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Effectiveness of Interventions to Control Transmission of Reemergent Cases of COVID-19 — Jilin Province, China, 2020

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Effectiveness of Interventions to Control Transmission of Reemergent Cases of COVID-19 — Jilin Province, China, 2020 China CDC Weekly Preplanned Studies Effectiveness of Interventions to Control Transmission of Reemergent Cases of COVID-19 — Jilin Province, China, 2020 Qinglong Zhao1,&; Meng Yang2,&; Yao Wang2; Laishun Yao1; Jianguo Qiao3; Zhiyong Cheng3; Hanyin Liu4; Xingchun Liu2; Yuanzhao Zhu2; Zeyu Zhao2; Jia Rui2; Tianmu Chen2,# interventions, and to provide experience for other Summary provinces or cities in China, or even for other countries What is already known about this topic? to deal with the second outbreak of COVID-19 COVID-19 has a high transmissibility calculated by outbreaks. mathematical model. The dynamics of the disease and Based on our previous study (2–5), we developed a the effectiveness of intervention to control the Susceptible-Exposed-Infectious-Asymptomatic- transmission remain unclear in Jilin Province, China. Removed (SEIAR) model to fit the data in Jilin What is added by this report? Province and to perform the assessment. In the SEIAR This is the first study to report the dynamic model, individuals were divided into five characteristics and to quantify the effectiveness of compartments: Susceptible (S), Exposed (E), Infectious interventions implemented in the second outbreak of (I), Asymptomatic (A), and Removed (R), and the COVID-19 in Jilin Province, China. The effective equations of the model were shown as follows: reproduction number of the disease before and after dS May 10 was 4.00 and p<0.01, respectively. The = −βS (I + κA) (1) combined interventions reduced the transmissibility of dt dE ¬ COVID-19 by 99% and the number of cases by = βS (I + κA) − p! E − ( − p) !E (2) dt 98.36%. dI = ( − p) !E − γI − fI (3) What are the implications for public health dt practice? dA ¬ ¬ = p! E − γ A (4) The findings of this study would add data on the dt dR ¬ transmission of COVID-19 and provide evidence to = γI + γ A (5) prepare the second outbreak transmission of the disease dt in other areas of China even in many other countries. There are eight parameters (β, κ, ω, ω', p, γ, γ', and f ) in the model. The transmission rate, β, China has successfully controlled the first outbreak was estimated by fitting the reported data. Since only of the coronavirus disease 2019 (COVID-19) due to limited secondary transmission was observed due to A, the strictly implemented public health policy including in this study, we assumed that the transmissibility of A active case finding with case management (1). was 5% of that of I. Therefore, the parameter κ, the However, it has become an essential public health relative transmissibility coefficient of A compared with concern that whether there would be a second I, was set as 0.05 in this study. According to the outbreak of COVID-19 in China, and how to control reported data in the outbreak, we investigated the the second outbreak? Jilin Province, locating in the following parameters: A) the incubation period (1/ω) north east of China, has also controlled its first and the latent period (1/ω') was 8 days and 6 days, outbreak of COVID-19 successfully (2). On May 7, respectively; B) the infectious periods of A and I were 2020, an outbreak of COVID-19 was reported in both set as 3 days; C) the parameter p, the proportion Shulan City, Jilin Province, China. The outbreak is the of A, was 6.52%; D) and the parameter f, the case second outbreak in the province. Therefore, it has fatality rate, was 2.17%. public health significance to quantify the Commonly, we used the basic reproduction number transmissibility, to assess the effectiveness of (R0) to assess the transmissibility of COVID-19. R0 Chinese Center for Disease Control and Prevention CCDC Weekly / Vol. 2 / No. 34 651 China CDC Weekly was defined as the expected number of secondary exposure date and symptoms onset date, the median infections that result from introducing a single infected incubation period of the cases was calculated as 6 days individual into an otherwise susceptible population(4). (range: 2–11 days). The epidemic spread to five However, if intervention was implemented, R0 should districts and cities in Jilin Province (Shulan, Fengman, be replaced as effective reproduction number (Reff) Chuanying, Changyi, and Gaoxin). About 48.84% which could be calculated by the following equation: confirmed cases had an age of 25–46 years. The main − p κp occupation of patients was housework and R = βS ( + ¬ ) (6) eff γ + f γ unemployment, cadres and staff, and business services (Table 1). Berkeley Madonna 8.3.18 (developed by Robert The SEIAR model fitted the data well (R2=0.29, Macey and George Oster of the University of p<0.01). The value of R before and after May 10 was California at Berkeley; Copyright © 1993–2001 eff 4.00 and p<0.01, respectively. Therefore, the Robert I. Macey & George F. Oster, University of combined interventions reduced the transmissibility of California, Berkeley, CA) was employed to perform the COVID-19 by 99% in the area (Figure 1). According curve fitting and simulation. to the simulation results, if the comprehensive The data were collected including all reported cases intervention measures were not taken on May 10, as of in Jilin Province from April 25, 2020 to June 4, 2020. June 4, the predicted cumulative number of cases The data included the basic information (sex, age, would be 2,833. occupation, address), the classification (asymptomatic Three further scenarios were simulated as follows: infection and confirmed cases), key date point (contact Scenario A: the duration from onset to diagnosed date date, symptom onset date, hospitalization date, and was shortened by 50% after May 10; Scenario B: the diagnosed date), and the number of close contacts of value of Reff was shortened by 50% after May 10; each case. Scenario C: all the cases (exception asymptomatic From April 25, 2020 to June 4, 2020, a total of 43 infections) were isolated after May 10. The results confirmed cases and 3 asymptomatic infections were showed that: under the circumstance of Scenario A, the reported in the province. The epidemic peak of number of cumulative cases would be 503 with a outbreak was during May 8 to May 10 (Figure 1). The reduction of 82.24%; under the circumstance of outbreak lasted 7 generations. The secondary attack Scenario B, the number of cumulative cases would be rate (TAR) of the index case and its following 309 with a reduction of 89.09%; under the generations was 40.00%, 2.59%, 4.55%, 5.09%, circumstance of Scenario C, the number of cumulative 1.19%, and 0.55%, respectively. Based on the cases would be 211 with a reduction of 92.55%. The information of some cases which had the exact reported cumulative number of cases was 46 (43 10 Confirmed cases Confirmed cases appeared and initiated an emergency response Asymptomatic infections Shulan City had been adjusted as a medium-risk area, and the intensity of risk prevention and control had been increased 8 Simulated cases Shulan City had been adjusted as a high-risk area Strict epidemic prevention and control measures 6 had been adopted throughout Jilin Province Expanded contact tracing and screening tests 4 Reff=4.00 Number of cases Reff<0.01 2 0 18 20 22 24 26 28 30 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 1 3 April May June Onset date FIGURE 1. Curve fitting results of Susceptible-Exposed-Infectious-Asymptomatic-Removed (SEIAR) model to fit the data of COVID-19 cases in Jilin Province, China. 652 CCDC Weekly / Vol. 2 / No. 34 Chinese Center for Disease Control and Prevention China CDC Weekly TABLE 1. The epidemiological characteristics of 46 COVID-19 cases or infections in Jilin Province, China. Confirmed cases Asymptomatic infections Variables n % n % Areas Shulan City in Jilin City 20 46.51 0 0.00 Fengman District in Jilin City 16 37.21 2 66.67 Chuanying District in Jilin City 3 6.98 0 0.00 Changyi District in Jilin City 1 2.32 0 0.00 Gaoxin District in Jilin City 3 6.98 0 0.00 Kuancheng District in Changchun City 0 0.00 1 33.33 Sex Male 19 44.19 1 33.33 Female 24 55.81 2 66.67 Age (years) ≤24 3 6.98 2 66.67 25–46 21 48.84 1 33.33 47–68 12 27.90 0 0.00 ≥69 7 16.28 0 0.00 Occupation Housework and unemployment 14 32.56 0 0.00 Cadres and staff 8 18.60 1 33.33 Business services 6 13.95 0 0.00 Retired personnel 5 11.63 0 0.00 Farmer 5 11.63 0 0.00 Teacher 2 4.64 0 0.00 Scattered children 1 2.33 2 66.67 Student 1 2.33 0 0.00 Medical staff 1 2.33 0 0.00 600 60 Confirmed cases Simulated cases 500 50 No intervention 40 Isolating all the cases Shortening the value of R by 50% 400 30 eff Shortening the duration from onset 20 to diagnosed date by 50% 300 10 0 25 26 27 28 29 30 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 2223 Number of cases 200 Arp May 100 0 25 26 27 28 29 30 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 1 2 3 4 April May June Date FIGURE 2.
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