Natural history and cycle threshold values analysis of COVID-19 in Xiamen City, China

Deng, Bin; Liu, Weikang; Guo, Zhinan; Luo, Lei; Yang, Tianlong; Huang, Jiefeng; Abudunaibi, Buasiyamu; Zhang, Yidun; Ouyang, Xue; Wang, Demeng; Su, Chenghao; Chen, Tianmu

doi:10.1016/j.idm.2022.07.007

Cited by 2 publications

(3 citation statements)

References 32 publications

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“…The infection coefficient β is obtained by fitting the actual data in the model. The incubation period of the Delta variant is 3-7 days (4,21) and that of the Omicron variant is 2 days (18,22); therefore, we set the incubation period coefficient for the Delta variant as 1/ω = 0.33 and the Omicron variant as 1/ω = 0.4. As there were not any asymptomatic infections during the outbreaks in regions P, X, and Z, we set the proportion of asymptomatic infections as p = 0.…”

Section: Parameter Estimationmentioning

confidence: 99%

“…This pandemic has been a great challenge for not only people's health and the global healthcare system but also for the socio-economy. In the last 2 years, multiple mutant variants of SARS-CoV-2 have emerged, which affects the transmissibility and severity of the virus greatly (3,4). In China, due to the implementation of nonpharmaceutical (NPIs, such as quarantine, nucleic-acid testing, and social distancing) and pharmaceutical interventions (PIs, such as medication as well as vaccination) during the prevention and control of COVID-19, there were some satisfying results (1,(5)(6)(7).…”

Section: Introductionmentioning

confidence: 99%

“…At the beginning of 2020, researchers calculated R 0 of COVID-19 in Wuhan, China with the definition method, to demonstrate the transmissibility of this emerging infectious disease (EID) for the first time ( 9 ). Recently, there have been some studies published about obtaining R eff and R t by applying the next-generation matrix and serial interval ( SI ) ( 4 , 10 , 11 ). Simultaneously, there are studies about R eff of COVID-19 by establishing transmission dynamics models, such as SEIAR models ( 1 , 12 , 13 ).…”

Section: Introductionmentioning

confidence: 99%

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A comparative study on the three calculation methods for reproduction numbers of COVID-19

Abudunaibi

Liu

Guo³

et al. 2023

Front. Med.

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ObjectiveThis study uses four COVID-19 outbreaks as examples to calculate and compare merits and demerits, as well as applicational scenarios, of three methods for calculating reproduction numbers.MethodThe epidemiological characteristics of the COVID-19 outbreaks are described. Through the definition method, the next-generation matrix-based method, and the epidemic curve and serial interval (SI)-based method, corresponding reproduction numbers were obtained and compared.ResultsReproduction numbers (Reff), obtained by the definition method of the four regions, are 1.20, 1.14, 1.66, and 1.12. Through the next generation matrix method, in region H Reff = 4.30, 0.44; region P Reff = 6.5, 1.39, 0; region X Reff = 6.82, 1.39, 0; and region Z Reff = 2.99, 0.65. Time-varying reproduction numbers (Rt), which are attained by SI of onset dates, are decreasing with time. Region H reached its highest Rt = 2.8 on July 29 and decreased to Rt < 1 after August 4; region P reached its highest Rt = 5.8 on September 9 and dropped to Rt < 1 by September 14; region X had a fluctuation in the Rt and Rt < 1 after September 22; Rt in region Z reached a maximum of 1.8 on September 15 and decreased continuously to Rt < 1 on September 19.ConclusionThe reproduction number obtained by the definition method is optimal in the early stage of epidemics with a small number of cases that have clear transmission chains to predict the trend of epidemics accurately. The effective reproduction number Reff, calculated by the next generation matrix, could assess the scale of the epidemic and be used to evaluate the effectiveness of prevention and control measures used in epidemics with a large number of cases. Time-varying reproduction number Rt, obtained via epidemic curve and SI, can give a clear picture of the change in transmissibility over time, but the conditions of use are more rigorous, requiring a greater sample size and clear transmission chains to perform the calculation. The rational use of the three methods for reproduction numbers plays a role in the further study of the transmissibility of COVID-19.

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Section: Parameter Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A comparative study on the three calculation methods for reproduction numbers of COVID-19

Abudunaibi

Liu

Guo³

et al. 2023

Front. Med.

Self Cite

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Assessing changes in incubation period, serial interval, and generation time of SARS-CoV-2 variants of concern: a systematic review and meta-analysis

Kummer

Zhao

et al. 2023

Preprint

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Background: After the first COVID-19 wave caused by the ancestral lineage, the pandemic has been fueled from the continuous emergence of new SARS-CoV-2 variants. Understanding key time-to-event periods for each emerging variant of concern is critical as it can provide insights into the future trajectory of the virus and help inform outbreak preparedness and response planning. Here, we aim to examine how the incubation period, serial interval, and generation time have changed from the ancestral SARS-CoV-2 lineage to different variants of concern. Methods: We conducted a systematic review and meta-analysis that synthesized the estimates of incubation period, serial interval, and generation time (both realized and intrinsic) for the ancestral lineage, Alpha, Beta, and Omicron variants of SARS-CoV-2. Results: Our study included 274 records obtained from 147 household studies, contact tracing studies or studies where epidemiological links were known. With each emerging variant, we found a progressive shortening of each of the analyzed key time-to-event periods. Specifically, we found that Omicron had the shortest pooled estimates for the incubation period (3.63 days, 95%CI: 3.25-4.02 days), serial interval (3.19 days, 95%CI: 2.95-3.43 days), and realized generation time (2.96 days, 95%CI: 2.54-3.38 days) whereas the ancestral lineage had the highest pooled estimates for each of them. We also observed shorter pooled estimates for the serial interval compared to the incubation period across the virus lineages. We found considerable heterogeneities (I2 > 80%) when pooling the estimates across different virus lineages, indicating potential unmeasured confounding from population factors (e.g., social behavior, deployed interventions). Conclusion: Our study supports the importance of conducting contact tracing and epidemiological investigations to monitor changes in SARS-CoV-2 transmission patterns. Our findings highlight a progressive shortening of the incubation period, serial interval, and generation time, which can lead to epidemics that spread faster, with larger peak incidence, and harder to control. We also consistently found a shorter serial interval than incubation period, suggesting that a key feature of SARS-CoV-2 is the potential for pre-symptomatic transmission. These observations are instrumental to plan for future COVID-19 waves. Keywords: COVID-19, variants of concern, incubation period, serial interval, realized generation time, intrinsic generation time, systematic review, meta-analysis

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Natural history and cycle threshold values analysis of COVID-19 in Xiamen City, China

Cited by 2 publications

References 32 publications

A comparative study on the three calculation methods for reproduction numbers of COVID-19

A comparative study on the three calculation methods for reproduction numbers of COVID-19

Assessing changes in incubation period, serial interval, and generation time of SARS-CoV-2 variants of concern: a systematic review and meta-analysis

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