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Environmental controls, oceanography and population dynamics of pathogens and harmful algal blooms: connecting sources to human exposure.

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Title: Environmental controls, oceanography and population dynamics of pathogens and harmful algal blooms: connecting sources to human exposure.
Authors: Dyble J (AUTHOR), Bienfang P (AUTHOR), Dusek E (AUTHOR), Hitchcock G (AUTHOR), Holland F (AUTHOR), Laws E (AUTHOR), Lerczak J (AUTHOR), McGillicuddy DJ Jr (AUTHOR), Minnett P (AUTHOR), Moore SK (AUTHOR), O'Kelly C (AUTHOR), Solo-Gabriele H (AUTHOR), Wang JD (AUTHOR), Dyble, Julianne1 (AUTHOR), Bienfang, Paul (AUTHOR), Dusek, Eva (AUTHOR), Hitchcock, Gary (AUTHOR), Holland, Fred (AUTHOR), Laws, Ed (AUTHOR), Lerczak, James (AUTHOR)
Source: Environmental Health: A Global Access Science Source. 2008 Supplement 2, Vol. 7, pS5-S5. 1p.
Abstract: Coupled physical-biological models are capable of linking the complex interactions between environmental factors and physical hydrodynamics to simulate the growth, toxicity and transport of infectious pathogens and harmful algal blooms (HABs). Such simulations can be used to assess and predict the impact of pathogens and HABs on human health. Given the widespread and increasing reliance of coastal communities on aquatic systems for drinking water, seafood and recreation, such predictions are critical for making informed resource management decisions. Here we identify three challenges to making this connection between pathogens/HABs and human health: predicting concentrations and toxicity; identifying the spatial and temporal scales of population and ecosystem interactions; and applying the understanding of population dynamics of pathogens/HABs to management strategies. We elaborate on the need to meet each of these challenges, describe how modeling approaches can be used and discuss strategies for moving forward in addressing these challenges. [ABSTRACT FROM AUTHOR]
Copyright of Environmental Health: A Global Access Science Source is the property of BioMed Central and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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An: 105530579
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  Data: Environmental controls, oceanography and population dynamics of pathogens and harmful algal blooms: connecting sources to human exposure.
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  Data: Coupled physical-biological models are capable of linking the complex interactions between environmental factors and physical hydrodynamics to simulate the growth, toxicity and transport of infectious pathogens and harmful algal blooms (HABs). Such simulations can be used to assess and predict the impact of pathogens and HABs on human health. Given the widespread and increasing reliance of coastal communities on aquatic systems for drinking water, seafood and recreation, such predictions are critical for making informed resource management decisions. Here we identify three challenges to making this connection between pathogens/HABs and human health: predicting concentrations and toxicity; identifying the spatial and temporal scales of population and ecosystem interactions; and applying the understanding of population dynamics of pathogens/HABs to management strategies. We elaborate on the need to meet each of these challenges, describe how modeling approaches can be used and discuss strategies for moving forward in addressing these challenges. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Environmental Health: A Global Access Science Source is the property of BioMed Central and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: 2008 Supplement 2
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