Phenomenological and mechanistic models for predicting early transmission data of COVID-19

DOI10.3934/mbe.2022096zbMath1489.92169OpenAlexW4206636700MaRDI QIDQ2130277

Hiroshi Nishiura, Takeshi Miyama, Yichi Yang, Andrei R. Akhmetzhanov, Baoyin Yuan, Taishi Kayano, Asami Anzai, Sung-mok Jung, Natalie M. Linton, Katsuma Hayashi, Ryo Kinoshita, Tetsuro Kobayashi, Ayako Suzuki

Publication date: 25 April 2022

Published in: Mathematical Biosciences and Engineering (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.3934/mbe.2022096

zbMATH Keywords

projectionforecastingmathematical modelepidemiologynon-pharmaceutical interventionlockdowncoronavirus disease 2019

Mathematics Subject Classification ID

Epidemiology (92D30)

Uses Software

• R

Cites Work

• Model selection and evaluation based on emerging infectious disease data sets including A/H1N1 and ebola
• Effective containment explains subexponential growth in recent confirmed COVID-19 cases in China
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