A bayesian approach for inferring the dynamics of partially observed endemic infectious diseases from space-time-genetic data

Proceedings of the Royal Society B: Biological Sciences

Volumn 281, Issue 1782, 2014, Pages

  Mollentze, Nardus ^a,b   Nel, Louis H ^a   Townsend, Sunny ^b   le Roux, Kevin ^c   Hampson, Katie ^b   Haydon, Daniel T ^b   Soubeyrand, Samuel ^d  

^a UNIVERSITY OF PRETORIA   (South Africa)

^b UNIVERSITY OF GLASGOW   (United Kingdom)

^c KwaZulu Natal Department of Agriculture   (South Africa)

^d INRA   (France)

Author keywords

Endemic disease; Rabies virus; Spatial epidemiology; Surveillance; Transmission trees

Indexed keywords

ACCURACY ASSESSMENT; ANTHROPOGENIC EFFECT; BAYESIAN ANALYSIS; CANID; DISEASE TRANSMISSION; EPIDEMIOLOGY; GENOME; INFECTIOUS DISEASE; INFECTIVITY; RABIES; RECONSTRUCTION; VIRUS;

SOUTH AFRICA;

ANIMAL; BAYES THEOREM; BIOLOGICAL MODEL; COMMUNICABLE DISEASES; DISEASE TRANSMISSION; DOG; GENETICS; MOLECULAR GENETICS; NUCLEOTIDE SEQUENCE; RABIES; RABIES VIRUS; SOUTH AFRICA; STATISTICS AND NUMERICAL DATA; TIME; TRANSMISSION; VETERINARY;

ANIMALS; BASE SEQUENCE; BAYES THEOREM; COMMUNICABLE DISEASES; DISEASE TRANSMISSION, INFECTIOUS; DOGS; EPIDEMIOLOGIC METHODS; MODELS, BIOLOGICAL; MOLECULAR SEQUENCE DATA; RABIES; RABIES VIRUS; SOUTH AFRICA; TIME;

EID: 85003050686     PISSN: 09628452     EISSN: 14712954     Source Type: Journal    
DOI: 10.1098/rspb.2013.3251     Document Type: Article

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