Clinical Malaria Transmission Trends and Its Association with Climatic Variables in Tubu Village,Botswana: A Retrospective Analysis

Good knowledge on the interactions between climatic variables and malaria can be very useful for predicting outbreaks and preparedness interventions. We investigated clinical malaria transmission patterns and its temporal relationship with climatic variables in Tubu village, Botswana. A 5-year retrospective time series data analysis was conducted to determine the transmission patterns of clinical malaria cases at Tubu Health Post and its relationship with rainfall, flood discharge, flood extent, mean minimum, maximum and average temperatures. Data was obtained from clinical records and respective institutions for the period July 2005 to June 2010, presented graphically and analysed using the Univariate ANOVA and Pearson cross-correlation coefficient tests. Peak malaria season occurred between October and May with the highest cumulative incidence of clinical malaria cases being recorded in February. Most of the cases were individuals aged >5 years. Associations between the incidence of clinical malaria cases and several factors were strong at lag periods of 1 month; rainfall (r = 0.417), mean minimum temperature (r = 0.537), mean average temperature (r = 0.493); and at lag period of 6 months for flood extent (r = 0.467) and zero month for flood discharge (r = 0.497). The effect of mean maximum temperature was strongest at 2-month lag period (r = 0.328). Although malaria transmission patterns varied from year to year the trends were similar to those observed in sub-Saharan Africa. Age group >5 years experienced the greatest burden of clinical malaria probably due to the effects of the national malaria elimination programme. Rainfall, flood discharge and extent, mean minimum and mean average temperatures showed some correlation with the incidence of clinical malaria cases.     

AVHRR NDVI与气候因子的相关分析

对中国１６０个气象站１０ａ的连续ＡＶＨＲＲ ＮＤＶＩ数据、气象观测数据进行相关分析,并结合植被覆盖类型资料深入探讨了ＡＶＨＲＲ ＮＤＶＩ／气温和ＡＶＨＲＲ ＮＤＶＩ／降水相关系数的地区差异及其随植被类型变化规律。研究结果表明,对中国的大部分地区,气温对植被的影响超过降水。就自然植被而言,其对降水的敏感性趋势为草本植被大于灌木植被,灌木植被大于乔木植被。就农作物而言,降水影响取决于耕作制度、作物种类     

Disease forecasting model for newly emerging bacterial seed and boll rot of cotton disease and its vector (Dysdercus cingulatus)

Bacterial seed and boll rot disease is a newly emerging threat to the cotton growers. Disease prediction model was devised to predict the disease progression impacted by the vector (Dysdercus cingulatus) and environmental variables (maximum air temperature, minimum air temperature, relative humidity and rainfall) on four varieties to minimise its losses and disease management cost. Impact of a-biotic environmental variables (maximum and minimum air temperature, relative humidity and rainfall) was assessed on bacterial seed and rot of cotton disease and its vector (D. cingulatus) on FH-941, FH-942, MNH-886 and FH-114 cotton varieties. Maximum red cotton bug population was assessed at 29–31 °C maximum temperature and at 15–17 °C minimum temperature. Disease severity was noticed maximum when maximum and minimum temperature was measured at 28–29 °C and 13–14.5 °C, respectively. Vector population was maximum when relative humidity and rainfall were 63–66% and 1.50–2.5 mm, respectively. Relative humidity at 66–68% and 0.5–1.5 mm rainfall favoured disease development. With increase in number of bugs, increase in disease severity was noted, maximum disease severity 45–48% noticed when 7–8 bugs were recorded. Red cotton bug (Dysdercus cingulatus) population prediction model was devised based on a-biotic factors (maximum and minimum air temperature, relative humidity and rainfall) on four cotton varieties. Disease forecasting model was developed based on biotic (D. cingulatus) and a-biotic factors. A close resemblance between observed and the predicted red cotton bugs and disease severity was seen.     

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.     

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.     

Change in rainfall drives malaria re-emergence in anhui province, china

Malaria is re-emerging in Anhui Province, China after a decade long’ low level of endemicity. The number of human cases has increased rapidly since 2000 and reached its peak in 2006. That year, the malaria cases accounted for 54.5% of total cases in mainland China. However, the spatial and temporal patterns of human cases and factors underlying the re-emergence remain unclear. We established a database containing 20 years’ (1990–2009) records of monthly reported malaria cases and meteorological parameters. Spearman correlations were used to assess the crude association between malaria incidence and meteorological variables, and a polynomial distributed lag (PDL) time-series regression was performed to examine contribution of meteorological factors to malaria transmission in three geographic regions (northern, mid and southern Anhui Province), respectively. Then, a two-year (2008–2009) prediction was performed to validate the PDL model that was created by using the data collected from 1990 to 2007. We found that malaria incidence decreased in Anhui Province in 1990s. However, the incidence has dramatically increased in the north since 2000, while the transmission has remained at a relatively low level in the mid and south. Spearman correlation analyses showed that the monthly incidences of malaria were significantly associated with temperature, rainfall, relative humidity, and the multivariate El Niño/Southern Oscillation index with lags of 0–2 months in all three regions. The PDL model revealed that only rainfall with a 1–2 month lag was significantly associated with malaria incidence in all three regions. The model validation showed a high accuracy for the prediction of monthly incidence over a 2-year predictive period. Malaria epidemics showed a high spatial heterogeneity in Anhui Province during the 1990–2009 study periods. The change in rainfall drives the reemergence of malaria in the northern Anhui Province.     

Molecular surveillance of Plasmodium vivax dhfr and dhps mutations in isolates from Afghanistan

Background

In Burundi, malaria is a major public health issue in terms of both morbidity and mortality with around 2.5 million clinical cases and more than 15,000 deaths each year. It is the single main cause of mortality in pregnant women and children below five years of age. Due to the severe health and economic cost of malaria, there is still a growing need for methods that will help to understand the influencing factors. Several studies have been done on the subject yielding different results as which factors are most responsible for the increase in malaria. The purpose of this study has been to undertake a spatial/longitudinal statistical analysis to identify important climatic variables that influence malaria incidences in Burundi.

Methods

This paper investigates the effects of climate on malaria in Burundi. For the period 1996-2007, real monthly data on both malaria epidemiology and climate in the area of Burundi are described and analysed. From this analysis, a mathematical model is derived and proposed to assess which variables significantly influence malaria incidences in Burundi. The proposed modelling is based on both generalized linear models (GLM) and generalized additive mixed models (GAMM). The modelling is fully Bayesian and inference is carried out by Markov Chain Monte Carlo (MCMC) techniques.

Results

The results obtained from the proposed models are discussed and it is found that malaria incidence in a given month in Burundi is strongly positively associated with the minimum temperature of the previous month. In contrast, it is found that rainfall and maximum temperature in a given month have a possible negative effect on malaria incidence of the same month.

Conclusions

This study has exploited available real monthly data on malaria and climate over 12 years in Burundi to derive and propose a regression modelling to assess climatic factors that are associated with monthly malaria incidence. The results obtained from the proposed models suggest a strong positive association between malaria incidence in a given month and the minimum temperature (night temperature) of the previous month. An open question is, therefore, how to cope with high temperatures at night.     

Incidence of pests in jute (Corchorus olitorius L.) ecosystem and pest–weather relationships in West Bengal,India

Experiment on jute crop was conducted during pre-kharif to kharif seasons (April to August) at Bidhan Chandra Krishi Viswavidyalaya (BCKV), West Bengal, India in a view to record the pest incidence on olitorius jute and to determine the weather parameters impacting on pest population in jute under West Bengal conditions. Seventeen different species of pests belonging to insects, mites and nematodes were found feeding on jute. Among them, jute semilooper (Anomis sabulifera Guen.), Bihar hairy caterpillar (Spilarctia obliqua Wlk.), grey weevil (Myllocerus discolor Bohemus), yellow mite (Polyphagotarsonemus latus Banks), stem weevil (Apion corchori Marshall), and root knot nematode, Meloidogyne incognita are causing economic damage to the crop and other pests were considered as minor pests. Correlation studies with weather parameters showed that incidence of semilooper was negatively correlated (r = ?0.795 to ?0.725) with the maximum temperature but had positive significant association with minimum temperature (r = 0.528–0.715), morning relative humidity (RH) (r = 0.579–0.857) and afternoon RH (r = 0.876). Bihar hairy caterpillar incidence also exhibited positive significant relationship with morning RH (r = 0.577) and afternoon RH (r = 0.545). Morning and afternoon RH and rainfall also had significant positive correlation with the incidence of M. discolor with r = 0.535, 0.570, and 0.700, respectively. None of the meteorological parameters had any significant influence on the incidence of A. corchori. Yellow mite incidence showed positive association with morning RH (r = 0.563–0.679) and afternoon RH (r = 0.526–0.618). Rainfall was found favourable for proliferation and incidence of M. discolor only but had a negative effect on the incidence of stem weevil and yellow mite. Thus climatic factors particularly temperature, relative humidity and rainfall played a pivotal role on occurrence and existence of different pests on jute crop.     

Association of Climatic Variability,Vector Population and Malarial Disease in District of Visakhapatnam,India: A Modeling and Prediction Analysis

Background

Malarial incidence, severity, dynamics and distribution of malaria are strongly determined by climatic factors, i.e., temperature, precipitation, and relative humidity. The objectives of the current study were to analyse and model the relationships among climate, vector and malaria disease in district of Visakhapatnam, India to understand malaria transmission mechanism (MTM).

Methodology

Epidemiological, vector and climate data were analysed for the years 2005 to 2011 in Visakhapatnam to understand the magnitude, trends and seasonal patterns of the malarial disease. Statistical software MINITAB ver. 14 was used for performing correlation, linear and multiple regression analysis.

Results/Findings

Perennial malaria disease incidence and mosquito population was observed in the district of Visakhapatnam with peaks in seasons. All the climatic variables have a significant influence on disease incidence as well as on mosquito populations. Correlation coefficient analysis, seasonal index and seasonal analysis demonstrated significant relationships among climatic factors, mosquito population and malaria disease incidence in the district of Visakhapatnam, India. Multiple regression and ARIMA (I) models are best suited models for modeling and prediction of disease incidences and mosquito population. Predicted values of average temperature, mosquito population and malarial cases increased along with the year. Developed MTM algorithm observed a major MTM cycle following the June to August rains and occurring between June to September and minor MTM cycles following March to April rains and occurring between March to April in the district of Visakhapatnam. Fluctuations in climatic factors favored an increase in mosquito populations and thereby increasing the number of malarial cases. Rainfall, temperatures (20°C to 33°C) and humidity (66% to 81%) maintained a warmer, wetter climate for mosquito growth, parasite development and malaria transmission.

Conclusions/Significance

Changes in climatic factors influence malaria directly by modifying the behaviour and geographical distribution of vectors and by changing the length of the life cycle of the parasite.     

气象因素对心力衰竭的影响

目的:相关性分析近两年上海的气象因素及心力衰竭发病患者之间的关系,探讨影响心力衰竭高发的天气气候条件,为开展疾病预防和干预提供了理论和实践依据。方法:前瞻性统计2011年1月-2012年12月上海市胸科医院急诊的心力衰竭人数,并与同期气温,气压,湿度等气象资料进行相关性分析。结果:温度、气压、湿度对心衰的发生具有显著的影响。心力衰竭数与平均气温、最高气温、最低气温呈显著负相关,而与日平均气压呈显著正相关。前期相对湿度变化与心衰数显著相关。温度对心力衰竭数的影响具有滞后效应。结论:气温、气压及湿度与心力衰竭发病有明显相关,揭示了急性呼吸系统感染高发的天气气候条件,为开展疾病预防和干预提供了理论和实践依据。     

Forecasting model of bacterial leaf spot disease of mulberry caused by Xanthomonas campestris pv. mori

In order to develop weather-based forecasting model of bacterial leaf spot (BLS) disease of mulberry caused by Xanthomonas campestris pv. mori, weekly disease severity data were recorded for three years on the ruling cultivar S-1. Daily meteorological data viz. maximum temperature, minimum temperature, maximum relative humidity, minimum relative humidity, rainfall and number of rainy days were also recorded. It was observed that BLS appeared in April/May and continued up to November with maximum severity in July. The correlation coefficient between disease severity and meteorological parameters revealed that the BLS disease severity has significant positive correlation with minimum temperatures, maximum and minimum relative humidity, rainfall and number of rainy days and negative correlation with maximum temperature. Multiple regressions analysis revealed that average of maximum temperature, minimum temperature and rainfall of preceding seven days and maximum relative humidity, minimum relative humidity of previous 9–15 days was found to maximally influence BLS disease severity. The contribution of the meteorological factors was found to be highest of minimum temperature (40.65%) followed by maximum temperature (24.20%), maximum relative humidity (16.41%), minimum relative humidity (8.07%), rainfall (5.29%) and number of rainy days (5.38%).     

Modeling the role of environmental variables on the population dynamics of the malaria vector Anopheles gambiae sensu stricto

ABSTRACT: BACKGROUND: The impact of weather and climate on malaria transmission has attracted considerable attention in recent years, yet uncertainties around future disease trends under climate change remain. Mathematical models provide powerful tools for addressing such questions and understanding the implications for interventions and eradication strategies, but these require realistic modeling of the vector population dynamics and its response to environmental variables. METHODS: Published and unpublished field and experimental data are used to develop new formulations for modeling the relationships between key aspects of vector ecology and environmental variables. These relationships are integrated within a validated deterministic model of Anopheles gambiae s.s. population dynamics to provide a valuable tool for understanding vector response to biotic and abiotic variables. RESULTS: A novel, parsimonious framework for assessing the effects of rainfall, cloudiness, wind speed, desiccation, temperature, relative humidity and density-dependence on vector abundance is developed, allowing ease of construction, analysis, and integration into malaria transmission models. Model validation shows good agreement with longitudinal vector abundance data from Tanzania, suggesting that recent malaria reductions in certain areas of Africa could be due to changing environmental conditions affecting vector populations. CONCLUSIONS: Mathematical models provide a powerful, explanatory means of understanding the role of environmental variables on mosquito populations and hence for predicting future malaria transmission under global change. The framework developed provides a valuable advance in this respect, but also highlights key research gaps that need to be resolved if we are to better understand future malaria risk in vulnerable communities.     

Stochastic simulation of daily air temperature and precipitation from monthly normals in North America north of Mexico

A simple, stochastic daily temperature and precipitation generator (TEMPGEN) was developed to generate inputs for the study of the effects of climate change on models driven by daily weather information when climate data are available as monthly summaries. The model uses as input only 11 sets of monthly normal statistics from individual weather stations. It needs no calibration, and was parameterized and validated for use in Canada and the continental United States. Monthly normals needed are: mean and standard deviation of daily minimum and maximum temperature, first and second order autoregressive terms for daily deviations of minimum and maximum temperatures from their daily means, correlation of deviations of daily minimum and maximum temperatures, total precipitation, and the interannual variance of total precipitation. The statistical properties and distributions of daily temperature and precipitation data produced by this generator compared quite favorably with observations from 708 stations throughout North America (north of Mexico). The algorithm generates realistic seasonal patterns, variability and extremes of temperature, precipitation, frost-free periods and hot spells. However, it predicts less accurately the daily probability of precipitation, extreme precipitation events and the duration of extreme droughts.     

Prediction of cotton leaf curl virus disease and its management through resistant germplasm and bio-products

Cotton leaf curl virus disease reduces the cotton yield significantly every year and is transmitted by Bemisia tabaci. The study was designed to evaluate 15 varieties/lines against the disease. Multiple regression analysis was performed based on a-biotic environmental variables (maximum air temperature, minimum air temperature, relative humidity and rainfall) to predict disease incidence and its vector (Bemisia tabaci). Two bio-products were evaluated against the whitefly population to control the disease. Out of 15 cotton varieties/lines, no one was found highly resistant against the disease. Five varieties/lines (BT BT-980, BT-457, KIRAN, BT-666 and SLH-BT-6) exhibited moderately resistant response. Maximum air temperature (34–35.5 °C), minimum temperature (25.75–26.25 °C), relative humidity (64.14–66%), rainfall (1–2 mm) and wind speed (5.50–5.75 Kmh^?1) favoured the disease development. Maximum whitefly population was favoured by maximum air temperature from 34–35.5 °C, 25.8–26.2 °C minimum air temperature, 64.14–66% relative humidity, 1–2 mm from rainfall and 5.50–5.75 Kmh^?1 wind speed. Datura stramonium was found more effective as compared to Aviara (Homoeopathic) but not from the positive control (Acetamiprid).     

Seasonality of retinal detachment incidence and its associations with climate: an 11-year nationwide population-based study

This study aimed to examine the seasonal variability of retinal detachment (RD) in Taiwan by using an 11-yr nationwide population database. This study also investigated the association of weather conditions, i.e., ambient temperature, relative humidity, rainfall, monthly hours of sunshine, and atmospheric pressure, with RD. Data were retrospectively collected from the Taiwan National Health Insurance Research Database. The study sample included 23 718 RD hospitalizations between January 1999 and December 2009. The incidence rate of RD/100 000 people over the 132 months was computed according to sex and age groupings of <20, 20-39, 40-59, and ≥60 yrs. Then, the association between climatic factors and the monthly RD incidence rate was examined. The ARIMA (autoregressive integrated moving average) method was also employed to test the seasonality of RD incidence rates and their association with climatic factors. The annual RD incidence rates were between 7.8 and 10.8 cases/100 000 people during the study period. A fairly similar seasonal pattern of monthly RD incidence rates was apparent for males and females and males and females combined. Rates were highest August through October, decreasing in November, and lowest in February. After adjusting for time, trend, and month, the ARIMA regression models for the male, female, and males and females combined consistently revealed the monthly RD incidence rate was significantly and positively associated with ambient temperature, but negatively associated with atmospheric pressure. The authors conclude that the monthly RD incidence rates were significantly associated with seasonality. The monthly RD incidence rates were positively associated with ambient temperature and negatively associated with atmospheric pressure.     

Spatial and temporal distribution of Alternaria spores in the Iberian Peninsula atmosphere, and meteorological relationships: 1993–2009

This paper provides an updated of airborne Alternaria spore spatial and temporal distribution patterns in the Iberian Peninsula, using a common non-viable volumetric sampling method. The highest mean annual spore counts were recorded in Sevilla (39,418 spores), Mérida (33,744) and Málaga (12,947), while other sampling stations never exceeded 5,000. The same cities also recorded the highest mean daily spore counts (Sevilla 109 spores m^?3; Mérida 53 spores m^?3 and Málaga 35 spores m^?3) and the highest number of days on which counts exceeded the threshold levels required to trigger allergy symptoms (Sevilla 38 % and Mérida 30 % of days). Analysis of annual spore distribution patterns revealed either one or two peaks, depending on the location and prevailing climate of sampling stations. For all stations, average temperature was the weather parameter displaying the strongest positive correlation with airborne spore counts, whilst negative correlations were found for rainfall and relative humidity.     

The use of ZIP and CART to model cryptosporidiosis in relation to climatic variables

This research assesses the potential impact of weekly weather variability on the incidence of cryptosporidiosis disease using time series zero-inflated Poisson (ZIP) and classification and regression tree (CART) models. Data on weather variables, notified cryptosporidiosis cases and population size in Brisbane were supplied by the Australian Bureau of Meteorology, Queensland Department of Health, and Australian Bureau of Statistics, respectively. Both time series ZIP and CART models show a clear association between weather variables (maximum temperature, relative humidity, rainfall and wind speed) and cryptosporidiosis disease. The time series CART models indicated that, when weekly maximum temperature exceeded 31°C and relative humidity was less than 63%, the relative risk of cryptosporidiosis rose by 13.64 (expected morbidity: 39.4; 95% confidence interval: 30.9–47.9). These findings may have applications as a decision support tool in planning disease control and risk-management programs for cryptosporidiosis disease.     

Statistical Modeling Reveals the Effect of Absolute Humidity on Dengue in Singapore

Weather factors are widely studied for their effects on indicating dengue incidence trends. However, these studies have been limited due to the complex epidemiology of dengue, which involves dynamic interplay of multiple factors such as herd immunity within a population, distinct serotypes of the virus, environmental factors and intervention programs. In this study, we investigate the impact of weather factors on dengue in Singapore, considering the disease epidemiology and profile of virus serotypes. A Poisson regression combined with Distributed Lag Non-linear Model (DLNM) was used to evaluate and compare the impact of weekly Absolute Humidity (AH) and other weather factors (mean temperature, minimum temperature, maximum temperature, rainfall, relative humidity and wind speed) on dengue incidence from 2001 to 2009. The same analysis was also performed on three sub-periods, defined by predominant circulating serotypes. The performance of DLNM regression models were then evaluated through the Akaike''s Information Criterion. From the correlation and DLNM regression modeling analyses of the studied period, AH was found to be a better predictor for modeling dengue incidence than the other unique weather variables. Whilst mean temperature (MeanT) also showed significant correlation with dengue incidence, the relationship between AH or MeanT and dengue incidence, however, varied in the three sub-periods. Our results showed that AH had a more stable impact on dengue incidence than temperature when virological factors were taken into consideration. AH appeared to be the most consistent factor in modeling dengue incidence in Singapore. Considering the changes in dominant serotypes, the improvements in vector control programs and the inconsistent weather patterns observed in the sub-periods, the impact of weather on dengue is modulated by these other factors. Future studies on the impact of climate change on dengue need to take all the other contributing factors into consideration in order to make meaningful public policy recommendations.     

Association of Weather and Anthropogenic Factors for Transmission of Japanese Encephalitis in an Endemic Area of India

Weather and anthropogenic factors are important determinants for Japanese encephalitis (JE) transmission. During 2008–2010, an increasing trend of JE was observed in Dibrugarh district of Northeast India. The JE cases were found to be clustered between June to October in each year. Monthly minimum temperature and rainfall were significantly associated with JE transmission at 1 and 2 months lagged. However, the relationship was more prominent at a lag of 1 month than that of two. Regression analysis suggested that rainfall, minimum and maximum temperature, and relative humidity at 6:00 h are significant predictors (P < 0.05) of quarterly occurrence of JE cases. Additional anthropogenic risk factors including the conditions such as pig sty/cattle shed around and lower part of the houses and proximity of rice field to the dwelling houses (P < 0.05) were also found to be predictors for JE occurrence. Meteorological and anthropogenic risk factors can be used to forecast JE outbreaks in Assam which in turn can help the local health authorities to protect communities in JE prone areas.