NASA-modified precipitation products to improve USEPA nonpoint source water quality modeling for the Chesapeake Bay.

The USEPA has estimated that over 20,000 water bodies within the United States do not meet water quality standards. One of the regulations in the Clean Water Act of 1972 requires states to monitor the total maximum daily load, or the amount of pollution that can be carried by a water body before it is determined to be "polluted," for any watershed in the United States (Copeland, 2005). In response to this mandate, the USEPA developed Better Assessment Science Integrating Nonpoint Sources (BASINS) as a decision support tool for assessing pollution and to guide the decision-making process for improving water quality. One of the models in BASINS, the Hydrological Simulation Program-Fortran (HSPF), computes continuous streamflow rates and pollutant concentration at each basin outlet. By design, precipitation and other meteorological data from weather stations serve as standard model input. In practice, these stations may be unable to capture the spatial heterogeneity of precipitation events, especially if they are few and far between. An attempt was made to resolve this issue by substituting station data with NASA-modified/NOAA precipitation data. Using these data within HSPF, streamflow was calculated for seven watersheds in the Chesapeake Bay Basin during low flow periods, convective storm periods, and annual flows. In almost every case, the modeling performance of HSPF increased when using the NASA-modified precipitation data, resulting in better streamflow statistics and, potentially, in improved water quality assessment.

Surveys of rodent-borne disease in Thailand with a focus on scrub typhus assessment.

The epidemiology of many rodent-borne diseases in South-East Asia remains ill-defined. Scrub typhus and lep-tospirosis are common and medically significant, while other zoonotic diseases, such as spotted fever group Rickettsiae have been identified, but their overall medical significance is unknown. Rodent surveillance was conducted from June 2002 to July 2004 in 18 provinces from Thailand. Traps were set up for one to three nights. Blood and serum samples and animal tissue samples (liver, spleen, kidney and urinary bladder) were collected. Chiggermites, ticks and fleas were removed from captured rodents. A total of 4536 wild-caught rodents from 27 species were captured over two years of animal trapping. Rattus rattus was the dominant species, followed by Rattus exulans and Bandicota indica. Almost 43 000 ectoparasites were removed from the captured animals. Approximately 98% of the ectoparasites were chigger-mites, of which 46% belonged to the genus Leptotrombidium (scrub typhus vector). Other genera included Schoengastia and Blankaartia. Tick and flea specimens together comprised less than 1% of the sample. Among the five species of ticks collected, Haemaphysalis bandicota was the predominant species caught, followed by Ixodes granulatus other Haemaphysalis spp., Rhipicephalus spp. and Dermacentor spp. Only two species of fleas were collected and Xenopsylla cheopis (rat flea) was the predominant species. Using both commercial diagnostic kits and in-house molecular assays, animal tissue samples were examined and screened for zoonotic diseases. Seven zoonotic diseases were detected: scrub typhus, leptospirosis, murine typhus, tick typhus, bartonella, babesiosis and trypanosomiasis. Most samples were positive for scrub typhus. Other zoonotic diseases still under investigation include borrelosis, ehrlichiosis, the plague, and other rickettsial diseases. Using geographic information systems, global positioning systems and remote sensing technology, epidemiological and environmental data were combined to assess the relative risk in different biotopes within highly endemic areas of scrub typhus in Thailand.

Meteorological, environmental remote sensing and neural network analysis of the epidemiology of malaria transmission in Thailand.

In many malarious regions malaria transmission roughly coincides with rainy seasons, which provide for more abundant larval habitats. In addition to precipitation, other meteorological and environmental factors may also influence malaria transmission. These factors can be remotely sensed using earth observing environmental satellites and estimated with seasonal climate forecasts. The use of remote sensing usage as an early warning tool for malaria epidemics have been broadly studied in recent years, especially for Africa, where the majority of the world's malaria occurs. Although the Greater Mekong Subregion (GMS), which includes Thailand and the surrounding countries, is an epicenter of multidrug resistant falciparum malaria, the meteorological and environmental factors affecting malaria transmissions in the GMS have not been examined in detail. In this study, the parasitological data used consisted of the monthly malaria epidemiology data at the provincial level compiled by the Thai Ministry of Public Health. Precipitation, temperature, relative humidity, and vegetation index obtained from both climate time series and satellite measurements were used as independent variables to model malaria. We used neural network methods, an artificial-intelligence technique, to model the dependency of malaria transmission on these variables. The average training accuracy of the neural network analysis for three provinces (Kanchanaburi, Mae Hong Son, and Tak) which are among the provinces most endemic for malaria, is 72.8% and the average testing accuracy is 62.9% based on the 1994-1999 data. A more complex neural network architecture resulted in higher training accuracy but also lower testing accuracy. Taking into account of the uncertainty regarding reported malaria cases, we divided the malaria cases into bands (classes) to compute training accuracy. Using the same neural network architecture on the 19 most endemic provinces for years 1994 to 2000, the mean training accuracy weighted by provincial malaria cases was 73%. Prediction of malaria cases for 2001 using neural networks trained for 1994-2000 gave a weighted accuracy of 53%. Because there was a significant decrease (31%) in the number of malaria cases in the 19 provinces from 2000 to 2001, the networks overestimated malaria transmissions. The decrease in transmission was not due to climatic or environmental changes. Thailand is a country with long borders. Migrant populations from the neighboring countries enlarge the human malaria reservoir because these populations have more limited access to health care. This issue also confounds the complexity of modeling malaria based on meteorological and environmental variables alone. In spite of the relatively low resolution of the data and the impact of migrant populations, we have uncovered a reasonably clear dependency of malaria on meteorological and environmental remote sensing variables. When other contextual determinants do not vary significantly, using neural network analysis along with remote sensing variables to predict malaria endemicity should be feasible.

Plasmodium-infected Anopheles mosquitoes collected in Virginia and Maryland following local transmission of Plasmodium vivax malaria in Loudoun County, Virginia.

Two recent outbreaks of locally acquired, mosquito-transmitted malaria in Virginia in 1998 and 2002 demonstrate the continued risk of endemic mosquito-transmitted malaria in heavily populated areas of the eastern United States. Increasing immigration, growth in global travel, and the presence of competent anopheline vectors throughout the eastern United States contribute to the increasing risk of malaria importation and transmission. On August 23 and 25, 2002, Plasmodium vivax malaria was diagnosed in 2 teenagers in Loudoun County, Virginia. The Centers for Disease Control and Prevention (CDC) deemed these cases to be locally acquired because of the lack of risk factors for malaria, such as international travel, blood transfusion, organ transplantation, or needle sharing. The patients lived approximately 0.5 mi apart; however, 1 patient reported numerous visits to friends who lived directly across the street from the other patient. Two Anopheles quadrimaculatus s.l. female pools collected in Loudoun County, Virginia, and 1 An. punctipennis female pool collected in Fairfax County, Virginia, tested positive for P. vivax 210 with the VecTest panel assay and enzyme-linked immunosorbent assay (ELISA). In addition, 2 An. quadrimaculatus s.l. female pools collected in Montgomery, Maryland, tested positive for P. vivax 210. The CDC confirmed these initial results with the circumsporozoite ELISA. The authors believe that this is the 1st demonstration of Plasmodium-infected mosquitoes collected in association with locally acquired human malaria in the United States since the current national malaria surveillance system began in 1957.