Vaccination and three non-pharmaceutical interventions determine the dynamics of COVID-19 in the US
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Abstract
The rapid rollout of the COVID-19 vaccine raises the question of whether and when the ongoing pandemic could be eliminated with vaccination and non-pharmaceutical interventions (NPIs). Despite advances in the impact of NPIs and the conceptual belief that NPIs and vaccination control COVID-19 infections, we lack evidence to employ control theory in real-world social human dynamics in the context of disease spreading. We bridge the gap by developing a new analytical framework that treats COVID-19 as a feedback control system with the NPIs and vaccination as the controllers and a computational model that maps human social behaviors into input signals. This approach enables us to effectively predict the epidemic spreading in 381 Metropolitan statistical areas (MSAs) in the US by learning our model parameters utilizing the time series NPIs (i.e., the stay-at-home order, face-mask wearing, and testing) data. This model allows us to optimally identify three NPIs to predict infections accurately in 381 MSAs and avoid over-fitting. Our numerical results demonstrate our approach’s excellent predictive power with R 2 > 0.9 for all the MSAs regardless of their sizes, locations, and demographic status. Our methodology allows us to estimate the needed vaccine coverage and NPIs for achieving R e to a manageable level and how the variants of concern diminish the likelihood for disease elimination at each location. Our analytical results provide insights into the debates surrounding the elimination of COVID-19. NPIs, if tailored to the MSAs, can drive the pandemic to an easily containable level and suppress future recurrences of epidemic cycles.
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SciScore for 10.1101/2021.05.18.21257362: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Our study has some limitations. The first limitation is rooted in the dataset for COVID-19 and the dataset for NPIs. Given the mass mild or asymptomatic infections, the inaccuracy of reported infections and deaths would increase the uncertainty of the SIRD model-based feedback controllers. The second limitation is assuming each MSA as the closed population, which ignores the fluctuation of infections caused by case importation and …
SciScore for 10.1101/2021.05.18.21257362: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Our study has some limitations. The first limitation is rooted in the dataset for COVID-19 and the dataset for NPIs. Given the mass mild or asymptomatic infections, the inaccuracy of reported infections and deaths would increase the uncertainty of the SIRD model-based feedback controllers. The second limitation is assuming each MSA as the closed population, which ignores the fluctuation of infections caused by case importation and exportation. This simplification would influence the results from needed NPIs and PIs for suppressing COVID-19 to elimination test. The third limitation is assuming vaccination adoption follows the curve of diffusion of innovations without considering people’s attitude to vaccinations. As the attitudes towards vaccination vary by age, race, ethnicity, and education, it is hard to capture the full complexity. In our current work, we use feedback linearization to design the control signals, and we may improve the accuracy by considering other controller design strategies, for example, adaptive controller50, 51, model predictive control52, or intelligent control53. Nevertheless, our study provides practical insights into tightening or relaxing NPIs for the aim of living with COVID-19. Also, we provide the possibility of achieving “Zero COVID” in metropolitan areas if vaccination is stable and efficient enough.
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
Results from scite Reference Check: We found no unreliable references.
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