Demographic and epidemiological characteristics of SARS-Cov-2-infected cases
As of March 15, 2020, there are 115 patients (60 males and 55 females) diagnosed with COVID-19 (Table 1). Approximately 90% of the patients were middle-aged or elderly(> 40 years), which was consistent with previous reports 18,19. During the 70-d follow-up, 205 visits were conducted. The visits interval ranged from 2–41 d. Exposure explanations were grouped into eight categories, among which, more than 40% of the cases were related to Wuhan or Hubei Province and more than 35% of the cases reported contact history with diagnosed or suspected infected individuals. Meanwhile, transmission chains were analyzed and converted to generations of infection. Among 83 enrolled patients within a known transmission chain, those considered to be second-generation infections (48.19%) accounted for the greatest percentage, followed by mixed generation (30.12%), first-generation (15.66%), and finally third-generation infections (6.02%).
Table 1
Demographic and epidemiology characteristics of COVID-19 patients.
Factors | Characteristic values |
Cases enrolled |
Male | n = 60 (52.17%) |
Female | n = 55 (47.83%) |
Total | 115 |
Age distribution (years) |
Range | 2–89 |
Mean | 45.5 (SD = 15.69) |
< 18 | 2 (1.73%) |
19–29 | 10 (8.69%) |
30–39 | 39 (33.91%) |
40–59 | 34 (29.57%) |
≥ 60 | 30 (26.09%) |
Follow-up situations |
Observations | 205 |
Visits per case | 1–7 (x̅= 1.8) |
Time range (d) | 0–70 (x̅ = 17.7) |
Interval (d) | 2–41 (x̅ = 10.7) |
Generation of infection | |
Observations | 83 |
First generation | 13 (15.66%) |
Second generation | 40 (48.19%) |
Third generation | 5 (6.02%) |
Mixed generation | 25 (30.12%) |
Exposure explanation |
Observations | 94 |
Wuhan or Hubei Province related | 38 (40.43%) |
Contact with diagnosed individual | 30 (31.91%) |
Other provinces related | 10 (10.64%) |
Contact with suspected infected individuals | 8 (8.51%) |
Iatrogenic | 6 (6.38%) |
No specific exposure history | 4 (4.26%) |
Foreign travel history | 2 (2.12%) |
Baseline clinical manifestations
Baseline clinical manifestations were quantified and classified according to the clinically acceptable range (Table 2). Of the 115 patients, observations of TEMP, WBC, NEUT, LYMPH, CRP were 109 (94.78%), 100 (86.96%), 84 (73.04%), 71 (61.74%), 23 (20%) and 86 (74.78%), respectively. Of the 109 subjects observed for temp, 94.49% (CI 88.4–98%) had a temp higher than 37 ℃. For the 23 subjects with CRP observations, 56.52% (CI 34.49–76.81%) were defined abnormal.
Table 2
Baseline data of clinical manifestations of SARS-CoV-2-infected patients.
Clinical manifestations | Obs.a | Range | Mean | SD | Normalc | % | 95% CI | Abnormalc | % | 95% CI |
Tempb (℃) | 109 | 35.9–39.2 | 38 | 0.65 | 6 | 5.5 | 0.02–0.12% | 103 | 94.49 | 88.4–98% |
WBCb (109/L) | 100 | 1.31–19 | 5.24 | 2.25 | 83 | 83 | 74.18–89.77% | 17 | 17 | 10.23–25.83% |
NEUTb (%) | 84 | 27.62–88.7 | 63.49 | 12.58 | 47 | 55.95 | 44.7–66.78% | 37 | 44.05 | 33.22–55.3% |
LYMPHb (%) | 71 | 11.5–92 | 33.29 | 16.8 | 48 | 67.61 | 55.45–78.24% | 23 | 32.39 | 21.76–44.55% |
CRPb (mg/L) | 23 | 0.25–96 | 14.61 | 22.87 | 10 | 43.48 | 23.19–66.51% | 13 | 56.52 | 34.49–76.81% |
aObs., observations; SD, standard deviation;
bTemp, body temperature; WBC, white blood cell count; NEUT, neutrophyls; LYMPH, lymphocytes; CRP, C-reactive protein;
c Normal = Within the clinical reference range of specific indicators(Temp: ≤37 ℃; WBC: 4.0–10.0×109/L, NEUT: 50–70%; LYMPH: 20–40%; CRP ≤ 10mg/L);
Abnormal = Not within the clinical reference range of specific indicators.
For WBC-related indicators, we take 4.0–10.0 (109/L), 50–70%, and 20–40% as the clinical reference intervals of WBC count, NEUT, and LYMPH, respectively. Individuals whose detection value falls within the clinical reference range are defined as normal, and those exceeding the clinical reference value are set as abnormal. The setting above the upper limit of the reference range is above normal, and the setting below the lower limit of the reference range is below normal. For WBC counts, 18 of 100 subjects (18%) exhibited abnormal results, including 13 below normal and 5 above normal. All baseline clinical data were quantified and classified according to the clinically acceptable range.
Diagnostic roles of COVID-19 specific serological markers
NAb, IgM, and IgG were tested in all 115 cases; observations of NAb, IgM, and IgG were 206, 192, and 192, respectively (Table 3). Since all cases were diagnosed with SARS-CoV-2 RNA positive, the positive coincidence rate or seroconversion rate with SARS-CoV-2 RNA was used as an indicator of the sensitivity of the serological assay. As shown in Table 3, NAb had the highest (79.61%) positive coincidence rate, followed by IgG (60.42%), while IgM had the lowest (26.56%).
Table 3
Consistency of serological marker and RNA testing for SARS-Cov-2
Antibody type | Obs.a | Positive coincidence rateb | 95% CI | Reactived | Nonreactivec |
+ | % | ++ | % | +++ | % | ++++ | - | % |
NAb | 206 | 79.61% | 73.46–84.89% | | | | | | | | 42 | 20.39% |
IgM | 192 | 26.56% | 20.46–33.4% | 4 | 2.08% | 33 | 17.19% | 10 | 5.21% | 4 | 141 | 73.44% |
IgG | 192 | 60.42% | 53.12–67.38% | 8 | 4.17% | 17 | 8.85% | 84 | 43.75% | 7 | 76 | 39.58% |
aObs., observations; NAb, neutralizing antibody; reactive,
bPositive coincidence rate = cases tested positive/ cases tested
c- Nonreactive, cases tested negative
dReactive, cases tested positive; +, ++, +++, ++++: positive cases classified according to the reaction intensity, weakly positive, moderately reactive, strongly reactive and extremely reactive.
According to the qualitative results of antibodies, there were six different serological patterns. Among which, 56 (29.02%) observations were NAb positive alone, accounting for the largest proportion of all serological models, followed by 53 (27.46%) observations of both IgG and NAb positive, 43 (22.28%) were all-positive, 20 (10.36%) were all negative, 12 (6.22%) were IgG positive alone, and 8 observations (4.15%) were both IgM and IgG positive. No cases of IgM positive alone were found.
To characterize the dynamic changes in IgM, IgG, and NAb in the SARS-CoV-2-infected population, the 5-d mean seroconversion rates of the antibodies were plotted over the 70-d duration of infection (Fig. 1a, Supplementary table 1). The seroconversion rates of IgM, IgG, and NAb gradually increased from the estimated initiation of infection until about day 15, and peaking around day 20. The high positive rate persisted from day 20 to day 40; the NAb seroconversion rate was as high as 100%. After the 40th day, the positive rates of IgM, IgG, and NAb showed a slow decline with time and IgM decreased to 0% by day 70.
NAb titers and influencing factors
205 observations of NAb ID50 were obtained, NAb titers ranged from less than 30 (detection limit; cases with titers < 30 were regarded as 15) to 6271, with a geometric mean of 201. For the 163 cases above the NAb detection limit, the geometric mean was 393 (Fig. 1b). Figure 2 showed the dynamic changes of NAb quantitative individual follow-up observations/the overall 5-d geometric mean over time. The NAb titer distributions of different individuals and different sampling time points were diverse (Fig. 2a). Regardless of the majority of the individual observation curves or the overall geometric mean curve (Fig. 2b), the NAb titer of the initial stage of infection increased with time, reaching a peak in about 30–40 days, and then slowly declined.
127 observations of 71 patients were included in a mixed model. Log-transformed NAb ID50 values with a base of 10 were taken as dependent variables. Potential influencing factors including gender, age, duration of infection, generation of infection, onset interval, and IgM and IgG intensity were taken as independent variables. According to the results, the establishment of the model was statistically effective (P = 0). As detailed in Table 4, the correlation coefficients for gender, age, generation of infection, and onset interval were P > 0.05; thus, there was insufficient evidence to infer that they were the influencing factors of NAb. Three other independent variables including duration of infection, IgG antibody response intensity, and IgM antibody response intensity had regression coefficients of P < 0.05, among which, both the duration of infection and IgG antibody response had a value of P < 0.01. These results indicated a statistically significant correlation between NAb and the three factors. All regression coefficients of the three factors were greater than 0, reflecting that the NAb titers were positively correlated with IgG, IgM, and duration of infection. On the premise that the other independent variables in the model remain constant, IgG had the greatest influence on NAb titers. For each grade of IgG intensity increase, the NAb titer increased Log10(0.2274). The inferior influential factor is IgM and duration of infection, which increases by Log10(0.1276) and Log10(0.0186), respectively. We also established interaction terms of gender and age with the duration of infection, respectively. Interaction terms were then re-integrated into the model, to explore whether the influence of infection days on the NAb titer is influenced by genders and ages. We found the regression coefficients of the interaction terms are not statistically significant (P > 0.05), which showed that age and gender do not interfere with the correlation.
Table 4
COVID-19 neutralizing antibody (NAb) titers and potentially related factorsa
Potential factors | Correlation coefficient | z values | P values | 95% CI |
Gender | -0.0455 | -0.42 | 0.671 | -0.2556 | 0.1645 |
Age | 0.0042 | 1.19 | 0.236 | -0.0028 | 0.0112 |
Duration of infection | 0.0186 | 3.90 | 0 | 0.0092 | 0.0279 |
Generation of infection | 0.0595 | 0.56 | 0.577 | -0.1495 | 0.2686 |
Onset interval | -0.0167 | -1.67 | 0.094 | -0.0363 | 0.0029 |
IgG | 0.2274 | 5.01 | 0 | 0.1384 | 0.3165 |
IgM | 0.1276 | 2.42 | 0.016 | 0.0242 | 0.2311 |
Duration of infection _ ageb | 0.0002 | 1.00 | 0.315 | -0.0002 | 0.0006 |
Duration of infection _genderb | -0.0071 | -0.89 | 0.371 | -0.0228 | 0.0085 |
Intercept | 1.3876 | 6.93 | 0 | 0.9954 | 1.7799 |
aNon-neutralizing serum was assigned an ID50 value of 15, i.e. one-half of the detection limit.
bInteractive variable formed between the duration of infection and age or gender.
Serological patterns and clinical manifestations
Cases with clinical manifestations data were grouped into five different serological patterns. among which, 28 cases were all negative, 1 case was IgM and IgG positive, 15 were IgG and NAb positive, 31 were NAb positive alone, and 25 were all-positive. WBC count, NEUT, LYMPH, and CRP were calculated and analyzed. Scatter diagrams are shown in Fig. 3. No significant difference was found between different serological patterns for WBC count, NEUT, or LYMPH. While for CRP, there was a significant difference between the all negative group and the all-positive group, and a higher significant difference (0.0031) between the NAb positive group and the all-positive group. Except for the ‘IgG and IgM’ group (because the observation less than 3), groups showing differences in CRP included all-positive, all negative, and ‘IgG and Nab’. Further, we grouped CRP data based on the reactivity of IgM, a highly significant difference (P < 0.0001) was found between IgM positive and negative group.
In Fig. 4, we grouped NAb IC50 according to the clinically acceptable range of clinical indicators. NAb observations were log-transformed to ensure that all data are normally distributed and they were divided into three groups according to the clinical reference value range, including normal group, below normal group, and above normal group. No significant differences were observed among different groups. Although no statistically significant differences were observed, we characterized the NAb titer distribution by standardizing the shape of the violin schematic. For all indicators in the normal group within clinically acceptable ranges compared with that of abnormal group, the NAb violins schematic exhibits a wider base. In other words, the negative rate of NAb was generally higher. The upper end of the NAb violins was thinner, indicating that for the observation of NAb positive, no matter what the titer was, the proportion was less than that of the abnormal group. For NEUT and LYMPH, the shapes of the violins of the below normal group and the above normal group were similar, but in the opposite position, which is consistent with the complementary calculation method of these two indicators. For WBCs, the prominent position of the violin was in the below normal group around Log10 (2), which may indicate that there are more relatively low concentrations of NAb detection values in this group.