ArticleEpidemics2022
Bayesian sequential data assimilation for COVID-19 forecasting.
Article in Epidemics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Real-time prediction of influenza and respiratory syncytial virus epidemics in primary care using the Gompertz model.Scientific reports · 2026Article
- A scalable variational method for estimating the latent infection-rate field of an outbreak.PloS one · 2026Article
- Combining data assimilation of states and parameters for more precise infectious disease prediction.One health (Amsterdam, Netherlands) · 2025Article
- Detecting outbreaks using a spatial latent field.PloS one · 2025Article
- Disentangling the role of virus infectiousness and awareness-based human behavior during the early phase of the COVID-19 pandemic in the European Union.Applied mathematical modelling · 2023Article
- Bayesian sequential approach to monitor COVID-19 variants through test positivity rate from wastewater.mSystems · 2023Article
- Article
- Bayesian sequential approach to monitor COVID-19 variants through positivity rate from wastewater.medRxiv : the preprint server for health sciences · 2023Article
- Data-driven estimation of the instantaneous reproduction number and growth rates for the 2022 monkeypox outbreak in Europe.PloS one · 2023Article
- Epidemic SI COVID-19 modeling in LMICs: Accompanying commentary.Epidemics · 2022Article
- A Bayesian predictive analytics model for improving long range epidemic forecasting during an infection wave.Healthcare analytics (New York, N.Y.) · 2022Article
- Modelling and forecasting new cases of Covid-19 in Nigeria: Comparison of regression, ARIMA and machine learning models.Scientific African · 2022Article
- Bayesian sequential data assimilation for COVID-19 forecasting.Epidemics · 2022Article
- Analysis of COVID-19 Spread in Tokyo through an Agent-Based Model with Data Assimilation.Journal of clinical medicine · 2022Article
- Bayesian data assimilation for estimating instantaneous reproduction numbers during epidemics: Applications to COVID-19.PLoS computational biology · 2022Article
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4 authors.
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Abstract
We introduce a Bayesian sequential data assimilation and forecasting method for non-autonomous dynamical systems. We applied this method to the current COVID-19 pandemic. It is assumed that suitable transmission, epidemic and observation models are available and previously validated. The transmission and epidemic models are coded into a dynamical system. The observation model depends on the dynamical system state variables and parameters, and is cast as a likelihood function. The forecast is sequentially updated over a sliding window of epidemic records as new data becomes available. Prior distributions for the state variables at the new forecasting time are assembled using the dynamical system, calibrated for the previous forecast. Epidemic outbreaks are non-autonomous dynamical systems depending on human behavior, viral evolution and climate, among other factors, rendering it impossible to make reliable long-term epidemic forecasts. We show our forecasting method's performance using a SEIR type model and COVID-19 data from several Mexican localities. Moreover, we derive further insights into the COVID-19 pandemic from our model predictions. The rationale of our approach is that sequential data assimilation is an adequate compromise between data fitting and dynamical system prediction.
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