Dose-response relationships for environmentally mediated infectious disease transmission models
Publication Date
April 07, 2017
Authors
Andrew F. Brouwer, Mark H. Weir, Marisa C. Eisenberg, Rafael Meza, et al
Volume
13
Issue
4
Pages
e1005481
DOI
https://dx.plos.org/10.1371/journal.pcbi.1005481
Publisher URL
http://journals.plos.org/ploscompbiol/article?id=10.1371%2Fjournal.pcbi.1005481
Scopus
85018313420
Mendeley
http://www.mendeley.com/research/doseresponse-relationships-environmentally-mediated-infectious-disease-transmission-models
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Mendeley | Further Information

{"title"=>"Dose-response relationships for environmentally mediated infectious disease transmission models", "type"=>"journal", "authors"=>[{"first_name"=>"Andrew F.", "last_name"=>"Brouwer", "scopus_author_id"=>"56967682100"}, {"first_name"=>"Mark H.", "last_name"=>"Weir", "scopus_author_id"=>"22137121000"}, {"first_name"=>"Marisa C.", "last_name"=>"Eisenberg", "scopus_author_id"=>"15762032100"}, {"first_name"=>"Rafael", "last_name"=>"Meza", "scopus_author_id"=>"8895339300"}, {"first_name"=>"Joseph N.S.", "last_name"=>"Eisenberg", "scopus_author_id"=>"7102686314"}], "year"=>2017, "source"=>"PLoS Computational Biology", "identifiers"=>{"doi"=>"10.1371/journal.pcbi.1005481", "sgr"=>"85018313420", "pui"=>"615827160", "scopus"=>"2-s2.0-85018313420", "issn"=>"15537358", "pmid"=>"28388665", "isbn"=>"1111111111"}, "id"=>"3c365416-9092-30ad-80c2-04512e337347", "abstract"=>"Environmentally mediated infectious disease transmission models provide a mechanistic approach to examining environmental interventions for outbreaks, such as water treatment or surface decontamination. The shift from the classical SIR framework to one incorporating the environment requires codifying the relationship between exposure to environmental pathogens and infection, i.e. the dose–response relationship. Much of the work characteriz-ing the functional forms of dose–response relationships has used statistical fit to experimen-tal data. However, there has been little research examining the consequences of the choice of functional form in the context of transmission dynamics. To this end, we identify four prop-erties of dose–response functions that should be considered when selecting a functional form: low-dose linearity, scalability, concavity, and whether it is a single-hit model. We find that i) middle-and high-dose data do not constrain the low-dose response, and different dose–response forms that are equally plausible given the data can lead to significant differ-ences in simulated outbreak dynamics; ii) the choice of how to aggregate continuous expo-sure into discrete doses can impact the modeled force of infection; iii) low-dose linear, concave functions allow the basic reproduction number to control global dynamics; and iv) identifiability analysis offers a way to manage multiple sources of uncertainty and leverage environmental monitoring to make inference about infectivity. By applying an environmen-tally mediated infectious disease model to the 1993 Milwaukee Cryptosporidium outbreak, we demonstrate that environmental monitoring allows for inference regarding the infectivity of the pathogen and thus improves our ability to identify outbreak characteristics such as pathogen strain. Author summary Many infectious disease interventions, including water treatment, hand hygiene, and sur-face decontamination, target pathogens in the environment. Explicitly modeling the con-centration of pathogens in the environment within transmission models can be a useful way to consider not only the impact of such mitigation efforts but also the spatial spread", "link"=>"http://www.mendeley.com/research/doseresponse-relationships-environmentally-mediated-infectious-disease-transmission-models", "reader_count"=>36, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Professor > Associate Professor"=>4, "Student > Doctoral Student"=>1, "Researcher"=>8, "Student > Ph. D. Student"=>12, "Other"=>2, "Student > Master"=>4, "Student > Bachelor"=>2, "Professor"=>2}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Professor > Associate Professor"=>4, "Student > Doctoral Student"=>1, "Researcher"=>8, "Student > Ph. D. Student"=>12, "Other"=>2, "Student > Master"=>4, "Student > Bachelor"=>2, "Professor"=>2}, "reader_count_by_subject_area"=>{"Unspecified"=>9, "Engineering"=>4, "Environmental Science"=>3, "Biochemistry, Genetics and Molecular Biology"=>1, "Nursing and Health Professions"=>1, "Mathematics"=>4, "Agricultural and Biological Sciences"=>5, "Medicine and Dentistry"=>3, "Veterinary Science and Veterinary Medicine"=>4, "Social Sciences"=>1, "Decision Sciences"=>1}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>4}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>3}, "Social Sciences"=>{"Social Sciences"=>1}, "Decision Sciences"=>{"Decision Sciences"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>5}, "Nursing and Health Professions"=>{"Nursing and Health Professions"=>1}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>1}, "Mathematics"=>{"Mathematics"=>4}, "Unspecified"=>{"Unspecified"=>9}, "Environmental Science"=>{"Environmental Science"=>3}, "Veterinary Science and Veterinary Medicine"=>{"Veterinary Science and Veterinary Medicine"=>4}}, "reader_count_by_country"=>{"United States"=>2}, "group_count"=>1}

Scopus | Further Information

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  • {"unique-ip"=>"32", "full-text"=>"33", "pdf"=>"3", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2021", "month"=>"1"}
  • {"unique-ip"=>"31", "full-text"=>"35", "pdf"=>"5", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2021", "month"=>"2"}
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Net::HTTPTooManyRequests

Source
Scopus
Time
2019-08-27 21:07:07 UTC
Target URL
https://api.elsevier.com/content/search/index:SCOPUS?query=DOI(10.1371%2Fjournal.pcbi.1005481)
Trace

/app/models/concerns/networkable.rb:21:in `get_result'
/app/models/source.rb:165:in `get_data'
/app/models/retrieval_status.rb:47:in `perform_get_data'
/app/jobs/source_job.rb:52:in `block (2 levels) in perform'
/app/jobs/source_job.rb:51:in `block in perform'
/app/jobs/source_job.rb:35:in `each'
/app/jobs/source_job.rb:35:in `perform'