Richard K. Kiang
Goddard Space Flight Center
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Featured researches published by Richard K. Kiang.
PLOS ONE | 2010
Radina P. Soebiyanto; Farida Adimi; Richard K. Kiang
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.
Malaria Journal | 2010
Farida Adimi; Radina Soebiyanto; Najibullah Safi; Richard K. Kiang
BackgroundMalaria is a significant public health concern in Afghanistan. Currently, approximately 60% of the population, or nearly 14 million people, live in a malaria-endemic area. Afghanistans diverse landscape and terrain contributes to the heterogeneous malaria prevalence across the country. Understanding the role of environmental variables on malaria transmission can further the effort for malaria control programme.MethodsProvincial malaria epidemiological data (2004-2007) collected by the health posts in 23 provinces were used in conjunction with space-borne observations from NASA satellites. Specifically, the environmental variables, including precipitation, temperature and vegetation index measured by the Tropical Rainfall Measuring Mission and the Moderate Resolution Imaging Spectoradiometer, were used. Regression techniques were employed to model malaria cases as a function of environmental predictors. The resulting model was used for predicting malaria risks in Afghanistan. The entire time series except the last 6 months is used for training, and the last 6-month data is used for prediction and validation.ResultsVegetation index, in general, is the strongest predictor, reflecting the fact that irrigation is the main factor that promotes malaria transmission in Afghanistan. Surface temperature is the second strongest predictor. Precipitation is not shown as a significant predictor, as it may not directly lead to higher larval population. Autoregressiveness of the malaria epidemiological data is apparent from the analysis. The malaria time series are modelled well, with provincial average R2 of 0.845. Although the R2 for prediction has larger variation, the total 6-month cases prediction is only 8.9% higher than the actual cases.ConclusionsThe provincial monthly malaria cases can be modelled and predicted using satellite-measured environmental parameters with reasonable accuracy. The Third Strategic Approach of the WHO EMRO Malaria Control and Elimination Plan is aimed to develop a cost-effective surveillance system that includes forecasting, early warning and detection. The predictive and early warning capabilities shown in this paper support this strategy.
PLOS ONE | 2014
Radina P. Soebiyanto; Wilfrido Clara; Jorge Jara; Leticia Castillo; Oscar Rene Sorto; Sidia Marinero; María E. Barnett de Antinori; John McCracken; Marc-Alain Widdowson; Eduardo Azziz-Baumgartner; Richard K. Kiang
Background The role of meteorological factors on influenza transmission in the tropics is less defined than in the temperate regions. We assessed the association between influenza activity and temperature, specific humidity and rainfall in 6 study areas that included 11 departments or provinces within 3 tropical Central American countries: Guatemala, El Salvador and Panama. Method/Findings Logistic regression was used to model the weekly proportion of laboratory-confirmed influenza positive samples during 2008 to 2013 (excluding pandemic year 2009). Meteorological data was obtained from the Tropical Rainfall Measuring Mission satellite and the Global Land Data Assimilation System. We found that specific humidity was positively associated with influenza activity in El Salvador (Odds Ratio (OR) and 95% Confidence Interval of 1.18 (1.07–1.31) and 1.32 (1.08–1.63)) and Panama (OR = 1.44 (1.08–1.93) and 1.97 (1.34–2.93)), but negatively associated with influenza activity in Guatemala (OR = 0.72 (0.6–0.86) and 0.79 (0.69–0.91)). Temperature was negatively associated with influenza in El Salvadors west-central departments (OR = 0.80 (0.7–0.91)) whilst rainfall was positively associated with influenza in Guatemalas central departments (OR = 1.05 (1.01–1.09)) and Panama province (OR = 1.10 (1.05–1.14)). In 4 out of the 6 locations, specific humidity had the highest contribution to the model as compared to temperature and rainfall. The model performed best in estimating 2013 influenza activity in Panama and west-central El Salvador departments (correlation coefficients: 0.5–0.9). Conclusions/Significance The findings highlighted the association between influenza activity and specific humidity in these 3 tropical countries. Positive association with humidity was found in El Salvador and Panama. Negative association was found in the more subtropical Guatemala, similar to temperate regions. Of all the study locations, Guatemala had annual mean temperature and specific humidity that were lower than the others.
Telematics and Informatics | 1993
Brian A. Telfer; Harold H. Szu; Richard K. Kiang
Abstract Two methods are tested for improving multispectral neural network classification: (a) new criterion functions and (b) incorporating contextual information. In the first approach, several energy functions for synthesizing neural networks are tested on 2-D synthetic data and on Landsat-4 thematic mapper data. These new energy functions, designed specifically for minimizing misclassification error, in some cases yield significant improvements in classification accuracy over the standard least mean squares energy function. In addition to operating on networks with one output unit per class, a new energy function is tested for binary encoded outputs, which result in smaller network sizes. The thematic mapper data (four bands were used) is classified on a single pixel basis, to provide a starting benchmark against which the contextual approach is compared. For single pixel classification, the best neural network result is 78.7%, compared with 71.7% for a classical nearest neighbor classifier. The 78.7% result also improves on several earlier neural network results on this data. In the contextual approach, several methods are tested, all employing the basic idea of concatenating the spectral values of neighboring pixels to the spectral values of the pixel to be classified. The best result was obtained by including spectral values from the 4 nearest (horizontal and vertical) neighbors, which increased the classification accuracy from 78.7% to 80.4%. Insight is provided into the nature of the classification errors by comparing the ground truth and spectral classification images. Approaches for further improving accuracy are discussed, including feature extraction methods for reducing the dimension of the feature vectors while still retaining contextual information.
IEEE Transactions on Geoscience and Remote Sensing | 1982
Richard K. Kiang
The effects of the Earths atmosphere on the Thematic Mapper (TM) measurements are studied with two radiative transfer models. A doubling model is used to compute the effective reflectance of the Earth-atmosphere system, as measured by the TM for the reflective bands. An emission-transmission model is used to compute the satellite -received radiance for the thermal band. The influences of the aerosol loading, the amount of water vapor, and the solar illumination angle on the effective reflectance are investigated. The effect of varying atmospheric water vapor on the measurements of the thermal band is studied. The scattering and absorption effects on TM bands are compared with those on Multispectral Scanner System (MSS) bands. While the changes in the aerosol loading introduce comparable variation of the effective reflectance for both sensors, the changes in the water vapor amount give less impact on TM4 than MSS7.
ISPRS international journal of geo-information | 2014
James G. Acker; Radina Soebiyanto; Richard K. Kiang; Steven Kempler
Abstract: The NASA Giovanni data analysis system ha s been recognized as a useful tool to access and analyze many different types of remote sensing data. The variety of environmental data types has allowed the use of Giovanni for different application areas, such as agriculture, hydrology, a nd air quality research. The us e of Giovanni for researching connections between public health issues and Earth’s environment and climate, potentially exacerbated by anthropogenic influence, has been increasingly demonstrated. In this communication, the pertinence of several different data parameters to public health will be described. This communication also provides a case study of the use of remote sensing data from Giovanni in assessing the associations between seasonal influenza and meteorological parameters. In this study, logistic regression was employed with precipitation, temperature and specific humidity as predictors. Specific humidity was found to be associated ( p < 0.05) with influenza activity in both temperate and tr opical climate. In the two temperate locations
PLOS ONE | 2015
Radina P. Soebiyanto; Diane Gross; Pernille Jorgensen; Silke Buda; Michal Bromberg; Zalman Kaufman; Katarina Prosenc; Maja Sočan; Tomás Vega Alonso; Marc-Alain Widdowson; Richard K. Kiang
Background Studies in the literature have indicated that the timing of seasonal influenza epidemic varies across latitude, suggesting the involvement of meteorological and environmental conditions in the transmission of influenza. In this study, we investigated the link between meteorological parameters and influenza activity in 9 sub-national areas with temperate and subtropical climates: Berlin (Germany), Ljubljana (Slovenia), Castile and León (Spain) and all 6 districts in Israel. Methods We estimated weekly influenza-associated influenza-like-illness (ILI) or Acute Respiratory Infection (ARI) incidence to represent influenza activity using data from each country’s sentinel surveillance during 2000–2011 (Spain) and 2006–2011 (all others). Meteorological data was obtained from ground stations, satellite and assimilated data. Two generalized additive models (GAM) were developed, with one using specific humidity as a covariate and another using minimum temperature. Precipitation and solar radiation were included as additional covariates in both models. The models were adjusted for previous weeks’ influenza activity, and were trained separately for each study location. Results Influenza activity was inversely associated (p<0.05) with specific humidity in all locations. Minimum temperature was inversely associated with influenza in all 3 temperate locations, but not in all subtropical locations. Inverse associations between influenza and solar radiation were found in most locations. Associations with precipitation were location-dependent and inconclusive. We used the models to estimate influenza activity a week ahead for the 2010/2011 period which was not used in training the models. With exception of Ljubljana and Israel’s Haifa District, the models could closely follow the observed data especially during the start and the end of epidemic period. In these locations, correlation coefficients between the observed and estimated ranged between 0.55 to 0.91and the model-estimated influenza peaks were within 3 weeks from the observations. Conclusion Our study demonstrated the significant link between specific humidity and influenza activity across temperate and subtropical climates, and that inclusion of meteorological parameters in the surveillance system may further our understanding of influenza transmission patterns.
PLOS ONE | 2016
Kristen Margevicius; Nicholas Generous; Esteban Abeyta; Ben Althouse; Howard Burkom; Lauren Castro; Ashlynn R. Daughton; Sara Y. Del Valle; Geoffrey Fairchild; James M. Hyman; Richard K. Kiang; Andrew P. Morse; Carmen M. Pancerella; Laura L. Pullum; Arvind Ramanathan; Jeffrey Schlegelmilch; Aaron E. Scott; Kirsten Taylor-McCabe; Alessandro Vespignani; Alina Deshpande
Epidemiological modeling for infectious disease is important for disease management and its routine implementation needs to be facilitated through better description of models in an operational context. A standardized model characterization process that allows selection or making manual comparisons of available models and their results is currently lacking. A key need is a universal framework to facilitate model description and understanding of its features. Los Alamos National Laboratory (LANL) has developed a comprehensive framework that can be used to characterize an infectious disease model in an operational context. The framework was developed through a consensus among a panel of subject matter experts. In this paper, we describe the framework, its application to model characterization, and the development of the Biosurveillance Analytics Resource Directory (BARD; http://brd.bsvgateway.org/brd/), to facilitate the rapid selection of operational models for specific infectious/communicable diseases. We offer this framework and associated database to stakeholders of the infectious disease modeling field as a tool for standardizing model description and facilitating the use of epidemiological models.
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery VIII | 2002
Richard K. Kiang
The spatial resolution of spaceborne instrument has increased substantially in the three decades since Landsat-1 was launched. Higher spatial resolution has made some applications possible. But it has also brought about new challenges in ground cover classification. At a resolution around 1 meter, vegetation often displays distinct textures. Hence texture may make differentiation among some cover types possible. Ikonos panchromatic and multispectral data are used to examine how spatial features improve classification accuracy. In this study, textural features are extracted from co-occurrence matrices, contextual features are derived from neighborhood properties, and maximum likelihood method is used for classifications. It is shown that for the test data both types of spatial features, and especially the contextual measures, can significantly improve the classification accuracies. Discrete wavelet transform is used to extract textural features for two types of vegetation. Transformed divergence, a measure of separability, is shown much enhanced when textural features are included.
Applications and science of artificial neural networks. Conference | 1997
Richard K. Kiang
Popularized by the images from weather satellites and other Earth observing satellites, remote sensing from space has already become a household term. Airborne remote sensing, however, still holds its important place in the development of the remote sensing technology and in many applications. Prototype, proof-of-concept instruments are flown on aircraft before their improved versions are deployed on space shuttles or satellites. Airborne remote sensing is also more practical for regional applications. Since an aircraft flies in the Earths atmosphere, factors contributing to geometric distortion are less systematic and more random. Substantial amount of effort is usually required to rectify the measurements. In this study, a scanner model is developed to generate simulated aircraft measurements. A backpropagation network and other variations are used to map the measurement space to the physical space. For measurements conducted over extensive area, techniques of anchoring the training data is developed such that geometric rectification can be performed in segments. Advantages of the neural network methods over the traditional method, and the need of constrained optimization are discussed.