Fig 1.
Epidemiological features and landscape between January 2020 and November 2022.
Figures (a)–(e) presented the daily cases and cumulative infection cases by age-group for the five COVID-19 outbreaks and the five variants: (a) Fujian epidemic (Jan 02–Mar 01, 2020); (b) Putian epidemic (Sep 08–Oct 01, 2021); (c) Quanzhou epidemic (Mar 10–Apr 14, 2022); (d) Xiapu epidemic (Jul 01–Jul 15, 2022); (e) Fuzhou epidemic (Oct 22–Nov 18, 2022). The names for the five epidemics were matched with their corresponding variants. The red curves stood for the cumulative numbers of infection cases for the five epidemics. The blue bar meant daily new infection cases for G1, the purple bar stood for daily new infection cases for G2. Figure (f) provided landscape for the five epidemics occurred in Fujian Province from January 2020 to November 2022, in which the gray bar meant the daily new infection cases detected by closed-loop management during dynamic zero-COVID policy, referred as the infection cases from other provinces of China and other countries abroad.
Fig 2.
Age distribution of infection cases to the five epidemics in Fujian Province.
The key population for the Fujian epidemic, the Quanzhou epidemic, the Xiapu epidemic, and the Fuzhou epidemic was 30–39 age-group, and the key population for the Putian epidemic was 40–49 age-group. School-oriented age-group included the infection cases who were under 20 years old. Job-oriented age-group clustered the infection cases whose ages ranged from 20 to 59. Home-oriented age-group collected the infection cases who were 60 years old and over. Age distribution of the Fuzhou epidemic revealed that home-oriented age-group was the key population for local governments and policymakers.
Fig 3.
Optimal fittings by least squares method for the five epidemics in Fujian Province.
Solid curves were optimal fittings, dashed curves were the surveillance data from Fujian CDC, blue curves denoted G1 of the local population, red curves meant G2 of the local population. The Pearson correlation coefficients (PCCs) of the optimal fittings were provided in S4 Table in S1 File.
Fig 4.
Epidemiological parameters of the five variants in Fujian Province.
The values for the main epidemiological parameters were normalized between 0 and 1 by L-Infinity Norm. The colors were same with the ones of landscape in Fig 1.
Table 1.
Scenario investigations of the five epidemics with the five variants.
Table 2.
The basic reproduction numbers for the five epidemics with the five variants.
Fig 5.
Peak values of and days spent from outbreak date to control date for the five epidemics of Fujian Province.
Fig 6.
Sensitivities and variations of with respect to parameters.
(a) The magnitude of log10 |Γ| indicated the sensitivity of against parameter. (b) Values of
varied with parameters and variants.
Fig 7.
Scenario A to Scenario D. (a)–(b) the XBB.1.9.1 variant and the EG.5 variant dominated for three months respectively. (c) the XBB.1.9.1 variant dominated one month, then the EG.5 variant dominated two months. (d) the XBB.1.9.1 variant dominated half-month, then the EG.5 variant dominated two and a half months.
Fig 8.
Cumulative percentage of infection cases.
Purple curves were for the XBB.1.9.1 variant under Scenario A, blue curves for the EG.5 variant under Scenario B.
Fig 9.
Effective reproduction numbers for G1 and G2.
The dates that the epidemics were controlled for the XBB.1.9.1 variant were three days later than those for the EG.5 variant under Scenario A and Scenario B.