ArticlePLoS computational biology2021
Estimating the time-varying reproduction number of COVID-19 with a state-space method.
Article in PLoS computational biology, 2021. 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.
- Data assimilation for estimating time-varying reproduction numbers.Journal of the Royal Society, Interface · 2025Article
- Robust uncertainty quantification in popular estimators of the instantaneous reproduction number.American journal of epidemiology · 2025Article
- A Primer on Inference and Prediction With Epidemic Renewal Models and Sequential Monte Carlo.Statistics in medicine · 2025Article
- Optimal COVID-19 vaccine prioritization by age depends critically on inter-group contacts and vaccination rates.Royal Society open science · 2024Article
- The optimal spatially-dependent control measures to effectively and economically eliminate emerging infectious diseases.PLoS computational biology · 2024Article
- Real-Time Epidemiology and Acute Care Need Monitoring and Forecasting for COVID-19 via Bayesian Sequential Monte Carlo-Leveraged Transmission Models.International journal of environmental research and public health · 2024Article
- Estimating effective reproduction number revisited.Infectious Disease Modelling · 2023Article
- A model of behavioural response to risk accurately predicts the statistical distribution of COVID-19 infection and reproduction numbers.Scientific reports · 2023Article
- Multivariate epidemic count time series model.PloS one · 2023Article
- Using a latent Hawkes process for epidemiological modelling.PloS one · 2023Article
- Assessing the heterogeneity in the transmission of infectious diseases from time series of epidemiological data.PloS one · 2023Article
- Improved time-varying reproduction numbers using the generation interval for COVID-19.Frontiers in public health · 2023Article
- Role of multiresolution vulnerability indices in COVID-19 spread in India: a Bayesian model-based analysis.BMJ open · 2022Observational
- Individual risk-aversion responses tune epidemics to critical transmissibility (Royal Society open science · 2022Article
- Fundamental limits on inferring epidemic resurgence in real time using effective reproduction numbers.PLoS computational biology · 2022Article
- Modeling COVID-19 Incidence by the Renewal Equation after Removal of Administrative Bias and Noise.Biology · 2022Article
- Model with transmission delays for COVID-19 control: Theory and empirical assessment.Journal of public economic theory · 2021Article
- Simple discrete-time self-exciting models can describe complex dynamic processes: A case study of COVID-19.PloS one · 2021Article
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Authors and funding
3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
After slowing down the spread of the novel coronavirus COVID-19, many countries have started to relax their confinement measures in the face of critical damage to socioeconomic structures. At this stage, it is desirable to monitor the degree to which political measures or social affairs have exerted influence on the spread of disease. Though it is difficult to trace back individual transmission of infections whose incubation periods are long and highly variable, estimating the average spreading rate is possible if a proper mathematical model can be devised to analyze daily event-occurrences. To render an accurate assessment, we have devised a state-space method for fitting a discrete-time variant of the Hawkes process to a given dataset of daily confirmed cases. The proposed method detects changes occurring in each country and assesses the impact of social events in terms of the temporally varying reproduction number, which corresponds to the average number of cases directly caused by a single infected case. Moreover, the proposed method can be used to predict the possible consequences of alternative political measures. This information can serve as a reference for behavioral guidelines that should be adopted according to the varying risk of infection.
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