Environmental and Socio-Demographic Determinants of Dengue Fever in Colombo City, Sri Lanka

Dengue fever has increased exponentially in Sri Lanka, from 24.4 cases per 100,000 in 2003 to 165.3 per 100,000 population in 2013. Although early warning systems using predictor models have been previously developed in other settings, it is important to develop such models in each local setting. Further, the ability of these models to be applicable at smaller geographic units will enhance current vector control and disease surveillance measures. The aim of this paper was to identify environmental and socio-economic status (SES) risk factors that may predict dengue fever at the Gram Niladhari Divisions (GND) level (smallest administrative unit) in Colombo city, Sri Lanka. These factors included landcover classes, amount of vegetation, population density, water access and neighborhood SES as determined by roof type. A geographically weighted regression (GWR) was used to develop the prediction model. A total 55 GND units covering an area of 37 sq km were investigated. We found that GND units with decreased vegetation, higher built-up area, higher population density and poor access to tap-water supply were associated with high risk of dengue; the pertinent GND units were concentrated in the center of the city. This is the first study in Sri Lanka to include both environmental and socio-demographic factors in prediction models for dengue fever. The methodology may be useful in enhancing ongoing dengue fever control measures in the country, and to be extended to other countries in the region that have an increasing incidence of dengue fever

LINKING SEASONAL PREDICTIONS TO DECISION-MAKING AND DISASTER MANAGEMENT IN THE GREATER HORN OF AFRICA

Seventy-six participants, including experts from seven countries from the Greater Horn of Africa (GHA) and project coinvestigators from the United States, met to discuss experimental seasonal prediction models and products for the GHA, to engage decision-makers and users in the assessment of hydroclimatic information requirements, and to use feedback to build a framework to support decision-making and disaster management. In pre- and postworkshop surveys, workshop participants were asked how the utility of forecasts to decision-makers might be improved. Their recommendations are presente

Sintomatologia ansiosa e depressiva em famílias com filhos adolescentes: Qual o papel da diferenciação do self dos pais?

A literatura tem realçado o impacto da história familiar de psicopatologia no desenvolvimento de perturbações emocionais nas gerações mais novas e a associação entre o nível de diferenciação do self e diversos quadros clínicos (e.g., ansiosos e depressivos). Com recurso a um desenho quantitativo transversal e a uma amostra de 104 tríades familiares (mãe, pai e filho/a adolescente), o presente estudo pretende analisar: a associação entre a sintomatologia depressiva e ansiosa de mães e pais e filhos/as adolescentes; e o papel mediador da diferenciação do self das mães e dos pais na relação entre a sintomatologia depressiva e ansiosa de mães e de pais e a sintomatologia depressiva e ansiosa dos/as filhos/as adolescentes. Os resultados mostram que: a sintomatologia depressiva e ansiosa do pai e da mãe são preditoras do nível de diferenciação do self do pai e da mãe; a relação entre a sintomatologia depressiva da mãe e dos/as filhos/as é mediada pela diferenciação do self da mãe; e a sintomatologia ansiosa da mãe é preditora da sintomatologia ansiosa dos/as filhos/as. Apesar de os resultados sugerirem que a mãe tenha também um papel central na transmissão de adversidade aos filhos, apontam para que a sintomatologia ansiosa e depressiva de pais e filhos se associem de formas diferentes. Este estudo tem implicações para a prática clínica e para a literatura na área da psicologia clínica e psicologia da família, ao relevar o impacto da sintomatologia da mãe e do seu nível de diferenciação do self no desenvolvimento de psicopatologia na adolescência.The literature has highlighted the impact of the family psychopathology history on the development of psychopathology in the younger generations and the association between the level of self differentiation and various clinical conditions (e.g., anxiety and depression). Using a quantitative cross-sectional design and a sample of 104 family triads (mother, father and adolescent child), the present study aims to analyze: the association between depressive and anxious symptomatology of mothers and fathers and adolescent children; and the mediating role of the self-differentiation of mothers and fathers in the relationship between the depressive and anxious symptomatology of mothers and fathers and the depressive and anxious symptomatology of the adolescent children. The results show that: the depressive and anxious symptomatology of the father and the mother are predictors of the level of differentiation of the self of the father and the mother; the relationship between the depressive symptomatology of the mother and the child is mediated by the differentiation of the mother's self; and the anxious symptomatology of the mother is a predictor of the anxious symptomatology of the child. Although the results suggest that the mother also has a central role in the transmission of adversity to the children, they point out that the anxious and depressive symptomatology of parents and children associate in different ways. This study has implications for clinical practice and for literature in clinical psychology and family psychology, by highlighting the impact of the mother's symptomatology and mothers’ level of self differentiation in the development of psychopathology in adolescence.Orientação: Ana Priost

Identifying and Addressing Land Surface Model Deficiencies with Data Assimilation

Land surface models (LSMs) encapsulate our understanding of terrestrial water and energy cycle physics and provide estimates of land surface states and fluxes when and where measurement gaps exist. Gaps in our understanding of the physics are a different issue. Data assimilation can address that issue both directly, through updating of prognostic model variables, or indirectly, when the simulated world conflicts with observation, necessitating adjustment of the model. Here we will focus on the latter case and present several examples, including (1) depth to bedrock adjustment to accommodate assimilated GRACE terrestrial water storage data; (2) steps to prevent immediate melting of assimilated snow cover; (3) irrigation's contribution to evapotranspiration; (4) lessons learned from soil moisture data assimilation; (5) the potential impact of satellite based runoff observatio

Fuel for growth and development

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/115971/1/sign854.pd

A Regional Drought Monitoring and Outlook System for South Asia

AbstractThe Regional Drought Monitoring and Outlook System (RDMOS) is an operational service which produces reliable drought indicators for the south Asia region with a specific focus on Afghanistan, Bangladesh, Nepal, and Pakistan. The system incorporates climatic models with suitable Earth observation data and land surface models to produce drought indices—precipitation, temperature, soil moisture, evapotranspiration—and vegetation conditions at 10-day intervals for near realtime monitoring of droughts. The RDMOS also provides seasonal outlooks at four-month intervals to support drought management and preparedness processes

Improved Rainfall Estimates and Predictions for 21st Century Drought Early Warning

As temperatures increase, the onset and severity of droughts is likely to become more intense. Improved tools for understanding, monitoring and predicting droughts will be a key component of 21st century climate adaption. The best drought monitoring systems will bring together accurate precipitation estimates with skillful climate and weather forecasts. Such systems combine the predictive power inherent in the current land surface state with the predictive power inherent in low frequency ocean-atmosphere dynamics. To this end, researchers at the Climate Hazards Group (CHG), in collaboration with partners at the USGS and NASA, have developed i) a long (1981-present) quasi-global (50degS-50degN, 180degW-180degE) high resolution (0.05deg) homogenous precipitation data set designed specifically for drought monitoring, ii) tools for understanding and predicting East African boreal spring droughts, and iii) an integrated land surface modeling (LSM) system that combines rainfall observations and predictions to provide effective drought early warning. This talk briefly describes these three components. Component 1: CHIRPS The Climate Hazards group InfraRed Precipitation with Stations (CHIRPS), blends station data with geostationary satellite observations to provide global near real time daily, pentadal and monthly precipitation estimates. We describe the CHIRPS algorithm and compare CHIRPS and other estimates to validation data. The CHIRPS is shown to have high correlation, low systematic errors (bias) and low mean absolute errors. Component 2: Hybrid statistical-dynamic forecast strategies East African droughts have increased in frequency, but become more predictable as Indo- Pacific SST gradients and Walker circulation disruptions intensify. We describe hybrid statistical-dynamic forecast strategies that are far superior to the raw output of coupled forecast models. These forecasts can be translated into probabilities that can be used to generate bootstrapped ensembles describing future climate conditions. Component 3: Assimilation using LSMs CHIRPS rainfall observations (component 1) and bootstrapped forecast ensembles (component 2) can be combined using LSMs to predict soil moisture deficits. We evaluate the skill such a system in East Africa, and demonstrate results for 2013

The Climate of the Mediterranean Region in Future Climate Projections

Future climate change over the Mediterranean area is investigated by means of climate model simulations covering the twenty-first century that take into account different anthropogenic greenhouse-gas-emission scenarios. This chapter first gives some new insights on these projections coming from the use of new methods, including the coupling at the regional scale of the atmospheric component to a Mediterranean Sea component. A synthesis of the expected changes of key aspects of the Mediterranean regional climate, obtained with a wide range of models and downscaling methods, is then presented. This includes an overview of not only expected changes in the mean climate and climate extremes but also possible changes in Mediterranean Sea temperature, salinity, circulation, water and heat budgets, and sea level. The chapter ends with some advanced results on the way to deal with uncertainties in climate projections and some discussion on the confidence that we can attribute to these projections

Ongoing Development of NASA's Global Land Data Assimilation System

NASA's Global Land Data Assimilation System (GLDAS) produces global fields of land surface states (e.g., soil moisture and temperature) and fluxes (e.g., latent heat flux and runoff) by driving offline land surface models with observation-based inputs, using the Land Information System (LIS) software. Since production began in 2001, GLDAS has supported more than 100 scientific investigations and applications. Some examples are GEWEX and NASA Energy and Water Cycle Study (NEWS) global water and energy budget analyses, interpretations of hydrologic data derived from the Gravity Recovery and Climate Experiment (GRACE) satellite mission, and forecast model initiation studies at NOAA and NASA. At the same time, the GLDAS team has continued improve results through the development of new modeling and data assimilation techniques. Here we describe several recent and ongoing innovations. These include global implementation of a runoff routing procedure, GRACE data assimilation, advanced snow cover assimilation, and irrigation modeling

Methods for estimating population density in data-limited areas: evaluating regression and tree-based models in Peru.

Obtaining accurate small area estimates of population is essential for policy and health planning but is often difficult in countries with limited data. In lieu of available population data, small area estimate models draw information from previous time periods or from similar areas. This study focuses on model-based methods for estimating population when no direct samples are available in the area of interest. To explore the efficacy of tree-based models for estimating population density, we compare six different model structures including Random Forest and Bayesian Additive Regression Trees. Results demonstrate that without information from prior time periods, non-parametric tree-based models produced more accurate predictions than did conventional regression methods. Improving estimates of population density in non-sampled areas is important for regions with incomplete census data and has implications for economic, health and development policies