Interaction of Mean Temperature and Daily Fluctuation Influences Dengue Incidence in Dhaka,Bangladesh

Local weather influences the transmission of the dengue virus. Most studies analyzing the relationship between dengue and climate are based on relatively coarse aggregate measures such as mean temperature. Here, we include both mean temperature and daily fluctuations in temperature in modelling dengue transmission in Dhaka, the capital of Bangladesh. We used a negative binomial generalized linear model, adjusted for rainfall, anomalies in sea surface temperature (an index for El Niño-Southern Oscillation), population density, the number of dengue cases in the previous month, and the long term temporal trend in dengue incidence. In addition to the significant associations of mean temperature and temperature fluctuation with dengue incidence, we found interaction of mean and temperature fluctuation significantly influences disease transmission at a lag of one month. High mean temperature with low fluctuation increases dengue incidence one month later. Besides temperature, dengue incidence was also influenced by sea surface temperature anomalies in the current and previous month, presumably as a consequence of concomitant anomalies in the annual rainfall cycle. Population density exerted a significant positive influence on dengue incidence indicating increasing risk of dengue in over-populated Dhaka. Understanding these complex relationships between climate, population, and dengue incidence will help inform outbreak prediction and control.     

Modeling and Predicting Seasonal Influenza Transmission in Warm Regions Using Climatological Parameters

Background

Influenza transmission is often associated with climatic factors. As the epidemic pattern varies geographically, the roles of climatic factors may not be unique. Previous in vivo studies revealed the direct effect of winter-like humidity on air-borne influenza transmission that dominates in regions with temperate climate, while influenza in the tropics is more effectively transmitted through direct contact.

Methodology/Principal Findings

Using time series model, we analyzed the role of climatic factors on the epidemiology of influenza transmission in two regions characterized by warm climate: Hong Kong (China) and Maricopa County (Arizona, USA). These two regions have comparable temperature but distinctly different rainfall. Specifically we employed Autoregressive Integrated Moving Average (ARIMA) model along with climatic parameters as measured from ground stations and NASA satellites. Our studies showed that including the climatic variables as input series result in models with better performance than the univariate model where the influenza cases depend only on its past values and error signal. The best model for Hong Kong influenza was obtained when Land Surface Temperature (LST), rainfall and relative humidity were included as input series. Meanwhile for Maricopa County we found that including either maximum atmospheric pressure or mean air temperature gave the most improvement in the model performances.

Conclusions/Significance

Our results showed that including the environmental variables generally increases the prediction capability. Therefore, for countries without advanced influenza surveillance systems, environmental variables can be used for estimating influenza transmission at present and in the near future.     

Socio-Economic Factors of Bacillary Dysentery Based on Spatial Correlation Analysis in Guangxi Province,China

Background

In the past decade, bacillary dysentery was still a big public health problem in China, especially in Guangxi Province, where thousands of severe diarrhea cases occur every year.

Methods

Reported bacillary dysentery cases in Guangxi Province were obtained from local Centers for Diseases Prevention and Control. The 14 socio-economic indexes were selected as potential explanatory variables for the study. The spatial correlation analysis was used to explore the associations between the selected factors and bacillary dysentery incidence at county level, which was based on the software of ArcGIS10.2 and GeoDA 0.9.5i.

Results

The proportion of primary industry, the proportion of younger than 5-year-old children in total population, the number of hospitals per thousand persons and the rates of bacillary dysentery incidence show statistically significant positive correlation. But the proportion of secondary industry, per capital GDP, per capital government revenue, rural population proportion, popularization rate of tap water in rural area, access rate to the sanitation toilets in rural, number of beds in hospitals per thousand persons, medical and technical personnel per thousand persons and the rate of bacillary dysentery incidence show statistically significant negative correlation. The socio-economic factors can be divided into four aspects, including economic development, health development, medical development and human own condition. The four aspects were not isolated from each other, but interacted with each other.     

基于MaxEnt模型西南地区高山植被对气候变化的响应评估

采用1∶100万的中国植被类型图以及19个气候环境变量数据,基于最大熵(MaxEnt)算法和ArcGIS空间分析模块构建西南地区高山植被地理分布的气候适宜性预测模型,模拟其在基准期(1960—2000年)和不同气候情景下(A2、A1B和B1)的气候适宜性分布格局,并评价其对气候变化的适应性。结果表明:MaxEnt模型分析研究区高山植被地理分布气候适宜性的适用性非常高(AUC=0.93);最暖月均温、最湿季均温、最冷月均温等温度变量是限制其地理分布的主要气候因子;研究区高山植被地理分布的气候适宜区主要集中在西藏自治区、青海省、四川省西部及云南省西北部的部分地区;完全适宜、中度适宜、轻度适宜、不适宜的面积所占总面积比例约为1∶1∶2∶5;1960—2050年研究区高山植被潜在地理分布的气候适宜性面积有不同定程度的减少;未来3种气候变化情景下高山植被地理分布对气候变化的适应性分布格局基本一致,均为不适应区所占总面积比例较大;伴随气候变化,研究区高山植被的适应性减弱,体现在其潜在地理分布对气候变化的适应区分布范围减少;海拔5000—5500m适应性较强,适应区所占面积比例最大(53%左右);3500—4500m适应性最弱,适应区所占面积比例最小(5%左右)。     

Application of a Hybrid Model for Predicting the Incidence of Tuberculosis in Hubei,China

Background

A prediction model for tuberculosis incidence is needed in China which may be used as a decision-supportive tool for planning health interventions and allocating health resources.

Methods

The autoregressive integrated moving average (ARIMA) model was first constructed with the data of tuberculosis report rate in Hubei Province from Jan 2004 to Dec 2011.The data from Jan 2012 to Jun 2012 were used to validate the model. Then the generalized regression neural network (GRNN)-ARIMA combination model was established based on the constructed ARIMA model. Finally, the fitting and prediction accuracy of the two models was evaluated.

Results

A total of 465,960 cases were reported between Jan 2004 and Dec 2011 in Hubei Province. The report rate of tuberculosis was highest in 2005 (119.932 per 100,000 population) and lowest in 2010 (84.724 per 100,000 population). The time series of tuberculosis report rate show a gradual secular decline and a striking seasonal variation. The ARIMA (2, 1, 0) × (0, 1, 1)₁₂ model was selected from several plausible ARIMA models. The residual mean square error of the GRNN-ARIMA model and ARIMA model were 0.4467 and 0.6521 in training part, and 0.0958 and 0.1133 in validation part, respectively. The mean absolute error and mean absolute percentage error of the hybrid model were also less than the ARIMA model.

Discussion and Conclusions

The gradual decline in tuberculosis report rate may be attributed to the effect of intensive measures on tuberculosis. The striking seasonal variation may have resulted from several factors. We suppose that a delay in the surveillance system may also have contributed to the variation. According to the fitting and prediction accuracy, the hybrid model outperforms the traditional ARIMA model, which may facilitate the allocation of health resources in China.     

Predicting Local Dengue Transmission in Guangzhou,China, through the Influence of Imported Cases,Mosquito Density and Climate Variability

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.     

气候变暖背景下春兰和蕙兰的适生区分布预测

为了阐明气候变暖背景下春兰(Cymbidium goeringii)和蕙兰(C. faberi)在我国的适生区分布变化情况,根据157条分布记录和19个生物气候变量,应用最大熵物种分布模型,对2070年4种温室气体排放情景下春兰和蕙兰在我国的适生区分布进行预测,并筛选影响其地理分布的主要气候因子。结果表明:(1)2070年春兰和蕙兰分布点的年均温(bio1)、最冷月最低温度(bio6)和最冷季平均温度(bio11)等均升高,气候有变暖趋势;(2)受试者工作特征曲线下面积(AUC)值在0.9—1.0之间,模型预测结果可信度较高;(3)影响春兰、蕙兰当前和2070年地理分布的限制性气候因子主要有最冷月最低温度(bio6)、最冷季平均温度(bio11)、年均降水量(bio12)和最干月份降水量(bio14);(4)气候变暖将会对春兰和蕙兰的适宜生境范围和面积产生影响。预测2070年春兰的适宜生境面积将会有所减小,而蕙兰的适宜生境面积将会增加,且整体有向北迁移的趋势。研究结果为野生春兰和蕙兰的生态风险评价和引种提供了重要依据。     

阿尔泰山萨彦岭4种优势树种径向生长对气候因子的响应

气候变化深刻地影响森林树木的生长,而树种对气候变化敏感度的差异可能影响了气候变化下的森林生态系统响应。因此,研究优势树种间生长对气候变化的敏感度差异,对正确认识气候变化下林分生长动态及分布格局十分重要。基于树木年代学的方法,研究了阿尔泰山萨彦岭西伯利亚落叶松（Larix sibirica）、西伯利亚红松（Pinus sibirica）、西伯利亚冷杉（Abies sibirica）以及西伯利亚云杉（Picea obovata）4种优势树种的径向生长-气候关系。结果显示：（1）西伯利亚冷杉径向生长与上一年10-11月、当年1-9月的干旱指数、2-4月的降水显著正相关,与1月的平均温和最高温呈显著负相关关系,与当年4、6月份的水汽压正相关;（2）西伯利亚落叶松径向生长与上一年8月和当年8月的平均温、最高温以及当年8月的最低温显著负相关,而与当年6月的最低温则正相关,与8月份的水汽压显著负相关;（3）西伯利亚红松径向生长与3月降水、7月最低温、上一年10月的水汽压显著正相关;（4）西伯利亚云杉径向生长与6月平均温、最高温、水汽压正相关,与上一年10-11月、当年2-4月和9月的干旱指数正相关,同时与3、4月的降水量显著正相关。西伯利亚冷杉和西伯利亚云杉、西伯利亚云杉和西伯利亚落叶松、西伯利亚云杉和西伯利亚红松对于特定气候因子表现出相似的响应结果,与年表间相关性的结果一致。但差异也是明显的,西伯利亚冷杉和西伯利亚云杉对区域水分变化敏感,而西伯利亚落叶松和西伯利亚红松主要对区域温度变化敏感。综上所述,气候变化下,该区域优势树种对气候变化响应的差异可能导致区域林分动态和格局的改变,因此,多树种径向生长-气候关系研究有助于正确反映森林动态。研究结果可以为区域森林管理与生态保护工作提供理论依据。     

Predicting Malaria occurrence in Southwest and North central Nigeria using Meteorological parameters

Malaria is a major public health problem especially in the tropics with the potential to significantly increase in response to changing weather and climate. This study explored the impact of weather and climate and its variability on the occurrence and transmission of malaria in Akure, the tropical rain forest area of southwest and Kaduna, in the savanna area of Nigeria. We investigate this supposition by looking at the relationship between rainfall, relative humidity, minimum and maximum temperature, and malaria at the two stations. This study uses monthly data of 7 years (2001–2007) for both meteorological data and record of reported cases of malaria infection. Autoregressive integrated moving average (ARIMA) models were used to evaluate the relationship between weather factors and malaria incidence. Of all the models tested, the ARIMA (1, 0, 1) model fits the malaria incidence data best for Akure and Kaduna according to normalized Bayesian information criterion (BIC) and goodness-of-fit criteria. Humidity and rainfall have almost the same trend of association in all the stations while maximum temperature share the same negative association at southwestern stations and positive in the northern station. Rainfall and humidity have a positive association with malaria incidence at lag of 1 month. In all, we found that minimum temperature is not a limiting factor for malaria transmission in Akure but otherwise in the other stations.     

珍稀濒危植物野生莲的适生分布区预测

基于野生莲（Nelumbo nucifera Gaertn.）136个分布点的数据和14个环境因子参数,运用规则集遗传算法（GARP）和最大熵（MaxEnt）两个生态位模型对他们在我国的适生分布区进行预测。结果显示：根据GARP和MaxEnt模型计算得到的ROC曲线下面积的AUC均值分别为0.861和0.964,其中MaxEnt模型的AUC值更大,预测结果更精准。MaxEnt模型预测结果表明,莲的最适分布区主要集中在四川、湖北、湖南等地的大部分地区,江西北部,以及黑龙江、辽宁、浙江、广东等地的小部分地区。刀切法（Jackknife）检测结果表明,影响莲适生分布区的主要环境因子包括：水汽压、海拔、年平均气温、多年平均降水量、最热季节平均温度、最冷季节平均温度、最干月降水量、最冷月最低温和最热月最高温等。适生区环境因子的统计分析结果显示,野生莲最适宜生长在海拔1~2216 m、年降水量丰富（1202.50 mm）、年均温约为16.19℃、最热月温度范围在24.60℃~35.10℃、最冷月均温不低于-0.53℃的地区。研究结果可为有效保护中国野生莲资源提供有利依据。     

Weather factors in the short-term forecasting of daily ambulance calls

The daily ambulance demand for Hong Kong is rising, and it has been shown that weather factors (temperature and humidity) play a role in the demand for ambulance services. This study aimed at developing short-term forecasting models of daily ambulance calls using the 7-day weather forecast data as predictors. We employed the autoregressive integrated moving average (ARIMA) method to analyze over 1.3 million cases of emergency attendance in May 2006 through April 2009 and the 7-day weather forecast data for the same period. Our results showed that the ARIMA model could offer reasonably accurate forecasts of daily ambulance calls at 1–7 days ahead of time and with improved accuracy by including weather factors. Specifically, the inclusion of average temperature alone in our ARIMA model improved the predictability of the 1-day forecast when compared to that of a simple ARIMA model (8.8 % decrease in the root mean square error, RMSE?=?53 vs 58). The improvement in the 7-day forecast with average temperature as a predictor was more pronounced, with a 10 % drop in prediction error (RMSE?=?62 vs 69). These findings suggested that weather forecast data can improve the 1- to 7-day forecasts of daily ambulance demand. As weather forecast data are readily accessible from Hong Kong Observatory’s official website, there is virtually no cost to including them in the ARIMA models, which yield better prediction for forward planning and deployment of ambulance manpower.     

Forecast Model Analysis for the Morbidity of Tuberculosis in Xinjiang,China

Tuberculosis is a major global public health problem, which also affects economic and social development. China has the second largest burden of tuberculosis in the world. The tuberculosis morbidity in Xinjiang is much higher than the national situation; therefore, there is an urgent need for monitoring and predicting tuberculosis morbidity so as to make the control of tuberculosis more effective. Recently, the Box-Jenkins approach, specifically the autoregressive integrated moving average (ARIMA) model, is typically applied to predict the morbidity of infectious diseases; it can take into account changing trends, periodic changes, and random disturbances in time series. Autoregressive conditional heteroscedasticity (ARCH) models are the prevalent tools used to deal with time series heteroscedasticity. In this study, based on the data of the tuberculosis morbidity from January 2004 to June 2014 in Xinjiang, we establish the single ARIMA (1, 1, 2) (1, 1, 1)₁₂ model and the combined ARIMA (1, 1, 2) (1, 1, 1)₁₂-ARCH (1) model, which can be used to predict the tuberculosis morbidity successfully in Xinjiang. Comparative analyses show that the combined model is more effective. To the best of our knowledge, this is the first study to establish the ARIMA model and ARIMA-ARCH model for prediction and monitoring the monthly morbidity of tuberculosis in Xinjiang. Based on the results of this study, the ARIMA (1, 1, 2) (1, 1, 1)₁₂-ARCH (1) model is suggested to give tuberculosis surveillance by providing estimates on tuberculosis morbidity trends in Xinjiang, China.     

Tracing the future of epidemics: Coincident niche distribution of host animals and disease incidence revealed climate-correlated risk shifts of main zoonotic diseases in China

Climate has critical roles in the origin, pathogenesis and transmission of infectious zoonotic diseases. However, large-scale epidemiologic trend and specific response pattern of zoonotic diseases under future climate scenarios are poorly understood. Here, we projected the distribution shifts of transmission risks of main zoonotic diseases under climate change in China. First, we shaped the global habitat distribution of main host animals for three representative zoonotic diseases (2, 6, and 12 hosts for dengue, hemorrhagic fever, and plague, respectively) with 253,049 occurrence records using maximum entropy (Maxent) modeling. Meanwhile, we predicted the risk distribution of the above three diseases with 197,098 disease incidence records from 2004 to 2017 in China using an integrated Maxent modeling approach. The comparative analysis showed that there exist highly coincident niche distributions between habitat distribution of hosts and risk distribution of diseases, indicating that the integrated Maxent modeling is accurate and effective for predicting the potential risk of zoonotic diseases. On this basis, we further projected the current and future transmission risks of 11 main zoonotic diseases under four representative concentration pathways (RCPs) (RCP2.6, RCP4.5, RCP6.0, and RCP8.5) in 2050 and 2070 in China using the above integrated Maxent modeling with 1,001,416 disease incidence records. We found that Central China, Southeast China, and South China are concentrated regions with high transmission risks for main zoonotic diseases. More specifically, zoonotic diseases had diverse shift patterns of transmission risks including increase, decrease, and unstable. Further correlation analysis indicated that these patterns of shifts were highly correlated with global warming and precipitation increase. Our results revealed how specific zoonotic diseases respond in a changing climate, thereby calling for effective administration and prevention strategies. Furthermore, these results will shed light on guiding future epidemiologic prediction of emerging infectious diseases under global climate change.     

Seasonal Variation in Ischemic Stroke Incidence and Association with Climate: A Six‐Year Population‐Based Study

Questions about the seasonality of stroke remain controversial. Using a nationwide population‐based dataset, this study presents a time series analysis of seasonal patterns in ischemic stroke occurrence, along with their association with climate in Taiwan. Using data from the Taiwan National Health Insurance Research Database, a total of 168,977 visits to emergency departments between 1998 and 2003 for ischemic stroke were identified for patients ranging between 20 and 84 yrs of age. Monthly stroke incidences were calculated for 72 months, by sex and stroke subtype, and for the age groups 20–54, 55–64, 65–74, and ≥75 yrs per 100,000 of the population. We performed auto‐regressive integrated moving average (ARIMA) analysis to investigate the presence of seasonality and any association with climate for acute ischemic stroke events. We found no significant seasonal variation in the incidence of ischemic stroke for any age or sex groups. Furthermore, after adjusting for seasonality, month, and trend, the ARIMA regression model revealed only associations between ischemic stroke incidence and atmospheric pressure. We conclude that seasonality of ischemic stroke does not exist in Taiwan. Ischemic stroke incidence is, however, significantly related to atmospheric pressure.     

银环蛇咬伤致自主呼吸衰竭984小时抢救成功1例报告

本文报道了我院抢救成功１例煞环蛇咬伤致深昏迷、自主呼吸衰竭９８４小时,并有ＡＲＤＳ、肺部感染、上消化道出血,颈部气管切口周围峰窝组织炎、右侧气胸、纵隔气肿、颈胸部皮下气肿、细菌性痢疾等并发症的病人,讨论了其救治经验及教训。     

Applied Mixed Generalized Additive Model to Assess the Effect of Temperature on the Incidence of Bacillary Dysentery and Its Forecast

Background

Association between bacillary dysentery (BD) disease and temperature has been reported in some studies applying Poisson regression model, however the effect estimation might be biased due to the data autocorrelation. Furthermore the temperature effect distributed in the time of different lags has not been studied either. The purpose of this work was to obtaining the association between the BD counts and the climatic factors such as temperature in the form of the weighted averages, concerning the autocorrelation pattern of the model residuals, and to make short term predictions using the model. The data was collected in the city of Shanghai from 2004 to 2008.

Methods

We used mixed generalized additive model (MGAM) to analyze data on bacillary dysentery, temperature and other covariates with autoregressive random effect. Short term predictions were made using MGAM with the moving average of the BD counts.

Main Results

Our results showed that temperature was significant linearly associated with the logarithm of BD count for temperature in the range from 12°C to 22°C. Optimal weights in the temperature effect have been obtained, in which the one of 1-day-lag was close to 0, and the one of 2-days-lag was the maximum (p-value of the difference was less than 0.05). The predictive model was showing good fitness on the internal data with R² value 0.875, and the good short term prediction effect on the external data with correlation coefficient to be 0.859.

Conclusion

According to the model estimation, corresponding Risk Ratio to affect BD was close to 1.1 when temperature effect goes up for 1°C in the range from 12°C to 22°C. And the 1-day incubation period could be inferred from the model estimation. Good prediction has been made using the predictive MGAM.     

Ecological footprint simulation and prediction by ARIMA model—A case study in Henan Province of China

Ecological footprint (EF), as one of the sustainable development indicators, has received considerable attention. However, it has mostly been used as a static indicator. The accurate quantitative analysis on its development trend is still rare. Thus, the autoregressive integrated moving average (ARIMA) model was introduced to enhance the forecasting capacity of EF indicator. Taking Henan Province of China as a study area, we firstly computed the EF and the ecological carrying capacity (EC) in 1949–2006. Based on the computed results, the simulating process of the ARIMA model and the fitting and forecasting results were explained in detail. The final results demonstrated that ARIMA model could be used effectively in the simulation and prediction of EF and the predicted EF could help the decision-makers make a package of better planning for regional ecological balance or sustainable future.     

New animal model of shigellosis in the Guinea pig: its usefulness for protective efficacy studies

It has been difficult to evaluate the protective efficacy of vaccine candidates against shigellosis, a major form of bacillary dysentery caused by Shigella spp. infection, because of the lack of suitable animal models. To develop a proper animal model representing human bacillary dysentery, guinea pigs were challenged with virulent Shigella flexneri serotype 2a (strains 2457T or YSH6000) or S. flexneri 5a (strain M90T) by the intrarectal (i.r.) route. Interestingly, all guinea pigs administered these Shigella strains developed severe and acute rectocolitis. They lost approximately 20% of their body weight and developed tenesmus by 24 h after Shigella infection. Shigella invasion and colonization of the distal colon were seen at 24 h but disappeared by 48 h following i.r. infection. Histopathological approaches demonstrated significant damage and destruction of mucosal and submucosal layers, thickened intestinal wall, edema, erosion, infiltration of neutrophils, and depletion of goblet cells in the distal colon. Furthermore, robust expression of IL-8, IL-1beta, and inducible NO synthase mRNA was detected in the colon from 6 to 24 h following Shigella infection. Most importantly, in our new shigellosis model, guinea pigs vaccinated with an attenuated S. flexneri 2a SC602 strain possessing high levels of mucosal IgA Abs showed milder symptoms of bacillary dysentery than did animals receiving PBS alone after Shigella infection. In the guinea pig, administration of Shigella by i.r. route induces acute inflammation, making this animal model useful for assessing the protective efficacy of Shigella vaccine candidates.     

The formulation of epidemiological hypotheses by using the methods of multivariate statistical analysis

The work demonstrates the main approaches to the use of the methods of multidimensional analysis for the creation of a hypothesis on the mechanism of the epidemiological process of dysentery in organized groups. The main risk factors have been established, and their role in the formation of annual, all-the-year-round and seasonal dysentery morbidity has been quantitatively evaluated. The results of analysis show the existence of diverse variants of the alimentary route of the transmission of infection, maintaining the epidemic process of dysentery, and the necessity of differentiating measures for the prophylaxis of all-the-year-round and seasonal morbidity.