On fluctuating characteristics of global COVID-19 cases and identification of inflection points

Purpose The emergence of a coronavirus disease 2019 (COVID-19) epidemic has had a tremendous impact on the world, and the characteristics of its evolution need to be better understood. Design/methodology/approach To explore the changes of cases and control them effectively, this paper analyzes and models the fluctuation and dynamic characteristics of the daily growth rate based on the data of newly confirmed cases around the world. Based on the data, the authors identify the inflection points and analyze the causes of the new daily confirmed cases and deaths worldwide. Findings The study found that the growth sequence of the number of new confirmed COVID-19 cases per day has a significant cluster of fluctuations. The impact of previous fluctuations in the future is gradually attenuated and shows a relatively gentle long-term downward trend. There are four inflection points in the global time series of new confirmed cases and the number of deaths per day. And these inflection points show the state of an accelerated rise, a slowdown in the rate of decline, a slowdown in the rate of growth and an accelerated decline in turn. Originality/value This paper has a certain guiding and innovative significance for the dynamic research of COVID-19 cases in the world.

[Analysis on the cluster epidemic of coronavirus disease 2019 in Guangdong Province].

Objective: Analysis of clustering characteristics of coronavirus disease 2019 (COVID-19) in Guangdong Province. Methods: The COVID-19 cases in Guangdong Province onset from January 1 to February 29, 2020 were collected from Chinese information system for disease control and prevention and Emergency Public Reporting System. Obtain the epidemiological survey data of the cluster epidemic situation, and clarify the scale of cluster epidemic situation, the characteristics of the index cases, family and non-family subsequent cases. Calculate serial interval according to the onset time of the index cases and subsequent cases, secondary attack rate based on the close contacts tracking results, the characteristics of different cases in the clustered epidemic were compared. Results: A total of 283 cluster were collected, including 633 index cases, 239 subsequent cases. Families are mainly clustered, the total number involved in each cluster is in the range of 2-27, M (P25, P75) are 2.0 (2.0, 4.0). During January 15 to February 29, the secondary attack rate is 2.86% (239/8 363) in Guangdong Province, the family secondary attack rate was 4.84% (276/3 697), and the non-family secondary attack rate was 1.32% (61/4 632). According to the reporting trend of the number of cases in Guangdong Province, it can be divided into four stages, the rising stage, the high platform stage, the descending stage and the low level fluctuation period. The secondary attack rate of the four stages were 3.5% (140/3 987), 2.3% (55/2 399), 2.6% (37/1 435), 1.3% (7/542), respectively. The difference was statistically significant (P=0.003). Conclusion: COVID-19 cluster mainly occurs in families in Guangdong Province. The scale of the clustered epidemic was small; the serial interval was short; and the overall secondary attack rate was low.

[Risk assessment and early warning of imported COVID-19 in Guangdong province].

Objective: To assess the imported risk of COVID-19 in Guangdong province and its cities, and conduct early warning. Methods: Data of reported COVID-19 cases and Baidu Migration Index of 21 cities in Guangdong province and other provinces of China as of February 25, 2020 were collected. The imported risk index of each city in Guangdong province were calculated, and then correlation analysis was performed between reported cases and the imported risk index to identify lag time. Finally, we classified the early warming levels of epidemic by imported risk index. Results: A total of 1 347 confirmed cases were reported in Guangdong province, and 90.0% of the cases were clustered in the Pearl River Delta region. The average daily imported risk index of Guangdong was 44.03. Among the imported risk sources of each city, the highest risk of almost all cities came from Hubei province, except for Zhanjiang from Hainan province. In addition, the neighboring provinces of Guangdong province also had a greater impact. The correlation between the imported risk index with a lag of 4 days and the daily reported cases was the strongest (correlation coefficient: 0.73). The early warning base on cumulative 4-day risk of each city showed that Dongguan, Shenzhen, Zhongshan, Guangzhou, Foshan and Huizhou have high imported risks in the next 4 days, with imported risk indexes of 38.85, 21.59, 11.67, 11.25, 6.19 and 5.92, and the highest risk still comes from Hubei province. Conclusions: Cities with a large number of migrants in Guangdong province have a higher risk of import. Hubei province and neighboring provinces in Guangdong province are the main source of the imported risk. Each city must strengthen the health management of migrants in high-risk provinces and reduce the imported risk of Guangdong province.

[Clinical outcomes of COVID-19 cases and influencing factors in Guangdong province].

Objective: To analyze the clinical courses and outcomes of COVID-19 cases and the influencing factors in Guangdong province and provide basis for the formulation or adjustment of medical care and epidemic control strategy for COVID-19. Methods: We collected demographic data, medical histories, clinical courses and outcomes of 1 350 COVID-19 patients reported in Guangdong as of 4 March 2020 via epidemiological investigation and process tracking. Disease severity and clinical course characteristics of the patients and influencing factors of severe illness were analyzed in our study. Results: Among 1 350 cases of COVID-19 cases in Guangdong, 72 (5.3%) and 1 049 (77.7%) were mild and ordinary cases, 164 (12.1%) were severe cases, 58 (4.3%) were critical cases and 7 (0.5%) were fatal. The median duration of illness were 23 days (P(25), P(75): 18, 31 days) and the median length of hospitalization were 20 days (P(25), P(75): 15,27 days). For severe cases, the median time of showing severe manifestations was on the 12(th) day after onset (P(25), P(75): 9(th) to 15(th) days), and the median time of severe manifestation lasted for 8 days (P(25), P(75): 4, 14 days). Among 1 066 discharged/fetal cases, 36.4% (36/99) and 1.0% (1/99) of the mild cases developed to ordinary cases and severe cases respectively after admission; and 5.2% (50/968) and 0.6% (6/968) of the ordinary cases developed to severe cases, and critical cases respectively after admission. In severe cases, 11.4% developed to critical cases (10/88). The influencing factors for severe illness or worse included male (aHR=1.87, 95%CI: 1.43-2.46), older age (aHR=1.67, 95%CI: 1.51-1.85), seeking medical care on day 2-3 after onset (aHR=1.73, 95%CI: 1.20-2.50) pre-existing diabetes (aHR=1.75, 95%CI: 1.12-2.73) and hypertension (aHR=1.49, 95%CI: 1.06-2.09). Conclusions: The course of illness and length of hospitalization of COVID-19 cases were generally long and associated with severity of disease clinical outcomes. The severe cases were mainly occurred in populations at high risk. In the epidemic period, classified management of COVID-19 cases should be promoted according to needs for control and prevention of isolation and treatment for the purpose of rational allocation of medical resources.