[Epidemiological characteristics of two local COVID-19 outbreaks caused by 2019-nCoV Omicron variant in Guangzhou, China].

Objective: To understand the epidemiological characteristics of two local COVID-19 outbreaks caused by 2019-nCoV Omicron variant in Guangzhou, such as incubation period, serial interval, basic reproductive number (R0) and the influence of gathering places on R0, and provide evidence for the prevention and control of Omicron variant infection. Methods: The data of daily confirmed cases of Omicron variant infection from April 8 to May 8, 2022 in two COVID-19 outbreaks in Guangzhou were collected for model fitting. Weibull, Gamma and lognormal distribution were used to estimate incubation period and serial interval. Exponential growth method and the maximum likelihood estimation were used to estimate R0. Results: The median of incubation period was 2.94 (95%CI: 2.52-3.38) days and median of serial interval was 3.32 (95%CI: 2.89-3.81) days. The estimated R0 in small-size place was 4.40 (95%CI: 3.95-4.85), while the estimated R0 at airport was 11.35 (95%CI: 11.02-11.67). Conclusion: The incubation period of Omicron variant in two local COVID-19 outbreaks in Guangzhou is significantly shorter than that of delta variant. The higher the gathering degree in a place, the larger the R0. Due to its rapid transmission, COVID-19 epidemic is prone to occur. Therefore, the COVID-19 prevention and control strategy should be dynamically adjusted in time.

[Application of artificial intelligence in prevention and control of COVID-19 in Guangzhou city].

Since the outbreak of COVID-19, it has spread rapidly throughout the country and even in the world. As a first tier city, Guangzhou is also the South Gate of China, with large population mobility and severe and complex prevention and control situation. Guangzhou CDC, together with Tencent and Alibaba Cloud, has made full use of its artificial intelligence technology to carry out a series of practices in case transmission chain analysis, close contact and other key personnel management, front-line staff prevention and control skills training and convenience services. Through the application of artificial intelligence in different scenarios, the difficulties and challenges in the prevention and control are solved, and the speed of epidemic prevention and control is increased.

[Contagiousness and secondary attack rate of 2019 novel coronavirus based on cluster epidemics of COVID-19 in Guangzhou].

Objective: To analyze the contagiousness and secondary attack rate of 2019 novel coronavirus in cluster epidemics in Guangzhou and provide evidence for the prevention and control of COVID-19. Methods: All the individuals identified to be infected with 2019-nCoV in Guangzhou, including confirmed cases and asymptomatic cases, were included and classified as imported cases and local cases. The first case of each cluster epidemic was defined as index case, and the number of subsequent infections was calculated to evaluate the contagiousness and secondary attack rate of 2019 novel coronavirus in the shortest incubation period of 1-3 days. Results: As of 18 February, 2020, a total of 349 cases of 2019-nCoV infection, including 339 confirmed cases (97.13%) and 10 asymptomatic cases (2.87%) were reported in Guangzhou. There were 68 clusters involving 217 2019-nCoV infection cases (210 confirmed cases and 7 asymptomatic cases). The median number of subsequent infections caused by an index case in a cluster epidemic was 3, among which 2 were confirmed cases and 1 was asymptomatic cases, respectively. The average number of contagiousness was 2.18 in shorted incubation period of 1-3 days (The average number of infected cases were 2.18 cases by the index case in a cluster epidemic), the average infection number in family members was 1.86, and the infection ratio of family member transmission was 85.32% (1.86/2.18). The secondary attack rate in close contacts with shortest incubation period of 1-3 days was 17.12%-18.99%, the secondary attack rate in family members was 46.11%-49.56%. Conclusions: The cluster epidemic of COVID-19 in Guangzhou mainly occurred in families, the contagiousness was high. It is necessary to strengthen the prevention and control to reduce the community transmission of COVID-19.

Imported cases and minimum temperature drive dengue transmission in Guangzhou, China: evidence from ARIMAX model.

Dengue is the fastest spreading mosquito-transmitted disease in the world. In China, Guangzhou City is believed to be the most important epicenter of dengue outbreaks although the transmission patterns are still poorly understood. We developed an autoregressive integrated moving average model incorporating external regressors to examine the association between the monthly number of locally acquired dengue infections and imported cases, mosquito densities, temperature and precipitation in Guangzhou. In multivariate analysis, imported cases and minimum temperature (both at lag 0) were both associated with the number of locally acquired infections (P < 0.05). This multivariate model performed best, featuring the lowest fitting root mean squared error (RMSE) (0.7520), AIC (393.7854) and test RMSE (0.6445), as well as the best effect in model validation for testing outbreak with a sensitivity of 1.0000, a specificity of 0.7368 and a consistency rate of 0.7917. Our findings suggest that imported cases and minimum temperature are two key determinants of dengue local transmission in Guangzhou. The modelling method can be used to predict dengue transmission in non-endemic countries and to inform dengue prevention and control strategies.

[Phylogenetic analysis of envelope gene of dengue virus serotype 2 in Guangzhou, 2001-2015].

Objective: To investigate the molecular characteristics of dengue virus serotype 2 (DENV2) in Guangzhou during 2001-2015, and analyze the E gene of the strains isolated, the phylogenetic tree and molecular clock were constructed to know about the evolution of the strains. Methods: The serum samples of the patients were detected by real time PCR, and positive samples were used to isolate dengue virus by using C6/36 cells. The E gene of the isolated strains were sequenced. The phylogenetic tree was constructed by using software Mega 4.0, and the molecular clock was drawn by using software BEASTv1.8.2. Results: Twenty-six dengue virus strains were isolated between 2001 and 2015. They were all clustered into 2 genotypes, i.e. cosmopolitan genotype and Asian genotype Ⅰ. The strains isolated in Guangzhou shared high homology with Southeast Asian strains. The cosmopolitan genotype was divided into 2 sub-genotype at about 46 and 35 years ago. The substitution rate of dengue virus serotype 2 in Guangzhou was 7.1 × 10(-4) per year per site. Conclusions: There were close relationship between the Guangzhou strains and Southeast Asian strains. Guangzhou was at high risk of imported dengue fever, outbreak of dengue hemorrhagic fever and dengue shock syndrome. There might be two ways of introduction of cosmopolitan genotype. The substitution rate of the strains in Guangzhou was similar to that in the neighbor countries.

Molecular characterization and genotype shift of dengue virus strains between 2001 and 2014 in Guangzhou.

We studied the evolution, genotypes, and the molecular clock of dengue virus serotype 1 (DENV-1), between 2001 and 2014 in Guangzhou, China. The analysis of the envelope (E) gene sequences of 67 DENV-1 strains isolated in Guangzhou, together with 58 representative sequences downloaded from NCBI, have shown shifts in viral genotypes. The genotype changed several times, from genotype I to IV in 2002, from IV to I in 2005, and from I to V in 2014. These genotype shifts may be the cause of DENV outbreaks. The diversity of genotypes and clades demonstrates a high risk of future outbreaks in Guangzhou. The mean rate of virus nucleotide substitution in Guangzhou was determined to be 7·77 × 10-4 per site per year, which represents a medium substitution rate compared to two other countries. Our research can point to different ancestors of the isolated strains, which may further reveal the different origins and transmission of DENV-1 strains in Guangzhou.

Risk assessment of dengue fever in Zhongshan, China: a time-series regression tree analysis.

Dengue fever (DF) is the most prevalent and rapidly spreading mosquito-borne disease globally. Control of DF is limited by barriers to vector control and integrated management approaches. This study aimed to explore the potential risk factors for autochthonous DF transmission and to estimate the threshold effects of high-order interactions among risk factors. A time-series regression tree model was applied to estimate the hierarchical relationship between reported autochthonous DF cases and the potential risk factors including the timeliness of DF surveillance systems (median time interval between symptom onset date and diagnosis date, MTIOD), mosquito density, imported cases and meteorological factors in Zhongshan, China from 2001 to 2013. We found that MTIOD was the most influential factor in autochthonous DF transmission. Monthly autochthonous DF incidence rate increased by 36·02-fold [relative risk (RR) 36·02, 95% confidence interval (CI) 25·26-46·78, compared to the average DF incidence rate during the study period] when the 2-month lagged moving average of MTIOD was >4·15 days and the 3-month lagged moving average of the mean Breteau Index (BI) was ⩾16·57. If the 2-month lagged moving average MTIOD was between 1·11 and 4·15 days and the monthly maximum diurnal temperature range at a lag of 1 month was <9·6 °C, the monthly mean autochthonous DF incidence rate increased by 14·67-fold (RR 14·67, 95% CI 8·84-20·51, compared to the average DF incidence rate during the study period). This study demonstrates that the timeliness of DF surveillance systems, mosquito density and diurnal temperature range play critical roles in the autochthonous DF transmission in Zhongshan. Better assessment and prediction of the risk of DF transmission is beneficial for establishing scientific strategies for DF early warning surveillance and control.