Real-time Privacy Preserving Framework for Covid-19 Contact Tracing

The recent unprecedented threat from COVID-19 and past epidemics,such as SARS,AIDS,and Ebola,has affected millions of people in multiple countries.Countries have shut their borders,and their nationals have been advised to self-quarantine.The variety of responses to the pandemic has given rise to data privacy concerns.Infection prevention and control strategies as well as disease control measures,especially real-time contact tracing for COVID-19,require the identification of people exposed to COVID-19.Such tracing frameworks use mobile apps and geolocations to trace individuals.However,while the motive may be well intended,the limitations and security issues associated with using such a technology are a serious cause of concern.There are growing concerns regarding the privacy of an individual’s location and personal identifiable information(PII)being shared with governments and/or health agencies.This study presents a real-time,trust-based contact-tracing framework that operateswithout the use of an individual’sPII,location sensing,or gathering GPS logs.The focus of the proposed contact tracing framework is to ensure real-time privacy using the Bluetooth range of individuals to determine others within the range.The research validates the trust-based framework using Bluetooth as practical and privacy-aware.Using our proposed methodology,personal information,health logs,and location data will be secure and not abused.This research analyzes 100,000 tracing dataset records from 150 mobile devices to identify infected users and active users.

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.

Extensive contact tracing and screening to control the spread of vancomycin-resistant Enterococcus faecium ST414 in Hong Kong

Background Proactive infection control management is crucial in preventing the introduction of multiple drug resistant organisms in the healthcare setting. In Hong Kong, where vancomycin-resistant enterococci （VRE） endemicity is not yet established, contact tracing and screening, together with other infection control measures are essential in limiting intra- and inter-hospital transmission. The objective of this study was to illustrate the control measures used to eradicate a VRE outbreak in a hospital network in Hong Kong. Methods We described an outbreak of VRE in a healthcare region in Hong Kong, involving a University affiliated hospital and a convalescent hospital of 1600 and 550 beds respectively. Computer-assisted analysis was utilized to facilitate contact tracing, followed by VRE screening using chromogenic agar. Multi-locus sequence typing （MLST） was performed to assess the clonality of the VRE strains isolated. A case-control study was conducted to identify the risk factors for nosocomial acquisition of VRE. Results Between November 26 and December 17, 2011, 11 patients （1 exogenous case and 10 secondary cases） in two hospitals with VRE colonization were detected during our outbreak investigation and screening for 361 contact patients, resulting in a clinical attack rate of 2.8% （10/361）. There were 8 males and 3 females with a median age of 78 years （range, 40-87 years）. MLST confirmed sequence type ST414 in all isolates. Case-control analysis demonstrated that VRE positive cases had a significantly longer cumulative length of stay （P 〈0.001）, a higher proportion with chronic cerebral and cardiopulmonary conditions （P=0.001）, underlying malignancies （P 〈0.001）, and presence of urinary catheter （P 〈0.001）, wound or ulcer （P 〈0.001）, and a greater proportion of these patients were receiving β-lactam/ β-1actamase inhibitors （P=0.009）, carbapenem group （P 〈0.001）, fluoroquinolones （P=0.003）, or vancomycin （P=0.001） when compared with the controls. Conclusion Extensive contact tracing and screening with a ＂search-and-confine＂ strategy was a successful tool for outbreak control in our healthcare reqion.

Supporting tuberculosis program in active contact tracing:a case study from Pakistan

Tuberculosis(TB)is on the rise in Pakistan and there could be multiple reasons including poverty,difficulty in access to TB treatment services,non-compliance with treatment,social stigma etc.According to the TB program managers,limited treatment and testing sites for tuberculosis and lack of trained human resources play a major role in compromising TB management.A major lacuna in the TB control program is the absence of active contact tracing strategy.This is essential for a disease where positive cases are known to be able to infect a further 10-15 individuals in a year.Tackling tuberculosis in Pakistan has been beleaguered by funding challenges and other systems’bottlenecks such as lack of skilled human resources and insufficient supply of medicines,despite the fact that disease burden is one of the highest in the world.Although it is a notifiable disease,active case finding,contact tracing and reporting is notoriously low throughout the country.Access to diagnostics and treatment facilities has been limited and stigma attached to the disease remains deeply entrenched among the communities.Researchers have shown that enhanced and active approaches to contact investigation effectively identifies additional patients with TB among household contacts at a relatively modest cost.USAID’s Integrated Health Systems Strengthening and Service Delivery Activity extended support to the Health Departments of Sindh and Khyber Pakhtunkhwa provinces.In collaboration with the two provincial TB programs,community based active contact tracing was conducted on 17,696 individuals,based on the index cases.Among the contacts traced,243 cases were diagnosed as drug sensitive or drug resistant TB.Awareness sessions were conducted to sensitize people on the various aspects of disease and importance of getting tested.The project also supported establishing three satellite Programmatic Management of Drug Resistant Tuberculosis(PMDT)sites for drug resistant TB treatment,enhancing the programs’diagnostic and testing capacity.

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.

Widespread testing, case isolation and contact tracing may allow safe school reopening with continued moderate physical distancing: A modeling analysis of King County, WA data

Background:In late March 2020,a“Stay Home,Stay Healthy”order was issued in Washington State in response to the COVID-19 pandemic.On May 1,a 4-phase reopening plan began.We investigated whether adjunctive prevention strategies would allow less restrictive physical distancing to avoid second epidemic waves and secure safe school reopening.Methods:We developed a mathematical model,stratifying the population by age,infection status and treatment status to project SARS-CoV-2 transmission during and after the reopening period.The model was parameterized with demographic and contact data from King County,WA and calibrated to confirmed cases,deaths and epidemic peak timing.Adjunctive prevention interventions were simulated assuming different levels of pre-COVID physical interactions(pC_PI)restored.Results:The best model fit estimated~35%pC_PI under the lockdown which prevented~17,000 deaths by May 15.Gradually restoring 75%pC_PI for all age groups between May 15-July 15 would have resulted in~350 daily deaths by early September 2020.Maintaining<45%pC_PI was required with current testing practices to ensure low levels of daily infections and deaths.Increased testing,isolation of symptomatic infections,and contact tracing permitted 60%pC_PI without significant increases in daily deaths before November and allowed opening of schools with<15 daily deaths.Inpatient antiviral treatment was predicted to reduce deaths significantly without lowering cases or hospitalizations.Conclusions:We predict that widespread testing,contact tracing and case isolation would allow relaxation of physical distancing,as well as opening of schools,without a surge in local cases and deaths.

Contact tracing-Old models and new challenges

Contact tracing is an effective method to control emerging infectious diseases.Since the 1980’s,modellers are developing a consistent theory for contact tracing,with the aim to find effective and efficient implementations,and to assess the effects of contact tracing on the spread of an infectious disease.Despite the progress made in the area,there remain important open questions.In addition,technological developments,especially in the field of molecular biology(genetic sequencing of pathogens)and modern communication(digital contact tracing),have posed new challenges for the modelling community.In the present paper,we discuss modelling approaches for contact tracing and identify some of the current challenges for the field.

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.

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.

Blockchain and IIoT Enabled Solution for Social Distancing and Isolation Management to Prevent Pandemics

Pandemics have always been a nightmare for humanity,especially in developing countries.Forced lockdowns are considered one of the effective ways to deal with spreading such pandemics.Still,developing countries cannot afford such solutions because these may severely damage the country’s econ-omy.Therefore,this study presents the proactive technological mechanisms for business organizations to run their standard business processes during pandemic-like situations smoothly.The novelty of this study is to provide a state-of-the-art solution to prevent pandemics using industrial internet of things(IIoT)and blockchain-enabled technologies.Compared to existing studies,the immutable and tamper-proof contact tracing and quarantine management solution is proposed.The use of advanced technologies and information security is a critical area for practitioners in the internet of things(IoT)and corresponding solutions.Therefore,this study also emphasizes information security,end-to-end solution,and experimental results.Firstly,a wearable wristband is proposed,incorporating 4G-enabled ultra-wideband(UWB)technology for smart contact tracing mechanisms in industries to comply with standard operating procedures outlined by the world health organization(WHO).Secondly,distributed ledger technology(DLT)omits the centralized dependency for transmitting contact tracing data.Thirdly,a privacy-preserving tracing mechanism is discussed using a public/private key cryptography-based authentication mechanism.Lastly,based on geofencing techniques,blockchain-enabled machine-to-machine(M2M)technology is proposed for quarantine management.The step-by-step methodology and test results are proposed to ensure contact tracing and quarantine management.Unlike existing research studies,the security aspect is also considered in the realm of blockchain.The practical implementation of the proposed solution also obtains the results.The results indicate the successful implementation of blockchain-enabled contact tracing and isolation management using IoT and geo-fencing techniques,which could help battle pandemic situations.Researchers can also consider the 5G-enabled narrowband internet of things(NB-IoT)technologies to implement contact tracing solutions.

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.

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.

Spatio-temporal analysis of leprosy risks in a municipality in the state of Mato Grosso-Brazilian Amazon: results from the leprosy post-exposure prophylaxis program in Brazil

Background Leprosy post-exposure prophylaxis(LPEP)with single dose rifampicin(SDR)can be integrated into different leprosy control program set-ups once contact tracing has been established.We analyzed the spatio-temporal changes in the distribution of index cases(IC)and co-prevalent cases among contacts of leprosy patients(CP)over the course of the LPEP program in one of the four study areas in Brazil,namely the municipality of Alta Floresta,state of Mato Grosso,in the Brazilian Amazon basin.Methods Leprosy cases were mapped,and socioeconomic indicators were evaluated to explain the leprosy distribution of all leprosy cases diagnosed in the period 2016–2018.Data were obtained on new leprosy cases[Notifiable diseases information system(Sinan)],contacts traced by the LPEP program,and socioeconomic variables[Brazilian Institute of Geography and Statistics(IBGE)].Kernel,SCAN,factor analysis and spatial regression were applied to analyze changes.Results Overall,the new case detection rate(NCDR)was 20/10000 inhabitants or 304 new cases,of which 55 were CP cases among the 2076 examined contacts.Changes over time were observed in the geographic distribution of cases.The highest concentration of cases was observed in the northeast of the study area,including one significant cluster(Relative risk=2.24;population 27427,P-value<0.001)in an area characterized by different indicators associated with poverty as identified through spatial regression(Coefficient 3.34,P-value=0.01).Conclusions The disease distribution was partly explained by poverty indicators.LPEP influences the spatial dynamic of the disease and results highlighted the relevance of systematic contact surveillance for leprosy elimination.

Grappling with COVID-19 by imposing and lifting nonpharmaceutical interventions in Sri Lanka: A modeling perspective

The imposition and lifting of non-pharmaceutical interventions(NPIs)to avert the COVID-19 pandemic have gained popularity worldwide and will continue to be enforced until herd immunity is achieved.We developed a linear regression model to ascertain the nexus between the time-varying reproduction number averaged over a time window of six days(R_(ts))and seven NPIs:contact tracing,quarantine efforts,social distancing and health checks,hand hygiene,wearing of facemasks,lockdown and isolation,and health-related supports.Our analysis suggests that the second wave that emerged in Sri Lanka in early October 2020 continued despite numerous NPIs.The model indicates that the most effective single NPI was lockdown and isolation.Conversely,the least effective individual NPIs were hand hygiene and wearing of facemasks.The model also demonstrates that to mitigate the second wave to a satisfactory level(R_(ts)<1),the best single NPI was the contact tracing with stringent imposition(%of improvement of R_(ts) was 69.43 against the base case).By contrast,the best combination of two NPIs was the lockdown&isolation with health-related supports(%of improvement was 31.92 against the base case).As such,many health authorities worldwide can use this model to successfully strategize the imposition and lifting of NPIs for averting the COVID-19 pandemic.

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.

Modeling the impact of surveillance activities combined with physical distancing interventions on COVID-19 epidemics at a local level

Physical distancing and contact tracing are two key components in controlling the COVID-19 epidemics.Understanding their interaction at local level is important for policymakers.We propose a flexible modeling framework to assess the effect of combining contact tracing with different physical distancing strategies.Using scenario tree analyses,we compute the probability of COVID-19 detection using passive surveillance,with and without contact tracing,in metropolitan Barcelona.The estimates of detection probability and the frequency of daily social contacts are fitted into an age-structured susceptible-exposed-infectious-recovered compartmental model to simulate the epidemics considering different physical distancing scenarios over a period of 26 weeks.With the original Wuhan strain,the probability of detecting an infected individual without implementing physical distancing would have been 0.465,0.515,0.617,and 0.665 in designated age groups(0e14,15e49,50e64,and>65),respectively.As the physical distancing measures were reinforced and the disease circulation decreased,the interaction between the two interventions resulted in a reduction of the detection probabilities;however,despite this reduction,active contact tracing and isolation remained an effective supplement to physical distancing.If we relied solely on passive surveillance for diagnosing COVID-19,the model required a minimal 50%(95%credible interval,39e69%)reduction of daily social contacts to keep the infected population under 5%,as compared to the 36%(95%credible interval,22e56%)reduction with contact tracing systems.The simulation with the B.1.1.7 and B.1.167.2 strains shows similar results.Our simulations showed that a functioning contact tracing program would reduce the need for physical distancing and mitigate the COVID-19 epidemics.

Outbreak of Crimean-Congo haemorrhagic fever with atypical clinical presentation in the Karak District of Khyber Pakhtunkhwa,Pakistan

Background:Crimean-Congo haemorrhagic fever(CCHF)is a potentially fatal disease endemic in Pakistan.The causative virus is transmitted by the bite of Hyalomma ticks or by contact with infected blood or tissue.First cases of the disease were reported in Pakistan in 1976 but regular outbreaks have been observed since the year 2000.A huge agricultural base with more than 175 million livestock,the concomitant presence of Hyalomma ticks and a lack of precautionary measures to prevent transmission lead to a considerable risk for exposed populations to contract CCHF in Pakistan.At the same time,secondary cases contracted by nosocomial transmission are reported from hospitals.Case presentation:Here we present an outbreak of CCHF with four of six patients succumbing to the disease before the suspicion for CCHF was raised.Importantly,the main clinical features of these cases were gastrointestinal symptoms without any clinical signs of bleeding.Only the last two patients in this outbreak presented with typical signs of bleeding disorder and were then confirmed being infected by CCHF.Confirmation of diagnosis was done at the National Institute of Health by real-time RT-PCR.Conclusions:This case series highlights the importance of early clinical suspicion for CCHF in exposed individuals and the need for improved precautionary measures against the spread of CCHF within the Pakistani population and hospitals.

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.