tetano
Editor, Senior Moderator
BMC Public Health
. 2023 Dec 2;23(1):2400.
doi: 10.1186/s12889-023-17327-7. Model-based analysis of the incidence trends and transmission dynamics of COVID-19 associated with the Omicron variant in representative cities in China
Yifei Ma[SUP] 1 [/SUP], Shujun Xu[SUP] 1 [/SUP], Yuxin Luo[SUP] 1 [/SUP], Jiantao Li[SUP] 2 [/SUP], Lijian Lei[SUP] 1 [/SUP], Lu He[SUP] 1 [/SUP], Tong Wang[SUP] 1 [/SUP], Hongmei Yu[SUP] 3 4 [/SUP], Jun Xie[SUP] 5 [/SUP]
Affiliations
Background: In 2022, Omicron outbreaks occurred at multiple sites in China. It is of great importance to track the incidence trends and transmission dynamics of coronavirus disease 2019 (COVID-19) to guide further interventions.
Methods: Given the population size, economic level and transport level similarities, two groups of outbreaks (Shanghai vs. Chengdu and Sanya vs. Beihai) were selected for analysis. We developed the SEAIQRD, ARIMA, and LSTM models to seek optimal modeling techniques for waves associated with the Omicron variant regarding data predictive performance and mechanism transmission dynamics, respectively. In addition, we quantitatively modeled the impacts of different combinations of more stringent interventions on the course of the epidemic through scenario analyses.
Results: The best-performing LSTM model showed better prediction accuracy than the best-performing SEAIQRD and ARIMA models in most cases studied. The SEAIQRD model had an absolute advantage in exploring the transmission dynamics of the outbreaks. Regardless of the time to inflection point or the time to R[SUB]t[/SUB] curve below 1.0, Shanghai was later than Chengdu (day 46 vs. day 12/day 54 vs. day 14), and Sanya was later than Beihai (day 16 vs. day 12/day 20 vs. day 16). Regardless of the number of peak cases or the cumulative number of infections, Shanghai was higher than Chengdu (34,350 vs. 188/623,870 vs. 2,181), and Sanya was higher than Beihai (1,105 vs. 203/16,289 vs. 3,184). Scenario analyses suggested that upgrading control level in advance, while increasing the index decline rate and quarantine rate, were of great significance for shortening the time to peak and R[SUB]t[/SUB] below 1.0, as well as reducing the number of peak cases and final affected population.
Conclusions: The LSTM model has great potential for predicting the prevalence of Omicron outbreaks, whereas the SEAIQRD model is highly effective in revealing their internal transmission mechanisms. We recommended the use of joint interventions to contain the spread of the virus.
Keywords: ARIMA model; COVID-19; Interventions; LSTM model; Omicron; Predictive performance; SEAIQRD model; Transmission dynamics.
. 2023 Dec 2;23(1):2400.
doi: 10.1186/s12889-023-17327-7. Model-based analysis of the incidence trends and transmission dynamics of COVID-19 associated with the Omicron variant in representative cities in China
Yifei Ma[SUP] 1 [/SUP], Shujun Xu[SUP] 1 [/SUP], Yuxin Luo[SUP] 1 [/SUP], Jiantao Li[SUP] 2 [/SUP], Lijian Lei[SUP] 1 [/SUP], Lu He[SUP] 1 [/SUP], Tong Wang[SUP] 1 [/SUP], Hongmei Yu[SUP] 3 4 [/SUP], Jun Xie[SUP] 5 [/SUP]
Affiliations
- PMID: 38042794
- DOI: 10.1186/s12889-023-17327-7
Background: In 2022, Omicron outbreaks occurred at multiple sites in China. It is of great importance to track the incidence trends and transmission dynamics of coronavirus disease 2019 (COVID-19) to guide further interventions.
Methods: Given the population size, economic level and transport level similarities, two groups of outbreaks (Shanghai vs. Chengdu and Sanya vs. Beihai) were selected for analysis. We developed the SEAIQRD, ARIMA, and LSTM models to seek optimal modeling techniques for waves associated with the Omicron variant regarding data predictive performance and mechanism transmission dynamics, respectively. In addition, we quantitatively modeled the impacts of different combinations of more stringent interventions on the course of the epidemic through scenario analyses.
Results: The best-performing LSTM model showed better prediction accuracy than the best-performing SEAIQRD and ARIMA models in most cases studied. The SEAIQRD model had an absolute advantage in exploring the transmission dynamics of the outbreaks. Regardless of the time to inflection point or the time to R[SUB]t[/SUB] curve below 1.0, Shanghai was later than Chengdu (day 46 vs. day 12/day 54 vs. day 14), and Sanya was later than Beihai (day 16 vs. day 12/day 20 vs. day 16). Regardless of the number of peak cases or the cumulative number of infections, Shanghai was higher than Chengdu (34,350 vs. 188/623,870 vs. 2,181), and Sanya was higher than Beihai (1,105 vs. 203/16,289 vs. 3,184). Scenario analyses suggested that upgrading control level in advance, while increasing the index decline rate and quarantine rate, were of great significance for shortening the time to peak and R[SUB]t[/SUB] below 1.0, as well as reducing the number of peak cases and final affected population.
Conclusions: The LSTM model has great potential for predicting the prevalence of Omicron outbreaks, whereas the SEAIQRD model is highly effective in revealing their internal transmission mechanisms. We recommended the use of joint interventions to contain the spread of the virus.
Keywords: ARIMA model; COVID-19; Interventions; LSTM model; Omicron; Predictive performance; SEAIQRD model; Transmission dynamics.