Modeling and Evaluation of the Joint Prevention and Control Mechanism for Curbing COVID-19 in Wuhan

Abstract

The spread of COVID-19 in Wuhan was successfully curbed under the strategy of “Joint Prevention and Control Mechanism.” To understand how this measure stopped the epidemics in Wuhan, we establish a compartmental model with time-varying parameters over different stages. In the early stage of the epidemic, due to resource limitations, the number of daily reported cases may lower than the actual number. We employ a dynamic-based approach to calibrate the accumulated clinically diagnosed data with a sudden jump on February 12 and 13. The model simulation shows reasonably good match with the adjusted data which allows the prediction of the cumulative confirmed cases. Numerical results reveal that the “Joint Prevention and Control Mechanism” played a significant role on the containment of COVID-19. The spread of COVID-19 cannot be inhibited if any of the measures was not effectively implemented. Our analysis also illustrates that the Fangcang Shelter Hospitals are very helpful when the beds in the designated hospitals are insufficient. Comprised with Fangcang Shelter Hospitals, the designated hospitals can contain the transmission of COVID-19 more effectively. Our findings suggest that the combined multiple measures are essential to curb an ongoing epidemic if the prevention and control measures can be fully implemented.

Analysis of the Protective Effect of Infection Controllers Supervising Third-Party Personnel Entering and Leaving Shanghai Fangcang Shelter Hospital

“…In terms of quality control management, continuous improvement measures are proposed for problems, and continuous improvement is implemented to form a virtuous circle. [13][14][15] In terms of effective communication, to ensure that all communication can be fed back to the people for the first time, the reported problems are solved in a timely manner.…”

A data-driven hybrid ensemble AI model for COVID-19 infection forecast using multiple neural networks and reinforced learning

“…It can be seen that the current COVID-19 prediction tools are mainly of the following types: Models based on kinetic (or dynamics) modeling, which further clarify the pattern of changes in the number of a specific population by dividing the population into Susceptible (S) , Infected (I) , Recovered (R) compartments, etc., and defining the transition relationships among them through several differential equations. Since the onset of the COVID-19 epidemic, multi-generational kinetic models have emerged, whose evolution route could be summarized as: SIR→SEIR [ 26 ], SLIR [ 27 ] (considering close contacts or the latent) → SEI A IR [ 28 ] (considering asymptomatic infected persons), SAHQD [ 19 ] (considering quarantine policies) → SEIRMH [ 29 ], SEPIAHR [ 30 ] (considering medical-related factors) → SCUAQIHMRD [ 31 ] (considering COVID-19 hierarchical treatment). Time series analysis.…”

“… N.P. Wuhan, China [ 31 ] SEPIAHR model (Susceptible, exposed, pre-symptomatic infectious, ascertained infectious, unascertained infectious, isolation in hospital and removed N.P. N.P.…”

“…First, as an ensemble model, the higher the variability of the sub-model, the higher the accuracy of the integrated model obtained in general, and a larger integrated model can be explored in the future, although it will take more time to train the model and to get the forecasting results, but it is feasible for COVID-19 morbidity prediction, a task that does not require high real-time performance (generally forecast in days). Second, integration with other modeling approaches (e.g., time series models such as ARIMA, STAR models, dynamic models such as SAHQD model [ 19 ], SEIAIR model [ 28 ], even the SCUAQIHMRD model [ 31 ]) can also be performed, where reinforcement learning can still provide an integration framework. Third, the data of other modalities, such as Mobility Report released by Google [ 78 ], search interest [ 51 ], local weather data, human contact data [ 33 ], etc., could also considered as meaningful input into the model [ 79 ], although the inaccessibility and incompleteness of some data modalities may limit the power and generalizability of the model with integrated multi-modalities.…”

Analysis of a COVID-19 Epidemic Model with Seasonality

“…By the theory of asymptotically periodic semi-flow (see (Zhao 2017, Theorem 3.2.1)), it can be concluded that…”