Wenbiao Hu

Dengue transmission in the Asia‐Pacific region: impact of climate change and socio‐environmental factors

Objective  To review the scientific evidence about the impact of climate change and socio‐environmental factors on dengue transmission, particularly in the Asia‐Pacific region.

Epidemiologic Features of Severe Fever With Thrombocytopenia Syndrome in China, 2011–2012

Severe fever with thrombocytopenia syndrome (SFTS), an emerging vector-borne disease, is caused by a novel bunyavirus belonging to the genus Phlebovirus [1, 2]. SFTS infections can be life-threatening and are characterized by sudden onset of fever, thrombocytopenia, gastrointestinal symptoms, and leukocytopenia. The tick Haemaphysalis longicornis is generally considered to be the vector of SFTS, which is widely distributed in China [2]. Person-to-person transmission through direct contact with contaminated blood has also been reported as a possible means of SFTS transmission [3–5]. Currently, there is no specific treatment other than supportive care [6]...

Dengue fever and El Niño/Southern Oscillation in Queensland, Australia: a time series predictive model.

Background It remains unclear over whether it is possible to develop an epidemic forecasting model for transmission of dengue fever in Queensland, Australia. Objectives To examine the potential impact of El Niño/Southern Oscillation on the transmission of dengue fever in Queensland, Australia and explore the possibility of developing a forecast model of dengue fever. Methods Data on the Southern Oscillation Index (SOI), an indicator of El Niño/Southern Oscillation activity, were obtained from the Australian Bureau of Meteorology. Numbers of dengue fever cases notified and the numbers of postcode areas with dengue fever cases between January 1993 and December 2005 were obtained from the Queensland Health and relevant population data were obtained from the Australia Bureau of Statistics. A multivariate Seasonal Auto-regressive Integrated Moving Average model was developed and validated by dividing the data file into two datasets: the data from January 1993 to December 2003 were used to construct a model and those from January 2004 to December 2005 were used to validate it. Results A decrease in the average SOI (ie, warmer conditions) during the preceding 3–12 months was significantly associated with an increase in the monthly numbers of postcode areas with dengue fever cases (β=−0.038; p = 0.019). Predicted values from the Seasonal Auto-regressive Integrated Moving Average model were consistent with the observed values in the validation dataset (root-mean-square percentage error: 1.93%). Conclusions Climate variability is directly and/or indirectly associated with dengue transmission and the development of an SOI-based epidemic forecasting system is possible for dengue fever in Queensland, Australia.

Climate variability and Ross River virus transmission in Townsville Region, Australia, 1985-1996

Background  How climate variability affects the transmission of infectious diseases at a regional level remains unclear. We assess the impact of climate variation on the Ross River virus (RRv) transmission in the Townsville region, Queensland, north‐east Australia.

Spatial patterns and socioecological drivers of dengue fever transmission in Queensland, Australia.

Background: Understanding how socioecological factors affect the transmission of dengue fever (DF) may help to develop an early warning system of DF. Objectives: We examined the impact of socioecological factors on the transmission of DF and assessed potential predictors of locally acquired and overseas-acquired cases of DF in Queensland, Australia. Methods: We obtained data from Queensland Health on the numbers of notified DF cases by local government area (LGA) in Queensland for the period 1 January 2002 through 31 December 2005. Data on weather and the socioeconomic index were obtained from the Australian Bureau of Meteorology and the Australian Bureau of Statistics, respectively. A Bayesian spatial conditional autoregressive model was fitted at the LGA level to quantify the relationship between DF and socioecological factors. Results: Our estimates suggest an increase in locally acquired DF of 6% [95% credible interval (CI): 2%, 11%] and 61% (95% CI: 2%, 241%) in association with a 1-mm increase in average monthly rainfall and a 1°C increase in average monthly maximum temperature between 2002 and 2005, respectively. By contrast, overseas-acquired DF cases increased by 1% (95% CI: 0%, 3%) and by 1% (95% CI: 0%, 2%) in association with a 1-mm increase in average monthly rainfall and a 1-unit increase in average socioeconomic index, respectively. Conclusions: Socioecological factors appear to influence the transmission of DF in Queensland, but the drivers of locally acquired and overseas-acquired DF may differ. DF risk is spatially clustered with different patterns for locally acquired and overseas-acquired cases.

Projecting the impact of climate change on dengue transmission in Dhaka, Bangladesh.

Weather variables, mainly temperature and humidity influence vectors, viruses, human biology, ecology and consequently the intensity and distribution of the vector-borne diseases. There is evidence that warmer temperature due to climate change will influence the dengue transmission. However, long term scenario-based projections are yet to be developed. Here, we assessed the impact of weather variability on dengue transmission in a megacity of Dhaka, Bangladesh and projected the future dengue risk attributable to climate change. Our results show that weather variables particularly temperature and humidity were positively associated with dengue transmission. The effects of weather variables were observed at a lag of four months. We projected that assuming a temperature increase of 3.3°C without any adaptation measure and changes in socio-economic condition, there will be a projected increase of 16,030 dengue cases in Dhaka by the end of this century. This information might be helpful for the public health authorities to prepare for the likely increase of dengue due to climate change. The modelling framework used in this study may be applicable to dengue projection in other cities.

Climate Change and Children's Health—A Call for Research on What Works to Protect Children

Climate change is affecting and will increasingly influence human health and wellbeing. Children are particularly vulnerable to the impact of climate change. An extensive literature review regarding the impact of climate change on children’s health was conducted in April 2012 by searching electronic databases PubMed, Scopus, ProQuest, ScienceDirect, and Web of Science, as well as relevant websites, such as IPCC and WHO. Climate change affects children’s health through increased air pollution, more weather-related disasters, more frequent and intense heat waves, decreased water quality and quantity, food shortage and greater exposure to toxicants. As a result, children experience greater risk of mental disorders, malnutrition, infectious diseases, allergic diseases and respiratory diseases. Mitigation measures like reducing carbon pollution emissions, and adaptation measures such as early warning systems and post-disaster counseling are strongly needed. Future health research directions should focus on: (1) identifying whether climate change impacts on children will be modified by gender, age and socioeconomic status; (2) refining outcome measures of children’s vulnerability to climate change; (3) projecting children’s disease burden under climate change scenarios; (4) exploring children’s disease burden related to climate change in low-income countries; and (5) identifying the most cost-effective mitigation and adaptation actions from a children’s health perspective.

Spatiotemporal Transmission Dynamics of Hemorrhagic Fever with Renal Syndrome in China, 2005–2012

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 Kulldorffs 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 Morans 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.

Extreme temperatures and paediatric emergency department admissions

Background Children are particularly vulnerable to the effects of extreme temperatures. Objective To examine the relationship between extreme temperatures and paediatric emergency department admissions (EDAs) in Brisbane, Australia, during 2003–2009. Methods A quasi-Poisson generalised linear model combined with a distributed lag non-linear model was used to examine the relationships between extreme temperatures and age-, gender- and cause-specific paediatric EDAs, while controlling for air pollution, relative humidity, day of the week, influenza epidemics, public holiday, season and long-term trends. The model residuals were checked to identify whether there was an added effect due to heat waves or cold spells. Results There were 131 249 EDAs among children during the study period. Both high (RR=1.27; 95% CI 1.12 to 1.44) and low (RR=1.81; 95% CI 1.66 to 1.97) temperatures were significantly associated with an increase in paediatric EDAs in Brisbane. Male children were more vulnerable to temperature effects. Children aged 0–4 years were more vulnerable to heat effects and children aged 10–14 years were more sensitive to both hot and cold effects. High temperatures had a significant impact on several paediatric diseases, including intestinal infectious diseases, respiratory diseases, endocrine, nutritional and metabolic diseases, nervous system diseases and chronic lower respiratory diseases. Low temperatures were significantly associated with intestinal infectious diseases, respiratory diseases and endocrine, nutritional and metabolic diseases. An added effect of heat waves on childhood chronic lower respiratory diseases was seen, but no added effect of cold spells was found. Conclusions As climate change continues, children are at particular risk of a variety of diseases which might be triggered by extremely high temperatures. This study suggests that preventing the effects of extreme temperature on children with respiratory diseases might reduce the number of EDAs.

Mosquito species (Diptera: Culicidae) and the transmission of ross river virus in Brisbane, Australia

Abstract This study aimed to identify the major mosquito vectors of Ross River virus (family Togaviridae, genus Alphavirus, RRV) and to explore the threshold of mosquito abundance necessary for RRV transmission in Brisbane, Australia. Data on the monthly counts of RRV cases by statistical local areas from the Queensland Health and the monthly mosquito abundance in Brisbane between November 1998 and December 2001 from the Brisbane City Council were used to assess the pairwise relationship between mosquito abundance and the incidence of RRV disease over a range of time lags using cross-correlations. We used time series Poisson regression models to identify major mosquito species associated with incidence of RRV after adjusting for overdispersion, maximum temperature, autocorrelation, and seasonality. Our results show that Aedes vigilax (Skuse) (relative risk [RR] = 1.32; 95% CI = 1.01–1.74 per 100 mosquitoes per trap) and Culex annulirostris (Skuse) (RR = 1.14, 95% CI = 1.04–1.24 per 100 mosquitoes per trap) were most strongly associated with RRV transmission at a lag of 1 mo. Classification and regression tree (CART) analyses indicate that the occurrence of RRV was associated with an average monthly mosquito abundance of Aedes vigilax above 72 and Cx. annulirostris above 52. The validation analyses indicate that the crude agreement between predicted values and actual observations was 76% (sensitivity, 61%; specificity, 80%). The results may have applications as a decision support tool in planning disease control and risk-management programs.