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Data · dataset · 2023

rachaelpung/covid_missed_infections

Listed in LSHTM Data Compass

This repository contains code and aggregate data to reconstruct the COVID-19 pandemic trajectory in Singapore.

Description

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.

Links

Where it is published

Catalogue records · 1

Topics

From keywords
Earth & Environmental Science · Humanities · Life Sciences · Medicine & Health · Social Science
Inferred from text
Epidemiology 69%
Provenance · 1 source records, 9 field assertions
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LSHTM Data Compassoai:datacompass.lshtm.ac.uk:252812 d agoJSON v1
FieldAssertionExtractorEvidence
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concepts[field].local:field:life-sciencesmapping · lshtm ac ukconnector:lshtm_ac_uk@1.0.0
concepts[field].local:field:medicine-healthmapping · lshtm ac ukconnector:lshtm_ac_uk@1.0.0
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