Novel social distancing detector using local binary pattern in comparison with principal component analysis to improve accuracy

The objective is to detect Novel Social Distancing using Local Binary Pattern (LBP) in comparison with Principal Component Analysis (PCA). Social Distance deduction is performed using Local Binary Pattern(N=20) and Principal Component Analysis(N=20) algorithms. Google AI open Images dataset is used for image detection. Dataset contains more than 10,000 images. Accuracy of Principal Component Analysis is 89.8% and Local Binary Pattern is 93.9%. There exists a statistical significant difference between LBP and PCA with (p<0.05). Local Binary Pattern appears to perform significantly better than Principal Component Analysis for Social Distancing Detection. © 2023 Author(s).

Understanding the Impact Mechanism of User Behavior Pattern toward Real Estate APP Platforms in the Post-COVID Era: A Quantitative Analysis

While technology factors are the main driver of the booming real estate APP platforms with important implications for user behavior pattern during and post-pandemic contexts, there is a lack of adequate research. In response, this study explores the user behavior pattern of real estate APP platforms to promote user mental health by taking the real estate APP platforms users as the participants based on theory of technology readiness and acceptance model. Data collected from offline surveys are analyzed using PLS-SEM. The results reveal the technology readiness index positively affects individuals' perceived usefulness and satisfaction, ultimately positively affects individuals' continuance intention with real estate APP platforms;satisfaction with real estate APP platforms mediated the relationship between technology readiness index, perceived ease of use, perceived usefulness, and individuals' continuance intention with real estate APP platforms. However, the group comparison finds no significant difference in user behavior patterns by gender. The contribution of this study is to reveal the influence mechanisms of digital technology on users' behavioral patterns toward real estate APP platforms, which can help guide the sustainable development of real estate APP platforms and promote user mental health and wellbeing in the post-COVID era.

Impact of the new corona virus (SARS-CoV-2) pandemic on climateric women's health: websurvey

The COVID-19 pandemic has impacted society: causing the collapse of health systems around the world, and also had a significant impact on the economy, personal care, mental health and the quality of life of the population. Few studies have been done about pandemic and the climacteric population, and the impact on quality of life and health. Our objective was to Investigate changes in the health and health care of climacteric women residing in Brazil during the pandemic period. Cross-sectional study with climacteric women aged between 40 and 70 years, residing in Brazil. The evaluation was carried out using a Google Docs electronic form with questions related to sociodemographic, clinical, gynecological data, treatments, access to health services and consultations, as well as changes in behavior. The Menopause Rating Scale - MRS was applied to assess climacteric symptoms, validated for Portuguese. Result(s): 419 women answered the questionnaire. More than 45% were between 51 and 60 years of age, 56.6% being married and residing in Brazilian capitals. 60% of participants reported weight gain during the pandemic. 50.8% of participants reported a decrease in the weekly practice of physical activity More than 80% reported worsening mental health during this period, and 66.1% had a change in their sleep pattern. More than half reported having difficulty accessing gynecological consultations. Women living in capital cities reported a greater increase in alcohol consumption (p=0.002). Food intake increased for 54.9%;the category of civil servant was associated with a significant increase in consumption in relation to other professions (p=0.038). Women whose family incomes changed during the pandemic had a higher prevalence of weight gain (p=0.033) and also had a higher occurrence of changes in sleep quality (72.6% vs. 61.5%;p=0.018). Women with a high school education had a higher occurrence of alterations in personal and health care outcomes (p<0.001). Conclusion(s): We observed an important reduction in the health care of climacteric women during the pandemic period. Changes in life habits, such as increased food consumption and reduced physical activity, were quite prevalent. There was a deterioration in mental health, with a high prevalence of anxiety symptoms and changes in sleep quality. Despite the attenuation of the pandemic, attention should be given to the health care of this population, as the changes may have repercussions for many years.Copyright © 2023

Spatiotemporal Modeling of the Association between Neighborhood Factors and COVID-19 Incidence Rates in Scotland

This study aims to investigate the association between neighborhood-level factors and COVID-19 incidence in Scotland from a spatiotemporal perspective. The outcome variable is the COVID-19 incidence in Scotland. Based on the identification of the wave peaks for COVID-19 cases between 2020 and 2021, confirmed COVID-19 cases in Scotland can be divided into four phases. To model the COVID-19 incidence, sixteen neighborhood factors are chosen as the predictors. Geographical random forest models are used to examine spatiotemporal variation in major determinants of COVID-19 incidence. The spatial analysis indicates that proportion of religious people is the most strongly associated with COVID-19 incidence in southern Scotland, whereas particulate matter is the most strongly associated with COVID-19 incidence in northern Scotland. Also, crowded households, prepandemic emergency admission rates, and health and social workers are the most strongly associated with COVID-19 incidence in eastern and central Scotland, respectively. A possible explanation is that the association between predictors and COVID-19 incidence might be influenced by local context (e.g., people's lifestyles), which is spatially variant across Scotland. The temporal analysis indicates that dominant factors associated with COVID-19 incidence also vary across different phases, suggesting that pandemic-related policy should take spatiotemporal variations into account. © 2023 by American Association of Geographers.

The Study of the Correlation Betweenthe on the Diet, Sleep Quality and Perceived Stress among College Students in Taichung during the COVID-19 Pandemic Distance Learning.

Research indicates the COVID-19 epidemic changes people's health and diet, However, this has not yet been well discussed in Taiwan, especially in college students. Therefore, the purpose of this study is to investigate the impact of distance learning on college students' dietary patterns, sleep quality and perception of stress during the COVID-19 epidemic in Taiwan. 265 college students from a university in Taichung were recruited in this study. The self-administered online questionnaire was used to investigate the changes in eating behavior, sleep quality, and perception of stress before and one month after distance learning, and further analyzed the relationship among them. The questionnaire contains demographic information, dietary questionnaires (including six categories of food intake behaviors, convenience food intake frequency), the Pittsburgh Sleep Quality Index (PSQI) and Perceived Stress Scale (Chinese 14-item PSS). The results showed that the proportion of college students to meet the recommended Taiwan Dietary Guidelines amount in vegetables (21.9%), fruits (27.5%), meats and dairy products (15.8%), and nuts and seeds (11.3%) were lower during distance learning. The frequency of convenient food intake was lower during distance learning (13.31 +/- 6.10 points;never to occasionally). During the distance learning period, there was a significant negative correlation between dietary patterns and sleep quality (r = -0.160, p = 0.009), It shows that college students with higher dietary pattern scores have better sleep quality. During the distance learning period, there was a significant positive correlation between sleep quality and perceived stress (r = 0.320, p < 0.001), It shows that college students with higher levels of stress had poorer sleep quality. This study found that the lower the perceived stress of college students, the better their diet and sleep quality;conversely, the higher the perceived stress, the worse their diet and sleep quality. Studies have shown that a healthy, balanced diet can reduce the risk of getting various diseases. Therefore, in the post-epidemic era, it is recommended that schools increase the accessibility and availability of vegetables, fruits, dairy products, nuts and seeds on campus to make it easier for teachers and students to obtain such healthy food in order to achieve the goal of promoting balanced diet.Copyright © 2022 Nutrition Society in Taipei. All rights reserved.

A Review on the analysis of Behavioral pattern of Students during pandemic COVID-19

The widespread of (covid-19) has become the major reason for many physical illnesses in addition to psychological encounters to the whole world. The psychological challenges brought in due to the Covid-19 pandemic have resulted in decrease in the learning curve of students to a very large extent risking the academic ability of students due to psychological/mental health. Hence it is a challenge to identify valid cues for disorientation in the learning ability of the student at the right time and to suggest necessary support and guidance. This paper aims to describe about the work done so far and analyzes the future challenges to be addressed based on the learning curve of a student and gives an insight of how a student can be identified to be psychologically disturbed. © 2023 IEEE.

ASSOCIATION RULE MINING FOR PREDICTION OF COVID-19

COVID-19 is a raging pandemic that has created havoc with its impact ranging from loss of millions of human lives to social and economic disruptions of the entire world. Therefore, error-free prediction, quick diagnosis, disease identification, isolation and treatment of a COVID patient have become extremely important. Nowadays, mining knowledge and providing scientific decision making for diagnosis of diseases from clinical datasets has found wide-ranging applications in healthcare sector. In this direction, among different data mining tools, association rule mining has already emerged out as a popular technique to extract invaluable information and develop important knowledge-base to help in intelligent diagnosis of distinct diseases quickly and automatically. In this paper, based on 5434 records of COVID cases collected from a popular data science community and using Rapid Miner Studio software, an attempt is put forward to develop a predictive model based on frequent pattern growth algorithm of association rule mining to determine the likelihood of COVID-19 in a patient. It identifies breathing problem, fever, dry cough, sore throat, abroad travel and attended large gathering as the main indicators of COVID-19. Employing the same clinical dataset, a linear regression model is also proposed having a moderately high coefficient of determination of 0.739 in accurately predicting the occurrence of COVID-19. A decision support system can also be developed using the association rules to ease out and automate early detection of other diseases. © 2023 by the authors.

An Exploratory Study of Masked Face Recognition with Machine Learning Algorithms

Automated face recognition is a widely adopted machine learning technology for contactless identification of people in various processes such as automated border control, secure login to electronic devices, community surveillance, tracking school attendance, workplace clock in and clock out. Using face masks have become crucial in our daily life with the recent world-wide COVID-19 pandemic. The use of face masks causes the performance of conventional face recognition technologies to degrade considerably. The effect of mask-wearing in face recognition is yet an understudied issue. In this paper, we address this issue by evaluating the performance of a number of face recognition models which are tested by identifying masked and unmasked face images. We use six conventional machine learning algorithms, which are SVC, KNN, LDA, DT, LR and NB, to find out the ones which perform best, besides the ones which poorly perform, in the presence of masked face images. Local Binary Pattern (LBP) is utilized as the feature extraction operator. We generated and used synthesized masked face images. We prepared unmasked, masked, and half-masked training datasets and evaluated the face recognition performance against both masked and unmasked images to present a broad view of this crucial problem. We believe that our study is unique in elaborating the mask-aware facial recognition with almost all possible scenarios including half_masked-to-masked and half_masked-to-unmasked besides evaluating a larger number of conventional machine learning algorithms compared the other studies in the literature. © 2023 IEEE.

Change detection of travel behavior: a case study of COVID-19

Travel behavior is important in many fields, such as urban management and disaster management. Since the breakout of COVID-19, many people have changed their preference in travel, which is called travel behavior pattern, to respond to the impact of COVID-19. Understanding when, how, and why people change their travel behavior patterns is significant for antiepidemic and estimating the impact of COVID-19 on human society. However, most current studies ignore that travel behavior is multi-dimensions, and it can be a barrier to understanding travel behavior change. To fill up the vacuum of current research, we used an online Bayesian change detection method to detect individual travel behavior pattern change from big mobile trajectory data. For the low data quality problem caused by various and uneven, we design a novel Monte Carlo data grading framework to assess data quality and filter useable data and thus avoid unreliable results. The analysis result shows Tokyo experienced 6 phases of travel behavior change since 2020, and the change was driven by policies to some extent, especially in the frequency dimension and spatial dimension. Also, the correlation analysis indicates the correlation between four travel behavior dimension dimensions, and the infection number provides us with knowledge about how people will make a change in their travel behavior in the COVID-19 period. © 2023 Elsevier Inc. All rights reserved.

GIS-Based Analysis of the Spatial Distribution and Influencing Factors of Traditional Villages in Hebei Province, China

Traditional villages are a valuable cultural asset that occupy an important position in Chinese traditional culture. This study focuses on 206 traditional villages in Hebei Province and aims to explore their spatial distribution characteristics and influencing factors using ArcGIS spatial analysis. The analysis shows that traditional villages in Hebei Province were distributed in clusters during different historical periods, and eventually formed three core clusters in Shijiazhuang, Zhangjiakou and Xingtai-Handan after different historical periods. Moreover, the overall distribution of traditional villages in Hebei Province is very uneven, with clear regional differences, and most of them are concentrated in the eastern foothills of the Taihang Mountains. To identify the factors influencing traditional villages, natural environmental factors, socio-economic factors, and historical and cultural factors are considered. The study finds that socio-economic and natural environmental factors alternate in the spatial distribution of traditional villages in Hebei Province. The influence of the interaction of these factors increases significantly, and socio-economic factors have a stronger influence on the spatial distribution. Specifically, the spatial distribution of traditional villages in Hebei Province is influenced by natural environmental factors, while socio-economic factors act as drivers of spatial distribution. Historical and cultural factors act as catalysts of spatial distribution, and policy directions are external forces of spatial distribution. Overall, this study provides valuable insights into the spatial distribution characteristics and influencing factors of traditional villages in Hebei Province, which can be used to develop effective strategies for rural revitalisation in China.

Spatio-Temporal Evolution and Driving Factors of "Non-Grain Production” in Hubei Province Based on a Non-Grain Index

Non-grain production (NGP) on cultivated land has become a common phenomenon due to the prosperity of the rural economy and the optimisation of the agricultural structure. However, the excessive use of cultivating land for NGP has threatened food production and the sustainable use of cultivated land. To halt this trend and to ensure food security, the authors of this paper applied a novel non-grain index to measure NGP, which could reflect multiple NGP activities;designated Hubei Province as its object of research;and revealed NGP's spatio-temporal patterns of the past 30 years. We then assessed the characteristics of NGP based on spatial autocorrelation analysis, the Theil index, and geographically weighted regression. The results showed that the value of the non-grain index grew from 0.497 to 1.113 as NGP increased significantly in Hubei Province. The number of high-NGP counties increased, spatial agglomeration became obvious, and the eastern and western sides of Hubei Province witnessed an observable growth in NGP. As a result, the NGP in the eastern and western regions overtook production in the central region. Despite a series of historical subsidy policies and agricultural modernisation initiatives that promoted the planting of grain crops, the policy of "grain on valuable cultivated land” could be better implemented. We conclude by making some suggestions for reducing NGP and protecting cultivated land.

Automated flood inundation mapping over Ganga basin

The worldwide appearance of COVID-19 halted all activity and caused the longest statewide lockdown. These wreaked havoc on people's livelihoods. The July 2020 floods also caused severe challenges. It adds anguish to the lives of those seeking to regulate COVID-19. It reduces catastrophe risk in other industries. Real-time information from space-based sensors is needed for a quick response. Using a cloud-based platform like Google earth engine (GEE), SAR pictures are analyzed automatically. This research shows the possibilities of automated procedures and algorithms on cloud-based systems. The findings provide flood extent maps for the lower Ganga basin, in India. Severe floods affected a large population in Bihar and West Bengal. This research provides a rapid and exact estimate of flooded regions to aid in risk assessment, notably during COVID-19. © 2023 Elsevier Inc. All rights reserved.

Antiviral Effects of Fructans

Infectious diseases of viral origin have never received so much interest globally since the emergence of the COVID-19 pandemic disease. In contrast to bacterial infections, antibiotic treatments do not have any effect on viral infections, requiring alternative solutions to reduce the impact of viral spread on animal populations. More important than curing, preventing viral replication before disease development is probably the best strategy to minimalize the negative effects of viruses on a global scale. Fructans, known to stimulate the immune system (by either interacting directly or indirectly with the immune system), may be interesting candidates as part of this broader prevention strategy. This chapter discusses the potential antiviral properties of fructans in relation to their well-described immunomodulating, antioxidant and prebiotic attributes, as well as a possible role as protein binders which may disturb the proper function of viral proteins, and thus reduce the infection ability of certain viral strains. © 2023 Elsevier Inc. All rights reserved.

A system model for real-time monitoring and geospatial data for the simulation of surveillance of COVID-19 in Makassar, Indonesia

The rapid spread of COVID-19 requires rapid management. Prompt treatment is needed to prevent the spread of this disease, which could be minimized or isolated in one place so that it does not spread to other places. This study was conducted to discover a model of the surveillance system in real time and to analyze the change in its distribution pattern. This study was conducted in the city of Makassar, South Sulawesi, Indonesia, involving 30 volunteers. Two devices were used, the Internet reverse transcription loop-mediated isothermal amplification (iRTLAMP) and IoT button application, to provide spatial data in the form of patient points exposed to COVID-19. Furthermore, three scenarios were applied to see the pattern of data distribution. The data recorded in the cloud database were retrieved with a created application and then analyzed using Kernel Density Estimation (KDE) and Point Pattern Analysis (PPA) to observe the distribution of patterns in real time. The analysis utilizing KDE with the Gaussian kernel function as the kernel revealed significant changes in the probability distribution, which could be seen from color changes in the map. The centrographic analysis revealed that the mean and median points of the three scenarios changed in various ways within approximately 700 m to 1.7 km. Meanwhile, the radius of minimal bounding circle behaved similarly and appeared to change depending on the scenario, from a radius of 5.57 (initial) km to 6.55 km (scenario 1), 5.57 km (scenario 2) and 6.22 km (scenario 3). The standard distance also showed a change from 4.53 km to 4.60 km (scenario 1), 4.70 km (scenario 2) and 5.40 km (scenario 3). Simulations carried out using the developed system showed that the use of internet devices could help monitor people exposed to COVID-19 by changing patterns and distribution points. Therefore, decision makers could take preventive actions earlier so that this disease does not spread quickly. © 2023, Mahidol University - ASEAN Institute for Health Development. All rights reserved.

A Low-Cost Wireless Body Area Network for Human Activity Recognition in Healthy Life and Medical Applications

Moved by the necessity, also related to the ongoing COVID-19 pandemic, of the design of innovative solutions in the context of digital health, and digital medicine, Wireless Body Area Networks (WBANs) are more and more emerging as a central system for the implementation of solutions for well-being and healthcare. In fact, by elaborating the data collected by a WBAN, advanced classification models can accurately extract health-related parameters, thus allowing, as examples, the implementations of applications for fitness tracking, monitoring of vital signs, diagnosis, and analysis of the evolution of diseases, and, in general, monitoring of human activities and behaviours. Unfortunately, commercially available WBANs present some technological and economic drawbacks from the point of view, respectively, of data fusion and labelling, and cost of the adopted devices. To overcome existing issues, in this paper, we present the architecture of a low-cost WBAN, which is built upon accessible off-the-shelf wearable devices and an Android application. Then, we report its technical evaluation concerning resource consumption. Finally, we demonstrate its versatility and accuracy in both medical and well-being application scenarios. Author

Les expériences de gain-de-fonction des virus pathogènes émergents1

Résumé Dans la nature, des virus adaptés à la transmission circulent dans les espèces animales (chauves-souris, oiseaux, rongeurs, primates, etc.). Le franchissement de la barrière des espèces peut se faire par contamination d'autres espèces animales, dont l'homme. Des manipulations génétiques ont été réalisées sur des virus sauvages pour faciliter le passage interespèces et augmenter la virulence virale. Le but était d'identifier les gènes critiques pour la pathogénicité. Ces manipulations ont été réalisées sur des agents pathogènes potentiellement épidémiques, comme Myxovirus influenzae de la grippe aviaire et les coronavirus des épidémies de SRAS et de MERS. Ces expériences dangereuses ont fait l'objet d'un moratoire aux États-Unis (2014-2017). Trois ans après l'émergence du Covid-19, l'origine du SARS-CoV2 d'emblée très contagieux reste un mystère. Il existe deux scénarios pour expliquer son émergence. Les partisans de l'origine naturelle avancent que le virus de la chauve-souris aurait pu infecter directement l'homme, se propageant silencieusement à un faible niveau chez l'homme pendant des années, sans éliminer l'existence d'hôtes intermédiaires non détectés. Cela n'explique pas l'origine à Wuhan, loin des réservoirs naturels de virus. Le site furin serait apparu spontanément à partir d'autres coronavirus. Le scénario alternatif est celui d'un accident de laboratoire à Wuhan, après des expériences de gain-de-fonction à partir d'un SARS-like CoV, voire même la survenue d'une contamination humaine par un virus CoV sauvage recuilli sur le terrain, lors de cultures cellulaires ou des tests sur les animaux à Wuhan. Summary In nature, viruses are well-adapted to transmission in wild animal species (bats, birds, rodents, primates...). The crossing of the species barrier can be done by contamination of other animal species, including humans. Genetic manipulations have been carried out on wild viruses to facilitate interspecies passage and increase viral virulence. The aim was to identify genes critical for pathogenicity. These manipulations have been performed on potentially epidemic pathogens, such as Myxovirus influenzae from avian influenza and coronaviruses from the SARS and MERS epidemics. These dangerous experiments were placed under a moratorium in the United States (2014-2017). Three years after the emergence of Covid-19, the origin of the highly contagious SARS-CoV2 remains a mystery. There are two scenarios to explain its emergence. Proponents of the natural origin argue that the bat virus could have directly infected humans, spreading silently at a low level in humans for years, without eliminating the possibility of undetected intermediate hosts. The furin site would have appeared spontaneously from other coronaviruses. However, this does not explain the specific origin in Wuhan, far from natural virus reservoirs. The alternative scenario is that of a laboratory accident in Wuhan, after gain-of-function experiments with an SARS-like CoV, or even the occurrence of human contamination by a wild CoV virus collected in the field, during cell cultures or animal tests in Wuhan.

17th Annual Sickle Cell and Thalassaemia Conference

The proceedings contain 115 papers. The topics discussed include: clinical and genetic predictors of sickle cell nephropathy in Malawi;clinicohematological characteristics of iron deficiency anemia and hemoglobinopathies in Pakistan;an experience of non-hospital based laboratory;assessment of hematological parameters of petrol filling workers at petrol stations in Ethiopia: a comparative cross-sectional study;burden and risk factor to acute myocardial ischemia in children with sickle cell anemia;dyslipidemia in transfusion-dependent-thalassemia patients and its correlation with serum vitamin D level;impact of COVID-19 pandemic to pre-transfusion hemoglobin level and frequency of transfusion in transfusion-dependent thalassemia patients in Indonesia;retinopathy in Egyptian patients with sickle cell disease;and dietary pattern, socio-demographic characteristics and nutritional status of pregnant women attending Barau Dikko teaching hospital and the need to develop recommended dietary allowance and dietary reference intakes for sickle cell disease patients.

Infection metallomics for critical care in the post-COVID era.

Infection metallomics is a mass spectrometry (MS) platform we established based on the central concept that microbial metallophores are specific, sensitive, noninvasive, and promising biomarkers of invasive infectious diseases. Here we review the in vitro, in vivo, and clinical applications of metallophores from historical and functional perspectives, and identify under-studied and emerging application areas with high diagnostic potential for the post-COVID era. MS with isotope data filtering is fundamental to infection metallomics; it has been used to study the interplay between "frenemies" in hosts and to monitor the dynamic response of the microbiome to antibiotic and antimycotic therapies. During infection in critically ill patients, the hostile environment of the host's body activates secondary bacterial, mycobacterial, and fungal metabolism, leading to the production of metallophores that increase the pathogen's chance of survival in the host. MS can reveal the structures, stability, and threshold concentrations of these metal-containing microbial biomarkers of infection in humans and model organisms, and can discriminate invasive disease from benign colonization based on well-defined thresholds distinguishing proliferation from the colonization steady state.

The transmission pattern and clinical course of the first 417 patients with COVID-19 infection in Shenzhen, China.

OBJECTIVE: To explore the transmission patterns and clinical course of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that was first identified in Wuhan, China in December 2019 as clustered and non-clustered cases of coronavirus disease (COVID-19) emerged in Shenzhen, China. METHODS: This retrospective study included the patients that were confirmed by laboratory detection of SARS-CoV-2 in Shenzen between 19 January 2020 and 21 February 2020. Data on the epidemiological and clinical characteristics were analysed. The patients were divided into non-clustered and clustered groups. The time course, intervals between first and second COVID-19 cases and other transmission patterns were compared between the groups. RESULTS: The 417 patients were divided into clustered (n = 235) and non-clustered groups (n = 182). Compared with the non-clustered group, the clustered group had significantly more young (≤20 years) and old (>60 years) patients. The clustered group had significantly more severe cases (nine of 235; 3.83%) compared with the non-clustered group (three of 182; 1.65%). Patients with severe disease spent 4-5 more days of hospitalization than patients with moderate and mild disease. CONCLUSION: This retrospective study analysed the transmission patterns and clinical course of the first wave of COVID-19 infection in Shenzhen, China.

Superspreading, overdispersion and their implications in the SARS-CoV-2 (COVID-19) pandemic: a systematic review and meta-analysis of the literature.

BACKGROUND: A recurrent feature of infectious diseases is the observation that different individuals show different levels of secondary transmission. This inter-individual variation in transmission potential is often quantified by the dispersion parameter k. Low values of k indicate a high degree of variability and a greater probability of superspreading events. Understanding k for COVID-19 across contexts can assist policy makers prepare for future pandemics. METHODS: A literature search following a systematic approach was carried out in PubMed, Embase, Web of Science, Cochrane Library, medRxiv, bioRxiv and arXiv to identify publications containing epidemiological findings on superspreading in COVID-19. Study characteristics, epidemiological data, including estimates for k and R0, and public health recommendations were extracted from relevant records. RESULTS: The literature search yielded 28 peer-reviewed studies. The mean k estimates ranged from 0.04 to 2.97. Among the 28 studies, 93% reported mean k estimates lower than one, which is considered as marked heterogeneity in inter-individual transmission potential. Recommended control measures were specifically aimed at preventing superspreading events. The combination of forward and backward contact tracing, timely confirmation of cases, rapid case isolation, vaccination and preventive measures were suggested as important components to suppress superspreading. CONCLUSIONS: Superspreading events were a major feature in the pandemic of SARS-CoV-2. On the one hand, this made outbreaks potentially more explosive but on the other hand also more responsive to public health interventions. Going forward, understanding k is critical for tailoring public health measures to high-risk groups and settings where superspreading events occur.