COVID-19 Clusters Research Articles

Applying Spatio-temporal Scan Statistics and Spatial Autocorrelation Statistics to identify Covid-19 clusters in the world - A Vaccination Strategy?

With the whole world being affected by the pandemic, it is a matter of great importance that studies about spatial and spatio-temporal aspects of the COVID-19 (Sars-Cov-2) pandemic should be conducted, therefore the main goal of this paper is to present the Global Moran's I and the Local Moran's I used to evaluate spatial association in the number of deaths and infections by COVID-19, and a spatio-temporal Poisson scan statistic used to identify emerging or “alive” clusters of infections by Sars-Cov-2 in space and time. As of January 2021 vaccination against COVID-19 already started, since the use of spatial clustering methods to identify non-vaccinated populations is not new among studies on vaccination coverage strategies, this paper also aims to discuss the implementation of spatial and spatio-temporal clustering methods in early vaccination.

Epidemiology and Regional Predictors of COVID-19 Clusters: A Bayesian Spatial Analysis Through a Nationwide Contact Tracing Data.

Purpose: Revealing the clustering risks of COVID-19 and prediction is essential for effective quarantine policies, since clusters can lead to rapid transmission and high mortality in a short period. This study aimed to present which regional and social characteristics make COVID-19 cluster with high risk.Methods: By analyzing the data of all confirmed cases (14,423) in Korea between January 10 and August 3, 2020, provided by the Korea Disease Control and Prevention Agency, we manually linked each case and discovered clusters. After classifying the cases into clusters as nine types, we compared the duration and size of clusters by types to reveal high-risk cluster types. Also, we estimated odds for the risk factors for COVID-19 clustering by a spatial autoregressive model using the Bayesian approach.Results: Regarding the classified clusters (n = 539), the mean size was 19.21, and the mean duration was 9.24 days. The number of clusters was high in medical facilities, workplaces, and nursing homes. However, multilevel marketing, religious facilities, and restaurants/business-related clusters tended to be larger and longer when an outbreak occurred. According to the spatial analysis in COVID-19 clusters of more than 20 cases, the global Moran's I statistics value was 0.14 (p < 0.01). After adjusting for population size, the risks of COVID-19 clusters were related to male gender (OR = 1.29) and low influenza vaccination rate (OR = 0.87). After the spatial modeling, the predicted probability of forming clusters was visualized and compared with the actual incidence and local Moran's I statistics 2 months after the study period.Conclusions: COVID-19 makes different sizes of clusters in various contact settings; thus, precise epidemic control measures are needed. Also, when detecting and screening for COVID-19 clusters, regional risks such as vaccination rate should be considered for predicting risk to control the pandemic cost-effectively.

Economic impact of targeted government responses to COVID-19: Evidence from the large-scale clusters in Seoul

We estimate the economic impact of South Korea's targeted responses to the large-scale COVID-19 clusters in a highly concentrated business area (Guro) and a highly concentrated entertainment area (Itaewon) in Seoul, respectively. We find that foot traffic and retail sales decreased only within a 300 m radius and recovered to their pre-outbreak level after four weeks in the case of the Guro cluster. The reductions appear to be driven by temporary business closures rather than by citizens' risk avoidance behavior. However, the adverse economic impacts measured by foot traffic and retail sales of another outbreak of the COVID-19 cluster in Itaewon were persistent. Our results imply that the effects of less intense but more targeted COVID-19 interventions, such as pinpointed, temporary closures of businesses, can differ by underlying geographical characteristics.

A sensitive, simple, and low-cost method for COVID-19 wastewater surveillance at an institutional level

SARS-CoV-2 is a respiratory virus, but it is also detected in a significant proportion of fecal samples from COVID-19 cases. Recent studies have shown that wastewater surveillance can be a low-cost tool compared to massive diagnostic testing for tracking COVID-19 outbreaks in communities, but most studies have focused on sampling from wastewater treatment plants. Institutional level wastewater surveillance may serve well for early warning purposes because specific geographic areas/populations with emerging cases can be tracked and immediate action can be executed in the event of a positive wastewater signal. In this study, a novel Moore swab method was developed and used for wastewater surveillance of COVID-19 at an institutional level. Of the 442 swab samples tested, 148 (33.5%) swabs collected from the three campuses and two buildings were positive for SARS-CoV-2 RNA. Further study of the quarantine building with a known number of cases indicated that this method was sensitive enough to detect few cases in the building. In addition, comparison between grab samples and Moore swab samples from the hospital sewage line indicated that Moore swabs were more sensitive than grab samples and offer a simple, inexpensive method for obtaining a composite sample of virus in wastewater over a 24–48 h period. These results suggest that collection and analyses of Moore swabs for SARS-CoV-2 RNA detection is a sensitive, low-cost, and easy to use tool for COVID-19 surveillance that is useful for institutional settings and could be deployed in low-resource settings to identify emerging COVID-19 clusters in communities.

Identification of Workers at Increased Risk of Infection During a COVID-19 Outbreak in a Meat Processing Plant, France, May 2020

On 13 May 2020, a COVID-19 cluster was detected in a French processing plant. Infected workers were described. The associations between the SARS-CoV-2 infection and the socio-demographic and occupational characteristics were assessed in order to implement risk management measures targeting workers at increased risk of contamination. Workers were tested by RT-PCR from samples taken during screening campaigns. Workers who tested positive were isolated and their contacts were quarantined. Workers were described and associations with the SARS-CoV-2 infection were assessed through risk ratios using multivariable Poisson regression. Of the 1347 workers, 87.5% were tested: 140 cases were identified; 4 were hospitalised, including 2 admitted to intensive care. In the company, the cluster remained limited to deboning and cutting activities. The attack rate was 11.9% in the company, reaching 16.6% in the cutting department. Being an employee of a subcontractor significantly increased the risk of infection by 2.98 [1.81–4.99]. In the cutting department, an association with virus infection was found for a group of non-French speaking workers from the same Eastern European country (RR = 2.67 [1.76–4.05]). They shared accommodation or carpooled more frequently than the other cases. The outbreak investigation revealed a significantly increased risk of SARS-CoV-2 infection for workers of subcontractors and some foreign-born workers. There are many such populations in meat processing plants; the observed associations and the ways in which these workers are contaminated need to be confirmed by further work. Prevention campaigns should now target these workers. Environmental risk factors in the workplace setting remain to be clarified.

COVID-19 hotspots through clusters analysis in France (may\u2013October 2020): where should we track the virus to mitigate the spread?

BackgroundIn France, the lifting of the lockdown implemented to control the COVID-19 first wave in 2020 was followed by a reinforced contact-tracing (CT) strategy for the early detection of cases and transmission chains. We developed a reporting system of clusters defined as at least three COVID-19 cases, within seven days and belonging to the same community or having participated in the same gathering, whether they know each other or not. The aim of this study was to describe the typology and criticality of clusters reported between the two lockdowns in France to guide future action prioritisation.MethodsIn this study we describe the typology and criticality of COVID-19 clusters between the two lockdowns implemented in France (between May and end of October 2020). Clusters were registered in a national database named “MONIC” (MONItoring des Clusters), established in May 2020. This surveillance system identified the most affected communities in a timely manner. A level of criticality was defined for each cluster to take into consideration the risk of spreading within and outside the community of occurrence, and the health impact within the community. We compared the level of criticality according to the type of community in which the cluster occurred using Pearson’s chi-square tests.ResultsA total of 7236 clusters were reported over the study period, particularly in occupational environment (25.1%, n = 1813), elderly care structures (21.9%, n = 1586), and educational establishments (15.9%, n = 1154). We show a shift over time of the most affected communities in terms of number of clusters. Clusters reported in occupational environment and the personal sphere had increased during summer while clusters reported in educational environment increased after the start of the school year. This trend mirrors change of transmission pattern overtime according to social contacts. Among all reported clusters, 43.1% had a high level of criticality with significant differences between communities (p < 0.0001). A majority of clusters had a high level of criticality in elderly care structures (82.2%), in disability care centres (56.6%), and health care facilities (51.7%).ConclusionThese results highlight the importance of targeting public health action based on timely sustained investigations, testing capacity and targeted awareness campaigns. The emergence of new SARS-CoV-2 variants strengthen these public health recommendations and the need for rapid and prioritise vaccination campaigns.

Geographically masking addresses to study COVID-19 clusters

ABSTRACT The spatial analysis of health data usually raises geoprivacy issues. Due to the virulence of COVID-19, scientists and crisis managers do need to analyze the distribution and spread of the disease with spatially precise data. In particular, it is useful to locate each case on a map to identify clusters of cases. To allow such analyses without breach of geoprivacy, geomasking techniques are necessary. This paper experiments with the geomasking techniques from the literature to solve this problem: masking the real address of positive cases while preserving the local spatial cluster patterns. In particular, two different approaches based on aggregation and perturbation are adapted to the geomasking of addresses in areas with different densities of population. A new simulated cluster crowding method is also proposed to preserve clusters as much as possible. The results show that geomasking techniques can spatially anonymize addresses while preserving clusters, and the best geomasking method depends on the use of the anonymized data.

Knowledge, Attitude, and Behavior of Parents toward School Reopening Amidst Coronavirus Disease 2019 Pandemic in Indonesia

Objectives: Recently, the subject of reopening schools has raised several concerns, especially on the possibility of new COVID-19 cluster cases. The parent's role is essential in this matter. Therefore, this study aims to understand the parents' knowledge, attitude, and behavior toward COVID-19 in Indonesia.&#x0D; Methods: A cross-sectional study was conducted on 206 Indonesian parents. A 55-items questionnaire was used to assess the knowledge, attitude, and behavior of subjects. In this study, median split tests, Kruskal-Wallis, Cronbach's α, and Pearson correlation test were used.&#x0D; Results: Subjects were found to have high levels of knowledge, especially in the knowledge of COVID-19 as a contagious viral disease (98.5%), and the necessity for children to maintain personal hygiene to prevent COVID-19 (98.5%). Subjects' attitudes were highest at understanding how to prevent COVID-19 (95.1%) and teaching children how to avoid COVID-19 (98.1%). Subjects' attitude was lowest at hearing the news of the death of COVID-19 scares them (72.8%), and on how their child(ren) are getting bored at home because of COVID-19 (69.9%). Subjects' are shown to have high levels of behavior in every question. This study found a significant correlation between socioeconomic status and subjects' knowledge (p = 0.007).&#x0D; Conclusion: This study revealed a lack of understanding of prevention strategies among low-income households. Hence, there is a need for targeted messaging and health education in low-income settings that consider the challenges they may face when attempting their children to practice COVID-19 prevention if schools reopen.&#x0D; Keywords: COVID-19, Knowledge, Attitude, Behavior, Parents, Indonesia

Detection of Spatiotemporal Clusters of COVID-19-Associated Symptoms and Prevention Using a Participatory Surveillance App: Protocol for the @choum Study.

BackgroundThe early detection of clusters of infectious diseases such as the SARS-CoV-2–related COVID-19 disease can promote timely testing recommendation compliance and help to prevent disease outbreaks. Prior research revealed the potential of COVID-19 participatory syndromic surveillance systems to complement traditional surveillance systems. However, most existing systems did not integrate geographic information at a local scale, which could improve the management of the SARS-CoV-2 pandemic.ObjectiveThe aim of this study is to detect active and emerging spatiotemporal clusters of COVID-19–associated symptoms, and to examine (a posteriori) the association between the clusters’ characteristics and sociodemographic and environmental determinants.MethodsThis report presents the methodology and development of the @choum (English: “achoo”) study, evaluating an epidemiological digital surveillance tool to detect and prevent clusters of individuals (target sample size, N=5000), aged 18 years or above, with COVID-19–associated symptoms living and/or working in the canton of Geneva, Switzerland. The tool is a 5-minute survey integrated into a free and secure mobile app (CoronApp-HUG). Participants are enrolled through a comprehensive communication campaign conducted throughout the 12-month data collection phase. Participants register to the tool by providing electronic informed consent and nonsensitive information (gender, age, geographically masked addresses). Symptomatic participants can then report COVID-19–associated symptoms at their onset (eg, symptoms type, test date) by tapping on the @choum button. Those who have not yet been tested are offered the possibility to be informed on their cluster status (information returned by daily automated clustering analysis). At each participation step, participants are redirected to the official COVID-19 recommendations websites. Geospatial clustering analyses are performed using the modified space-time density-based spatial clustering of applications with noise (MST-DBSCAN) algorithm.ResultsThe study began on September 1, 2020, and will be completed on February 28, 2022. Multiple tests performed at various time points throughout the 5-month preparation phase have helped to improve the tool’s user experience and the accuracy of the clustering analyses. A 1-month pilot study performed among 38 pharmacists working in 7 Geneva-based pharmacies confirmed the proper functioning of the tool. Since the tool’s launch to the entire population of Geneva on February 11, 2021, data are being collected and clusters are being carefully monitored. The primary study outcomes are expected to be published in mid-2022.ConclusionsThe @choum study evaluates an innovative participatory epidemiological digital surveillance tool to detect and prevent clusters of COVID-19–associated symptoms. @choum collects precise geographic information while protecting the user’s privacy by using geomasking methods. By providing an evidence base to inform citizens and local authorities on areas potentially facing a high COVID-19 burden, the tool supports the targeted allocation of public health resources and promotes testing.International Registered Report Identifier (IRRID)DERR1-10.2196/30444

The important role of Type A Hospital in efforts to reduce the covid-19 cluster by improving the dimension of service quality in building satisfaction and loyalty of covid-19 patients in Surabaya

COVID-19 in Surabaya has reached 20,832 cases. Of these, 19,270 people have recovered, and 1,313 people have died and 249 people are being treated on the Surabaya COVID-19 Response website. In addition, the Surabaya City Government is also still working continuously in the vaccination process which until now recorded 34,869 people have been vaccinated in the first stage. Therefore, this study was conducted to know the development of health services hospital type A referral COVID-19 in Surabaya. The purpose of this study is to evaluate and analyze behavioral intentions of COVID-19 survivors at COVID-19 referral type A hospitals in Surabaya. Based on the purpose of the study, the expected benefit of this study is to increase knowledge in the field of management, in this case related to variables Technical Quality, Infrastructural Quality, Interactional Quality, Personnel Quality, Social Support Quality, Patient Satisfaction, and Behavioral Intention. This research is a causal study with quantitative research methods. The data analysis process uses the process of validity test analysis, reliability test, classical assumption test using SPSS 22.0 software. The data collection in this study was conducted by spreading questionnaires through google form to more than 140 respondents with characteristics who areCOVID-19 survivors at COVID-19 referral type A Hospital in Surabaya.

Prediction of epidemics dynamics on networks with partial differential equations: A case study for COVID-19 in China* *Project supportedby the National Natural Science Foundation of China (Grant Nos. 61672298, 61873326, and 61802155) and the Philosophy Social Science Research Key ProjectFund of Jiangsu University (Grant No. 2018SJZDI142).

Since December 2019, the COVID-19 epidemic has repeatedly hit countries around the world due to various factors such as trade, national policies and the natural environment. To closely monitor the emergence of new COVID-19 clusters and ensure high prediction accuracy, we develop a new prediction framework for studying the spread of epidemic on networks based on partial differential equations (PDEs), which captures epidemic diffusion along the edges of a network driven by population flow data. In this paper, we focus on the effect of the population movement on the spread of COVID-19 in several cities from different geographic regions in China for describing the transmission characteristics of COVID-19. Experiment results show that the PDE model obtains relatively good prediction results compared with several typical mathematical models. Furthermore, we study the effectiveness of intervention measures, such as traffic lockdowns and social distancing, which provides a new approach for quantifying the effectiveness of the government policies toward controlling COVID-19 via the adaptive parameters of the model. To our knowledge, this work is the first attempt to apply the PDE model on networks with Baidu Migration Data for COVID-19 prediction.

Population Density and Covid Spatial Dynamics: A Critical Assessment of Indian Districts

In this article, a critical assessment of urban density and Covid-19 incidences in Indian cities is explored. The top hundred Covid-19 reported districts are analysed. The ArcGIS 10.1 statistical tool Getis-Ord Gi* is used in the identification of statistically significant Covid-19 clusters across India. Attempts are made to empirically establish the correlation between the urban density, the number of reported cases, and their possible impact on health infrastructure in general and planning in specific. Based on the results from 164 out of 693 district datasets, analyses have shown high positive spatial autocorrelation, which is more than 24% of the districts analysed. Further, the results show that southern districts are more affected than the Central and northern districts of India. Although a positive association between reported cases and the urban density was found, in high-density urban areas, the relationship with infection rate varied, which should be looked at together with other variables such as people’s activities and behaviours.

First known COVID-19 case and contact tracing efforts in İstanbul, Turkey

Background/aim COVID-19 has now become a global pandemic. Understanding the routes of transmission is vital in the mitigation and suppression of the disease. İstanbul has become one of the disease’s epicenters. This study aims to describe the first COVID-19 case and contact tracing efforts around it in İstanbul.Materials and methods The descriptive study was conducted in İstanbul, Turkey. The first COVID-19 cases and those associated with them were investigated with contact tracing, and primary and secondary cases were described. Results The source case was an individual who returned to Turkey from international travel at the beginning of March and tested PCR (–). The index case is the brother of the source case and is considered the first PCR (+) case diagnosed in İstanbul. Contact tracing revealed 23 PCR (+) cases, 14 of which resulted in hospitalization and three deaths.ConclusionsThis study described cases of the first COVID-19 cluster in İstanbul. Moreover, contact tracing was used in this first cluster. This contributed to contact tracing algorithms in Turkey.

Detection of a COVID-19 cluster by universal SARS-CoV-2 testing in a geriatric hospital and implications for infection control management.

Hospital-level COVID-19 testing strategies may require rapid changes depending on the dynamics of local epidemiology. A COVID-19 outbreak in a hospital near the authors' institution, a Geriatric Medicine centre in Berlin, Germany, prompted the implementation of comprehensive SARS-CoV-2 testing. Serial nasopharyngeal swabs were collected from all patients and staff and analysed for SARS-CoV-2 virus RNA using quantitative real-time PCR. In total, 11 of 84 patients (13.1%) and 1 of 147 employees (0.68%) tested positive. Of the 11 positive patients, 9 (81.8%) were asymptomatic at the time of testing. The data were used as the basis for adjusting local COVID-19 containment measures. Only one staff-to-staff transmission was observed, and there was no patient-to-patient, patient-to-staff or staff-to-patient transmission. Three patients infected with SARS-CoV2 died, two deaths being attributable to advanced malignant disease and one to COVID-19-associated myocarditis. In conclusion, universal SARS-CoV-2 testing revealed a cluster of predominantly asymptomatic infections, enabled appropriate local COVID-19 containment decisions and presumably contributed to the prevention of in-hospital SARS-CoV-2 transmission.

Clustering of COVID-19 data for knowledge discovery using c-means and fuzzy c-means

In this work, the partitioning clustering of COVID-19 data using c-Means (cM) and Fuzy c-Means (Fc-M) algorithms is carried out. Based on the data available from January 2020 with respect to location, i.e., longitude and latitude of the globe, the confirmed daily cases, recoveries, and deaths are clustered. In the analysis, the maximum cluster size is treated as a variable and is varied from 5 to 50 in both algorithms to find out an optimum number. The performance and validity indices of the clusters formed are analyzed to assess the quality of clusters. The validity indices to understand all the COVID-19 clusters' quality are analysed based on the Zahid SC (Separation Compaction) index, Xie-Beni Index, Fukuyama–Sugeno Index, Validity function, PC (performance coefficient), and CE (entropy) indexes. The analysis results pointed out that five clusters were identified as a major centroid where the pandemic looks concentrated. Additionally, the observations revealed that mainly the pandemic is distributed easily at any global location, and there are several centroids of COVID-19, which primarily act as epicentres. However, the three main COVID-19 clusters identified are 1) cases with value <50,000, 2) cases with a value between 0.1 million to 2 million, and 3) cases above 2 million. These centroids are located in the US, Brazil, and India, where the rest of the small clusters of the pandemic look oriented. Furthermore, the Fc-M technique seems to provide a much better cluster than the c-M algorithm.

In-flight transmission of wild-type SARS-CoV-2 and the outbreak potential of imported clusters of COVID-19: a review of published evidence

International air travel has been highlighted as a concern since the beginning of the COVID-19 pandemic with respect to importation of cases. We summarise the available evidence for in-flight transmission of wild type SARS-CoV-2 during 2020, and for imported COVID-19 clusters to cause outbreaks. This paper provides a data baseline prior to the emergence of new mutations causing SARS-CoV-2 variants of concern, whose characteristics may increase the potential risk of in-flight transmission and imported outbreaks. The evidence on in-flight transmission of wild-type SARS-CoV-2 is limited, and is described in a small number of published reports. Most of the available evidence pertains to the early phase of the COVID-19 pandemic, during a period without non-pharmaceutical interventions such as distancing and in-flight mask wearing. There is considerable potential for outbreaks of COVID-19 from imported cases or clusters when public health guidance around quarantine of travellers and self-isolation of cases is not adhered to. Risks can be mitigated by measures such as: avoiding non-essential travel, targeted testing and quarantine of travellers from high incidence regions or regions of concern, managed quarantine processes, and protocols for rapid investigation and control of transmission from a possible variant of concern. Measures should be dynamically assessed and proportionate to the level of risk.Graphical abstract

Modifications to the delivery of NHS face-to-face general practice consultations during the COVID-19 pandemic in England

Background: To minimise transmission of SARS-CoV-2, the virus causing COVID-19, delivery of English general practice consultations was modified in March 2020 to enable separation of diagnosed or suspected COVID-19 patients from others. Remote triage and consultations became the default, with adapted face-to-face contact used only when clinically necessary. This study aimed to identify the modified face-to-face delivery models used nationwide in spring/summer 2020. Information was also sought concerning COVID-19 outbreaks linked to English general practice. Methods: In June 2020, a survey was sent by email to the 135 Clinical Commissioning Groups (CCGs) in England to identify local organisation of face-to-face general practice consultations since March 2020. An email was sent to Public Health England (PHE) requesting data on COVID-19 outbreaks linked to general practice. Results: All CCGs responded. Between March and July 2020, separation of COVID-19 patients from others was achieved using combinations of the following models: zoned surgeries (reported by 47% of CCGs), where COVID-19 and other patients were separated within their own practice;‘hot’ or ‘cold’ hubs (reported by 90% of CCGs), separate sites where COVID-19 or other patients registered at one of several collaborating practices were seen;‘hot’ and ‘cold’ home visits (reported by 70% of CCGs). One of seven combinations of these models was used across each CCG, with some flexibility shown according to changing demand through hub availability. PHE data indicated 25 possible or confirmed COVID-19 outbreaks or clusters in English general practice to July 31st 2020. Conclusions: Varied, flexible ways of delivering face-to-face general practice consultations were identified. Analysis of the modified delivery in terms of management of COVID-19 and other conditions, and impacts on staff and patients, together with learning from investigations into confirmed COVID-19 outbreaks, may both aid future pandemic management and identify beneficial elements for practice beyond this.

Sustained seroprevalence of SARS-CoV-2 antibodies one year after infection: one of the first COVID-19 cluster cases in Bosnia and Herzegovina.

Severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) is a novel virus that has been identified as a causal agent of COVID-19, an emergent infectious disease which brought about a new pandemic in the 21st century. The immune responses and clinical features of individuals infected with SARS-CoV-2 have not yet been fully described. Thus, in this study, we compare the seroprevalence and define the correlation between symptoms and serological results in the first COVID-19 cluster in the city of Konjic, Bosnia, and Herzegovina. Of the total number, 93% of real-time polymerase chain reaction-positive participants had positive immunoglobulin G (IgG) serology and 75% of them developed symptoms of COVID-19. We found that there was no significant alteration in specific IgG (p = 0.504) antibody levels during the 1-year period after COVID-19. Our results indicate that symptomatic COVID-19 patients have a higher rate of seroconversion (p < 0.01). The IgG seroconversion was correlated with high fever (p = 0.002) and headache (p = 0.007), suggesting that these symptoms could be considered as indicators of a better immune response. This study has demonstrated persistence of sustained levels of specific SARS-CoV-2 antibodies after recovering from COVID-19 infection. However, to gain a better insight into the immune response to SARS-CoV-2, further systematic studies should be focused on quality and longevity analyses.

COVID-19 in Workplaces: Secondary Transmission.

Coronavirus disease (COVID-19) contagion at work is well studied for healthcare workers, however it is not enough assessed in other working settings. Very little is known, indeed, about the risk of COVID-19 transmission through occupational exposure in non-health working places. This study aims to describe a COVID-19 cluster among workers in an office in Italy. This was a retrospective observational study on a cluster of COVID-19 that occurred from 20 November through 3 December 2020 in a group of six colleagues (A-F) working in the same office full time 5 days a week, 8 h a day. The workers used the following prevention measures: social distancing (desks were >1 m, 1.76-5.01 m range), plexiglas panels, hands disinfection, and use of face mask. However, they did not wear face mask when in static position sitting at their desk and they did not aerate the place frequently. The disease spread from one worker (subject A) to four (80%) of the five colleagues (subjects B-F). Only subject D was negative to COVID-19 on 14 days after last contact with subject A (20 November 2020) as confirmed by nasopharyngeal swab testing. Subject D, in particular, did not contact subject A in the 48 h before symptoms onset. COVID-19 positivity of subject A was promptly communicated to the colleagues, who started self-isolation from their relatives and none of their households were infected. COVID-19 transmission was observed only in households of subject A. The rapid communication of COVID-19 positivity to the colleagues and the prompt isolation of index case's close contacts allowed to eliminate the secondary transmission to their households. The contagion of index case's colleagues occurred from second day before symptoms onset. Distancing of >1 m, use of plexiglass panels, sanitizing hand gel, and inconstant use of face mask may not be enough for infection prevention in closed places with poor ventilation and high occupancy.

The re-emergence from the COVID-19 epidemic of Beijing Xinfadi Market.

To provide references for global pandemic prevention and control, this study aimed to analyze the epidemiological characteristics and clinical manifestations of 103 new confirmed cases between June 12 and June 15, 2020, in Beijing. All confirmed cases in this study were tested with a positive SARS-CoV-2 reverse transcriptase-polymerase chain reaction and extracting data from the Beijing Municipal Health Commission (June 11 to July 6, 2020). We selected the 103 typical confirmed cases (excluding imported cases) between June 12 and June 15 for statistical analysis and explored differences among different clinical cases. A cluster of COVID-19 was reported in Beijing between June 12 and June 15, 2020, involving 103 confirmed cases. Patients aged 21 to 65 years old and the mean age was 42.38 ± 11.507, the male-to-female sex ratio was 1.40:1. All confirmed cases had a direct or indirect exposure history in the Beijing Xinfadi Market (BXM), and the clinical manifestations of 97% confirmed cases was diagnosed as mild or moderate. Different clinical classification in age (P = .041), exposure history (P = .025), fever (P = .020), and cough (P = .000) were the statistically significant difference, but there was no statistically significant difference in gender (P = .501), the type of diagnosis (P = .478), expectoration (P = .979), fatigue (P = .906), dizziness or headache (P = .848), muscle pain (P = .825), sore throat or throat discomfort (P = .852), chills (P = .933), diarrhea (P = .431) and runny nose or nasal congestion (P = .898). This study shows that Beijing's epidemic scope was mainly concentrated in the Xinfadi Market. The initial cases were epidemiologically related to the BXM, the clinical classification of most cases was mild and moderate, and the differences in age, exposure history, fever, and cough among different clinical cases were statistically significant.