key: cord-309032-idjdzs97
authors: Zhou, Feng; You, Chong; Zhang, Xiaoyu; Qian, Kaihuan; Hou, Yan; Gao, Yanhui; Zhou, Xiao-Hua
title: Epidemiological Characteristics and Factors Associated with Critical Time Intervals of COVID-19 in Eighteen Provinces, China: A Retrospective Study
date: 2020-10-09
journal: Int J Infect Dis
DOI: 10.1016/j.ijid.2020.09.1487
sha: 
doc_id: 309032
cord_uid: idjdzs97

Background As COVID-19 ravages continuously around the world, more information on the epidemiological characteristics and factors associated with time interval between critical events is needed to contain the pandemic and to assess the effectiveness of interventions. Methods Individual information on confirmed cases from January 21 to March 2 was collected from provincial or municipal health commissions. We identified the difference between imported and local cases in the epidemiological characteristics. Two models were established to estimate the factors associated with time interval from symptom onset to hospitalization (TOH) and length of hospital stay (LOS) respectively. Results Among 7,042 cases, 3392 (48.17%) were local cases and 3304 (46.92%) were imported cases. Since the first intervention was adopted in Hubei on January 23, the daily reported imported cases reached a peak on January 28 and gradually decreased since then. Imported cases were on average younger (41 vs. 48), and had more male (58.66% vs. 47.53%) compared to local cases. Furthermore, imported cases had more contacts with other confirmed cases (2.80 ± 2.33 vs. 2.17 ± 2.10), which were mainly within family members (2.26 ± 2.18 vs. 1.57 ± 2.06). The TOH and LOS were 2.67 ± 3.69 and 18.96 ± 7.63 days respectively, and a longer TOH was observed in elderly living in the provincial capital cities that were higher migration intensity with Hubei. Conclusions Measures to restrict traffic can effectively reduce imported spread. However, household transmission is still not controlled, particularly for the infection of imported cases to elderly women. It is still essential to surveil and educate patients about the early admission or isolation.

As of September 20, 2020, a total of more than 30 million confirmed cases of coronavirus disease 2019 , as well as more than 900,000 deaths had been reported by World Health Organization (WHO) in the worldwide (Organization, 2020a) . At the same time, China had reported 85,291 lab confirmed cases with 4,634 deaths (China National Health Commission of the People's Republic of, 2020a). Despite the WHO and international community declared and took many efforts to control this pandemic in time, our knowledge about the COVID-19 is still very limited, and the number of daily reported cases is still increasing sharply worldwide (Organization, 2020b) .

In the context of the rapid spread of COVID-19, a full understanding of the epidemiological characteristics of this infectious disease is crucial in epidemic control and public policy practices. Several studies conducted in China, Italy and the United States have reported some epidemiological characteristics of COVID-19 in the initial phase (Grasselli et al., 2020 , Liang et al., 2020 , Price-Haywood et al., 2020 , Richardson et al., 2020 , Wu and McGoogan, 2020 , However, there is still a lack of research on the space-time characteristics in the populations of imported and local cases respectively which is of great significance. Imported cases play a very important role in the disease spreading, especially it is an indicator for predicting new clusters of infections. Understanding its epidemiological characteristics would help us to assess the possible effect of non-pharmaceutical interventions (NPIs), such as travel restrictions (Desjardins et al., 2020 , Gilbert et al., 2020 . Furthermore, considering the changes in susceptible populations, exposure opportunity and intervention of disease over epidemic progresses and locations, the epidemiological characteristics of disease should hence be estimated spatiotemporally in order to better describe the epidemic (Zhang J. et al., 2020) . For example, the space-time characteristics of COVID-19 revealed by previous studies can prioritize locations and the best time for different NPIs (Desjardins et al., 2020 , Lai S. et al., 2020 , Masrur et al., 2020 . Therefore, exploring the epidemiological characteristics of J o u r n a l P r e -p r o o f imported cases from a space-time perspective is critical and provides guidance for countries on interventions taken at different periods and regions, specifically in resource-scarce countries and regions.

As a highly contagious disease, early detection, isolation, hospitalization and diagnosis of COVID-19 are also important for control and they can effectively reduce the risk of disease transmission (Bi et al., 2020 , Rong et al., 2020 , Thompson, 2020 . Delay in hospitalization or isolation may lead to prolonged periods of infectiousness, and increase the difficulty and burden of infectious disease control. Previous studies have described some characteristics of patients with COVID-19 including the time interval between key events (Liang et al., 2020 , Tian et al., 2020 . In addition, existing literature also brought to light the reduction in the time interval from symptom onset to hospitalization/isolation after various interventions , Zhang J. et al., 2020 . However, little is known about individual-level influence factors associated with delaying hospital admission and length of hospital stay. Identifying these factors would not only help us predict the medical burden and reasonably allocate medical resources, but also would inform response efforts across the world.

In this study, we described the spatiotemporal distribution of the COVID-19 in eighteen provinces of China (outside Hubei province) and investigated the epidemiological characteristics in the population of imported cases and local cases, from the beginning of this epidemic until it was under good control. We further assessed the critical influence factors associated with time interval from symptom onset to hospitalization (TOH) and length of hospital stay (LOS), including demographic and temporal and spatial characteristics.

J o u r n a l P r e -p r o o f

We constructed a retrospective cohort study for COVID-19 confirmed cases, based on the detailed information published by the provincial or municipal health commissions in eighteen provinces of China (outside Hubei province) from January 21 to March 2. The details of sampling and data collection are shown in Figure 1 . Data collectors were trained and divided into five groups of two according to provinces to collect timely epidemiological data of confirmed cases. LinkMed EDC were used for data entry, the two collectors in each group entered the same data, and we conducted data verification and consistency test in real-time.

Specifically, demographic characteristics, epidemiological history and date of critical event were extracted from the official report of the confirmed case details. (1) Demographic information including age, gender, residence at the time of diagnosis and type of symptoms were included in our analysis. (2) Epidemiological history includes history of travel or residence in other regions and contact history of confirmed cases. According to whether the patient had a travel or residence history in other regions within 14 days before diagnosis and likely exposure to pathogens in that regions, the patient was divided into imported and local cases. Similarly, we can identify whether patients had contacted with confirmed cases of family and non-family members. (3) The dates of events include the date of symptoms onset, hospitalization/isolation, CDC diagnosis and recovery/death. Hospitalization/isolation is defined as a patient receiving regular hospital treatment (not includes small medical institutions such as clinics and community health service centers), or a mandatory isolation measure implemented by the community. In this study, we used the time interval between two events to analyze this data, including time interval from symptom onset to hospitalization (TOH) and length of hospital stay (LOS).

Additionally, we also collected information on the intensity of migration from Hubei to these 18 provinces in the week before January 23, which was obtained from the Baidu J o u r n a l P r e -p r o o f migration map (Baidu, 2020) . Migration intensity between provinces and Hubei was categorized into four levels: strong connection (≥0.15%), medium connection [0.05%-0.15%), weak connection [0.03-0.05%) and very weak connection (<0.03). Finally, according to the daily trend of new cases and date of intervention, we divided the entire epidemic into five periods from the beginning of the epidemic (Jan 21) to Mar 2. The first period is before January 23, when Wuhan took measures of traffic restrictions and lockdown, since then every week works as one period, until the last period is a recession of this epidemic after February 14.

We described the epidemic scale in 18 provinces and the proportion of imported cases spatiotemporally. Meantime, the demographic characteristics of imported and local cases were reported. In addition, two models were established to identify and quantify the relevant sociodemographic factors to TOH and LOS respectively. In the first model, we estimated the factors associated with TOH using a generalized linear model with a Poisson distribution and a log link. Besides, the odds ratio (OR) and their 95% confidence intervals (CI) were calculated after incorporating multiple variables (Coxe et al., 2009 , SAS, 2016 . In the second model, an accelerated failure time (AFT) model was used to handle the survival data with both left and right censored (Kalbfleisch, 2002 , Paul, 2010 . In our study of analyzing factors associated with LOS, left censoring would occur if we know that a patient recovered before Marth 2, but the exact time cannot be obtained. Similarly, right censoring would occur for patients who are confirmed in the later phase of the epidemic. Moreover, we included the TOH in the model and used the hazard ratio (HR) and their 95% CIs to identify the difference in LOS among recovered patients with different characteristics. Based on the distribution of LOS which is denoted by T, we established the Weibull model, written as,

where ε is a random disturbance term, and β 0 ,...,β , and σ are parameters to be estimated.

Then we applied a likelihood function with censored to estimate the parameter values. Inc., North Carolina, USA). P<0.05 was considered statistically significant.

Among 7,042 cases, 3392 (48.17%) of patients were local cases and 3304 (46.92%) of patients were imported cases, and less than 5% (346) of other patients were unable to confirm their travel history within 14 days before diagnosis. The temporal and spatial distribution of imported and local cases is shown in Figure 2 . From panel A, we can see that the greater the intensity of migration with Hubei, the more cases in the province. For provinces with migration intensity greater than 0.03%, the proportion of imported cases to total cases was about 50%. However, for provinces including Tianjin, Ningxia and Hebei with very weak connection (<0.03%) with Hubei, they had more local cases than imported cases. Since the first intervention was adopted in Hubei on January 23, the daily reported imported cases reached the highest on January 28, and the proportion of imported cases to the total cases gradually decreased over time, reaching 50% on February 2 ( Figure 2B ).

J o u r n a l P r e -p r o o f 44.13%). For time interval, the frequency and best-fitting probability density function for TOH and LOS are present in Figure 3 respectively. As shown in 

The top half of 

The left panel of Table 3 shows the results of the first model for the influence factors of TOH.

A longer TOH was observed in older and provincial capital cases. The older the case is , the longer the TOH. As compared with the cases younger than 20, especially for cases older than Furthermore, patients who lived in regions with lower migration intensity with Hubei province had shorter TOH. Particularly, as for patients living in regions where had the migration intensity more than 0.15%, migration intensity (1) between 0.05% and 0.15%, had down to 0.87 times decreased risk of longer time, (2) between 0.03% and 0.05%, had down to 0.74 times, (3) less than 0.03%, had down to 0.69 times. In addition, there is no significant differences in TOH between imported and local cases.

The right panel of Table 3 gives the HR estimates of related factors associated with LOS.

There were no significant differences in LOS among different gender or age groups. It also showed that differences in LOS relative to city type and fever symptoms were not statistically significant. While, patients clearly contacted with family-confirmed case had a longer LOS (HR=1.05; 95% CI: 1.01,1.09) than patients who did not clearly contact. Moreover, we found J o u r n a l P r e -p r o o f that local patients had a shorter hospital stay than imported cases (HR=0.95; 95% CI: 0.91,0.99). Furthermore, patients reported in the later periods of this epidemic had a shorter hospital stay than patients in the initial epidemic (HR=0.66; 95% CI: 0.57,0.77). Compared with patients whose TOH was less than or equal to one day, LOS of patients whose TOH was more than 4 days was reduced by 0.05 percentage. And the similar result appeared in patients whose TOH was 2-3 days (HR=0.94; 95% CI: 0.89,0.99).

Comprehensive epidemiological characteristics of the COVID-19 covering the entire periods of epidemic and summaries of the experience from China are useful in public health control.

In this study, we described the epidemiological characteristics of imported and local cases, including temporal and spatial characteristics. Indeed, regions with greater migration intensity with Hubei had more imported cases. After the lockdown measures taken by cities in Hubei since January 23 towards the interruption of sustained COVID-19 transmission outside Hubei

Province (Nie et al., 2020) . We found the daily reported imported cases reached a peak on January 28 and gradually decreased since then. These suggest that traffic restrictions or lockdown in the epicenter can effectively reduce the export of cases (Islam et al., 2020 , Zhang J. et al., 2020 . Moreover, outside of the epicenter, it is also obvious that timely restriction and quarantine of suspicious imported individuals with a travel history of epicenter can effectively reduce the transmission by imported cases in local , Kwok et al., 2020 , Lai C. K. C. et al., 2020 . Even in the provinces that were not in close contact with Hubei, the surveillance of imported cases could not still be overlooked. Taking Tianjin, Ningxia and Hebei province as examples, local cases were twice as large as imported cases, which was related to the several local gathering events of imported cases , Dong et al., 2020 , Zhang S. X. et al., 2020 .

This study confirms previously described characteristics (Liang et al., 2020, Wu and McGoogan, 2020) , but also highlights the difference between imported and local cases.

Throughout this epidemic, imported patients focused on younger, had a higher proportion of male and had more provincial capital residents compared to local cases. This may match the situation that labor exports are mainly the young and middle-aged male in China. This result also insinuates older women living in non-provincial capital cities were at greater risk of exposure when the epidemic spreads to the local. A study on household transmission also founded similar results (Xu et al., 2020) . Moreover, the proportion of clearly confirmed case contact history in local cases was higher than that in imported cases. This may be due to the complicated epidemic chain in Hubei Province in the initial phase of the epidemic, which made it difficult to track the contact history of imported cases. Nonetheless, approximately 40% of local cases may be attributed to the household transmission. Among the patients who were clearly exposed to confirmed cases, imported cases had more contacts with other confirmed cases than local cases on average, and contacts were mainly family members.

Although we are unable to determine the infectious relationship between them, it might partly explain household transmission caused by imported cases was more prominent. This suggests that after NPIs such as restricting population movement were taken. More effective interventions were still needed to be taken to control household transmission simultaneously, especially for the infection of imported cases to elderly woman in non-provincial capital cities.

Indeed, the Chinese government encouraged people to stay at home as much as possible (Lai S. et al., 2020) . While, the cases that have migrated out from Hubei before January 23 still have the risk of household transmission in local. Therefore, emergency measures were taken by local governments across China to strengthen the tracking and isolation of recent travelers from Hubei (China National Health Commission of the People's Republic of, 2020b, China

The State Council of the People's Republic of, 2020), which reduced this risk to a certain extent. Moreover, our study showed that the daily local cases reached a peak on the 14th day (February 6) after the lockdown, and then gradually declined. This also illustrates the early response of the government is very important for containing the local spread of imported cases.

Our findings show that there was a lag of 2.67 days from symptom onset to hospital admission, and the average length of hospital stay was about 19 days, which were similar to previous studies conducted in China (Khalili et al., 2020 , Liang et al., 2020 , Linton et al., 2020 . Surprisingly, we found that the older the patients are, the longer the hospitalization delays. Considering the situation that medical resources outside Hubei Province had not reached saturation, this might be related to the hospital admission pattern of viral respiratory diseases or the lack of recognition of the disease in elderly patients (Petrilli et al., 2020) .

Besides, the TOH at the later phase of the epidemic showed a rebound trend. Cases reported in the later phase of the epidemic had a slack attitude in seeking medical resources and the decline in control efforts were possible reasons. However, research in China (outside Hubei province) during January 21 to February 17 demonstrated a shorter hospital admission delay from January 28 to February 17 (4.4 vs. 2.6 days) (Zhang J. et al., 2020) . Before adjusting for other factors, our research also showed a slightly shorter hospital admission delays in the week after January 23. Except for the different study population and period, we consider this result may be affected by the confounder. Our research included the later phase of the epidemic and adjusted other demographic factors.

This study also confirms that patients living in provincial capital that closely connected to the epicenter had a longer TOH. This provides new demands on the epidemic prevention and control, that is, in provincial capital cities close to the epicenter, case tracking, surveillance and education of immediate admission/isolation should be emphasized. A mathematical model study showed that if the mean time from symptom onset to hospitalization can be halved by surveillance, then the probability that a case leads to transmission is very low (Thompson, 2020) . Interestingly, we found associations of clear Republic of, 2020b). In addition, our results also found that the average LOS of 19 days will not decrease by early admission. Perhaps it is related to the characteristics of the viral infectious disease. By contrast, the decrease in LOS in the later phase of the epidemic may be due to the continuous improvement of medical technology for this disease.

This study included a large study cases during an entire epidemic and used a novel methodology. However, there are some limitations. First, as a retrospective study, since the date of symptom onset is self-reported based, there may be recall bias. Second, although we made an effort to collect patient discharge information, we still could not obtain the discharge data of some patients. Fortunately, nearly 90% of patients were discharged from the hospital at the end-point of observation on March 2, which provides an opportunity for the statistical methodology using survival data with left censoring. Third, given the proportion of death cases in the study population was particularly small, which is less than 1%, the impact of death truncation was not considered when analyzing the length of hospitalization. Finally, our study did not include the southeast provinces, but Henan and Zhejiang province were similar to those provinces in intensity of migration and scale of epidemic, and our results are also consistent with several studies conducted in Shenzhen and Hong Kong in epidemiological characteristics during the same period (Bi et al., 2020 , Lai C. K. C. et al., 2020 .

In patients' education about early admission or isolation should still be attached great importance in the future prevention and control, especially for the elderly living in provincial capital cities that were more closely connected with the epicenter.

Feng Zhou: data collection, data analysis, writing. Chong You: data collection, writing.

Xiaoyu Zhang: data analysis. Kaihuan Qian: data collection. Yan Hou: data collection. Yanhui

Gao: data analysis. Xiao-Hua Zhou: study design.

Not required. The study was anonymous, and individual information was collected from provincial or municipal health commissions, which is a public data to help control this epidemic.

No potential conflict of interest exits in the submission of this manuscript, and manuscript is approved by all authors for publication. Panel A shows the frequency (blue histograms) and best-fitting probability density function (Poisson, red curves) for time interval from symptom onset to hospitalization(≥0). Panel B shows the frequency (blue histograms) and best-fitting probability density function (Weibull, red curves) for length of hospital stay.

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Symptoms Fever #

Gender Male 2430 2.84(3.53) 2(0-4) Reference 654 19

Non-family-confirmed patient contact history # unclear 3962 2.87(3.55) 2(0-5) Reference

We thank Xueqing Liu, Yuying Li J o u r n a l P r e -p r o o f

J o u r n a l P r e -p r o o f
