Imported COVID-19 Cases Research Articles

An observational study on imported COVID-19 cases in Hong Kong during mandatory on-arrival hotel quarantine

BackgroundHong Kong enforced stringent travel restrictions during the COVID-19 pandemic. Understanding the characteristics of imported COVID-19 cases is important for establishing evidence-based control measures. MethodsRetrospective cohort study summarising the characteristics of imported cases detected in Hong Kong between 13 November 2020 and 31 January 2022, when compulsory quarantine was implemented. FindingsA total of 2269 imported COVID-19 cases aged 0–85 years were identified, of which 48.6 % detected on arrival. A shorter median delay from arrival to isolation was observed in Delta and Omicron cases (3 days) than in ancestral strain and other variants cases (12 days; p < 0.001). Lower Ct values at isolation were observed in Omicron cases than in ancestral strain or other variants cases. No Omicron cases were detected beyond 14 days after arrival. Cases detected after 14 days of quarantine (n=58, 2.6 %) were more likely asymptomatic at isolation and had higher Ct value during isolation, some of them indicating re-positivity or post-arrival infections. ConclusionsTesting inbound travellers at arrival and during quarantine can detect imported cases early, but may not prevent all COVID-19 introductions into the community. Public health measures should be adapted in response to the emergence of SARS-CoV-2 variants based on evidence from ongoing surveillance.

Prediction of cross-border spread of the COVID-19 pandemic: A predictive model for imported cases outside China.

The COVID-19 pandemic has been present globally for more than three years, and cross-border transmission has played an important role in its spread. Currently, most predictions of COVID-19 spread are limited to a country (or a region), and models for cross-border transmission risk assessment remain lacking. Information on imported COVID-19 cases reported from March 2020 to June 2022 was collected from the National Health Commission of China, and COVID-19 epidemic data of the countries of origin of the imported cases were collected on data websites such as WHO and Our World in Data. It is proposed to establish a prediction model suitable for the prevention and control of overseas importation of COVID-19. Firstly, the SIR model was used to fit the epidemic infection status of the countries where the cases were exported, and most of the r2 values of the fitted curves obtained were above 0.75, which indicated that the SIR model could well fit different countries and the infection status of the region. After fitting the epidemic infection status data of overseas exporting countries, on this basis, a SIR-multiple linear regression overseas import risk prediction combination model was established, which can predict the risk of overseas case importation, and the established overseas import risk model overall P <0.05, the adjusted R2 = 0.7, indicating that the SIR-multivariate linear regression overseas import risk prediction combination model can obtain better prediction results. Our model effectively estimates the risk of imported cases of COVID-19 from abroad.

PHDtools: A platform for pathogen detection and multi-dimensional genetic signatures decoding to realize pathogen genomics data analyses online

ObjectivesFacing the emerging diseases, rapid identification of the pathogen and multi-dimensional characterization of the genomic features at both isolate-level and population-level through high-throughput sequencing data can provide invaluable information to guide the development of antiviral agents and strategies. However, a user-friendly program is in urgent need for clinical laboratories without bioinformatics background to decode the complex big genomics data. MethodsIn this study, we developed an interactive online platform named PHDtools with a total of 15 functions to analyze metagenomics data to identify the potential pathogen and decode multi-dimensional genetic signatures including intra-/inter-host variations and lineage-level variations. The platform was applied to analyze the meta-genomic data of the samples collected from the 172 imported COVID-19 cases. ResultsAccording to the analytical results of mNGS, 27 patients were found to have the co-infections of SARS-CoV-2 with either influenza virus (n = 9) or human picobirnavirus (n = 19). Enough coverages of all the assembled SARS-CoV-2 genomes provided the sub-lineages of Omicron variant, and the number of mutations in the non-structural genes and M gene was increased, as well as the intra-host variations occurred in E and M gene were under positive selection (Ka/Ks > 1). These findings of increased or changed mutations in the SARS-CoV-2 genome characterized the current adaptive evolution patterns of Omicron sub-lineages, and revealed the evolution speed of these sub-lineages might increase. ConclusionsConsequently, the application of PHDtools has proved that this platform is accurate, user-friendly and convenient for clinical users who are deficient in bioinformatics, and the clinical monitor of SARS-CoV-2 genomes by PHDtools also highlighted the potential evolution features of current SARS-CoV-2 and indicated that the development of anti-SARS-CoV-2 agents and new-designed vaccines should incorporate the gene variations other than S gene.

“Low-risk groups” deserve more attention than “high-risk groups” in imported COVID-19 cases

ObjectiveTo estimate the optimal quarantine period for inbound travelers and identify key risk factors to provide scientific reference for emerging infectious diseases.MethodsA parametric survival analysis model was used to calculate the time interval between entry and first positive nucleic acid test of imported cases in Guangzhou, to identify the influencing factors. And the COVID-19 epidemic risk prediction model based on multiple risk factors among inbound travelers was constructed.ResultsThe approximate 95th percentile of the time interval was 14 days. Multivariate analysis found that the mean time interval for inbound travelers in entry/exit high-risk occupations was 29% shorter (OR 0.29, 95% CI 0.18–0.46, p &amp;lt; 0.0001) than that of low-risk occupations, those from Africa were 37% shorter (OR 0.37, 95% CI 0.17–0.78, p = 0.01) than those from Asia, those who were fully vaccinated were 1.88 times higher (OR 1.88, 95% CI 1.13–3.12, p = 0.01) than that of those who were unvaccinated, and those in other VOC periods were lower than in the Delta period. Decision tree analysis showed that a combined entry/exit low-risk occupation group with Delta period could create a high indigenous epidemic risk by 0.24.ConclusionDifferent strata of imported cases can result in varying degrees of risk of indigenous outbreaks. “low-risk groups” with entry/exit low-risk occupations, fully vaccinated, or from Asia deserve more attention than “high-risk groups.”

A multivariate spatio-temporal model for the incidence of imported COVID-19 cases and COVID-19 deaths in Cuba

To monitor the COVID-19 epidemic in Cuba, data on several epidemiological indicators have been collected on a daily basis for each municipality. Studying the spatio-temporal dynamics in these indicators, and how they behave similarly, can help us better understand how COVID-19 spread across Cuba. Therefore, spatio-temporal models can be used to analyze these indicators. Univariate spatio-temporal models have been thoroughly studied, but when interest lies in studying the association between multiple outcomes, a joint model that allows for association between the spatial and temporal patterns is necessary. The purpose of our study was to develop a multivariate spatio-temporal model to study the association between the weekly number of COVID-19 deaths and the weekly number of imported COVID-19 cases in Cuba during 2021. To allow for correlation between the spatial patterns, a multivariate conditional autoregressive prior (MCAR) was used. Correlation between the temporal patterns was taken into account by using two approaches; either a multivariate random walk prior was used or a multivariate conditional autoregressive prior (MCAR) was used. All models were fitted within a Bayesian framework.

Epidemiological characteristics of overseas imported COVID-19 cases into China: A scoping literature review.

Previous studies investigating the characteristics of imported cases were mostly limited to a certain province/city or a specific sub-group during a certain period with a small sample size, which may not provide an overall picture of the characteristics of imported cases. In this scoping literature review, we comprehensively synthesized the epidemiological characteristics of overseas imported COVID-19 cases into China by retrieving six literature databases, with aims to provide implications for more targeted control, prevention, and medical treatment of this disease. After dropping duplicates and reviewing titles, abstracts, and full-texts, 50 articles were included in the review finally, including 26 (52%) articles in English and 24 (48%) articles in Chinese. According to the type of data sources, the 50 studies were divided into three categories: 13 (26%) articles using data sourced from the Chinese Infectious Diseases Online Reporting System, 15 (30%) articles using data from the websites of national/local health departments, and 22 (44%) articles using hospital admission data. Most of the overseas imported COVID-19 cases were young and middle-aged Chinese students and businessmen returning from the United States, Europe, and some neighboring countries. Airport routine health screening measures could not identify COVID-cases effectively, although scheduled multiple nucleic acid tests were required before boarding. Almost all imported cases were identified during the hotel quarantine period. Although a large proportion of imported cases were asymptomatic or with mild symptoms in the published literature, they may be due to participant selection bias. The exact proportion of asymptomatic cases may need to be further investigated especially through population-based large-scale studies.

The Applications of Game Theory in Natural Disaster Management: a Case Study of COVID-19 Pandemic

As of 2022, mankind has been struggling with the COVID-19 pandemic for three years. This paper is based on the application of game theory in natural disaster prevention and control. Using a case study approach, the paper specifically examines the game process between policy makers (states), policy transmitters (hospitals) and policy implementers (individuals) in the prevention and control of the pandemic, as well as the answers to three questions: why governments have coercive measures, how hospital decisions at the beginning of the pandemic caused the spread of the coronavirus, and the impact of uneven control efforts between countries. It is found that from the individual perspective, which can include individuals in the pandemic prevention and control or a particular hospital in the healthcare system, the rational choice of interest of the individual is inconsistent with the rational choice of interest of the whole and a Nash equilibrium is thus reached. From a national perspective, in a world of countries experiencing progressively globalized and integrated, the unevenness of control efforts has led to the fact that no country can be left alone, thereby imported COVID-19 cases are always present. This is also reflected in whether or not to collaborate on vaccine development.

ASSESSING EGYPTIAN AIRPORTS CLOSURE FOR INTERNATIONAL FLIGHTS AS A TRANSPORTATION MEASURE TO CONTROL THE COVID-19 SPREAD IN THE COUNTRY

One of the measures applied by the Egyptian government to control the spread of the COVID-19 infection is performing flight suspension at the Egyptian Airports against international flights to/from Egypt. This policy was launched on March 19th, 2020. This paper develops a method to evaluate the benefits of this measure regarding the disease control. The methodology is based on mathematical models to assess the policy regarding a) reduced the number of the imported COVID-19 infections, b) reduced probability risk of a major epidemic occurrence, c) gained days to a major epidemic initializing, d) reduced probability of epidemic occurrence in each day after the airport closure. The application of the model for five scenarios of 10%, 30%, 60%, 70%, or 90% of infected cases contacts that were traced and successfully isolated showed that airport closure reduced the number of the imported COVID-19 cases by about 78.6% during the first 10 days of application. It also reduces the probability of major epidemic occurrence to about 62% for contact trace and an isolation percentage of 90%, with a 40.1% reduction as opposed to the situation in case of no airport closure. This reduction provides a delay of about 4 days, 1 day in epidemic occurrence for 90% and 70% contact tracing and isolation percentages, respectively. The probability of epidemic occurrence each day is also estimated. The study presents a method for developing an applicable model for any country tending to assess the impact of airport blocking against international flights on the COVID-19 spreading. Keywords: COVID-19, Epidemic, Egypt, Airport Closure, Flight Suspension DOI： https://doi.org/10.35741/issn.0258-2724.58.1.3

Analysis of 394 COVID-19 cases infected with Omicron variant in Shenzhen: impact of underlying diseases to patient’s symptoms

ObjectivesThe emergence of new variants of SARS-CoV-2 is continuously posing pressure to the epidemic prevention and control in China. The Omicron variant of SARS-CoV-2 having stronger infectivity, immune escape ability, and capability causing repetitive infection spread to many countries and regions all over the world including South Africa, United States and United Kingdom etc., in a short time. The outbreaks of Omicron variant also occurred in China. The aim of this study is to understand the epidemiological characteristics of Omicron variant infection in Shenzhen and to provide scientific basis for effective disease control and prevention.MethodsThe clinical data of 394 imported COVID-19 cases infected with Omicron variant from 16 December 2021 to 24 March 2022 admitted to the Third People’s hospital of Shenzhen were collected and analyzed retrospectively. Nucleic acid of SARS-CoV-2 of nasopharyngeal swabs and blood samples was detected using 2019-nCoV nucleic acid detection kit. Differences in Ct values of N gene were compared between mild group and moderate group. The specific IgG antibody was detected using 2019-nCoV IgG antibody detection kit. Statistical analysis was done using SPSS software and graphpad prism.ResultsPatients were categorized into mild group and moderate group according to disease severity. The data on the general conditions, underlying diseases, COVID-19 vaccination and IgG antibody, viral load, laboratory examination results, and duration of hospitalization, etc., were compared among disease groups. Mild gorup had higher IgG level and shorter nucleic acid conversion time. Patients with underlying diseases have 4.6 times higher probability to progress to moderate infection.ConclusionIn terms of epidemic prevention, immunization coverage should be strengthened in the population with underlying diseases. In medical institutions, more attention needs to be paid to such vulnerable population and prevent further deterioration of the disease.

Risk assessment of imported COVID-19 in China: A modelling study in Sichuan Province.

The importation of COVID‐19 cases in China is due to the returning of Chinese citizens abroad, where the majority of cases stand. This study aimed to evaluate the risk of importing COVID‐19 into the Sichuan Province of China and conduct a short‐term risk prediction assessment and analysis. Data on COVID‐19 cases in each country and Sichuan were collected, as well as visitors to Sichuan, population, area, and medical resources in each city in Sichuan province. According to different control strategies of entry aviation and quarantine control, we built models of epidemic transmission to estimate the risk for imported COVID‐19 cases in 21 cities of Sichuan. Within 140 days of the policy change's implementation, the number of susceptible, infected, and recovered people in all cities followed the same pattern over time: (1) the number of susceptible people declined slowly at first, then accelerated to reach a stable value; (2) the number of infections gradually increased to a peak, then decreased; and (3) the number of recovered patients gradually increased to a stable value. Under the four different scenarios, there were no significant differences between the risk peaks because the social distance did not change. However, the peak time would be delayed due to the implementation of flight control and nucleic acid detection measures. The improvement of foreign epidemics (reduction of attenuation factors) all delayed the arrival of the peak risk value in Chengdu by about 20 days; however, the size of the peak value did not change significantly. The improvement of nucleic acid detection accuracy delayed the arrival of the peak risk value in Chengdu, but the size of the peak value did not change significantly. Therefore, flight control and the improvement of nucleic acid detection accuracy and overseas epidemic situations have positively affected the prevention and control of the epidemic in Sichuan.

A new approach to modeling pre-symptomatic incidence and transmission time of imported COVID-19 cases evolving with SARS-CoV-2 variants.

There is paucity of the statistical model that is specified for data on imported COVID-19 cases with the unique global information on infectious properties of SARS-CoV-2 variant different from local outbreak data used for estimating transmission and infectiousness parameters via the established epidemic models. To this end, a new approach with a four-state stochastic model was proposed to formulate these well-established infectious parameters with three new parameters, including the pre-symptomatic incidence rate, the median of pre-symptomatic transmission time (MPTT) to symptomatic state, and the incidence (proportion) of asymptomatic cases using imported COVID-19 data. We fitted the proposed stochastic model to empirical data on imported COVID-19 cases from D614G to Omicron with the corresponding calendar periods according to the classification GISAID information on the evolution of SARS-CoV-2 variant between March 2020 and Jan 2022 in Taiwan. The pre-symptomatic incidence rate was the highest for Omicron followed by Alpha, Delta, and D614G. The MPTT (in days) increased from 3.45 (first period) ~ 4.02 (second period) of D614G until 3.94–4.65 of VOC Alpha but dropped to 3.93–3.49 of Delta and 2 days (only first period) of Omicron. The proportion of asymptomatic cases increased from 29% of D-614G period to 59.2% of Omicron. Modeling data on imported cases across strains of SARS-CoV-2 not only bridges the link between the underlying natural infectious properties elucidated in the previous epidemic models and different disease phenotypes of COVID-19 but also provides precision quarantine and isolation policy for border control in the face of various emerging SRAS-CoV-2 variants globally.

Preliminary Estimation of the Basic Reproduction Number of SARS-CoV-2 in the Middle East

Up to April 9, 2020, 142490 cases have been confirmed as COVID-19 infection including 5705 associated deaths in the Middle East. Most of the countries, such as Qatar, Bahrain, Iraq, Kuwait, United Arab Emirates (UAE), Oman, Lebanon, and Saudi Arabia have imported COVID-19 cases from Iran. Using the available data from WHO webpage, up to 9 April 2020, we traced epidemic curves and estimated the basic reproduction number ( ) of COVID-19 through the susceptible-infectious-recovered (SIR) model for the Middle East countries. Epidemic curves for Middle East countries and territory show similar trend as Iran, with a couple of weeks’ delay in time. In SIR model, ranged between 7.41 as in Turkey to lowest as 2.60 for Oman whereas basic reproduction number for Iran, Kuwait, Bahrain, Qatar, Saudi Arabia, United Arab Emirates (UAE), Oman, Jordan, Egypt, Lebanon, Syria, Israel, West Bank and Gaza Strip territory, and Cyprus were 4.13, 2.71, 3.39, 4.18, 4.45, 2.75, 2.60, 3.52, 3.35, 3.16, 4.99, 4.08, 2.89, and 4.05, respectively. This study indicates an important trend on an early outbreak of COVID-19 based on estimated for the Middle East countries, mean 3.76 for COVID-19, with median and interquartile range (IQR) in the Middle East.

A measure to estimate the risk of imported COVID-19 cases and its application for evaluating travel-related control measures

High connectivity between nations facilitates the spread of infectious diseases. We introduce an improved measure to estimate the risk of COVID-19 importation. The measure was applied to identify the effectiveness of travel-related control measures. We estimated the risk of importation, using the product of air-travel volume and COVID-19 prevalence in the area-of-origin. Travel volumes were acquired through real-time mobile data, and prevalence was calculated considering the time-varying strength of the COVID-19 testing policy. With the measure, the number of expected-imported cases was calculated, and compared with the reported-imported COVID-19 cases before and after post-entry quarantine for all entrants. The expected and reported-imported cases were well fitted (R2 = 0.8). A maximum of 35 undetected-imported cases was estimated to have entered Seoul, before the first imported COVID-19 case was confirmed. With the travel-related control measures, at most, 48 (73%) imported cases could be isolated from the local community. Our measure predicted trends in imported COVID-19 cases well. The method used to develop the measure can be applied to future emerging infectious diseases. Our results provide a ‘real-world’ evidence that travel-related control measures are effective at curbing further COVID-19 transmission.

The comparison of clinical characteristics between imported and native cases of COVID-19 in China

The study aimed to determine the trends in the manifestations and severity over the epidemic course of imported COVID-19 cases, with comparison to native cases. The clinical characteristics of imported and native Chinese COVID-19 cases included in the study were assessed and compared. The association was analyzed using Mann-Whitney U test for categorical variables, Kruskal-Wallis H test for continuous variables, and Spearman's correlation test for disease severity. A total of 247 imported patients were enrolled, with an average age of 29 years, and 41.3% were female. The imported patients were younger than the native patients (29 vs 47 years) and included a lower proportion of fever (44.1%), chills (5.3%), fatigue (10.1%), leukopenia (14.6%), lymphopenia (39.3%), elevated C-reactive protein (CRP) (7.3%), elevated D-dimer (16.3%), and pneumonia (65.6%). Among patients with moderate severity, imported cases had a lower proportion of fever (44.2%), dyspnea (8.3%), and increased CRP (7.7%) than native cases. COVID-19 infection was less severe in imported cases than that in native cases.

Optimal use of COVID-19 Ag-RDT screening at border crossings to prevent community transmission: A modeling analysis.

Countries around the world have implemented restrictions on mobility, especially cross-border travel to reduce or prevent SARS-CoV-2 community transmission. Rapid antigen testing (Ag-RDT), with on-site administration and rapid turnaround time may provide a valuable screening measure to ease cross-border travel while minimizing risk of local transmission. To maximize impact, we developed an optimal Ag-RDT screening algorithm for cross-border entry. Using a previously developed mathematical model, we determined the daily number of imported COVID-19 cases that would generate no more than a relative 1% increase in cases over one month for different effective reproductive numbers (Rt) and COVID-19 prevalence within the recipient country. We then developed an algorithm-for differing levels of Rt, arrivals per day, mode of travel, and SARS-CoV-2 prevalence amongst travelers-to determine the minimum proportion of people that would need Ag-RDT testing at border crossings to ensure no greater than the relative 1% community spread increase. When daily international arrivals and/or COVID-19 prevalence amongst arrivals increases, the proportion of arrivals required to test using Ag-RDT increases. At very high numbers of international arrivals/COVID-19 prevalence, Ag-RDT testing is not sufficient to prevent increased community spread, especially when recipient country prevalence and Rt are low. In these cases, Ag-RDT screening would need to be supplemented with other measures to prevent an increase in community transmission. An efficient Ag-RDT algorithm for SARS-CoV-2 testing depends strongly on the epidemic status within the recipient country, volume of travel, proportion of land and air arrivals, test sensitivity, and COVID-19 prevalence among travelers.

Establishment of a rapid risk assessment system for imported COVID-19 cases

Objective: To develop a rapid risk assessment tool for imported COVID-19 cases and provide reference evidences for prevention and control of COVID-19 at ports. Methods: The information about COVID-19 pandemic and control strategies of 12 concerned countries was collected during July to August 2021, and 12 indexes were selected to assess the importation risk of COVID-19 by risk matrix. Results: The risk for imported COVID-19 cases from 12 countries to China was high or extremely high, and the risk from Russia and the USA was highest. Conclusions: The developed rapid risk assessment tool based on the risk matrix method can be used to determine the risk level of countries for imported COVID-19 cases to China at ports, and the risk of imported COVID-19 was high at Beijing port in August 2021.

A pre-symptomatic incubation model for precision strategies of screening, quarantine, and isolation based on imported COVID-19 cases in Taiwan

Facing the emerging COVID viral variants and the uneven distribution of vaccine worldwide, imported pre-symptomatic COVID-19 cases play a pivotal role in border control strategies. A stochastic disease process and computer simulation experiments with Bayesian underpinning was therefore developed to model pre-symptomatic disease progression during incubation period on which we were based to provide precision strategies for containing the resultant epidemic caused by imported COVID-19 cases. We then applied the proposed model to data on 1051 imported COVID-19 cases among inbound passengers to Taiwan between March 2020 and April 2021. The overall daily rate (per 100,000) of pre-symptomatic COVID-19 cases was estimated as 106 (95% credible interval (CrI): 95–117) in March–June 2020, fell to 37 (95% CrI: 28–47) in July–September 2020 (p < 0.0001), resurged to 141 (95% CrI: 118–164) in October–December 2020 (p < 0.0001), and declined to 90 (95% CrI: 73–108) in January–April 2021 (p = 0.0004). Given the median dwelling time, over 82% cases would progress from pre-symptomatic to symptomatic phase in 5-day quarantine. The time required for quarantine given two real-time polymerase chain reaction (RT-PCR) tests depends on the risk of departing countries, testing and quarantine strategies, and whether the passengers have vaccine jabs. Our proposed four-compartment stochastic process and computer simulation experiments design underpinning Bayesian MCMC algorithm facilitated the development of precision strategies for imported COVID-19 cases.

Epidemiological characteristics of imported COVID-19 cases in Beijing

Objective: To analyze the epidemiological characteristics of imported COVID-19 cases and the effect of vaccination on virus load and disease severity of the cases in Beijing. Methods: The data of the imported COVID-19 cases in Beijing were collected from the National Notifiable Infectious Disease Reporting System of China Information for Disease Control and Prevention and Epidemiology investigation. The data were processed and analyzed by Excel 2010 and SPSS 22.0. Results: From June 1 to September 30, 2021, a total of 171 imported COVID-19 cases were reported in Beijing, of which 66.67% (114/171) were asymptomatic. The cases were mainly from the Philippines, the United Arab Emirates, the United Kingdom and the Russian Federation, accounting for 67.84% (116/171). The male to female ratio of the cases was 2∶1 (114∶57). The median age M (Q1, Q3) of the cases was 28 (23, 36) years. The cases of Chinese accounted for 80.12% (137/171). The sequencing of the whole genome of the virus in 47 imported COVID-19 cases showed that the proportion of Delta variant was 76.60% (36/47). The COVID-19 vaccination coverage rate in the cases was 60.82% (104/171), but the full vaccination coverage rate was 53.80% (92/171). In the imported COVID-19 cases, 13.53% (23/170) were screened to be SARS-CoV-2 nucleic acid positive on the day when they arrived in Beijing, and all the cases were positive for 2019-nCoV nucleic acid within 28 days. The severity of the disease was higher in the unvaccinated group than in the partially vaccinated group and fully vaccinated group (P<0.001). In the unvaccinated group, there were 1 severe case and 1 critical case. The median Ct values M (Q1, Q3) of N gene and ORFlab gene in unvaccinated group were 32.51 (23.23, 36.06) and 32.78 (24.00, 36.38), respectively. There was no significant difference in the median of double-gene Ct value between the partially vaccinated group and the fully vaccinated group. Conclusions: During the study period, most of the imported COVID-19 cases in Beijing were asymptomatic. No matter vaccinated or not, the viral loads in the COVID-19 cases were similar, but the vaccination could reduce the severity of the disease.

Modelling the Risk of Imported COVID-19 Infections at Maritime Ports Based on the Mobility of International-Going Ships

Maritime ports are critical logistics hubs that play an important role when preventing the transmission of COVID-19-imported infections from incoming international-going ships. This study introduces a data-driven method to dynamically model infection risks of international ports from imported COVID-19 cases. The approach is based on global Automatic Identification System (AIS) data and a spatio-temporal clustering algorithm that both automatically identifies ports and countries approached by ships and correlates them with country COVID-19 statistics and stopover dates. The infection risk of an individual ship is firstly modeled by considering the current number of COVID-19 cases of the approached countries, increase rate of the new cases, and ship capacity. The infection risk of a maritime port is mainly calculated as the aggregation of the risks of all of the ships stopovering at a specific date. This method is applied to track the risk of the imported COVID-19 of the main cruise ports worldwide. The results show that the proposed method dynamically estimates the risk level of the overseas imported COVID-19 of cruise ports and has the potential to provide valuable support to improve prevention measures and reduce the risk of imported COVID-19 cases in seaports.

Clinical features of 375 COVID-19 cases imported from Russia through the Suifenhe port and countermeasures.

Background and objectivesAt present, the focus of the fighting against COVID-19 in China is shifting to strictly prevent the entrance of cases from abroad and disease transmission. Therefore, it is extremely urgent to better understand the clinical features of imported cases from overseas countries, which is conductive to formulate the corresponding countermeasures. This study aimed to describe the clinical features of COVID-19 cases imported from Russia through the Suifenhe port, in order to identify baseline and clinical data associated with disease progression and present corresponding countermeasures.MethodsAll COVID-19 cases imported from Russia through the Suifenhe port were included in this retrospective study. According to the “Diagnosis and Treatment Protocol for Novel Coronavirus Pneumonia (seventh edition)”, imported COVID-19 cases were divided into asymptomatic infection, mild, moderate, severe, and critical groups. Baseline and clinical data, including age, gender, comorbidities, disease severity, symptoms at onset, body temperature, white blood cell (WBC) count, lymphocyte (LYMPH) count, lymphocyte percentage (LYM%), C-reactive protein (CRP), oxygenation index (OI), and the use therapeutic modalities were obtained on admission, and then compared between groups.ResultsA total of 375 COVID-19 cases imported from Russia through Suifenhe port were included, of whom the asymptomatic infection, mild, moderate, severe, and critical groups accounted for 4.0%, 13.9%, 75.5%, 5.3%, and 1.3%, respectively. The majority of the imported COVID-19 cases were men (61.9%) with a median age of 38.72 years who had no comorbidity (87.7%). Nearly one-third of them (33.1%) were asymptomatic at onset, and common initial symptoms included fever (36.5%), cough (36.0%), pharyngeal discomfort (12.3%), expectoration (8.0%), and chest tightness (5.3%). In total, 180 (48%) and 4 (1.1%) enrolled imported cases received nasal tube oxygen inhalation therapy and high-flow oxygen absorption, respectively; the remaining patients did not undergo oxygen therapy. The values of age, body temperature, WBC, LYMPH, LYM%, CRP, and OI were 38.72 ± 10.50, 35.10 ± 7.92, 5.59 ± 1.97, 1.67 ± 0.68, 31.05 ± 10.22, 8.00 ± 14.75, and 389.03 ± 74.07, respectively. Gender, age, LYMPH, LYM%, symptoms at onset, cough, fever, other rare symptoms, and oxygen therapy showed significant differences between groups (P = 0.036, < 0.001, < 0.001, < 0.001, < 0.001, < 0.001, < 0.001, = 0.045, < 0.001, respectively).ConclusionsCompared with domestic confirmed patients, COVID-19 patients who arrived at China from Russia through the Suifenhe port had significantly different clinical features, and the differences in gender, age, LYMPH, LYM%, symptoms at onset, cough, fever, other rare symptoms, and oxygen therapy between groups were statistically significant. Therefore, detailed and comprehensive countermeasures were developed to manage and prevent another outbreak based on these clinical features.