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      Anonymity Preserving IoT-Based COVID-19 and Other Infectious Disease Contact Tracing Model

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          Abstract

          Automated digital contact tracing is effective and efficient, and one of the non-pharmaceutical complementary approaches to mitigate and manage epidemics like Coronavirus disease 2019 (COVID-19). Despite the advantages of digital contact tracing, it is not widely used in the western world, including the US and Europe, due to strict privacy regulations and patient rights. We categorized the current approaches for contact tracing, namely: mobile service-provider-application, mobile network operators’ call detail, citizen-application, and IoT-based. Current measures for infection control and tracing do not include animals and moving objects like cars despite evidence that these moving objects can be infection carriers. In this article, we designed and presented a novel privacy anonymous IoT model. We presented an RFID proof-of-concept for this model. Our model leverages blockchain’s trust-oriented decentralization for on-chain data logging and retrieval. Our model solution will allow moving objects to receive or send notifications when they are close to a flagged, probable, or confirmed diseased case, or flagged place or object. We implemented and presented three prototype blockchain smart contracts for our model. We then simulated contract deployments and execution of functions. We presented the cost differentials. Our simulation results show less than one-second deployment and call time for smart contracts, though, in real life, it can be up to 25 seconds on Ethereum public blockchain. Our simulation results also show that it costs an average of $1.95 to deploy our prototype smart contracts, and an average of $0.34 to call our functions. Our model will make it easy to identify clusters of infection contacts and help deliver a notification for mass isolation while preserving individual privacy. Furthermore, it can be used to understand better human connectivity, model similar other infection spread network, and develop public policies to control the spread of COVID-19 while preparing for future epidemics.

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          Most cited references26

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          Feasibility of controlling COVID-19 outbreaks by isolation of cases and contacts

          Summary Background Isolation of cases and contact tracing is used to control outbreaks of infectious diseases, and has been used for coronavirus disease 2019 (COVID-19). Whether this strategy will achieve control depends on characteristics of both the pathogen and the response. Here we use a mathematical model to assess if isolation and contact tracing are able to control onwards transmission from imported cases of COVID-19. Methods We developed a stochastic transmission model, parameterised to the COVID-19 outbreak. We used the model to quantify the potential effectiveness of contact tracing and isolation of cases at controlling a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-like pathogen. We considered scenarios that varied in the number of initial cases, the basic reproduction number (R 0), the delay from symptom onset to isolation, the probability that contacts were traced, the proportion of transmission that occurred before symptom onset, and the proportion of subclinical infections. We assumed isolation prevented all further transmission in the model. Outbreaks were deemed controlled if transmission ended within 12 weeks or before 5000 cases in total. We measured the success of controlling outbreaks using isolation and contact tracing, and quantified the weekly maximum number of cases traced to measure feasibility of public health effort. Findings Simulated outbreaks starting with five initial cases, an R 0 of 1·5, and 0% transmission before symptom onset could be controlled even with low contact tracing probability; however, the probability of controlling an outbreak decreased with the number of initial cases, when R 0 was 2·5 or 3·5 and with more transmission before symptom onset. Across different initial numbers of cases, the majority of scenarios with an R 0 of 1·5 were controllable with less than 50% of contacts successfully traced. To control the majority of outbreaks, for R 0 of 2·5 more than 70% of contacts had to be traced, and for an R 0 of 3·5 more than 90% of contacts had to be traced. The delay between symptom onset and isolation had the largest role in determining whether an outbreak was controllable when R 0 was 1·5. For R 0 values of 2·5 or 3·5, if there were 40 initial cases, contact tracing and isolation were only potentially feasible when less than 1% of transmission occurred before symptom onset. Interpretation In most scenarios, highly effective contact tracing and case isolation is enough to control a new outbreak of COVID-19 within 3 months. The probability of control decreases with long delays from symptom onset to isolation, fewer cases ascertained by contact tracing, and increasing transmission before symptoms. This model can be modified to reflect updated transmission characteristics and more specific definitions of outbreak control to assess the potential success of local response efforts. Funding Wellcome Trust, Global Challenges Research Fund, and Health Data Research UK.
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            Susceptibility of ferrets, cats, dogs, and other domesticated animals to SARS–coronavirus 2

            Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes the infectious disease COVID-19, which was first reported in Wuhan, China in December, 2019. Despite the tremendous efforts to control the disease, COVID-19 has now spread to over 100 countries and caused a global pandemic. SARS-CoV-2 is thought to have originated in bats; however, the intermediate animal sources of the virus are completely unknown. Here, we investigated the susceptibility of ferrets and animals in close contact with humans to SARS-CoV-2. We found that SARS-CoV-2 replicates poorly in dogs, pigs, chickens, and ducks, but ferrets and cats are permissive to infection. We found experimentally that cats are susceptible to airborne infection. Our study provides important insights into the animal models for SARS-CoV-2 and animal management for COVID-19 control.
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              Another Decade, Another Coronavirus

              For the third time in as many decades, a zoonotic coronavirus has crossed species to infect human populations. This virus, provisionally called 2019-nCoV, was first identified in Wuhan, China, in persons exposed to a seafood or wet market. The rapid response of the Chinese public health, clinical, and scientific communities facilitated recognition of the clinical disease and initial understanding of the epidemiology of the infection. First reports indicated that human-to-human transmission was limited or nonexistent, but we now know that such transmission occurs, although to what extent remains unknown. Like outbreaks caused by two other pathogenic human respiratory coronaviruses (severe acute respiratory syndrome coronavirus [SARS-CoV] and Middle East respiratory syndrome coronavirus [MERS-CoV]), 2019-nCoV causes respiratory disease that is often severe. 1 As of January 24, 2020, there were more than 800 reported cases, with a mortality rate of 3% (https://promedmail.org/). As now reported in the Journal, Zhu et al. 2 have identified and characterized 2019-nCoV. The viral genome has been sequenced, and these results in conjunction with other reports show that it is 75 to 80% identical to the SARS-CoV and even more closely related to several bat coronaviruses. 3 It can be propagated in the same cells that are useful for growing SARS-CoV and MERS-CoV, but notably, 2019-nCoV grows better in primary human airway epithelial cells than in standard tissue-culture cells, unlike SARS-CoV or MERS-CoV. Identification of the virus will allow the development of reagents to address key unknowns about this new coronavirus infection and guide the development of antiviral therapies. First, knowing the sequence of the genome facilitates the development of sensitive quantitative reverse-transcriptase–polymerase-chain-reaction assays to rapidly detect the virus. Second, the development of serologic assays will allow assessment of the prevalence of the infection in humans and in potential zoonotic sources of the virus in wet markets and other settings. These reagents will also be useful for assessing whether the human infection is more widespread than originally thought, since wet markets are present throughout China. Third, having the virus in hand will spur efforts to develop antiviral therapies and vaccines, as well as experimental animal models. Much still needs to be learned about this infection. Most important, the extent of interhuman transmission and the spectrum of clinical disease need to be determined. Transmission of SARS-CoV and MERS-CoV occurred to a large extent by means of superspreading events. 4,5 Superspreading events have been implicated in 2019-nCoV transmission, but their relative importance is unknown. Both SARS-CoV and MERS-CoV infect intrapulmonary epithelial cells more than cells of the upper airways. 4,6 Consequently, transmission occurs primarily from patients with recognized illness and not from patients with mild, nonspecific signs. It appears that 2019-nCoV uses the same cellular receptor as SARS-CoV (human angiotensin-converting enzyme 2 [hACE2]), 3 so transmission is expected only after signs of lower respiratory tract disease develop. SARS-CoV mutated over the 2002–2004 epidemic to better bind to its cellular receptor and to optimize replication in human cells, enhancing virulence. 7 Adaptation readily occurs because coronaviruses have error-prone RNA-dependent RNA polymerases, making mutations and recombination events frequent. By contrast, MERS-CoV has not mutated substantially to enhance human infectivity since it was detected in 2012. 8 It is likely that 2019-nCoV will behave more like SARS-CoV and further adapt to the human host, with enhanced binding to hACE2. Consequently, it will be important to obtain as many temporally and geographically unrelated clinical isolates as possible to assess the degree to which the virus is mutating and to assess whether these mutations indicate adaptation to the human host. Furthermore, if 2019-nCoV is similar to SARS-CoV, the virus will spread systemically. 9 Obtaining patient samples at autopsy will help elucidate the pathogenesis of the infection and modify therapeutic interventions rationally. It will also help validate results obtained from experimental infections of laboratory animals. A second key question is identification of the zoonotic origin of the virus. Given its close similarity to bat coronaviruses, it is likely that bats are the primary reservoir for the virus. SARS-CoV was transmitted to humans from exotic animals in wet markets, whereas MERS-CoV is transmitted from camels to humans. 10 In both cases, the ancestral hosts were probably bats. Whether 2019-nCoV is transmitted directly from bats or by means of intermediate hosts is important to understand and will help define zoonotic transmission patterns. A striking feature of the SARS epidemic was that fear played a major role in the economic and social consequences. Although specific anticoronaviral therapies are still in development, we now know much more about how to control such infections in the community and hospitals, which should alleviate some of this fear. Transmission of 2019-nCoV probably occurs by means of large droplets and contact and less so by means of aerosols and fomites, on the basis of our experience with SARS-CoV and MERS-CoV. 4,5 Public health measures, including quarantining in the community as well as timely diagnosis and strict adherence to universal precautions in health care settings, were critical in controlling SARS and MERS. Institution of similar measures will be important and, it is hoped, successful in reducing the transmission of 2019-nCoV.
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                Author and article information

                Contributors
                Journal
                IEEE Access
                IEEE Access
                0063500
                ACCESS
                IAECCG
                Ieee Access
                IEEE
                2169-3536
                2020
                31 August 2020
                : 8
                : 159402-159414
                Affiliations
                [1 ] departmentDepartment of Computer Information System (CIS), institutionFaculty of Information Communication Technology (ICT), University of Malta, institutionringgold 37563; 2080 Msida Malta
                [2 ] divisionCollege of Engineering, institutionAlfaisal University, institutionringgold 101686; Riyadh 50927 Saudi Arabia
                [3 ] departmentDepartment of Electronics and Communication Engineering, institutionBirla Institute of Technology at Mesra; Ranchi 835215 India
                [4 ] institutionABV-Indian Institute of Information Technology and Management, institutionringgold 121807; Gwalior 474015 India
                Article
                10.1109/ACCESS.2020.3020513
                8545304
                34786286
                7ab4eefd-d3c0-4fea-b2a7-55458f44249e
                This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/

                This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/

                History
                : 26 August 2020
                : 28 August 2020
                : 14 September 2020
                Page count
                Figures: 10, Tables: 2, Equations: 11, References: 39, Pages: 13
                Funding
                Funded by: Alfaisal University under Internal Research Grant C20220 and in part by the University of Malta’s Research Innovation & Development Trust (RIDT);
                Award ID: E18LO77-01
                This work was supposed in part by Alfaisal University under Internal Research Grant C20220 and in part by the University of Malta’s Research Innovation & Development Trust (RIDT) under Research Grant E18LO77-01.
                Categories
                Biomedical Engineering
                Communications technology

                contact tracing,rfid,iot,blockchain,hospitals,telemedicine,digital health,privacy,covid-19

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