Remotely sensed surrogates of meteorological data for the study of the distribution and abundance of arthropod vectors of disease

Hay, Simon I.; Tucker, Compton J.; Rogers, David J.; Packer, M.J.

doi:10.1080/00034983.1996.11813021

Cited by 140 publications

(107 citation statements)

References 61 publications

Supporting

Mentioning

105

Contrasting

Unclassified

Order By: Relevance

“…An example of the use of remote sensing for vector-borne disease is the study on human African trypanosomiasis by Rogers and colleagues. Vegetation indices (specifically the NDVI, normalised vegetation index) obtained from satellite images and tsetse fly abundance have been correlated for regions in Central and East Africa (Rogers and Randolph, 1991;Hay et al, 1996). Two important points are evident.…”

Section: Box 104 Example: Malaria Transmission In Sri Lankamentioning

confidence: 99%

Temperature Mediated Moose Survival in Northeastern Minnesota

et al. 2009

View full text Add to dashboard Cite

The earth is in the midst of a pronounced warming trend and temperatures in Minnesota, USA, as elsewhere, are projected to increase. Northern Minnesota represents the southern edge to the circumpolar distribution of moose (Alces alces), a species intolerant of heat. Moose increase their metabolic rate to regulate their core body temperature as temperatures rise. We hypothesized that moose survival rates would be a function of the frequency and magnitude that ambient temperatures exceeded the upper critical temperature of moose. We compared annual and seasonal moose survival in northeastern Minnesota between 2002 and 2008 with a temperature metric. We found that models based on January temperatures above the critical threshold were inversely correlated with subsequent survival and explained >78% of variability in spring, fall, and annual survival. Models based on late‐spring temperatures also explained a high proportion of survival during the subsequent fall. A model based on warm‐season temperatures was important in explaining survival during the subsequent winter. Our analyses suggest that temperatures may have a cumulative influence on survival. We expect that continuation or acceleration of current climate trends will result in decreased survival, a decrease in moose density, and ultimately, a retreat of moose northward from their current distribution.

show abstract

Section: Box 104 Example: Malaria Transmission In Sri Lankamentioning

confidence: 99%

Temperature Mediated Moose Survival in Northeastern Minnesota

et al. 2009

View full text Add to dashboard Cite

show abstract

“…Kawa and Sabroza (2002) and Werneck and Maguire (2002) have analyzed historical and spatial determinants, in the city of Rio de Janeiro, Brazil, and Teresina, state of Piauí, Brazil, respectively, for implementation, maintenance, and spread of ACL and their correlation with urban organization and occupation in the periphery of those cities. Hay et al (1997), Connor et al (1998), andKing et al (2004) Therefore, data collection through geoprocessing techniques has contributed to monitor and specially to identify effective and priority control measures for tropical diseases. The present work aims to provide clarification on visceral leishmaniasis in the city of Belo Horizonte, and give subsidies for the central organs of the state in order to promote improvements to the disease control plans.…”

mentioning

confidence: 99%

Epidemiology of visceral leishmaniasis through spatial analysis, in Belo Horizonte municipality, state of Minas Gerais, Brazil

Margonari

Freitas²,

Ribeiro³

et al. 2006

Mem. Inst. Oswaldo Cruz

100

View full text Add to dashboard Cite

The geographic information system approach has permitted integration between demographic, socio-economic and environmental data, providing In the 1990s, an increasing capacity of data analysis and ease of information accessibility through cheap and simple computational systems was remarkable. Such technology represents a breakthrough in data bank organization, mainly regarding health.Geoprocessing is a broad term that is applied to several technologies of manipulation and processing of geographical data through computational programs. System of geographical information (SGI) is one of the geoprocessing techniques, the most widely used, once it gathers organized data at the stages of data capture by remote sensing, GPS or organization of digital cartographic basis, and organizes systems, which are able to obtain new information and improve knowledge. SGI comprises computational systems used for understanding facts and phenomena that occur in the geographical space. Its capacity of gathering data sets of conventional spatial expression, structuring and integrating them adequately, makes it an essential tool for manipulating geographical information (Pina 1994).Applications of SGI in the field of health have been reported in studies on epidemiological surveys, health service assessment, urbanization, and environment. Moreover, evaluation of endemic diseases from the perspective of several elements involved in the transmission cycle, such as historical, environmental, and social determinants of disease foci, became easier with Geoprocessing techniques (Sabroza et al. 1992, Albuquerque 1993, Thomson & Connor 2000.Geoprocessing technology has enabled scientists to map vectors and analyze environmental factors that affect spatial and temporal distribution of insects. Such techniques have been used to monitor diseases such as malaria, trypanosomiasis, and leishmaniases (Elnaiem et al. 1998, Thomson & Connor 2000.American cutaneous leishmaniasis (ACL) and visceral leishmaniasis (VL) have been studied through geoprocessing techniques by several investigators: Cross et al. (1996), by gathering data from 136 scientific papers, have generated a distribution model of Phlebotomus papatasi in Southeast Asia throughout the year. By using satellite images and field-collected data in Sudan, Elnaiem et al. (1998Elnaiem et al. ( , 2003 and Thomson et al. (1999) observed that several ecological factors are crucial for the presence of Phlebotomus orientalis, the vector of VL in that country. Kawa and Sabroza (2002) and Werneck and Maguire (2002) have analyzed historical and spatial determinants, in the city of Rio de Janeiro, Brazil, and Teresina, state of Piauí, Brazil, respectively, for implementation, maintenance, and spread of ACL and their correlation with urban organization and occupation in the periphery of those cities. Hay et al. (1997), Connor et al. (1998), andKing et al. (2004) Therefore, data collection through geoprocessing techniques has contributed to monitor and specially to identify effective and priority contro...

show abstract

“…In previous studies, rainfall surrogates were often used for modeling when no suitable remotely sensed or ground based measurements were available (Hay et al, 1996;Thomson et al, 1996;Hay et al, 1998). The most frequently used surrogates include the Cold Cloud Duration (CCD) derived from Meteosat remote sensing measurements (Snijders, 1991) and the Normalized Difference Vegetation Index (NDVI) derived from Advanced Very High Resolution Radiometer (AVHRR) measurements.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2006

View full text Add to dashboard Cite

Abstract. 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 vari...

show abstract

Remotely sensed surrogates of meteorological data for the study of the distribution and abundance of arthropod vectors of disease

Cited by 140 publications

References 61 publications

Temperature Mediated Moose Survival in Northeastern Minnesota

Temperature Mediated Moose Survival in Northeastern Minnesota

Epidemiology of visceral leishmaniasis through spatial analysis, in Belo Horizonte municipality, state of Minas Gerais, Brazil

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

Contact Info

Product

Resources

About