Climate controls on valley fever incidence in Kern County, California

Coccidiodomycosis (valley fever) is a systemic infection caused by inhalation of airborne spores from Coccidioides immitis, a soil-dwelling fungus found in the southwestern United States, parts of Mexico, and Central and South America. Dust storms help disperse C. immitis so risk factors for valley fever include conditions favorable for fungal growth (moist, warm soil) and for aeolian soil erosion (dry soil and strong winds). Here, we analyze and inter-compare the seasonal and inter-annual behavior of valley fever incidence and climate risk factors for the period 1980–2002 in Kern County, California, the US county with highest reported incidence. We find weak but statistically significant links between disease incidence and antecedent climate conditions. Precipitation anomalies 8 and 20 months antecedent explain only up to 4% of monthly variability in subsequent valley fever incidence during the 23 year period tested. This is consistent with previous studies suggesting that C. immitis tolerates hot, dry periods better than competing soil organisms and, as a result, thrives during wet periods following droughts. Furthermore, the relatively small correlation with climate suggests that the causes of valley fever in Kern County could be largely anthropogenic. Seasonal climate predictors of valley fever in Kern County are similar to, but much weaker than, those in Arizona, where previous studies find precipitation explains up to 75% of incidence. Causes for this discrepancy are not yet understood. Higher resolution temporal and spatial monitoring of soil conditions could improve our understanding of climatic antecedents of severe epidemics.     

Investigating the Relationship Between Climate and Valley Fever (Coccidioidomycosis)

Valley fever (coccidioidomycosis) is a disease caused by inhalation of spores from the soil-dwelling Coccidioides fungal species. The disease is endemic to semiarid areas in the western USA and parts of Central and South America. The region of interest for this study, Kern County, California, accounts for approximately 14% of the reported valley fever cases in the USA each year. It is hypothesized that the weather conditions that foster the growth and dispersal of the fungus influence the number of cases in the endemic area. This study uses regression-based analysis to model and assess the seasonal relationships between valley fever incidence and climatic variables including concurrent and lagged precipitation, temperature, Palmer Drought Severity Index, wind speed, and PM₁₀ using data from 2000 to 2015. We find statistically significant links between disease incidence and climate conditions in Kern County, California. The best performing seasonal model explains up to 76% of the variability in fall valley fever incidence based on concurrent and antecedent climate conditions. Findings are consistent with previous studies, suggesting that antecedent precipitation is an important predictor of disease. The significant relationships found support the “grow and blow” hypothesis for climate-related coccidioidomycosis incidence risk that was originally developed for Arizona.     

Coccidioidomycosis incidence in Arizona predicted by seasonal precipitation

The environmental mechanisms that determine the inter-annual and seasonal variability in incidence of coccidioidomycosis are unclear. In this study, we use Arizona coccidioidomycosis case data for 1995–2006 to generate a timeseries of monthly estimates of exposure rates in Maricopa County, AZ and Pima County, AZ. We reveal a seasonal autocorrelation structure for exposure rates in both Maricopa County and Pima County which indicates that exposure rates are strongly related from the fall to the spring. An abrupt end to this autocorrelation relationship occurs near the the onset of the summer precipitation season and increasing exposure rates related to the subsequent season. The identification of the autocorrelation structure enabled us to construct a “primary” exposure season that spans August-March and a “secondary” season that spans April–June which are then used in subsequent analyses. We show that October–December precipitation is positively associated with rates of exposure for the primary exposure season in both Maricopa County (R = 0.72, p = 0.012) and Pima County (R = 0.69, p = 0.019). In addition, exposure rates during the primary exposure seasons are negatively associated with concurrent precipitation in Maricopa (R = −0.79, p = 0.004) and Pima (R = −0.64, p = 0.019), possibly due to reduced spore dispersion. These associations enabled the generation of models to estimate exposure rates for the primary exposure season. The models explain 69% (p = 0.009) and 54% (p = 0.045) of the variance in the study period for Maricopa and Pima counties, respectively. We did not find any significant predictors for exposure rates during the secondary season. This study builds on previous studies examining the causes of temporal fluctuations in coccidioidomycosis, and corroborates the “grow and blow” hypothesis.     

Statistical modeling of valley fever data in Kern County,California

Coccidioidomycosis (valley fever) is a fungal infection found in the southwestern US, northern Mexico, and some places in Central and South America. The fungus that causes it (Coccidioides immitis) is normally soil-dwelling but, if disturbed, becomes air-borne and infects the host when its spores are inhaled. It is thus natural to surmise that weather conditions that foster the growth and dispersal of the fungus must have an effect on the number of cases in the endemic areas. We present here an attempt at the modeling of valley fever incidence in Kern County, California, by the implementation of a generalized auto regressive moving average (GARMA) model. We show that the number of valley fever cases can be predicted mainly by considering only the previous history of incidence rates in the county. The inclusion of weather-related time sequences improves the model only to a relatively minor extent. This suggests that fluctuations of incidence rates (about a seasonally varying background value) are related to biological and/or anthropogenic reasons, and not so much to weather anomalies.     

Impact of landscape predictors on climate change modelling of species distributions: a case study with Eucalyptus fastigata in southern New South Wales,Australia

Aim We consider three questions. (1) How different are the predicted distribution maps when climate‐only and climate‐plus‐terrain models are developed from high‐resolution data? (2) What are the implications of differences between the models when predicting future distributions under climate change scenarios, particularly for climate‐only models at coarse resolution? (3) Does the use of high‐resolution data and climate‐plus‐terrain models predict an increase in the number of local refugia? Location South‐eastern New South Wales, Australia. Methods We developed two species distribution models for Eucalyptus fastigata under current climate conditions using generalized additive modelling. One used only climate variables as predictors (mean annual temperature, mean annual rainfall, mean summer rainfall); the other used both climate and landscape (June daily radiation, topographic position, lithology, nutrients) variables as predictors. Predictions of the distribution under current climate and climate change were then made for both models at a pixel resolution of 100 m. Results The model using climate and landscape variables as predictors explained a significantly greater proportion of the deviance than the climate‐only model. Inclusion of landscape variables resulted in the prediction of much larger areas of existing optimal habitat. An overlay of predicted future climate on the current climate space indicated that extrapolation of the statistical models was not occurring and models were therefore more robust. Under climate change, landscape‐defined refugia persisted in areas where the climate‐only model predicted major declines. In areas where expansion was predicted, the increase in optimal habitat was always greater with landscape predictors. Recognition of extensive optimal habitat conditions and potential refugia was dependent on the use of high‐resolution landscape data. Main conclusions Using only climate variables as predictors for assessing species responses to climate change ignores the accepted conceptual model of plant species distribution. Explicit statements justifying the selection of predictors based on ecological principles are needed. Models using only climate variables overestimate range reduction under climate change and fail to predict potential refugia. Fine‐scale‐resolution data are required to capture important climate/landscape interactions. Extrapolation of statistical models to regions in climate space outside the region where they were fitted is risky.     

黑龙江省2010～2020年猩红热发病与气象因素的关联性研究

分析黑龙江省气象因素与猩红热发病的关系,建立时间序列模型,为今后制定更科学有效的猩红热防控策略提供参考依据。收集黑龙江省2010～2020年猩红热月发病数据以及同期气温、气压等气象资料,应用广义相加模型分析气象因素与猩红热发病之间的关联程度和形式。结果发现: 猩红热全年均有发病而且呈现出较为典型的双峰型特征,在春季的4～5月份和冬季的11～12月份发病数达到高峰;月平均气压、月平均相对湿度、月日照时数和月平均风速的P值均小于0.05,表明具有统计学意义。同时,RR(相对危险度Risk Ratio)值均小于1,即猩红热发病与四个气象因素呈负相关。黑龙江省猩红热发病每年存在两个流行高峰,主要以冬季为主,发病数随着月平均相对湿度、月日照时数、月平均风速与月平均气压的升高而降低。     

Animal Reservoir,Natural and Socioeconomic Variations and the Transmission of Hemorrhagic Fever with Renal Syndrome in Chenzhou,China, 2006–2010

Background

China has the highest incidence of hemorrhagic fever with renal syndrome (HFRS) worldwide. Reported cases account for 90% of the total number of global cases. By 2010, approximately 1.4 million HFRS cases had been reported in China. This study aimed to explore the effect of the rodent reservoir, and natural and socioeconomic variables, on the transmission pattern of HFRS.

Methodology/Principal Findings

Data on monthly HFRS cases were collected from 2006 to 2010. Dynamic rodent monitoring data, normalized difference vegetation index (NDVI) data, climate data, and socioeconomic data were also obtained. Principal component analysis was performed, and the time-lag relationships between the extracted principal components and HFRS cases were analyzed. Polynomial distributed lag (PDL) models were used to fit and forecast HFRS transmission. Four principal components were extracted. Component 1 (F1) represented rodent density, the NDVI, and monthly average temperature. Component 2 (F2) represented monthly average rainfall and monthly average relative humidity. Component 3 (F3) represented rodent density and monthly average relative humidity. The last component (F4) represented gross domestic product and the urbanization rate. F2, F3, and F4 were significantly correlated, with the monthly HFRS incidence with lags of 4 months (r = −0.289, P<0.05), 5 months (r = −0.523, P<0.001), and 0 months (r = −0.376, P<0.01), respectively. F1 was correlated with the monthly HFRS incidence, with a lag of 4 months (r = 0.179, P = 0.192). Multivariate PDL modeling revealed that the four principal components were significantly associated with the transmission of HFRS.

Conclusions

The monthly trend in HFRS cases was significantly associated with the local rodent reservoir, climatic factors, the NDVI, and socioeconomic conditions present during the previous months. The findings of this study may facilitate the development of early warning systems for the control and prevention of HFRS and similar diseases.     

Venom variability and envenoming severity outcomes of the Crotalus scutulatus scutulatus (Mojave rattlesnake) from Southern Arizona

Twenty-one Mojave rattlesnakes, Crotalus scutulatus scutulatus (C. s. scutulatus), were collected from Arizona and New Mexico U.S.A. Venom proteome of each specimen was analyzed using reverse-phase HPLC and SDS-PAGE. The toxicity of venoms was analyzed using lethal dose 50 (LD(50)). Health severity outcomes between two Arizona counties U.S.A., Pima and Cochise, were determined by retrospective chart review of the Arizona Poison and Drug Information Center (APDIC) database between the years of 2002 and 2009. Six phenotypes (A-F) were identified based on three venom protein families; Mojave toxin, snake venom metalloproteinases PI and PIII (SVMP), and myotoxin-A. Venom changed geographically from SVMP-rich to Mojave toxin-rich phenotypes as you move from south central to southeastern Arizona. Phenotypes containing myotoxin-A were only found in the transitional zone between the SVMP and Mojave toxin phenotypes. Venom samples containing the largest amounts of SVMP or Mojave toxin had the highest and lowest LD(50s), respectively. There was a significant difference when comparing the presence of neurotoxic effects between Pima and Cochise counties (p=0.001). No significant difference was found when comparing severity (p=0.32), number of antivenom vials administered (p=0.17), days spent in a health care facility (p=0.23) or envenomation per 100,000 population (p=0.06). Although not part of the original data to be collected, death and intubations, were also noted. There is a 10× increased risk of death and a 50× increased risk of intubations if envenomated in Cochise County.     

Experience of a Wyoming County Streptococcal Control Project

Figures from Natrona County, Wyoming, during the period 1957-1959 and from the Papago Indian Health Service in Arizona during the years 1970-1982 indicate that a vigorous control program targeted to school children that used throat culturing to detect group A streptococci and to recommend adequate treatment effectively lowered the incidence of first attacks of rheumatic fever. Statistics from the Wyoming Department of Public Health for the years 1972-1983 recorded a consistently lower rate of rheumatic fever in Natrona County, where such a control program was maintained, than for the rest of the state, although the national decline in rheumatic fever incidence makes these figures more difficult to assess. Experience gained in these programs may be valuable for third world countries where rheumatic heart disease is still a major cause of death and disability.     

A predictive model relating North American plant formations and climate

A discriminant model was produced that predicts North American plant formations with basic climatic variables (monthly mean temperatures, monthly precipitation, and latitude). The model is based on data from 176 weather stations. Climatic variables from 30 additional randomly-selected weather stations were used to test the model. The predicted formation and actual formation at each site were compared; four sites were classified into the wrong formations (87% accuracy). This predictive model indicates a strong correlation between climate and formations in North America. Vegetation-climate models produced by canonical discriminant analysis may be useful in detecting geographical localities where non-climatic factors are particularly influential.     

Climate change and spotted owls: potentially contrasting responses in the Southwestern United States

Developing strategies that reduce the impacts of climate change on biodiversity will require projections of the future status of species under alternative climate change scenarios. Demographic models based on empirical data that link temporal variation in climate with vital rates can improve the accuracy of such predictions and help guide conservation efforts. Here, we characterized how population dynamics and extinction risk might be affected by climate change for three spotted owl (Strix occidentalis) populations in the Southwestern United States over the next century. Specifically, we used stochastic, stage‐based matrix models parameterized with vital rates linked to annual variation in temperature and precipitation to project owl populations forward in time under three IPCC emissions scenarios relative to contemporary climate. Owl populations in Arizona and New Mexico were predicted to decline rapidly over the next century and had a much greater probability of extinction under all three emissions scenarios than under current climate conditions. In contrast, owl population dynamics in Southern California were relatively insensitive to predicted changes in climate, and extinction risk was low for this population under all scenarios. The difference in predicted climate change impacts between these areas was due to negative associations between warm, dry conditions and owl vital rates in Arizona and New Mexico, whereas cold, wet springs reduced reproduction in Southern California. Predicted changes in population growth rates were mediated more by weather‐induced changes in fecundity than survival, and were generally more sensitive to increases in temperature than declines in precipitation. Our results indicate that spotted owls in arid environments may be highly vulnerable to climate change, even in core parts of the owl's range. More broadly, contrasting responses to climate change among populations highlight the need to tailor conservation strategies regionally, and modeling efforts such as ours can help prioritize the allocation of resources in this regard.     

Jacob Corwin Blumer,Arizona botanist

Jacob Corwin Blumer, a Swiss-born botanist, collected several thousand plants in southern Arizona, concentrating on the Chiricahua Mountains, Cochise County, and the Rincon Mountains, Pima County. At least 28 of these collections were later designated as type specimens. He published 23 papers on a variety of topics, including plant ecology and plant geography. His botanical career was brief, lasting from 1906 to 1917. He spent the latter half of his life as a farmer in Minnesota.     

Investigating the Effects of Food Available and Climatic Variables on the Animal Host Density of Hemorrhagic Fever with Renal Syndrome in Changsha,China

Background

The transmission of hemorrhagic fever with renal syndrome (HFRS) is influenced by population dynamics of its main host, rodents. It is therefore important to better understand rodents’ characteristic in epidemic areas.

Methodology/Principal Findings

We examined the potential impact of food available and climatic variability on HFRS rodent host and developed forecasting models. Monthly rodent density of HFRS host and climate data in Changsha from January 2004 to December 2011 were obtained. Monthly normalized difference vegetation index (NDVI) and temperature vegetation dryness index (TVDI) for rice paddies were extracted from MODIS data. Cross-correlation analysis were carried out to explore correlation between climatic variables and food available with monthly rodent data. We used auto-regressive integrated moving average model with explanatory variables to examine the independent contribution of climatic variables and food supply to rodent density. The results indicated that relative rodent density of HFRS host was significantly correlated with monthly mean temperatures, monthly accumulative precipitation, TVDI and NDVI with lags of 1–6 months.

Conclusions/Significance

Food available plays a significant role in population fluctuations of HFRS host in Changsha. The model developed in this study has implications for HFRS control and prevention.     

Climate change and the incidence of food poisoning in England and Wales

In recent years there have been several spells of high temperatures providing analogues for the conditions that might become more common as a result of the enhanced greenhouse effect. Statistical models were developed of the relationship between the monthly incidence of food poisoning and temperatures and these were then used to provide estimates of the possible effects of future warmer summers. Routinely collected data on the number of reported cases of food poisoning were analysed for the years 1982–1991. Regression analysis was used to establish the relationship between the monthly incidence of food poisoning and temperatures of the same and the previous month. Published scenarios for future temperatures were applied to these statistical models to provide estimates of the possible impacts of warmer conditions. The monthly incidence of food poisoning was found to be significantly associated with the temperature of the same and of the previous month with the latter having the stronger effect. Using published data on the relationship between reported and actual numbers of cases of food poisoning, it is estimated that annually there might be an additional 179 000 cases of food poisoning by the year 2050 as a result of climate change. The observed relationship with the same month's temperature underlines the need for improvements in storage, preparation and hygiene close to the point of consumption. However, there was a much stronger relationship with the temperature of the previous month, indicating the importance of conditions earlier in the food production process. Improvements in areas such as animal husbandry and slaughtering may also be necessary to avoid the adverse effects of a warmer climate.     

Environmental vVariability and coccidioidomycosis (valley fever)

Coccidioidomycosis (valley fever) is a disease endemic to arid regions in the western hemisphere, and is caused by the soil-dwellingfungus Coccidioides immitis (C. immitis). In this paper, we provide an overview of the current state of knowledge regarding valley fever and C. immitis as related to climatic conditions and habitat requirements. Previous research shows there is a relationship between temperature and precipitation, and outbreaks of coccidioidomycosis. Incidence of the disease varies seasonally as well as annually due to changing climatic conditions. However, the specific environmental conditions that may produce an outbreak of coccidioidomycosis are not well understood in space and time. Previous studies have attempted characterize C. immitis' habitat. Temperature, moisture, salinity, and pH of the soil have all been considered separately in the geographic distribution of the fungus. Medical and proactive intervention are served best, however, by an integrative strategy that folds climate and surface variables into spatially-explicit models. We conclude with recommendations for future research directions.     

Climate teleconnections and recent patterns of human and animal disease outbreaks

Background

Recent clusters of outbreaks of mosquito-borne diseases (Rift Valley fever and chikungunya) in Africa and parts of the Indian Ocean islands illustrate how interannual climate variability influences the changing risk patterns of disease outbreaks. Although Rift Valley fever outbreaks have been known to follow periods of above-normal rainfall, the timing of the outbreak events has largely been unknown. Similarly, there is inadequate knowledge on climate drivers of chikungunya outbreaks. We analyze a variety of climate and satellite-derived vegetation measurements to explain the coupling between patterns of climate variability and disease outbreaks of Rift Valley fever and chikungunya.

Methods and Findings

We derived a teleconnections map by correlating long-term monthly global precipitation data with the NINO3.4 sea surface temperature (SST) anomaly index. This map identifies regional hot-spots where rainfall variability may have an influence on the ecology of vector borne disease. Among the regions are Eastern and Southern Africa where outbreaks of chikungunya and Rift Valley fever occurred 2004–2009. Chikungunya and Rift Valley fever case locations were mapped to corresponding climate data anomalies to understand associations between specific anomaly patterns in ecological and climate variables and disease outbreak patterns through space and time. From these maps we explored associations among Rift Valley fever disease occurrence locations and cumulative rainfall and vegetation index anomalies. We illustrated the time lag between the driving climate conditions and the timing of the first case of Rift Valley fever. Results showed that reported outbreaks of Rift Valley fever occurred after ∼3–4 months of sustained above-normal rainfall and associated green-up in vegetation, conditions ideal for Rift Valley fever mosquito vectors. For chikungunya we explored associations among surface air temperature, precipitation anomalies, and chikungunya outbreak locations. We found that chikungunya outbreaks occurred under conditions of anomalously high temperatures and drought over Eastern Africa. However, in Southeast Asia, chikungunya outbreaks were negatively correlated (p<0.05) with drought conditions, but positively correlated with warmer-than-normal temperatures and rainfall.

Conclusions/Significance

Extremes in climate conditions forced by the El Niño/Southern Oscillation (ENSO) lead to severe droughts or floods, ideal ecological conditions for disease vectors to emerge, and may result in epizootics and epidemics of Rift Valley fever and chikungunya. However, the immune status of livestock (Rift Valley fever) and human (chikungunya) populations is a factor that is largely unknown but very likely plays a role in the spatial-temporal patterns of these disease outbreaks. As the frequency and severity of extremes in climate increase, the potential for globalization of vectors and disease is likely to accelerate. Understanding the underlying patterns of global and regional climate variability and their impacts on ecological drivers of vector-borne diseases is critical in long-range planning of appropriate disease and disease-vector response, control, and mitigation strategies.     

Modeling climate effects on hip fracture rate by the multivariate GARCH model in Montreal region,Canada

Changes in extreme meteorological variables and the demographic shift towards an older population have made it important to investigate the association of climate variables and hip fracture by advanced methods in order to determine the climate variables that most affect hip fracture incidence. The nonlinear autoregressive moving average with exogenous variable-generalized autoregressive conditional heteroscedasticity (ARMAX-GARCH) and multivariate GARCH (MGARCH) time series approaches were applied to investigate the nonlinear association between hip fracture rate in female and male patients aged 40–74 and 75+ years and climate variables in the period of 1993–2004, in Montreal, Canada. The models describe 50–56 % of daily variation in hip fracture rate and identify snow depth, air temperature, day length and air pressure as the influencing variables on the time-varying mean and variance of the hip fracture rate. The conditional covariance between climate variables and hip fracture rate is increasing exponentially, showing that the effect of climate variables on hip fracture rate is most acute when rates are high and climate conditions are at their worst. In Montreal, climate variables, particularly snow depth and air temperature, appear to be important predictors of hip fracture incidence. The association of climate variables and hip fracture does not seem to change linearly with time, but increases exponentially under harsh climate conditions. The results of this study can be used to provide an adaptive climate-related public health program and ti guide allocation of services for avoiding hip fracture risk.     

Influence of climate and disturbance on the growth of Tsuga canadensis at its southern limit in eastern North America

It has long been hypothesized that trees growing at range limits likely also occur near the limit of their ecological amplitude and thus, should be more sensitive to climate variability than individuals growing nearer the range core. We developed a tree-ring chronology using Tsuga canadensis individuals from three disjunct stands at the species’ southern limit to quantify the influence of climate and disturbance on radial growth patterns. The tree-ring record extended 158 years from 1850 to 2007. Significant negative relationships were found between the STANDARD chronology and monthly mean temperature, monthly maximum temperature, and monthly minimum temperature during the previous and current summer, while significant positive relationships were documented between the STANDARD chronology and monthly minimum temperature for September and October of the current year. Also, significant positive relationships were documented between the STANDARD chronology and monthly total precipitation for September of the previous year and May of the current year. Response function analysis showed that monthly climate variables (r ² = 0.22) and prior growth (r ² = 0.40) explained 62% of the variance in the T. canadensis tree-ring chronology. A time series plot for the T. canadensis chronology showed that actual tree growth agreed relatively well with the predicted growth based on significant climate variables. However, positive departures from the predicted growth were noted. Dendroecological analysis revealed these departures were likely related to disturbance events. Our results indicated that T. canadensis individuals at its southernmost extent are sensitive to regional climate, but not more so than trees nearer the range core. We hypothesize that microenvironmental conditions of T. canadensis stands at its southern limit are similar to conditions within the contiguous distribution of the species, which may explain this pattern.     

Investigating the relationships between the distribution of British ground beetle species (Coleoptera, Carabidae) and temperature, precipitation and altitude

Aims We examine the relationships between the distribution of British ground beetle species and climatic and altitude variables with a view to developing models for evaluating the impact of climate change. Location Data from 1684 10‐km squares in Britain were used to model species–climate/altitude relationships. A validation data set was composed of data from 326 British 10‐km squares not used in the model data set. Methods The relationships between incidence and climate and altitude variables for 137 ground beetle species were investigated using logistic regression. The models produced were subjected to a validation exercise using the Kappa statistic with a second data set of 30 species. Distribution patterns for four species were predicted for Britain using the regression equations generated. Results As many as 136 ground beetle species showed significant relationships with one or more of the altitude and climatic variables but the amount of variation explained by the models was generally poor. Models explaining 20% or more of the variation in species incidence were generated for only 10 species. Mean summer temperature and mean annual temperature were the best predictors for eight and six of these 10 species respectively. Few models based on altitude, annual precipitation and mean winter temperature were good predictors of ground beetle species distribution. The results of the validation exercise were mixed, with models for four species showing good or moderate fits whilst the remainder were poor. Main conclusions Whilst there were many significant relationships between British ground beetle species distributions and altitude and climatic variables, these variables did not appear to be good predictors of ground beetle species distribution. The poor model performance appears to be related to the coarse nature of the response and predictor data sets and the absence of key predictors from the models.     

An accurate mathematical model predicting number of dengue cases in tropics

Dengue fever is a systemic viral infection of epidemic proportions in tropical countries. The incidence of dengue fever is ever increasing and has doubled over the last few decades. Estimated 50million new cases are detected each year and close to 10000 deaths occur each year. Epidemics are unpredictable and unprecedented. When epidemics occur, health services are over whelmed leading to overcrowding of hospitals. At present there is no evidence that dengue epidemics can be predicted. Since the breeding of the dengue mosquito is directly influenced by environmental factors, it is plausible that epidemics could be predicted using weather data. We hypothesized that there is a mathematical relationship between incidence of dengue fever and environmental factors and if such relationship exists, new cases of dengue fever in the succeeding months can be predicted using weather data of the current month. We developed a mathematical model using machine learning technique. We used Island wide dengue epidemiology data, weather data and population density in developing the model. We used incidence of dengue fever, average rain fall, humidity, wind speed, temperature and population density of each district in the model. We found that the model is able to predict the incidence of dengue fever of a given month in a given district with precision (RMSE between 18- 35.3). Further, using weather data of a given month, the number of cases of dengue in succeeding months too can be predicted with precision (RMSE 10.4—30). Health authorities can use existing weather data in predicting epidemics in the immediate future and therefore measures to prevent new cases can be taken and more importantly the authorities can prepare local authorities for outbreaks.