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Background Ross River virus (RRV) disease is Australia's most widespread vector-borne disease causing significant public health concern. The aim of this study was to identify the ecological covariates of RRV risk and to develop epidemic forecasting models in a disease hotspot region of South Australia. Methods Seasonal autoregressive integrated moving average models were used to predict the incidence of RRV disease in the Riverland region of South Australia, an area known to have a high incidence of the disease. The model was developed using data from January 2000 to December 2012 then validated using disease notification data on reported cases for the following year. Results Monthly numbers of the mosquito Culex annulirostris (β=0.033, p<0.001) and total rainfall (β=0.263, p=0.002) were significant predictors of RRV transmission in the study region. The forecasted RRV incidence in the predictive model was generally consistent with the actual number of cases in the study area. Conclusions A predictive model has been shown to be useful in forecasting the occurrence of RRV disease, with increased vector populations and rainfall being important factors associated with transmission. This approach may be useful in a public health context by providing early warning of vector-borne diseases in other settings. [ABSTRACT FROM AUTHOR]

Background: There was a dengue epidemic in several regions of China in 2013. No study has explored the dynamics of dengue transmission between different geographical locations with dengue outbreaks in China. The purpose of the study is to analyze the epidemiological characteristics and to explore the dynamic transmission of dengue in China, 2013. Methodology and Principal Findings: Records of dengue cases of 2013 were obtained from the China Notifiable Disease Surveillance System. Full E-gene sequences of dengue virus detected from the outbreak regions of China were download from GenBank. Geographical Information System and heatmaps were used to describe the epidemiological characteristics. Maximum Likelihood phylogenetic and Bayesian phylogeographic analyses were conducted to explore the dengue dynamic transmission. Yunnan Province and Guangdong Province had the highest imported cases in the 2013 epidemic. In the locations with local dengue transmission, most of imported cases occurred from June to November 2013 while local dengue cases developed from July to December, 2013. There were significant variations for the incidences of dengue, in terms of age distributions, among different geographic locations. However, gender differences were identified in Guangzhou, Foshan and Xishuangbanna. DENV 1–3 were detected in all locations with the disease outbreaks. Some genotypes were detected in more than one locations and more than one genotypes have been detected in several locations. The dengue viruses introduced to outbreak areas were predominantly from Southeast Asia. In Guangdong Province, the phylogeographical results indicated that dengue viruses of DENV 1 were transmitted to neighboring cities Foshan and Zhongshan from Guangzhou city, and then transmitted to Jiangmen city. The virus in DENV 3 was introduced to Guangzhou city, Guangdong Province from Xishuangbanna prefecture, Yunnan Province. Conclusions: Repeated dengue virus introductions from Southeast Asia and subsequent domestic dengue transmission within different regions may have contributed to the dengue epidemics in China, 2013. [ABSTRACT FROM AUTHOR]

Background After reviewing urethral gonorrhoea cases among men who have sex with men (MSM) at the South Australia Specialist Sexual Health (SASSH) in Adelaide, Australia, we noticed peaks of gonorrhoea among MSM occurred predominantly in the first quarter of the year (January-March). The aim of this study was to formally test this hypothesis against data from a similar period at three sexual health services, one each in Adelaide, Melbourne and Sydney.Methods: This study was a retrospective analysis of computerised records at the three Australian sexual health services. Potential risk factors for urethral gonorrhoea among MSM were also reviewed at the SASSH.Results: More peaks of gonorrhoea cases were observed in the first quarter of the year in Adelaide and Sydney and in the second and fourth quarter in Melbourne. Factors independently associated with urethral gonorrhoea at the SASSH were being a young MSM, especially those aged 25-29 (odds ratio (OR) 2.66, 95% confidence interval (CI): 2.00-3.54), having more than one sexual partner (OR 1.71, 95% CI: 1.43-2.04), having had sex interstate and overseas (OR 1.52, 95% CI: 1.06-2.17), and presenting in the first quarter (OR 1.30, 95% CI: 1.10-1.55).Conclusion: Our data suggest that gonorrhoea among MSM occurs in a seasonal pattern, particularly late summer into early autumn. This has implications for the provision of health services over the year and for the timing of health promotion activities. [ABSTRACT FROM AUTHOR]

Background: Hemorrhagic fever with renal syndrome (HFRS) is a rodent-borne disease caused by many serotypes of hantaviruses. In China, HFRS has been recognized as a severe public health problem with 90% of the total reported cases in the world. This study describes the spatiotemporal dynamics of HFRS cases in China and identifies the regions, time, and populations at highest risk, which could help the planning and implementation of key preventative measures. Methods: Data on all reported HFRS cases at the county level from January 2005 to December 2012 were collected from Chinese Center for Disease Control and Prevention. Geographic Information System-based spatiotemporal analyses including Local Indicators of Spatial Association and Kulldorff's space-time scan statistic were performed to detect local high-risk space-time clusters of HFRS in China. In addition, cases from high-risk and low-risk counties were compared to identify significant demographic differences. Results: A total of 100,868 cases were reported during 2005–2012 in mainland China. There were significant variations in the spatiotemporal dynamics of HFRS. HFRS cases occurred most frequently in June, November, and December. There was a significant positive spatial autocorrelation of HFRS incidence during the study periods, with Moran's I values ranging from 0.46 to 0.56 (P<0.05). Several distinct HFRS cluster areas were identified, mainly concentrated in northeastern, central, and eastern of China. Compared with cases from low-risk areas, a higher proportion of cases were younger, non-farmer, and floating residents in high-risk counties. Conclusions: This study identified significant space-time clusters of HFRS in China during 2005–2012 indicating that preventative strategies for HFRS should be particularly focused on the northeastern, central, and eastern of China to achieve the most cost-effective outcomes. [ABSTRACT FROM AUTHOR]

There have been increasing concerns about the challenge of emerging and re-emerging infectious diseases due to climate change, especially in developing countries including China. Health professionals play a significant role in the battle to control and prevent infectious diseases. This study therefore aims to investigate the perceptions and attitudes of health professionals at the Centers for Disease Control and Prevention (CDC) in different levels in China, and to consider adaptation measures to deal with the challenge of climate change. In 2013, a cross-sectional questionnaire survey was undertaken among 314 staff in CDCs in Shanxi Province, China, whose routine work involves disease control and prevention. Data were analyzed using descriptive methods and logistic regression. A majority of the CDC staff were aware of the health risks from climate change, especially its impacts on infectious disease transmission in their jurisdictions, and believed climate change might bring about both temporal and spatial change in transmission patterns. It was thought that adaptation measures should be established including: strengthening/improving currently existing disease surveillance systems and vector monitoring; building CDC capacity in terms of infrastructure and in-house health professional training; development and refinement of relevant legislation, policies and guidelines; better coordination among various government departments; the involvement of the community in infectious disease interventions; and collaborative research with other institutions. This study provides a snapshot of the understanding of CDC staff regarding climate change risks relevant to infectious diseases and adaptation in China. Results may help inform future efforts to develop adaptation measures to minimize infectious disease risks due to climate change. [ABSTRACT FROM AUTHOR]

Introduction: Each year there are approximately 390 million dengue infections worldwide. Weather variables have a significant impact on the transmission of Dengue Fever (DF), a mosquito borne viral disease. DF in mainland China is characterized as an imported disease. Hence it is necessary to explore the roles of imported cases, mosquito density and climate variability in dengue transmission in China. The study was to identify the relationship between dengue occurrence and possible risk factors and to develop a predicting model for dengue’s control and prevention purpose. Methodology and Principal Findings: Three traditional suburbs and one district with an international airport in Guangzhou city were selected as the study areas. Autocorrelation and cross-correlation analysis were used to perform univariate analysis to identify possible risk factors, with relevant lagged effects, associated with local dengue cases. Principal component analysis (PCA) was applied to extract principal components and PCA score was used to represent the original variables to reduce multi-collinearity. Combining the univariate analysis and prior knowledge, time-series Poisson regression analysis was conducted to quantify the relationship between weather variables, Breteau Index, imported DF cases and the local dengue transmission in Guangzhou, China. The goodness-of-fit of the constructed model was determined by pseudo-R2, Akaike information criterion (AIC) and residual test. There were a total of 707 notified local DF cases from March 2006 to December 2012, with a seasonal distribution from August to November. There were a total of 65 notified imported DF cases from 20 countries, with forty-six cases (70.8%) imported from Southeast Asia. The model showed that local DF cases were positively associated with mosquito density, imported cases, temperature, precipitation, vapour pressure and minimum relative humidity, whilst being negatively associated with air pressure, with different time lags. Conclusions: Imported DF cases and mosquito density play a critical role in local DF transmission, together with weather variables. The establishment of an early warning system, using existing surveillance datasets will help to control and prevent dengue in Guangzhou, China. [ABSTRACT FROM AUTHOR]

Objective: The aim of the study is to examine the spatiotemporal pattern of Japanese Encephalitis (JE) in mainland China during 2002–2010. Specific objectives of the study were to quantify the temporal variation in incidence of JE cases, to determine if clustering of JE cases exists, to detect high risk spatiotemporal clusters of JE cases and to provide evidence-based preventive suggestions to relevant stakeholders. Methods: Monthly JE cases at the county level in mainland China during 2002–2010 were obtained from the China Information System for Diseases Control and Prevention (CISDCP). For the purpose of the analysis, JE case counts for nine years were aggregated into four temporal periods (2002; 2003–2005; 2006; and 2007–2010). Local Indicators of Spatial Association and spatial scan statistics were performed to detect and evaluate local high risk space-time clusters. Results: JE incidence showed a decreasing trend from 2002 to 2005 but peaked in 2006, then fluctuated over the study period. Spatial cluster analysis detected high value clusters, mainly located in Southwestern China. Similarly, we identified a primary spatiotemporal cluster of JE in Southwestern China between July and August, with the geographical range of JE transmission increasing over the past years. Conclusion: JE in China is geographically clustered and its spatial extent dynamically changed during the last nine years in mainland China. This indicates that risk factors for JE infection are likely to be spatially heterogeneous. The results may assist national and local health authorities in the development/refinement of a better preventive strategy and increase the effectiveness of public health interventions against JE transmission. [ABSTRACT FROM AUTHOR]

Abstract: This study aims to quantify the relationship between climate variations and cases of Salmonella infection in subtropical and tropical areas in Australia. Brisbane in a subtropical area and Townsville in a tropical area of Queensland were selected as the study regions. Local meteorological variables and notified cases of Salmonella infection from January 1990 to July 2005 were provided by local authorities. Spearman correlation and time-series adjusted Poisson regression were applied controlling for autoregression, lag effects, seasonal variation and long-term trend. Natural cubic spline and Hockey Stick model were used to estimate a potential threshold temperature. Spearman correlation indicated that maximum and minimum temperatures, relative humidity at 9am and 3pm, and rainfall were all positively correlated with the number of cases in both Brisbane and Townsville, with the lag values of the effects up to 2weeks in Brisbane and 2months in Townsville. Only temperature and rainfall were significantly included in the regression models in both regions. The models suggested that a potential 1°C rise in maximum or minimum temperature may cause a very similar increase in the number of cases in the two regions. No threshold for the effect of maximum or minimum temperature on Salmonella infection was detected in either region. The association between climate variations and Salmonella infection could be very similar in subtropical and tropical regions in Australia. Temperature and rainfall may be used as key meteorological predictors for the number of cases in both regions. [Copyright &y& Elsevier]

Summary: Background: The relationship between weather and food-borne diseases has been of great concern recently. However, the impact of weather variations on food-borne disease may vary in different areas with various geographic, weather and demographic characteristics. This study was designed to quantify the relationship between weather variables and Campylobacter infections in two Australian cities with different local climatic conditions. Methods: An ecological–epidemiological study was conducted, using weekly disease surveillance data and meteorological data, over the period 1990–2005, to quantify the relationship between maximum and minimum temperatures, rainfall, relative humidity and notifications of Campylobacter infections in Adelaide, with a temperate Mediterranean climate, and Brisbane, with a sub-tropical climate. Spearman correlation and time-series adjusted Poisson regression analyses were performed taking into account seasonality, lag effects and long-term trends. Results: The results indicate that weekly maximum and minimum temperatures were inversely associated with the weekly number of cases in Adelaide, but positively correlated with the number of cases in Brisbane, with relevant lagged effects. The effects of rainfall and relative humidity on Campylobacter infection rates varied in the two cities. Conclusion: Weather might have different effect on Campylobacter infections in different cities. Further studies are needed for a better understanding of these relationships for they may indicate epidemiologic factors important for control of these infections. [Copyright &y& Elsevier]

To investigate determinants of hemorrhagic fever with renal syndrome (HFRS) in low-lying areas of China, the authors studied Chuigang and Wanggang communities in Anhui Province. These adjacent farming communities have a population of about 100,000. Data were collected from the two communities in 1961–1977 and from Yingshang County in 1983–1995; information covered the incidence of HFRS, amount of precipitation, differences in the water level of the Huai River, density of Apodemus agrarius, autumn crop production, and areas of inundated farmland. Correlation and multiple linear regression analyses were used to estimate the relation between seasonal rainfall, density of mice, occupational factors, and occurrence of the disease. Associations were observed between the incidence of HFRS and the amount of precipitation, the water level of the Huai River, and the areas of inundated farmland in Chuigang community. The smaller the water-level difference, the less farmland was inundated and the higher the incidence of HFRS. In Wanggang community, the density of A. agrarius (r1 = 0.63, p = 0.02), the water-level difference in the Huai River (r2 = −0.81, p = 0.007), and crop production (r3 = 0.96, p = 0.005) were correlated with the incidence of HFRS. The regression analyses based on Wanggang community suggested that these indexes could be used as predictive variables, and the results from the model were well calibrated with the actual incidence of HFRS in that community (R2 = 0.88, p < 0.01) and Yingshang County (R2 = 0.91, p < 0.01). Am J Epidemiol 1998;148:276–81. [ABSTRACT FROM AUTHOR]

This study aimed to investigate the effects of temperature variability on rotavirus infections among children under 5 years of age in Kathmandu, Nepal. Findings may inform infection control planning, especially in relation to the role of environmental factors in the transmission of rotavirus infection. Generalized linear Poisson regression equations with distributed lag non-linear model were fitted to estimate the effect of temperature (maximum, mean and minimum) variation on weekly counts of rotavirus infections among children under 5 years of age living in Kathmandu, Nepal, over the study period (2013 to 2016). Seasonality and long-term effects were adjusted in the model using Fourier terms up to the seventh harmonic and a time function, respectively. We further adjusted the model for the confounding effects of rainfall and relative humidity. During the study period, a total of 733 cases of rotavirus infection were recorded, with a mean of 3 cases per week. We detected an inverse non-linear association between rotavirus infection and average weekly mean temperature, with increased risk (RR: 1.52; 95% CI: 1.08–2.15) at the lower quantile (10th percentile) and decreased risk (RR: 0.64; 95% CI: 0.43–0.95) at the higher quantile (75th percentile). Similarly, we detected an increased risk [(RR: 1.93; 95% CI: 1.40–2.65) and (RR: 1.42; 95% CI: 1.04–1.95)] of rotavirus infection for both maximum and minimum temperature at their lower quantile (10th percentile). We estimated that 344 (47.01%) cases of rotavirus diarrhoea among the children under 5 years of age were attributable to minimum temperature. The significant effect of temperature on rotavirus infection was not observed beyond lag zero week. An inverse non-linear association was estimated between rotavirus incidence and all three indices of temperature, indicating a higher risk of infection during the cooler times of the year, and suggesting that transmission of rotavirus in Kathmandu, Nepal may be influenced by temperature. Unlabelled Image • Rotavirus infection risk increased with decreased in all three temperature indices. • An inverse nonlinear association was observed between rotavirus infection and temperature. • Relative risk was higher for lower percentiles of temperature. • 47.01% of cases (<5 years age) were attributable to minimum temperature. • No significant association was observed with rainfall and relative humidity. [ABSTRACT FROM AUTHOR]