Primary Contacts Identification for COVID-19 Carriers from Surveillance Videos

COVID-19 (Coronavirus disease of 2019) is caused by SARS-CoV2(Severe Acute Respiratory Syndrome Coronavirus 2) and it was first diagnosedin December 2019 in China. As of 25th Aug 2021, there are 165 million con-firmed COVID-19 positive cases and 4.4 million deaths globally. As of today,though there are approved COVID-19 vaccine candidates only 4 billion doseshave been administered. Until 100% of the population is safe, no one is safe. Eventhough these vaccines can provide protection against getting seriously ill anddying from the disease, it does not provide 100% protection from getting infectedand passing it on to others. The more the virus spreads;it has more opportunity tomutate. So, it is mandatory to follow all precautions like maintaining social distance, wearing mask, washing hands frequently irrespective of whether a person isvaccinated or not. To prevent spread of the virus, contact tracing based on socialdistance also becomes equally important. The work proposes a solution that canhelp with contact tracing/identification, knowing the infected persons recent travelhistory (even within the city) for few days before being assessed positive. Whilethe person would be able to give the known contacts with whom he/she has interacted with, he/she will not be aware of who all were in proximity if he/she hadbeen in public places. The proposed solution is to get the CCTV (Closed-CircuitTelevision) video clips from those public places for the specific date and time andidentify the people who were in proximity—i.e., not followed the safe distance tothe infected person. The approach uses YOLO V3 (You Only Look Once) whichuses darknet framework for people detection. Once the infected person is locatedfrom the video frames, the distance from that person to the other people in theframe is found, to check if there is a violation of social distance guideline. If thereis, then the people violating the distance are extracted and identified using Facialdetection and recognition algorithms. Two different solutions for Face detectionand Recognition are implemented and results compared—Dlib based modelsand OpenCV (Open Source Computer Vision Library) based models. The solutions were studied for two different CCTV footages and the results for Dlib basedmodels were better than OpenCV based models for the studied videos.

Modelling COVID-19 epidemic with confirmed cases-driven contact tracing quarantine

The pandemic of novel coronavirus disease 2019(COVID-19)has been a severe threat to public health.The policy of close contract tracing quarantine is an effective strategy in controlling the COVID-19 epidemic outbreak.In this paper,we developed a mathematical model of the COVID-19 epidemic with confirmed case-driven contact tracing quarantine,and applied the model to evaluate the effectiveness of the policy of contact tracing and quarantine.The model is established based on the combination of the compartmental model and individual-based model simulations,which results in a closed-form delay differential equation model.The proposed model includes a novel form of quarantine functions to represent the number of quarantine individuals following the confirmed cases every day and provides analytic expressions to study the effects of changing the quarantine rate.The proposed model can be applied to epidemic dynamics during the period of community spread and when the policy of confirmed cases-driven contact tracing quarantine is efficient.We applied the model to study the effectiveness of contact tracing and quarantine.The proposed delay differential equation model can describe the average epidemic dynamics of the stochastic-individual-based model,however,it is not enough to describe the diverse response due to the stochastic effect.Based on model simulations,we found that the policy of contact tracing and quarantine can obviously reduce the epidemic size,however,may not be enough to achieve zero-infectious in a short time,a combination of close contact quarantine and social contact restriction is required to achieve zeroinfectious.Moreover,the effect of reducing epidemic size is insensitive to the period of quarantine,there are no significant changes in the epidemic dynamics when the quarantine days vary from 7 to 21 days.

Clustering Indoor Location Data for Social Distancing and Human Mobility to Combat COVID-19

The world is experiencing the unprecedented time of a pandemic caused by the coronavirus disease(i.e.,COVID-19).As a countermeasure,contact tracing and social distancing are essential to prevent the transmission of the virus,which can be achieved using indoor location analytics.Based on the indoor location analytics,the human mobility on a site can be monitored and planned to minimize human’s contact and enforce social distancing to contain the transmission of COVID-19.Given the indoor location data,the clustering can be applied to cluster spatial data,spatio-temporal data and movement behavior features for proximity detection or contact tracing applications.More specifically,we propose the Coherent Moving Cluster(CMC)algorithm for contact tracing,the density-based clustering(DBScan)algorithm for identification of hotspots and the trajectory clustering(TRACLUS)algorithm for clustering indoor trajectories.The feature extraction mechanism is then developed to extract useful and valuable features that can assist the proposed system to construct the network of users based on the similarity of the movement behaviors of the users.The network of users is used to model an optimization problem to manage the human mobility on a site.The objective function is formulated to minimize the probability of contact between the users and the optimization problem is solved using the proposed effective scheduling solution based on OR-Tools.The simulation results show that the proposed indoor location analytics system outperforms the existing clustering methods by about 30%in terms of accuracy of clustering trajectories.By adopting this system for human mobility management,the count of close contacts among the users within a confined area can be reduced by 80%in the scenario where all users are allowed to access the site.

EPIDEM: A Technology-Enabled COVID-19 Elimination Strategy

Background: The novel Coronavirus disease 2019 (COVID-19) has rapidly evolved into a global emergency with far-reaching consequences. Multiple models predict mounting morbidity and mortality in the absence of appropriate interventions. Screening of sub-clinical cases through technological tools has the potential to eliminate virus containment in the population. Objective: Our aim was to develop an effective application that detected early COVID-19 infections and resulted in four-dimensional (4-D) data visualization systems to develop safe networks whilst respecting user privacy. Methods: We proposed an algorithm for a novel form of contact tracing and screening that can detect infection before manifestation of clinical symptoms and signs. We developed a simulation model that demonstrated impact on the pandemic through percent change in the “trigger point”. Results: A “trigger point” based on personal risk assessment generated a visual report to the community network. The following outcomes were rendered in the app: live surveillance of metadata, hotspot mapping, targeted live health messaging to a large population, an infection control passport technology, and personal hotspot avoidance warnings. Our model suggested that higher adoption of such strategies can potentially eliminate viral carriage in the population. Conclusions: Our proposed technology-enabled screening, detection, and elimination strategy presents a novel approach to eliminate the viral containment. This app could be applied to the COVID-19 pandemic as well as other outbreaks and epidemics in the future to control the unprecedented disease spread.

COVID-19 contact tracing in a tertiary care hospital: A retrospective chart review

Background:Contact tracing is one of the strategies used to control COVID-19 pandemic.It played an important role in the beginning to identify all contacts and minimise the spread of the infection.Methods:A retrospective chart review was carried out of contact tracing records during the one-month period,starting from the onset of the first lockdown in India.The largest wave of 372 contacts was analysed in detail to find out the association between the result of COVID-19 test and various factors(age,gender,type of contact).Results:A total of 372 contacts(214 males and 158 females)were traced and around 21%contacts were tested positive on COVID-19 RT-PCR test.Chi-square test didn’t find the significant difference between COVID-19 test result and proportions of male and female contacts,X^(2)(1)0.033,p=0.855.Female positive contacts had lower mean age compared to male positive contacts,though not statistically significant,t(75)=-1.809,p=0.0745.No difference was found in either median or mean age of contacts with respect to COVID-19 test result.Odds of tested COVID-19 positive among household contacts much higher than community contacts,OR=24.52,95%CI 12.45e48.29,p<0.05.Conclusion:No difference was noted in the rate of contracting infection with respect to age and gender of contacts.Type of contact,household or community,significantly affected the probability of becoming infected with the coronavirus.Occupation of primary case was probably responsible for large number of contacts found positive for COVID-19.

Simulating potential outbreaks of Delta and Omicron variants based on contact-tracing data:A modelling study in Fujian Province,China

Although studies have compared the relative severity of Omicron and Delta variants by assessing the relative risks,there are still gaps in the knowledge of the potential COVID-19 burden these variations may cause.And the contact patterns in Fujian Province,China,have not been described.We identified 8969 transmission pairs in Fujian,China,by analyzing a contact-tracing database that recorded a SARS-CoV-2 outbreak in September 2021.We estimated the waning vaccine effectiveness against Delta variant infection,contact patterns,and epidemiology distributions,then simulated potential outbreaks of Delta and Omicron variants using a multi-group mathematical model.For instance,in the contact setting without stringent lockdowns,we estimated that in a potential Omicron wave,only 4.7%of infections would occur in Fujian Province among individuals aged>60 years.In comparison,58.75%of the death toll would occur in unvaccinated individuals aged>60 years.Compared with no strict lockdowns,combining school or factory closure alone reduced cumulative deaths of Delta and Omicron by 28.5%and 6.1%,respectively.In conclusion,this study validates the need for continuous mass immunization,especially among elderly aged over 60 years old.And it confirms that the effect of lockdowns alone in reducing infections or deaths is minimal.However,these measurements will still contribute to lowering peak daily incidence and delaying the epidemic,easing the healthcare system's burden.

人工智能产品的适应性创新逻辑——哈贝马斯交往理论视角

新型冠状病毒肺炎疫情(COVID-19)迫使数字化接触者追踪APP成为平衡疫情控制与挽救经济的方案之一。基于哈贝马斯交往理论和产品适应性创新理论,以COVID-19追踪APP为例,进行单案例研究并通过扎根理论构建出“隐私保护与伦理遵守→数据驱动→适应性成长”的人工智能产品的适应性创新逻辑和成长机制。研究发现:对于人工智能产品的适应性创新,隐私保护与伦理遵守是实现人机和谐共生的前提,这也是人工智能产品创新区别于传统工业产品创新的重要特征之一,COVID-19追踪APP所掀起的全球大讨论只是将这一人工智能产品创新的前提凸显和放大出来。据此,在肖静华等提出的数字产品成长机制基础上提出人工智能产品的创新逻辑。

Risk estimation and prediction of the transmission of coronavirus disease-2019(COVID-19)in the mainland of China excluding Hubei province

Background In December 2019,an outbreak of coronavirus disease(later named as COVID-19)was identified in Wuhan,China and,later on,detected in other parts of China.Our aim is to evaluate the effectiveness of the evolution of interventions and self-protection measures,estimate the risk of partial lifting control measures and predict the epidemic trend of the virus in the mainland of China excluding Hubei province based on the published data and a novel mathematical model.Methods A novel COVID-19 transmission dynamic model incorporating the intervention measures implemented in China is proposed.COVID-19 daily data of the mainland of China excluding Hubei province,including the cumulative confirmed cases,the cumulative deaths,newly confirmed cases and the cumulative recovered cases between 20 January and 3 March 2020,were archived from the National Health Commission of China(NHCC).We parameterize the model by using the Markov Chain Monte Carlo(MCMC)method and estimate the control reproduction number(Rc),as well as the effective daily reproduction ratio-Re(t),of the disease transmission in the mainland of China excluding Hubei province.Results The estimation outcomes indicate that Rc is 3.36(95%CI:3.20–3.64)and Re(t)has dropped below 1 since 31 January 2020,which implies that the containment strategies implemented by the Chinese government in the mainland of China are indeed effective and magnificently suppressed COVID-19 transmission.Moreover,our results show that relieving personal protection too early may lead to a prolonged disease transmission period and more people would be infected,and may even cause a second wave of epidemic or outbreaks.By calculating the effective reproduction ratio,we prove that the contact rate should be kept at least less than 30%of the normal level by April,2020.Conclusions To ensure the pandemic ending rapidly,it is necessary to maintain the current integrated restrict interventions and self-protection measures,including travel restriction,quarantine of entry,contact tracing followed by quarantine and isolation and reduction of contact,like wearing masks,keeping social distance,etc.People should be fully aware of the real-time epidemic situation and keep sufficient personal protection until April.If all the above conditions are met,the outbreak is expected to be ended by April in the mainland of China apart from Hubei province.

The role of artificial intelligence technology in the care of diabetic foot ulcers: the past, the present, and the future

Foot ulcers are common complications of diabetes mellitus and substantially increase the morbidity and mortality due to this disease.Wound care by regular monitoring of the progress of healing with clinical review of the ulcers,dressing changes,appropriate antibiotic therapy for infection and proper offloading of the ulcer are the cornerstones of the management of foot ulcers.Assessing the progress of foot ulcers can be a challenge for the clinician and patient due to logistic issues such as regular attendance in the clinic.Foot clinics are often busy and because of manpower issues,ulcer reviews can be delayed with detrimental effects on the healing as a result of a lack of appropriate and timely changes in management.Wound photographs have been historically useful to assess the progress of diabetic foot ulcers over the past few decades.Mobile phones with digital cameras have recently revolutionized the capture of foot ulcer images.Patients can send ulcer photographs to diabetes care professionals electronically for remote monitoring,largely avoiding the logistics of patient transport to clinics with a reduction on clinic pressures.Artificial intelligence-based technologies have been developed in recent years to improve this remote monitoring of diabetic foot ulcers with the use of mobile apps.This is expected to make a huge impact on diabetic foot ulcer care with further research and development of more accurate and scientific technologies in future.This clinical update review aims to compile evidence on this hot topic to empower clinicians with the latest developments in the field.

Mathematical modelling of the spread of COVID-19 on a university campus

In this paper we present a deterministic transmission dynamic compartmental model for the spread of the novel coronavirus on a college campus for the purpose of analyzing strategies to mitigate an outbreak.The goal of this project is to determine and compare the utility of certain containment strategies including gateway testing,surveillance testing,and contact tracing as well as individual level control measures such as mask wearing and social distancing.We modify a standard SEIR-type model to reflect what is currently known about COVID-19.We also modify the model to reflect the population present on a college campus,separating it into students and faculty.This is done in order to capture the expected different contact rates between groups as well as the expected difference in outcomes based on age known for COVID-19.We aim to provide insight into which strategies are most effective,rather than predict exact numbers of infections.We analyze effectiveness by looking at relative changes in the total number of cases as well as the effect a measure has on the estimated basic reproductive number.We find that the total number of infections is most sensitive to parameters relating to student behaviors.We also find that contact tracing can be an effective control strategy when surveillance testing is unavailable.Lastly,we validate the model using data from Villanova University's online COVID-19 Dashboard from Fall 2020 and find good agreement between model and data when superspreader events are incorporated in the model as shocks to the number of infected individuals approximately two weeks after each superspreader event.

WLAN-log-based superspreader detection in the COVID-19 pandemic

Identifying“superspreaders”of disease is a pressing concern for society during pandemics such as COVID-19.Superspreaders represent a group of people who have much more social contacts than others.The widespread deployment of WLAN infrastructure enables non-invasive contact tracing via people’s ubiquitous mobile devices.This technology offers promise for detecting superspreaders.In this paper,we propose a general framework for WLAN-log-based superspreader detection.In our framework,we first use WLAN logs to construct contact graphs by jointly considering human symmetric and asymmetric interactions.Next,we adopt three vertex centrality mea-surements over the contact graphs to generate three groups of superspreader candidates.Finally,we leverage SEIR simulation to determine groups of superspreaders among these candidates,who are the most critical individuals for the spread of disease based on the simulation results.We have implemented our framework and evaluate it over a WLAN dataset with 41 million log entries from a large-scale university.Our evaluation shows superspread-ers exist on university campuses.They change over the first few weeks of a semester,but stabilize throughout the rest of the term.The data also demonstrate that both symmetric and asymmetric contact tracing can dis-cover superspreaders,but the latter performs better with daily contact graphs.Further,the evaluation shows no consistent differences among three vertex centrality measures for long-term(i.e.,weekly)contact graphs,which necessitates the inclusion of SEIR simulation in our framework.We believe our proposed framework and these results can provide timely guidance for public health administrators regarding effective testing,intervention,and vaccination policies.