Can Google Trends data improve forecasting of Lyme disease incidence?

Kapitány-Fövény, Máté; Ferenci, Tamás; Sulyok, Zita; Kegele, Josua; Richter, Hardy; Vályi-Nagy, István; Sulyok, Mihály

Kapitány-Fövény, Máté; Ferenci, Tamás; Sulyok, Zita; Kegele, Josua; Richter, Hardy; Vályi-Nagy, István; Sulyok, Mihály.

Afiliação

Kapitány-Fövény M; Faculty of Health Sciences, Semmelweis University, Budapest, Hungary.
Ferenci T; Nyíro Gyula National Institute of Psychiatry and Addictions, Budapest, Hungary.
Sulyok Z; John von Neumann Faculty of Informatics, Physiological Controls Group, Óbuda University, Budapest, Hungary.
Kegele J; Institute of Tropical Medicine, Eberhard Karls University, Tübingen, Germany.
Richter H; Department of Neurology and Epileptology, Neurology Clinics, Eberhard Karls University, Tübingen, Germany.
Vályi-Nagy I; Department of Neurology and Stroke, Neurology Clinics, Eberhard Karls University, Tübingen, Germany.
Sulyok M; South-Pest Central Hospital, National Institute of Hematology and Infectious Diseases, Budapest, Hungary.

Zoonoses Public Health ; 66(1): 101-107, 2019 02.

Article em En | MEDLINE | ID: mdl-30447056

Assuntos

Internet; Doença de Lyme/epidemiologia; Vigilância da População/métodos; Alemanha/epidemiologia; Humanos; Incidência; Fatores de Tempo

Palavras-chave

Lyme disease; Web browser; forecasting; mathematical modelling

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Doença de Lyme / Vigilância da População / Internet Tipo de estudo: Incidence_studies / Prognostic_studies / Risk_factors_studies / Screening_studies Limite: Humans País/Região como assunto: Europa Idioma: En Ano de publicação: 2019 Tipo de documento: Article

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