[Analysis on the infection source of the first local cluster epidemic caused by the VOC/Gamma variant of SARS-CoV-2 in China].

Objective: To investigate a SARS-CoV-2 epidemic reported in Rongcheng City, Weihai, Shandong Province. Methods: The SARS-CoV-2 nucleic acid positive patients and their close contacts were investigated, and the whole genome sequencing and genetic evolution analysis of 9 variant viruses were carried out. An infection source investigation and analysis were carried out from two sources of home and abroad, and three aspects of human, material and environment. Results: A total of 15 asymptomatic infections were reported in this epidemic, including 13 cases as employees of workshop of aquatic products processing company, with an infection rate of 21.67% (13/60). Two cases were infected people's neighbors in the same village (conjugal relation). The first six positive persons were processing workers engaged in the first process of removing squid viscera in the workshop of the company. The nucleic acid Ct value of the first time were concentrated between 15 and 29, suggesting that the virus load was high, which was suspected to be caused by one-time homologous exposure. The whole genome sequence of 9 SARS-CoV-2 strains was highly homologous, belonging to VOC/Gamma (Lineage P.1.15). No highly homologous sequences were found from previous native and imported cases in China. It was highly homologous with the six virus sequences sampled from May 5 to 26, 2021 uploaded by Chile. The infection source investigation showed that the company had used the squid raw materials captured in the ocean near Chile and Argentina from May to June 2021 over the last 14 days. Many samples of raw materials, products and their outer packages in the inventory were tested positive for nucleic acid. Conclusion: This epidemic is the first local epidemic caused by the VOC/Gamma of SARS-CoV-2 in China. It is speculated that the VOC/Gamma, which was prevalent in South America from May to June 2021, could be imported into China through frozen squid.

[Epidemiological characteristics of COVID-19 monitoring cases in Yinzhou district based on health big data platform].

Objective: To understand the epidemiological characteristics of COVID-19 monitoring cases in Yinzhou district based on health big data platform to provide evidence for the construction of COVID-19 monitoring system. Methods: Data on Yinzhou COVID-19 daily surveillance were collected. Information on patients' population classification, epidemiological history, COVID-19 nucleic acid detection rate, positive detection rate and confirmed cases monitoring detection rate were analyzed. Results: Among the 1 595 COVID-19 monitoring cases, 79.94% were community population and 20.06% were key population. The verification rate of monitoring cases was 100.00%. The total percentage of epidemiological history related to Wuhan city or Hubei province was 6.27% in total, and was 2.12% in community population and 22.81% in key population (P<0.001). The total COVID-19 nucleic acid detection rate was 18.24% (291/1 595), and 53.00% in those with epidemiological history and 15.92% in those without (P<0.001).The total positive detection rate was 1.72% (5/291) and the confirmed cases monitoring detection rate was 0.31% (5/1 595). The time interval from the first visit to the first nucleic acid detection of the confirmed monitoring cases and other confirmed cases was statistically insignificant (P>0.05). Conclusions: The monitoring system of COVID-19 based on the health big data platform was working well but the confirmed cases monitoring detection rate need to be improved.

[Application of healthcare big data in active case finding of COVID-19 in Yinzhou district of Ningbo].

During the prevention and control of the COVID-19 epidemic, identifying and controlling the source of infection has become one of the most important prevention and control measures to curb the epidemic in the absence of vaccines and specific therapeutic drugs. While actively taking traditional and comprehensive "early detection" measures, Yinzhou district implemented inter-departmental data sharing through the joint prevention and control mechanism. Relying on a healthcare big data platform that integrates the data from medical, disease control and non-health sectors, Yinzhou district innovatively explored the big data-driven COVID-19 case finding pattern with online suspected case screening and offline verification and disposal. Such effort has laid a solid foundation and gathered experience to conduct the dynamic and continuous surveillance and early warning for infectious disease outbreaks more effectively and efficiently in the future. This article introduces the exploration of this pattern in Yinzhou district and discusses the role of big data-driven disease surveillance in the prevention and control of infectious diseases.