Bayesian sequential data assimilation for COVID-19 forecasting

Daza–Torres, María L.; Capistrán, Marcos A.; Capella, Antonio; Christen, J. Andrés

doi:10.1016/j.epidem.2022.100564

Cited by 19 publications

(25 citation statements)

References 23 publications

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“…Result as obtained by [19] indicated that the hybridized VMD artificial intelligence forecasting models outperformed the single forecasting models. Other predictive models such as the Bayesian sequential data assimilation [20] , regression [21] , ARIMA models [22] , combination of regression, ARIMA and Machine Learning models [23] , Interpretable Temporal Attention Network [24] , Artificial Neural Network [25] have also been applied to Covid-19 data. Similarly, state neural based framework [26] , ensemble learning models coupled with urban mobility information [27] , space time ARIMA [28] among other predictive models have been proposed for forecasting cases of Covid-19.…”

Section: Introductionmentioning

confidence: 99%

Modelling and forecasting new cases of Covid-19 in Nigeria: Comparison of regression, ARIMA and machine learning models

Busari

Samson²

2022

Scientific African

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Section: Introductionmentioning

confidence: 99%

Modelling and forecasting new cases of Covid-19 in Nigeria: Comparison of regression, ARIMA and machine learning models

Busari

Samson²

2022

Scientific African

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“…The Bayesian sequential forecasting method in Daza-Torres, et al [ 14 ] is used to conduct parameter inference. The estimation is implemented by decoupling the model into two parts.…”

Section: Methodsmentioning

confidence: 99%

The impact of COVID-19 vaccination on California’s return to normalcy

et al. 2022

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SARS-CoV-2 has infected nearly 3.7 million and killed 61,722 Californians, as of May 22, 2021. Non-pharmaceutical interventions have been instrumental in mitigating the spread of the coronavirus. However, as we ease restrictions, widespread implementation of COVID-19 vaccines is essential to prevent its resurgence. In this work, we addressed the adequacy and deficiency of vaccine uptake within California and the possibility and severity of resurgence of COVID-19 as restrictions are lifted given the current vaccination rates. We implemented a real-time Bayesian data assimilation approach to provide projections of incident cases and deaths in California following the reopening of its economy on June 15, 2021. We implemented scenarios that vary vaccine uptake prior to reopening, and transmission rates and effective population sizes following the reopening. For comparison purposes, we adopted a baseline scenario using the current vaccination rates, which projects a total 11,429 cases and 429 deaths in a 15-day period after reopening. We used posterior estimates based on CA historical data to provide realistic model parameters after reopening. When the transmission rate is increased after reopening, we projected an increase in cases by 21.8% and deaths by 4.4% above the baseline after reopening. When the effective population is increased after reopening, we observed an increase in cases by 51.8% and deaths by 12.3% above baseline. A 30% reduction in vaccine uptake alone has the potential to increase cases and deaths by 35% and 21.6%, respectively. Conversely, increasing vaccine uptake by 30% could decrease cases and deaths by 26.1% and 17.9%, respectively. As California unfolds its plan to reopen its economy on June 15, 2021, it is critical that social distancing and public behavior changes continue to be promoted, particularly in communities with low vaccine uptake. The Centers for Disease Control and Prevention (CDC) recommendation to ease mask-wearing for fully vaccinated individuals despite major inequities in vaccine uptake in counties across the state highlights some of the logistical challenges that society faces as we enthusiastically phase out of this pandemic.

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“…At any given week, except the first one, posteriors inferred from the previous week were used as starting points for the current models. This practice of updating the priors with previous estimations is known as Sequential Bayesian Updating, or simply Bayesian Updating [43] , and has been used for similar purposes in related literature [44] , as well as in other academic fields [45] , [46] .…”

Section: Proposed Modelsmentioning

confidence: 99%