rachaelpung/covid_missed_infections
Pung, R
(2023).
rachaelpung/covid_missed_infections.
[Dataset].
Zenodo.
https://doi.org/10.5281/zenodo.7538046
This repository contains code and aggregate data to reconstruct the COVID-19 pandemic trajectory in Singapore. For each time period of the pandemic, we simulated the transmissions through a branching process model to compute the expected incidence over time and model outputs were fitted against the daily incidence of linked and unlinked local COVID-19 cases. Model parameters were estimated using a Bayesian MCMC framework.
Keywords
COVID-19, SARS-CoV-2, Coronavirus, Pandemic trajectoryItem Type | Dataset |
---|---|
Capture method | Simulation |
Date | 15 January 2023 |
Language(s) of written materials | English |
Creator(s) |
Pung, R |
LSHTM Faculty/Department | Faculty of Epidemiology and Population Health > Dept of Infectious Disease Epidemiology (-2023) |
Research Centre | Centre for the Mathematical Modelling of Infectious Diseases |
Participating Institutions | London School of Hygiene & Tropical Medicine, London, United Kingdom |
Date Deposited | 04 Oct 2021 09:05 |
Last Modified | 04 Apr 2023 09:04 |
Publisher | Zenodo |
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ORCID: https://orcid.org/0000-0002-1188-8231