Data-model fusion to better understand emerging pathogens and improve infectious disease forecasting

Ecological Applications

Volumn 21, Issue 5, 2011, Pages 1443-1460

  LaDeau, Shannon L ^a   Glass, Gregory E ^b   Hobbs, N Thompson ^c   Latimer, Andrew ^d   Ostfeld, Richard S ^a  

^a CARY INSTITUTE OF ECOSYSTEM STUDIES   (United States)

^b JOHNS HOPKINS BLOOMBERG SCHOOL OF PUBLIC HEALTH   (United States)

^c Department of Environmental and Radiological Health Sciences   (United States)

^d UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Bayesian; Climate change; Disease ecology; Emergent; Forecast; Hierarchical; Likelihood; Pathogen; State space models; Transmission; Vector; Zoonotic

Indexed keywords

BAYESIAN ANALYSIS; CHOLERA; CLIMATE CHANGE; CONCEPTUAL FRAMEWORK; DATA ASSIMILATION; DISEASE TRANSMISSION; DISEASE VECTOR; EPIDEMIOLOGY; FORECASTING METHOD; GLOBAL CHANGE; HEALTH RISK; HIERARCHICAL SYSTEM; INFECTIOUS DISEASE; PUBLIC HEALTH; PUMP; SPATIOTEMPORAL ANALYSIS;

ANIMAL; ARTICLE; BIOLOGICAL MODEL; COMMUNICABLE DISEASE; DENGUE; EPIDEMIC; FORECASTING; HEALTH; HUMAN; LYME DISEASE; METHODOLOGY; SEVERE ACUTE RESPIRATORY SYNDROME; STATISTICAL MODEL; STATISTICS; WEST NILE FEVER; ZOONOSIS;

ANIMALS; COMMUNICABLE DISEASES; COMMUNICABLE DISEASES, EMERGING; DENGUE; DISEASE OUTBREAKS; FORECASTING; HUMANS; LYME DISEASE; MODELS, BIOLOGICAL; MODELS, STATISTICAL; SEVERE ACUTE RESPIRATORY SYNDROME; WEST NILE FEVER; WORLD HEALTH; ZOONOSES;

ENGLAND; LONDON [ENGLAND]; UNITED KINGDOM;

EID: 79960373930     PISSN: 10510761     EISSN: None     Source Type: Journal    
DOI: 10.1890/09-1409.1     Document Type: Article

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