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NASA NTRS2005

Assessing Malaria Risks in Greater Mekong Subregion based on Environmental Parameters

At 4,200 km, the Mekong River is the tenth longest river in the world. It directly and indirectly influences the lives of hundreds of millions of inhabitants in its basin. The riparian countries - Thailand, Myanmar, Cambodia, Laos, Vietnam, and a small part of China - form the Greater Mekong Subregion (GMS). This geographical region has the misfortune of being the world's epicenter of falciparum malaria, which is the most severe form of malaria caused by Plasmodium falciparum. Depending on the country, approximately 50 to 90% of all malaria cases are due to this species. In the Malaria Modeling and Surveillance Project, we have been developing techniques to enhance public health s decision capability for malaria risk assessments and controls. The main objectives are: 1) identifying the potential breeding sites for major vector species; 2) implementing a malaria transmission model to identify the key factors that sustain or intensify malaria transmission; and 3) implementing a risk algorithm to predict the occurrence of malaria and its transmission intensity. The potential benefits are: 1) increased warning time for public health organizations to respond to malaria outbreaks; 2) optimized utilization of pesticide and chemoprophylaxis; 3) reduced likelihood of pesticide and drug resistance; and 4) reduced damage to environment. Environmental parameters important to malaria transmission include temperature, relative humidity, precipitation, and vegetation conditions. The NASA Earth science data sets that have been used for malaria surveillance and risk assessment include AVHRR Pathfinder, TRMM, MODIS, NSIPP, and SIESIP. Hindcastings based on these environmental parameters have shown good agreement to epidemiological records. Socioeconomic factors that may influence malaria transmissions will also be incorporated into the predictive models.

Kiang, Richard↗

NASA NTRS2010

Toward Malaria Risk Prediction in Afghanistan Using Remote Sensing

Malaria causes more than one million deaths every year worldwide, with most of the mortality in Sub-Saharan Africa. It is also a significant public health concern in Afghanistan, with approximately 60% of the population, or nearly 14 million people, living in a malaria-endemic area. Malaria transmission has been shown to be dependent on a number of environmental and meteorological variables. For countries in the tropics and the subtropics, rainfall is normally the most important variable, except for regions with high altitude where temperature may also be important. Afghanistan s diverse landscape contributes to the heterogeneous malaria distribution. Understanding the environmental effects on malaria transmission is essential to the effective control of malaria in Afghanistan. Provincial malaria data gathered by Health Posts in 23 provinces during 2004-2007 are used in this study. Remotely sensed geophysical parameters, including precipitation from TRMM, and surface temperature and vegetation index from MODIS are used to derive the empirical relationship between malaria cases and these geophysical parameters. Both neural network methods and regression analyses are used to examine the environmental dependency of malaria transmission. And the trained models are used for predicting future transmission. While neural network methods are intrinsically more adaptive for nonlinear relationship, the regression approach lends itself in providing statistical significance measures. Our results indicate that NDVI is the strongest predictor. This reflects the role of irrigation, instead of precipitation, in Afghanistan for agricultural production. The second strongest prediction is surface temperature. Precipitation is not shown as a significant predictor, contrary to other malarious countries in the tropics or subtropics. With the regression approach, the malaria time series are modelled well, with average R2 of 0.845. For cumulative 6-month prediction of malaria cases, the average provincial accuracy reaches 91%. The developed predictive and early warning capabilities support the Third Strategic Approach of the WHO EMRO Malaria Control and Elimination Plan.

Safi, N.↗

NASA NTRS2007

Surveillance and Control of Malaria Transmission in Thailand using Remotely Sensed Meteorological and Environmental Parameters

These slides address the use of remote sensing in a public health application. Specifically, this discussion focuses on the of remote sensing to detect larval habitats to predict current and future endemicity and identify key factors that sustain or promote transmission of malaria in a targeted geographic area (Thailand). In the Malaria Modeling and Surveillance Project, which is part of the NASA Applied Sciences Public Health Applications Program, we have been developing techniques to enhance public health's decision capability for malaria risk assessments and controls. The main objectives are: 1) identification of the potential breeding sites for major vector species; 2) implementation of a risk algorithm to predict the occurrence of malaria and its transmission intensity; 3) implementation of a dynamic transmission model to identify the key factors that sustain or intensify malaria transmission. The potential benefits are: 1) increased warning time for public health organizations to respond to malaria outbreaks; 2) optimized utilization of pesticide and chemoprophylaxis; 3) reduced likelihood of pesticide and drug resistance; and 4) reduced damage to environment. !> Environmental parameters important to malaria transmission include temperature, relative humidity, precipitation, and vegetation conditions. The NASA Earth science data sets that have been used for malaria surveillance and risk assessment include AVHRR Pathfinder, TRMM, MODIS, NSIPP, and SIESIP. Textural-contextual classifications are used to identify small larval habitats. Neural network methods are used to model malaria cases as a function of the remotely sensed parameters. Hindcastings based on these environmental parameters have shown good agreement to epidemiological records. Discrete event simulations are used for modeling the detailed interactions among the vector life cycle, sporogonic cycle and human infection cycle, under the explicit influences of selected extrinsic and intrinsic factors. The output of the model includes the individual infection status and the quantities normally observed in field studies, such as mosquito biting rates, sporozoite infection rates, gametocyte prevalence and incidence. Results are in good agreement with mosquito vector and human malaria data acquired by Coleman et al. over 4.5 years in Kong Mong Tha, a remote village in western Thailand. Application of our models is not restricted to the Greater Mekong Subregion. Our models have been applied to malaria in Indonesia, Korea, and other regions in the world with similar success.

Kiang, Richard K.↗

NASA NTRS2005

Malaria Modeling and Surveillance for the Greater Mekong Subregion

At 4,200 km, the Mekong River is the tenth longest river in the world. It directly and indirectly influences the lives of hundreds of millions of inhabitants in its basin. The riparian countries - Thailand, Myanmar, Cambodia, Laos, Vietnam, and a small part of China - form the Greater Mekong Subregion (GMS). This geographical region has the misfortune of being the world's epicenter of falciparum malaria, which is the most severe form of malaria caused by Plasmodium falciparum. Depending on the country, approximately 50 to 90% of all malaria cases are due to this species. In the Malaria Modeling and Surveillance Project, we have been developing techniques to enhance public health's decision capability for malaria risk assessments and controls. The main objectives are: 1) Identifying the potential breeding sites for major vector species; 2) Implementing a malaria transmission model to identify the key factors that sustain or intensify malaria transmission; and 3) Implementing a risk algorithm to predict the occurrence of malaria and its transmission intensity. The potential benefits are: 1) Increased warning time for public health organizations to respond to malaria outbreaks; 2) Optimized utilization of pesticide and chemoprophylaxis; 3) Reduced likelihood of pesticide and drug resistance; and 4) Reduced damage to environment. Environmental parameters important to malaria transmission include temperature, relative humidity, precipitation, and vegetation conditions. These parameters are extracted from NASA Earth science data sets. Hindcastings based on these environmental parameters have shown good agreement to epidemiological records.

Kiang, Richard↗

NASA NTRS2004

Malaria Modeling using Remote Sensing and GIS Technologies

Malaria has been with the human race since the ancient time. In spite of the advances of biomedical research and the completion of genomic mapping of Plasmodium falciparum, the exact mechanisms of how the various strains of parasites evade the human immune system and how they have adapted and become resistant to multiple drugs remain elusive. Perhaps because of these reasons, effective vaccines against malaria are still not available. Worldwide, approximately one to three millions deaths are attributed to malaria annually. With the increased availability of remotely sensed data, researchers in medical entomology, epidemiology and ecology have started to associate environmental and ecological variables with malaria transmission. In several studies, it has been shown that transmission correlates well with certain environmental and ecological parameters, and that remote sensing can be used to measure these determinants. In a NASA project, we have taken a holistic approach to examine how remote sensing and GIs can contribute to vector and malaria controls. To gain a better understanding of the interactions among the possible promoting factors, we have been developing a habitat model, a transmission model, and a risk prediction model, all using remote sensing data as input. Our objectives are: 1) To identify the potential breeding sites of major vector species and the locations for larvicide and insecticide applications in order to reduce costs, lessen the chance of developing pesticide resistance, and minimize the damage to the environment; 2) To develop a malaria transmission model characterizing the interactions among hosts, vectors, parasites, landcover and environment in order to identify the key factors that sustain or intensify malaria transmission, and 3) To develop a risk model to predict the occurrence of malaria and its transmission intensity using epidemiological data and satellite-derived or ground-measured environmental and meteorological data.

Kiang, Richard↗

NASA NTRS1997

Radar Monitoring of Wetlands for Malaria Control

Malaria is the most important vector-borne tropical disease (Collins and Paskewitz, 1995) and there is no simple and universally applicable form of vector control. While new methods such as malaria vaccine or genetic manipulation of mosquitoes are being explored in the laboratories, the need for more field research on malaria transmission remains very strong. For the foreseeable future many malaria programs must focus on controlling the vector, the anopheline mosquito, often under the specter of shrinking budgets. Therefore information on which human populations are at the greatest risk is especially valuable when allocating scarce resources. The goal of the Radar Monitoring of Wetlands for Malaria Control Project is to demonstrate the feasibility of using Radarsat or other comparable satellite radar imaging systems to determine where and when human populations are at greatest risk for contracting malaria. The study area is northern Belize, a region with abundant wetlands and a potentially serious malaria problem. A key aspect of this study is the analysis of multi-temporal satellite imagery to track seasonal flooding of anopheline mosquito breeding sites. Radarsat images of the test site in Belize have been acquired one to three times a month over the last year, however,, to date only one processed image has been received from the Alaska SAR Facility for analysis. Therefore analysis at this stage is focussed on determining the radar backscatter characteristics of known anopheline breeding sites, with future work to be dedicated toward seasonal changes.

Pope, Kevin O.↗

NASA NTRS1997

Radar Monitoring of Wetlands for Malaria Control

Malaria is perhaps the most serious human disease problem. It inflicts millions worldwide and is on the rise in many countries where it was once under control. This rise is in part due to the high costs, both economic and environmental, of current control programs. The search for more cost-effective means to combat malaria has focussed attention on new technologies, one of which is remote sensing. Remote sensing has become an important tool in the effort to control a variety of diseases worldwide and malaria is perhaps one of the most promising. This study is part of the malaria control effort in the Central American country of Belize, which has experienced a resurgence of malaria in the last two decades. The proposed project is a feasibility study of the use of Radarsat (and other similar radar systems) to monitor seasonal changes in the breeding sites of the anopheline mosquito, which is responsible for malaria transmission. We propose that spatial and temporal changes in anopheline mosquito production can be predicted by sensing where and when their breeding sites are flooded. Timely knowledge of anopheline mosquito production is a key factor in control efforts. Such knowledge can be used by local control agencies to direct their limited resources to selected areas and time periods when the human population is at greatest risk. Radar is a key sensor in this application because frequent cloud cover during the peak periods of malaria transmission precludes the use of optical sensors.

Pope, Kevin O.↗

NASA NTRS2007

Modeling Malaria Transmission in Thailand and Indonesia

Malaria Modeling and Surveillance is a project in the NASA Applied Sciences Public Health Applications Program. The main objectives of this project are: 1) identification of the potential breeding sites for major vector species: 2) implementation of a malaria transmission model to identify they key factors that sustain or intensify malaria transmission; and 3) implementation of a risk algorithm to predict the occurrence of malaria and its transmission intensity. Remote sensing and GIs are the essential elements of this project. The NASA Earth science data sets used in this project include AVHRR Pathfinder, TRMM, MODIS, NSIPP and SIESIP. Textural-contextual classifications are used to identify small larval habitats. Neural network methods are used to model malaria cases as a function of precipitation, temperatures, humidity and vegetation. Hindcastings based on these environmental parameters have shown good agreement to epidemiological records. Examples for spatio-temporal modeling of malaria transmissions in Southeast Asia are given. Discrete event simulations were used for modeling the detailed interactions among the vector life cycle, sporogonic cycle and human infection cycle, under the explicit influences of selected extrinsic and intrinsic factors. The output of the model includes the individual infection status and the quantities normally observed in field studies, such as mosquito biting rates, sporozoite infection rates, gametocyte prevalence and incidence. Results are in good agreement with mosquito vector and human malaria data acquired by Coleman et al. over 4.5 years in Kong Mong Tha, a remote village in western Thailand. Application of our models is not restricted to Southeast Asia. The model and techniques are equally applicable to other regions of the world, when appropriate epidemiological and vector ecological parameters are used as input.

Kiang, Richard↗

NASA NTRS2006

Agent-Based Simulations of Malaria Transmissions with Applications to a Study Site in Thailand

The dynamics of malaria transmission are driven by environmental, biotic and socioeconomic factors. Because of the geographic dependency of these factors and the complex interactions among them, it is difficult to generalize the key factors that perpetuate or intensify malaria transmission. Methods: Discrete event simulations were used for modeling the detailed interactions among the vector life cycle, sporogonic cycle and human infection cycle, under the explicit influences of selected extrinsic and intrinsic factors. Meteorological and environmental parameters may be derived from satellite data. The output of the model includes the individual infection status and the quantities normally observed in field studies, such as mosquito biting rates, sporozoite infection rates, gametocyte prevalence and incidence. Results were compared with mosquito vector and human malaria data acquired over 4.5 years (June 1999 - January 2004) in Kong Mong Tha, a remote village in Kanchanaburi Province, western Thailand. Results: Three years of transmissions of vivax and falciparum malaria were simulated for a hypothetical hamlet with approximately 1/7 of the study site population. The model generated results for a number of scenarios, including applications of larvicide and insecticide, asymptomatic cases receiving or not receiving treatment, blocking malaria transmission in mosquito vectors, and increasing the density of farm (host) animals in the hamlet. Transmission characteristics and trends in the simulated results are comparable to actual data collected at the study site.

Kiang, Richard K.↗

NASA NTRS1994

Remote Sensing as a Landscape Epidemiologic Tool to Identify Villages at High Risk for Malaria Transmission

A landscape approach using remote sensing and Geographic Information System (GIS) technologies was developed to discriminate between villages at high and low risk for malaria transmission, as defined by adult Anopheles albimanus abundance. Satellite data for an area in southern Chiapas, Mexico were digitally processed to generate a map of landscape elements. The GIS processes were used to determine the proportion of mapped landscape elements surrounding 40 villages where An. albimanus data had been collected. The relationships between vector abundance and landscape element proportions were investigated using stepwise discriminant analysis and stepwise linear regression. Both analyses indicated that the most important landscape elements in terms of explaining vector abundance were transitional swamp and unmanaged pasture. Discriminant functions generated for these two elements were able to correctly distinguish between villages with high ind low vector abundance, with an overall accuracy of 90%. Regression results found both transitional swamp and unmanaged pasture proportions to be predictive of vector abundance during the mid-to-late wet season. This approach, which integrates remotely sensed data and GIS capabilities to identify villages with high vector-human contact risk, provides a promising tool for malaria surveillance programs that depend on labor-intensive field techniques. This is particularly relevant in areas where the lack of accurate surveillance capabilities may result in no malaria control action when, in fact, directed action is necessary. In general, this landscape approach could be applied to other vector-borne diseases in areas where: 1. the landscape elements critical to vector survival are known and 2. these elements can be detected at remote sensing scales.

Beck, Louisa R.↗

NASA NTRS2001

Controlling Malaria and Other Diseases Using Remote Sensing

Remote sensing offers the vantage of monitoring a vast area of the Earth continuously. Once developed and launched, a satellite gives years of service in collecting data from the land, the oceans, and the atmosphere. Since the 1980s, attempts have been made to relate disease occurrence with remotely sensed environmental and geophysical parameters, using data from Landsat, SPOT, AVHRR, and other satellites. With higher spatial resolution, the recent satellite sensors provide a new outlook for disease control. At sub-meter to I 10m resolution, surface types associated with disease carriers can be identified more accurately. The Ikonos panchromatic sensor with I m resolution, and the Advanced Land Imager with 1 Om resolution on the newly launched Earth Observing-1, both have displayed remarkable mapping capabilities. In addition, an entire array of geophysical parameters can now be measured or inferred from various satellites. Airborne remote sensing, with less concerns on instrument weight, size, and power consumption, also offers a low-cost alternative for regional applications. NASA/GSFC began to collaborate with the Mahidol University on malaria and filariasis control using remote sensing in late 2000. The objectives are: (1) To map the breeding sites for the major vector species; (2) To identify the potential sites for larvicide and insecticide applications; (3) To explore the linkage of vector population and transmission intensity to environmental variables; (4) To monitor the impact of climate change and human activities on vector population and transmission; and (5) To develop a predictive model for disease distribution. Field studies are being conducted in several provinces in Thailand. Data analyses will soon begin. Malaria data in South Korea are being used as surrogates for developing classification techniques. GIS has been shown to be invaluable in making the voluminous remote sensing data more readily understandable. It will be used throughout this study to clearly demonstrate the spatial relationship between the disease intensities, geophysical variables, and socioeconomic parameters. Asides from malaria and filariasis, application of remote sensing to the control of other diseases have been vigorously pursued by NASA's Environment and Health Initiative. The current program includes projects on Rift Valley fever, St. Louis encephalitis, dengue fever, ebola, African dust and diseases, meningitis, asthma, bartonellosis, cholera, and urban health concerns. Results from these projects indicate that remote sensing will play an increasingly important role in disease control in the future.

Kiang, Richard K.↗

NASA NTRS2004

Communications: Mosquito Habitats, Land Use, and Malaria Risk in Belize from Satellite Imagery

Satellite imagery of northern Belize is used to examine the distribution of land use and breeding habitats of the malaria vector the Anopheles mosquito. A land cover classification based on multispectral SPOT and multitemporal Radarsat images identified eleven land cover classes, including agricultural, forest, and marsh types. Two of the land cover types, Typha domingensis marsh and flooded forest, are Anopheles vestitipennis larval habitats, and one, Eleocharis spp. marsh, is the larval habitat for Anopheles albimanus. Geographic Information Systems (GIS) analyses of land cover demonstrate that the amount of Typha domingensis in a marsh is positively correlated with the amount of agricultural land in the adjacent upland, and negatively correlated with the amount of adjacent forest. This finding is consistent with the hypothesis that nutrient (phosphorus) runoff from agricultural lands is causing an expansion of Typha domingensis in northern Belize. Thus, land use induced expansion of Anopheles vestitipennis larval habitat is potentially increasing malaria risk in Belize, and in other regions where Anopheles vestitipennis is a major malaria vector.

Pope, Kevin↗

NASA NTRS1991

Project MEDSAT: The design of a remote sensing platform for malaria research and control

Project MEDSAT was proposed with the specific goal of designing a satellite to remotely sense pertinent information useful in establishing strategies to control malaria. The 340 kg MEDSAT satellite is to be inserted into circular earth orbit aboard the Pegasus Air-Launched Space Booster at an inclination of 21 degrees and an altitude of 473 km. It is equipped with a synthetic aperture radar and a visible thermal/infrared sensor to remotely sense conditions at the target area of Chiapas, Mexico. The orbit is designed so that MEDSAT will pass over the target site twice each day. The data from each scan will be downlinked to Hawaii for processing, resulting in maps indicating areas of high malaria risk. These will be distributed to health officials at the target site. A relatively inexpensive launch by Pegasus and a design using mainly proven, off-the-shelf technology permit a low mission cost, while innovations in the satellite controls and the scientific instruments allow a fairly complex mission.

Source record↗

NASA NTRS1992

MEDSAT - A remote sensing satellite for malaria early warning and control

A remote sensing, medical satellite (MEDSAT) aids in the control of carrier (vector) borne disease. The prototype design is a light satellite to test for control of malaria. The design features a 340-kg satellite with visual/IR and SAR sensors in a low inclination orbit observing a number of worldwide test sites. The approach is to use four-band visual/IR and dual-polarized L-band SAR images obtained from MEDSAT in concert with in-situ data to estimate the temporal and spatial variations of malaria risk. This allows public health resources to focus on the most vulnerable areas at the appropriate time. It is concluded that a light-satellite design for a MEDSAT satellite with a Pegasus launch is feasible.

Vesecky, John↗

NASA NTRS2003

Predictability of Malaria Transmission Intensity in the Mpumalanga Province, South Africa, Using Land Surface Climatology and Autoregressive Analysis

There has been increasing effort in recent years to employ satellite remotely sensed data to identify and map vector habitat and malaria transmission risk in data sparse environments. In the current investigation, available satellite and other land surface climatology data products are employed in short-term forecasting of infection rates in the Mpumalanga Province of South Africa, using a multivariate autoregressive approach. The climatology variables include precipitation, air temperature and other land surface states computed by the Off-line Land-Surface Global Assimilation System (OLGA) including soil moisture and surface evaporation. Satellite data products include the Normalized Difference Vegetation Index (NDVI) and other forcing data used in the Goddard Earth Observing System (GEOS-1) model. Predictions are compared to long- term monthly records of clinical and microscopic diagnoses. The approach addresses the high degree of short-term autocorrelation in the disease and weather time series. The resulting model is able to predict 11 of the 13 months that were classified as high risk during the validation period, indicating the utility of applying antecedent climatic variables to the prediction of malaria incidence for the Mpumalanga Province.

Grass, David↗

NASA NTRS1996

Predictions of malaria vector distribution in Belize based on multispectral satellite data

Use of multispectral satellite data to predict arthropod-borne disease trouble spots is dependent on clear understandings of environmental factors that determine the presence of disease vectors. A blind test of remote sensing-based predictions for the spatial distribution of a malaria vector, Anopheles pseudopunctipennis, was conducted as a follow-up to two years of studies on vector-environmental relationships in Belize. Four of eight sites that were predicted to be high probability locations for presence of An. pseudopunctipennis were positive and all low probability sites (0 of 12) were negative. The absence of An. pseudopunctipennis at four high probability locations probably reflects the low densities that seem to characterize field populations of this species, i.e., the population densities were below the threshold of our sampling effort. Another important malaria vector, An. darlingi, was also present at all high probability sites and absent at all low probability sites. Anopheles darlingi, like An. pseudopunctipennis, is a riverine species. Prior to these collections at ecologically defined locations, this species was last detected in Belize in 1946.

Non-NASA Center↗

NASA NTRS2004

Does Magnetic Field Affect Malaria Parasite Replication in Human Red Blood Cells?

Digestion of red blood cell (RBC) hemoglobin by the malaria parasite results in the formation of paramagnetic hemazoin crystals inside the parasite body. A number of reports suggest that magnetic field interaction with hamazoin crystals significantly reduces the number of infected cells in culture, and thus magnetic field can be used to combat malaria. We studies the effects of magnetic filed on the Plasmodium falciparum asexual life cycle inside RBCs under various experimental conditions. No effect was found during prolonged exposure of infected RBCs to constant magnetic fields up to 6000 Gauss. Infected RBCs were also exposed, under temperature-controlled conditions, to oscillating magnetic fields with frequencies in the range of 500-20000 kHz, and field strength 30-600 Gauss. This exposure often changed the proportion of different parasite stages in treated culture compared to controls. However, no significant effect on parasitemia was observed in treated cultures. This result indicates that the magnetic field effect on Plasmodium falciparum is negligible, or that hypothetical negative and positive effects on different stages within one 48-hour compensate each other.

Chanturiya, Alexandr N.↗

NASA NTRS2007

Surveillance and Control of Malaria Transmission Using Remotely Sensed Meteorological and Environmental Parameters

Meteorological and environmental parameters important to malaria transmission include temperature, relative humidity, precipitation, and vegetation conditions. These parameters can most conveniently be obtained using remote sensing. Selected provinces and districts in Thailand and Indonesia are used to illustrate how remotely sensed meteorological and environmental parameters may enhance the capabilities for malaria surveillance and control. Hindcastings based on these environmental parameters have shown good agreement to epidemiological records.

Kiang, R.↗