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CALMS: Modelling the long-term health and economic impact of Covid-19 using agent-based simulation.
Mintram, Kate; Anagnostou, Anastasia; Anokye, Nana; Okine, Edward; Groen, Derek; Saha, Arindam; Abubakar, Nura; Islam, Tasin; Daroge, Habiba; Ghorbani, Maziar; Xue, Yani; Taylor, Simon J E.
Afiliación
  • Mintram K; Modelling and Simulation Group, Department of Computer Science, Brunel University London, Uxbridge, London, United Kingdom.
  • Anagnostou A; Modelling and Simulation Group, Department of Computer Science, Brunel University London, Uxbridge, London, United Kingdom.
  • Anokye N; Global Public Health, Department of Health Sciences, Brunel University London, Uxbridge, London, United Kingdom.
  • Okine E; Global Public Health, Department of Health Sciences, Brunel University London, Uxbridge, London, United Kingdom.
  • Groen D; Modelling and Simulation Group, Department of Computer Science, Brunel University London, Uxbridge, London, United Kingdom.
  • Saha A; Modelling and Simulation Group, Department of Computer Science, Brunel University London, Uxbridge, London, United Kingdom.
  • Abubakar N; Modelling and Simulation Group, Department of Computer Science, Brunel University London, Uxbridge, London, United Kingdom.
  • Islam T; Modelling and Simulation Group, Department of Computer Science, Brunel University London, Uxbridge, London, United Kingdom.
  • Daroge H; Modelling and Simulation Group, Department of Computer Science, Brunel University London, Uxbridge, London, United Kingdom.
  • Ghorbani M; Department of Electronic and Computer Engineering, Brunel University London, Uxbridge, London, United Kingdom.
  • Xue Y; Modelling and Simulation Group, Department of Computer Science, Brunel University London, Uxbridge, London, United Kingdom.
  • Taylor SJE; Modelling and Simulation Group, Department of Computer Science, Brunel University London, Uxbridge, London, United Kingdom.
PLoS One ; 17(8): e0272664, 2022.
Article en En | MEDLINE | ID: mdl-36037156
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: COVID-19 Tipo de estudio: Health_economic_evaluation / Observational_studies / Prognostic_studies / Risk_factors_studies Aspecto: Patient_preference Límite: Humans Idioma: En Revista: PLoS One Asunto de la revista: CIENCIA / MEDICINA Año: 2022 Tipo del documento: Article País de afiliación: Reino Unido
Texto completo
Añadir a Mi BVS
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XML
PubMed Links
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: COVID-19 Tipo de estudio: Health_economic_evaluation / Observational_studies / Prognostic_studies / Risk_factors_studies Aspecto: Patient_preference Límite: Humans Idioma: En Revista: PLoS One Asunto de la revista: CIENCIA / MEDICINA Año: 2022 Tipo del documento: Article País de afiliación: Reino Unido