ArticleNature communications2022
Incorporating temporal distribution of population-level viral load enables real-time estimation of COVID-19 transmission.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- A knowledge-guided deep learning framework for quantitative nucleic acid testing.Briefings in bioinformatics · 2026Article
- Article
- Nowcasting epidemic trends using hospital- and community-based virologic test data.Nature communications · 2025Article
- Generalizing population RT-qPCR cycle threshold values-informed estimation of epidemiological dynamics: Impact of surveillance practices and pathogen variability.PLoS computational biology · 2025Article
- Article
- Estimating the time-varying effective reproduction number via Cycle Threshold-based Transformer.PLoS computational biology · 2024Article
- SARS-CoV-2 Viral Load in the Nasopharynx at Time of First Infection Among Unvaccinated Individuals: A Secondary Cross-Protocol Analysis of 4 Randomized Trials.JAMA network open · 2024Article
- Long-term trends in cycle threshold values: a comprehensive analysis of COVID-19 dynamics, viral load, and reproduction number in South Korea.Frontiers in public health · 2024Article
- Personal characteristics and transmission dynamics associated with SARS-CoV-2 semi-quantitative PCR test results: an observational study from Belgium, 2021-2022.Frontiers in public health · 2024Observational
- Lineage replacement and evolution captured by 3 years of the United Kingdom Coronavirus (COVID-19) Infection Survey.Proceedings. Biological sciences · 2023Article
- Making waves: Integrating wastewater surveillance with dynamic modeling to track and predict viral outbreaks.Water research · 2023Article
- Trends in SARS-CoV-2 cycle threshold values in the Czech Republic from April 2020 to April 2022.Scientific reports · 2023Article
- Extreme differences in SARS-CoV-2 viral loads among respiratory specimen types during presumed pre-infectious and infectious periods.PNAS nexus · 2023Article
- Systematic Review on the Correlation Between SARS-CoV-2 Real-Time PCR Cycle Threshold Values and Epidemiological Trends.Infectious diseases and therapy · 2023Review
- Cross-sectional Ct distributions from qPCR tests can provide an early warning signal for the spread of COVID-19 in communities.Frontiers in public health · 2023Article
- Transmission Dynamics and Effectiveness of Control Measures during COVID-19 Surge, Taiwan, April-August 2021.Emerging infectious diseases · 2022Article
- Clinical Time Delay Distributions of COVID-19 in 2020-2022 in the Republic of Korea: Inferences from a Nationwide Database Analysis.Journal of clinical medicine · 2022Article
- The importance of the generation interval in investigating dynamics and control of new SARS-CoV-2 variants.Journal of the Royal Society, Interface · 2022Article
Corrections and comments
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Authors and funding
16 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Many locations around the world have used real-time estimates of the time-varying effective reproductive number ([Formula: see text]) of COVID-19 to provide evidence of transmission intensity to inform control strategies. Estimates of [Formula: see text] are typically based on statistical models applied to case counts and typically suffer lags of more than a week because of the latent period and reporting delays. Noting that viral loads tend to decline over time since illness onset, analysis of the distribution of viral loads among confirmed cases can provide insights into epidemic trajectory. Here, we analyzed viral load data on confirmed cases during two local epidemics in Hong Kong, identifying a strong correlation between temporal changes in the distribution of viral loads (measured by RT-qPCR cycle threshold values) and estimates of [Formula: see text] based on case counts. We demonstrate that cycle threshold values could be used to improve real-time [Formula: see text] estimation, enabling more timely tracking of epidemic dynamics.
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