Characteristics of viral shedding in people infected with SARS-CoV-2 during difference stages

Viral shedding of SARS-CoV-2 is a continuous dynamic process, which can be divided into latent stage, initial stage, peak stage and decreasing stage according to the characteristics of viral shedding. After being infected with SARS-CoV-2, the infected person generally stays in the latent period for 1-3 days, which is characterized by continuous negative nucleic acid test results and no infectiousness, and the risk of infection for close contacts is very low. At the initial stage of viral shedding is characterized by a rapid decline in the Ct value of nucleic acid tests in a short time, and clinical symptoms gradually appear. The infectiousness of the infected person gradually increases during this period, and the risk of infection for close contacts also gradually increases, but it is still in the early stage of infection, the possibility of viral shedding is low, and the risk of infection of secondary close contacts is low. The peak of viral shedding is characterized by low Ct value in nucleic acid test and obvious clinical symptoms;during this period, the infected person is the most infectious, and the risk of infection of the contact is the highest, so the scope of close contacts should be expanded appropriately. The decreasing period is characterized by the gradual increase of Ct value of nucleic acid test and the gradual disappearance of clinical symptoms;during this period, the infectiousness of the infected person gradually decreases to disappear. In an outbreak, an infected person in the decreasing phase is more likely to be an early infected person in the transmission chain. If infected individuals in the decreasing phase are found in an area without a SARS-CoV-2 epidemic, it suggests that the local outbreak epidemic has been spreading for some time and may be larger in scale. According to the characteristics of viral shedding, risk personnel can be determined more scientifically and accurately, so as to minimize the risk and reduce the waste of epidemic prevention resources.

Symptom Clustering Patterns and Population Characteristics of COVID-19 Based on Text Clustering Method.

Background: Descriptions of single clinical symptoms of coronavirus disease 2019 (COVID-19) have been widely reported. However, evidence of symptoms associations was still limited. We sought to explore the potential symptom clustering patterns and high-frequency symptom combinations of COVID-19 to enhance the understanding of people of this disease. Methods: In this retrospective cohort study, a total of 1,067 COVID-19 cases were enrolled. Symptom clustering patterns were first explored by a text clustering method. Then, a multinomial logistic regression was applied to reveal the population characteristics of different symptom groups. In addition, time intervals between symptoms onset and the first visit were analyzed to consider the effect of time interval extension on the progression of symptoms. Results: Based on text clustering, the symptoms were summarized into four groups. Group 1: no-obvious symptoms; Group 2: mainly fever and/or dry cough; Group 3: mainly upper respiratory tract infection symptoms; Group 4: mainly cardiopulmonary, systemic, and/or gastrointestinal symptoms. Apart from Group 1 with no obvious symptoms, the most frequent symptom combinations were fever only (64 cases, 47.8%), followed by dry cough only (42 cases, 31.3%) in Group 2; expectoration only (21 cases, 19.8%), followed by expectoration complicated with fever (10 cases, 9.4%) in Group 3; fatigue complicated with fever (12 cases, 4.2%), followed by headache complicated with fever was also high (11 cases, 3.8%) in Group 4. People aged 45-64 years were more likely to have symptoms of Group 4 than those aged 65 years or older (odds ratio [OR] = 2.66, 95% CI: 1.21-5.85) and at the same time had longer time intervals. Conclusions: Symptoms of COVID-19 could be divided into four clustering groups with different symptom combinations. The Group 4 symptoms (i.e., mainly cardiopulmonary, systemic, and/or gastrointestinal symptoms) happened more frequently in COVID-19 than in influenza. This distinction could help deepen the understanding of this disease. The middle-aged people have a longer time interval for medical visit and was a group that deserve more attention, from the perspective of medical delays.

SARS-CoV-2 Seroprevalence and Profiles Among Convalescents in Sichuan Province, China.

Objectives: To explore and understand the SARS-CoV-2 seroprevalence of convalescents, the association between antibody levels and demographic factors, and the seroepidemiology of convalescents of COVID-19 till March 2021. Methods: We recruited 517 voluntary COVID-19 convalescents in Sichuan Province and collected 1,707 serum samples till March 2021. Then we reported the seroprevalence and analyzed the associated factors. Results: Recent travel history was associated with IgM levels. Convalescents who had recent travel history were less likely to be IgM antibody negative [OR = 0.232, 95% CI: (0.128, 0.420)]. Asymptomatic cases had, approximately, twice the odds of being IgM antibody negative compared with symptomatic cases [OR = 2.583, 95% CI: (1.554, 4.293)]. Participants without symptoms were less likely to be IgG seronegative than those with symptoms [OR = 0.511, 95% CI: (0.293, 0.891)]. Convalescents aged 40-59 were less likely to be IgG seronegative than those aged below 20 [OR = 0.364, 95% CI: (0.138, 0.959)]. The duration of positive IgM antibodies persisted 365 days while the IgG persisted more than 399 days. Conclusions: Our findings suggested that recent travel history might be associated with the antibody levels of IgM, while age could be associated with the antibody levels of IgG. Infection type could be associated with both antibody levels of IgM and IgG that declined quicker in asymptomatic cases.