Prediction of different interventions on the burden of drug-resistant tuberculosis in China: a dynamic modelling study.

Xu, Ao; Wen, Ze-Xuan; Wang, Yue; Wang, Wei-Bing

Xu, Ao; Wen, Ze-Xuan; Wang, Yue; Wang, Wei-Bing.

Afiliación

Xu A; School of Public Health, Key Laboratory of Public Health Safety of Ministry of Education, Fudan University, Shanghai, China.
Wen ZX; School of Public Health, Key Laboratory of Public Health Safety of Ministry of Education, Fudan University, Shanghai, China.
Wang Y; School of Public Health, Key Laboratory of Public Health Safety of Ministry of Education, Fudan University, Shanghai, China.
Wang WB; School of Public Health, Key Laboratory of Public Health Safety of Ministry of Education, Fudan University, Shanghai, China. Electronic address: wwb@fudan.edu.cn.

J Glob Antimicrob Resist ; 29: 323-330, 2022 06.

Article en En | MEDLINE | ID: mdl-35351676

Asunto(s)

Tuberculosis Resistente a Múltiples Medicamentos; Tuberculosis; China/epidemiología; Humanos; Tuberculosis/epidemiología; Tuberculosis Resistente a Múltiples Medicamentos/tratamiento farmacológico; Tuberculosis Resistente a Múltiples Medicamentos/epidemiología; Tuberculosis Resistente a Múltiples Medicamentos/prevención & control; Organización Mundial de la Salud

Palabras clave

Basic reproductive number; Drug-resistant TB; End TB Strategy; SEIR model; Tuberculosis

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Tuberculosis / Tuberculosis Resistente a Múltiples Medicamentos Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Humans País/Región como asunto: Asia Idioma: En Revista: J Glob Antimicrob Resist Año: 2022 Tipo del documento: Article País de afiliación: China Pais de publicación: Países Bajos

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