ArticleInternational journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases2021
Predicting the effective reproduction number of COVID-19: inference using human mobility, temperature, and risk awareness.
Article in International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine Learning Used in Communicable Disease Control: A Scoping Review.Public health reviews · 2026Pooled it
- A novel approach to forecasting reproduction numbers of spatiotemporal stochastic epidemic spread using a PDE-based model and real-time infection data.Scientific reports · 2025Article
- EpiFusion: Joint inference of the effective reproduction number by integrating phylodynamic and epidemiological modelling with particle filtering.PLoS computational biology · 2024Article
- Public holidays increased the transmission of COVID-19 in Japan, 2020-2021: a mathematical modelling study.Epidemiology and health · 2024Article
- Real-time estimation of the effective reproduction number of COVID-19 from behavioral data.Scientific reports · 2023Article
- Association of Ambient Temperature and Absolute Humidity with the Effective Reproduction Number of COVID-19 in Japan.Pathogens (Basel, Switzerland) · 2023Article
- Evaluating the COVID-19 vaccination program in Japan, 2021 using the counterfactual reproduction number.Scientific reports · 2023Article
- Modeling the time-dependent transmission rate using gaussian pulses for analyzing the COVID-19 outbreaks in the world.Scientific reports · 2023Article
- Night-time population consistently explains the transmission dynamics of coronavirus disease 2019 in three megacities in Japan.Frontiers in public health · 2023Article
- Decrease in overdispersed secondary transmission of COVID-19 over time in Japan.Epidemiology and infection · 2022Article
- Go To Travel campaign and the geographic spread of COVID-19 in Japan.BMC infectious diseases · 2022Article
- Article
- Transmission Dynamics and Effectiveness of Control Measures during COVID-19 Surge, Taiwan, April-August 2021.Emerging infectious diseases · 2022Article
- Forecasting COVID-19 Epidemic Trends by Combining a Neural Network withEntropy (Basel, Switzerland) · 2022Article
- Incorporating the mutational landscape of SARS-COV-2 variants and case-dependent vaccination rates into epidemic models.Infectious Disease Modelling · 2022Article
- Modeling COVID-19 Incidence by the Renewal Equation after Removal of Administrative Bias and Noise.Biology · 2022Article
- Assessing Public Health and Social Measures Against COVID-19 in Japan From March to June 2021.Frontiers in medicine · 2022Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesThe effective reproduction number (R
methodsA linear regression model to predict R
resultsThe predicted mean values of R
conclusionsThe described model can potentially be used for monitoring the transmission dynamics of COVID-19 ahead of the formal estimates, subject to delay, providing essential information for timely planning and assessment of countermeasures.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.