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Modelling COVID-19.
Vespignani, Alessandro; Tian, Huaiyu; Dye, Christopher; Lloyd-Smith, James O; Eggo, Rosalind M; Shrestha, Munik; Scarpino, Samuel V; Gutierrez, Bernardo; Kraemer, Moritz U G; Wu, Joseph; Leung, Kathy; Leung, Gabriel M.
  • Vespignani A; Network Science Institute, Northeastern University, Boston, MA USA.
  • Tian H; ISI Foundation, Turin, Italy.
  • Dye C; State Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing, China.
  • Lloyd-Smith JO; Department of Zoology, University of Oxford, Oxford, UK.
  • Eggo RM; Department of Ecology and Evolutionary Biology, University of California, Los Angeles, Los Angeles, CA USA.
  • Shrestha M; Centre for the Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK.
  • Scarpino SV; Network Science Institute, Northeastern University, Boston, MA USA.
  • Gutierrez B; Network Science Institute, Northeastern University, Boston, MA USA.
  • Kraemer MUG; Department of Zoology, University of Oxford, Oxford, UK.
  • Wu J; School of Biological and Environmental Sciences, Universidad San Francisco de Quito USFQ, Quito, Ecuador.
  • Leung K; Department of Zoology, University of Oxford, Oxford, UK.
  • Leung GM; Harvard Medical School, Harvard University, Boston, MA USA.
Nat Rev Phys ; 2(6): 279-281, 2020.
Article in English | MEDLINE | ID: covidwho-1684119
ABSTRACT
As the COVID-19 pandemic continues, mathematical epidemiologists share their views on what models reveal about how the disease has spread, the current state of play and what work still needs to be done.
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Full text: Available Collection: International databases Database: MEDLINE Language: English Journal: Nat Rev Phys Year: 2020 Document Type: Article

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Full text: Available Collection: International databases Database: MEDLINE Language: English Journal: Nat Rev Phys Year: 2020 Document Type: Article