Browsing by Author "Moreno-Madriñán, Max J."

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    Applications of geospatial analysis techniques for public health
    (2016-05-02) Stanforth, Austin Curran; Filippelli, Gabriel; Johnson, Daniel P.; Wang, Lixin; Wilson, Jeffrey; Moreno-Madriñán, Max J.; Jacinthe, Pierre-André
    Geospatial analysis is a generic term describing several technologies or methods of computational analysis using the Earth as a living laboratory. These methods can be implemented to assess risk and study preventative mitigation practices for Public Health. Through the incorporation Geographic Information Science and Remote Sensing tools, data collection can be conducted at a larger scale, more frequent, and less expensive that traditional in situ methods. These techniques can be extrapolated to be used to study a variety of topics. Application of these tools and techniques were demonstrated through Public Health research. Although it is understand resolution, or scale, of a research project can impact a study’s results; further research is needed to understand the extent of the result’s bias. Extreme heat vulnerability analysis was studied to validate previously identified socioeconomic and environmental variables influential for mitigation studies, and how the variability of resolution impacts the results of the methodology. Heat was also investigated for the implication of spatial and temporal resolution, or aggregation, influence on results. Methods studying the physical and socioeconomic environments of Dengue Fever outbreaks were also studied to identify patters of vector emergence.
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    Correlating Remote Sensing Data with the Abundance of Pupae of the Dengue Virus Mosquito Vector, Aedes aegypti, in Central Mexico
    (2014-05) Moreno-Madriñán, Max J.; Crosson, William L.; Eisen, Lars; Estes, Sue M.; Estes Jr, Maurice G.; Hayden, Mary; Hemmings, Sarah N.; Irwin, Dan E.; Lozano-Fuentes, Saul; Monaghan, Andrew J.; Quattrochi, Dale; Welsh-Rodriguez, Carlos M.; Zielinski-Gutierrez, Emily
    Using a geographic transect in Central Mexico, with an elevation/climate gradient, but uniformity in socio-economic conditions among study sites, this study evaluates the applicability of three widely-used remote sensing (RS) products to link weather conditions with the local abundance of the dengue virus mosquito vector, Aedes aegypti (Ae. aegypti). Field-derived entomological measures included estimates for the percentage of premises with the presence of Ae. aegypti pupae and the abundance of Ae. aegypti pupae per premises. Data on mosquito abundance from field surveys were matched with RS data and analyzed for correlation. Daily daytime and nighttime land surface temperature (LST) values were obtained from Moderate Resolution Imaging Spectroradiometer (MODIS)/Aqua cloud-free images within the four weeks preceding the field survey. Tropical Rainfall Measuring Mission (TRMM)-estimated rainfall accumulation was calculated for the four weeks preceding the field survey. Elevation was estimated through a digital elevation model (DEM). Strong correlations were found between mosquito abundance and RS-derived night LST, elevation and rainfall along the elevation/climate gradient. These findings show that RS data can be used to predict Ae. aegypti abundance, but further studies are needed to define the climatic and socio-economic conditions under which the correlations observed herein can be assumed to apply.
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    Exploratory Analysis of Dengue Fever Niche Variables within the Río Magdalena Watershed
    (MDPI, 2016-09-19) Stanforth, Austin; Moreno-Madriñán, Max J.; Ashby, Jeffrey; Department of Environmental Health Sciences, FSPH
    Previous research on Dengue Fever have involved laboratory tests or study areas with less diverse temperature and elevation ranges than is found in Colombia; therefore, preliminary research was needed to identify location specific attributes of Dengue Fever transmission. Environmental variables derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Tropical Rainfall Measuring Mission (TRMM) satellites were combined with population variables to be statistically compared against reported cases of Dengue Fever in the Río Magdalena watershed, Colombia. Three-factor analysis models were investigated to analyze variable patterns, including a population, population density, and empirical Bayesian estimation model. Results identified varying levels of Dengue Fever transmission risk, and environmental characteristics which support, and advance, the research literature. Multiple temperature metrics, elevation, and vegetation composition were among the more contributory variables found to identify future potential outbreak locations.
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    Factors of Concern Regarding Zika and Other Aedes aegypti-Transmitted Viruses in the United States
    (Oxford Academic, 2017-03) Moreno-Madriñán, Max J.; Turell, Michael; Department of Environmental Health Sciences, Richard M. Fairbanks School of Public Health
    The recent explosive outbreaks of Zika and chikungunya throughout the Americas has raised concerns about the threats that these and similar diseases may pose to the United States (U.S.). The commonly accepted association between tropical climates and the endemicity of these diseases has led to concerns about the possibility of their redistribution due to climate change and transmission arising from cases imported from endemic regions initiating outbreaks in the United States. While such possibilities are indeed well founded, the analysis of historical records not only confirms the potential critical role of traveling and globalization but also reveals that the climate in the United States currently is suitable for local transmission of these viruses. Thus, the main factors preventing these diseases from occurring in the United States today are more likely socioeconomic such as lifestyle, housing infrastructure, and good sanitation. As long as such conditions are maintained, it seems unlikely that local transmission will occur to any great degree, particularly in the northern states. Indeed, a contributing factor to explain the current endemicity of these diseases in less-developed American countries may be well explained by socioeconomic and some lifestyle characteristics in such countries.
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    History of Mosquitoborne Diseases in the United States and Implications for New Pathogens
    (Emerging Infectious Diseases, 2018-05) Moreno-Madriñán, Max J.; Turell, Michael; Environmental Health Science, School of Public Health
    The introduction and spread of West Nile virus and the recent introduction of chikungunya and Zika viruses into the Americas have raised concern about the potential for various tropical pathogens to become established in North America. A historical analysis of yellow fever and malaria incidences in the United States suggests that it is not merely a temperate climate that keeps these pathogens from becoming established. Instead, socioeconomic changes are the most likely explanation for why these pathogens essentially disappeared from the United States yet remain a problem in tropical areas. In contrast to these anthroponotic pathogens that require humans in their transmission cycle, zoonotic pathogens are only slightly affected by socioeconomic factors, which is why West Nile virus became established in North America. In light of increasing globalization, we need to be concerned about the introduction of pathogens such as Rift Valley fever, Japanese encephalitis, and Venezuelan equine encephalitis viruses.
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    Improving Inland Water Quality Monitoring through Remote Sensing Techniques
    (2014-11) Ogashawara, Igor; Moreno-Madriñán, Max J.
    Chlorophyll-a (chl-a) levels in lake water could indicate the presence of cyanobacteria, which can be a concern for public health due to their potential to produce toxins. Monitoring of chl-a has been an important practice in aquatic systems, especially in those used for human services, as they imply an increased risk of exposure. Remote sensing technology is being increasingly used to monitor water quality, although its application in cases of small urban lakes is limited by the spatial resolution of the sensors. Lake Thonotosassa, FL, USA, a 3.45-km2 suburban lake with several uses for the local population, is being monitored monthly by traditional methods. We developed an empirical bio-optical algorithm for the Moderate Resolution Imaging Spectroradiometer (MODIS) daily surface reflectance product to monitor daily chl-a. We applied the same algorithm to four different periods of the year using 11 years of water quality data. Normalized root mean squared errors were lower during the first (0.27) and second (0.34) trimester and increased during the third (0.54) and fourth (1.85) trimesters of the year. Overall results showed that Earth-observing technologies and, particularly, MODIS products can also be applied to improve environmental health management through water quality monitoring of small lakes.
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    Niche Modeling of Dengue Fever Using Remotely Sensed Environmental Factors and Boosted Regression Trees
    (MDPI, 2017-04) Ashby, Jeffrey; Moreno-Madriñán, Max J.; Yiannoutsos, Constantin T.; Stanforth, Austin; Environmental Health Science, School of Public Health
    Dengue fever (DF), a vector-borne flavivirus, is endemic to the tropical countries of the world with nearly 400 million people becoming infected each year and roughly one-third of the world’s population living in areas of risk. The main vector for DF is the Aedes aegypti mosquito, which is also the same vector of yellow fever, chikungunya, and Zika viruses. To gain an understanding of the spatial aspects that can affect the epidemiological processes across the disease’s geographical range, and the spatial interactions involved, we created and compared Bernoulli and Poisson family Boosted Regression Tree (BRT) models to quantify the overall annual risk of DF incidence by municipality, using the Magdalena River watershed of Colombia as a study site during the time period between 2012 and 2014. A wide range of environmental conditions make this site ideal to develop models that, with minor adjustments, could be applied in many other geographical areas. Our results show that these BRT methods can be successfully used to identify areas at risk and presents great potential for implementation in surveillance programs.
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    Niche Modeling of Dengue Fever Using Remotely Sensed Environmental Factors and BRT
    (OJPHI, 2018) Ashby, Jeffrey L.; Moreno-Madriñán, Max J.; Health Policy and Management, School of Public Health
    In this paper we used Boosted Regression Tree analysis coupled with environmental factors gathered from satellite data, such as temperature, elevation, and precipitation, to model the niche of Dengue Fever (DF) in Colombia.
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    Performance of the MODIS FLH algorithm in estuarine waters: a multi-year (2003–2010) analysis from Tampa Bay, Florida (USA)
    (2013-06) Moreno-Madriñán, Max J.; Fischer, Andrew M.
    Although satellite technology promises great usefulness for the consistent monitoring of chlorophyll-α concentration in estuarine and coastal waters, the complex optical properties commonly found in these types of waters seriously challenge the application of this technology. Blue–green ratio algorithms are susceptible to interference from water constituents, different from phytoplankton, which dominate the remote-sensing signal. Alternatively, modelling and laboratory studies have not shown a decisive position on the use of near-infrared (NIR) algorithms based on the sun-induced chlorophyll fluorescence signal. In an analysis of a multi-year (2003–2010) in situ monitoring data set from Tampa Bay, Florida (USA), as a case, this study assesses the relationship between the fluorescence line height (FLH) product from the Moderate Resolution Imaging Spectrometer (MODIS) and chlorophyll-α.
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    Spatio-Temporal Variability in a Turbid and Dynamic Tidal Estuarine Environment (Tasmania, Australia): An Assessment of MODIS Band 1 Reflectance
    (MDPI, 2017-10-15) Fischer, Andrew M.; Pang, Daniel; Kidd, Ian M.; Moreno-Madriñán, Max J.; Environmental Health Sciences, School of Public Health
    Patterns of turbidity in estuarine environments are linked to hydrodynamic processes. However, the linkage between patterns and processes remains poorly resolved due to the scarcity of data needed to resolve fine scale highly dynamic processes in tidal estuaries. The application of remote sensing technology to monitor dynamic coastal areas such as estuaries offers important advantages in this regard, by providing synoptic maps of larger, constantly changing regions over consistent periods. In situ turbidity measurements were correlated against the Moderate Resolution Imaging Spectrometer Terra sensor 250 m surface reflectance product, in order to assess this product for examining the complex estuarine waters of the Tamar estuary (Australia). Satellite images were averaged to examine spatial, seasonal and annual patterns of turbidity. Relationships between in situ measurements of turbidity and reflectance is positively correlated and improves with increased tidal height, a decreased overpass-in situ gap, and one day after a rainfall event. Spatial and seasonal patterns that appear in seasonal and annual MODIS averages, highlighting the usefulness of satellite imagery for resource managers to manage sedimentation issues in a degraded estuary.