PCR test, with an overall infection rate of 7.14%. Multivariate logistic regression analysis indicated that orchards, rice agriculture and moderate elevation were significantly associated with the prevalence of HVs infection in rodents, while the forest was the only protective factor for the infection. The final logistic regression function for predicting the risk areas was Logit(P) = 1.059×Rice agriculture+0.115×Orchards+2.285 Moderate elevation-1.909 Forest. The constructed prediction risk map showed that the highest risk regions for HVs in rodents mainly focused on the downtown and several suburbs. Meanwhile, the locations of HFRS cases were used to test the validity of the constructed risk map.②In Elunchun and Molidawahaner county, the results of cross correlation analysis showed that monthly mean rainfall, land surface temperature, relative humidity, and MEI were significantly correlated with the monthly reported HFRS cases with lags of 3-5 months. In Elunchun county, after controlling for the autocorrelation, seasonality and long-term trend, rainfall at a lag of 3 months, LST at a lag of 4 months, RH at a lag of 3 months, and MEI at a lag of 4 months appeared to play significant roles in the transmission of HFRS. The final time-series Poisson regression model suggests that a 1°C increase in the monthly mean LST may be associated with an 11.4% increase in HFRS cases. A 1mm/day increase in monthly mean rainfall, 1% RH rise, and 1 unit MEI rise were associated with 1.1%, 2.9% and 55.3% increases in HFRS cases, respectively. The observed and expected number of cases from the final model matched reasonably well for Elunchun. The pseudo R2 value for the fitted model was 79.43%. In Molidawahaner county, after controlling for the autocorrelation, seasonality, and long-term trend, rainfall at a lag of 4 months, LST at a lag of 5 months, RH at a lag of 4 months, and MEI at a lag of 4 months were significantly associated with HFRS. The final model indicated that a 1°C increase in the monthly mean LST was associated with a 16.8% increase in HFRS cases. A 1mm/day increase in monthly mean rainfall, 1% RH rise, and 1 unit MEI rise were associated with 0.5%, 3.2% and 73.6% increases in HFRS cases, respectively. The pseudo R2 value for the fitted model was equal to 75.91%.③The incidence of malaria showed high variability at the county level. Malaria epidemic mainly occurred in the central parts of Anhui Province in the late 1990s, and then expanded to the northern region of this province since 2001. Trend analysis showed that the incidence of malaria changed obviously in the East-West and North-South directions. In general, the incidence in the north was higher than the south in this province, and the incidence in the East-West direction showed the"∩"type. The results of spatial autocorrelation showed the incidence of malaria in Anhui Province was clustered at the county level. Using the maximum spatial cluster size of < 50% of the total population and the maximum temporal cluster size of < 50% of the study period, the spatio-temporal cluster analysis identified a most likely cluster that included 13 counties, which all located in the north of Huai River. The highest endemic period occurred from June 2003 to October 2008. To investigate the possibility of smaller clusters, the same analysis was performed with a modification of the maximum spatial cluster size defined as < 25% total population and the maximum temporal cluster size of < 25% of the study period. A most likely cluster and one secondary cluster were identified. The most likely cluster was almost the same as in the 50% analysis. The secondary sub-cluster included 14 counties, which located in the central and eastern part of this province.④In Anhui province, the continuous wavelet transform (CWT) showed significant periodicity on the 1-y scale. High power was also present in the 5–6-y period and 12-y range. In Hainan province, the CWT showed significant periodicity on the 8-y scale. High power was also present on the 1-y scale, but did not reach significance compared to the null hypothesis. In Yunnan province, the CWT showed significant periodicity on the 1-y scale. We analyzed the relationship between MEI, local weather (monthly mean rainfall, monthly mean average temperature, monthly mean maximum temperature, monthly mean minimum temperature, monthly mean relative humidity), and malaria incidence in these three provinces using XWT and WTC analyses to identify time- and frequency-specific association. In Anhui province, malaria incidence showed significantly coherence with local weather on the annual scale with a 1–2-mo lag and with MEI in the 2–4-y mode with a 5-mo lag. However, the relationship between malaria incidence and climate wasn't consistent. In Yunnan province, malaria incidence showed consistent and strong coherence with the monthly rainfall and mean temperature and transient coherence with relative humidity and MEI. In Yunnan province, malaria incidence showed significantly coherence with local weather and MEI on the 1-y scale. Conclusion: This study clarified the spatial distribution of HV infection in rodent hosts in Beijing, and determined the environmental factors contributing to the presence of HV in rodent population, and also constructed a risk map of HFRS in Beijing. This study also evaluated the quantitative relationship between climate variation and the transmission of HFRS in northeastern China. Meanwhile, this study characterized the temporal and spatial distribution patterns of malaria in Anhui Province, and identified the distribution of the hot spots at the county level. Also, the periodicity of the malaria in the typical endemic areas in China was characterized and the association between climatic factors and malaria incidences was identified.