Can Google Trends data improve forecasting of Lyme disease incidence?

Zoonoses and Public Health

Volumn 66, Issue 1, 2019, Pages 101-107

  Kapitány Fövény, Máté ^a,b   Ferenci, Tamás ^c   Sulyok, Zita ^d   Kegele, Josua ^d   Richter, Hardy ^d   Vályi Nagy, István ^e   Sulyok, Mihály ^d  

^a SEMMELWEIS UNIVERSITY   (Hungary)

^b NATIONAL INSTITUTE OF PSYCHIATRY AND NEUROLOGY   (Hungary)

^c OBUDA UNIVERSITY   (Hungary)

^d UNIVERSITY OF TÜBINGEN   (Germany)

^e Saint Ladislaus Campus   (Hungary)

Author keywords

forecasting; Lyme disease; mathematical modelling; Web browser

Indexed keywords

ARTICLE; DATA BASE; FORECASTING; GERMANY; INCIDENCE; INTERNET; LYME DISEASE; PERFORMANCE; PREDICTION; PRIORITY JOURNAL; TIME SERIES ANALYSIS; WEB BROWSER; HEALTH SURVEY; HUMAN; PROCEDURES; TIME FACTOR;

GERMANY; HUMANS; INCIDENCE; INTERNET; LYME DISEASE; POPULATION SURVEILLANCE; TIME FACTORS;

EID: 85056624395     PISSN: 18631959     EISSN: 18632378     Source Type: Journal    
DOI: 10.1111/zph.12539     Document Type: Article

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