Applying contextual integrity to digital contact tracing and automated triage for hospitals during COVID-19

To control and minimise the spread of COVID-19, various technological solutions have been proposed. In this research, we focus on digital contact tracing and automated triage for hospitals. We conducted an online survey in Flanders (N = 1708) to investigate the perceived appropriateness of these systems based on the Contextual Integrity framework, as developed by Nissenbaum [1]. For digital contact tracing, significant differences were found between the appropriateness of using various types of data for different goals. Precise individual location data (i.e. GPS) was considered to be least appropriate and much less appropriate than proximity data (i.e. Bluetooth) or coarser location data (i.e. GSM). Goals for digital contact tracing with a high individual impact were considered to be less appropriate than goals with a low individual or societal impact. In addition, the data showed that respondents would find the usage of digital contact tracing to be less appropriate after the pandemic, underlining the temporality of this technological solution. For automated triage, the results indicated that gender is perceived to be significantly less appropriate than the other types of data, including age, to determine the priority of treatment.     

Effect of manual and digital contact tracing on COVID-19 outbreaks: a study on empirical contact data

Non-pharmaceutical interventions are crucial to mitigate the COVID-19 pandemic and contain re-emergence phenomena. Targeted measures such as case isolation and contact tracing can alleviate the societal cost of lock-downs by containing the spread where and when it occurs. To assess the relative and combined impact of manual contact tracing (MCT) and digital (app-based) contact tracing, we feed a compartmental model for COVID-19 with high-resolution datasets describing contacts between individuals in several contexts. We show that the benefit (epidemic size reduction) is generically linear in the fraction of contacts recalled during MCT and quadratic in the app adoption, with no threshold effect. The cost (number of quarantines) versus benefit curve has a characteristic parabolic shape, independent of the type of tracing, with a potentially high benefit and low cost if app adoption and MCT efficiency are high enough. Benefits are higher and the cost lower if the epidemic reproductive number is lower, showing the importance of combining tracing with additional mitigation measures. The observed phenomenology is qualitatively robust across datasets and parameters. We moreover obtain analytically similar results on simplified models.     

How necessary is a fast testkit for mitigation of pandemic flu?

It is widely feared that a novel, highly pathogenic, human transmissible influenza virus may evolve that could cause the next global pandemic. Mitigating the spread of such an influenza pandemic would require not only the timely administration of antiviral drugs to those infected, but also the implementation of suitable intervention policies for stunting the spread of the virus. Towards this end, mathematical modelling and simulation studies are crucial as they allow us to evaluate the predicted effectiveness of the various intervention policies before enforcing them. Diagnosis plays a vital role in the overall pandemic management framework by detecting and distinguishing the pathogenic strain from the less threatening seasonal strains and other influenza-like illnesses. This allows treatment and intervention to be deployed effectively, given limited antiviral supplies and other resources. However, the time required to design a fast and accurate testkit for novel strains may limit the role of diagnosis. Herein, we aim to investigate the cost and effectiveness of different diagnostic methods using a stochastic agent-based city-scale model, and then address the issue of whether conventional testing approaches, when used with appropriate intervention policies, can be as effective as fast testkits in containing a pandemic outbreak. We found that for mitigation purposes, fast and accurate testkits are not necessary as long as sufficient medication is given, and are generally recommended only when used with extensive contact tracing and prophylaxis. Additionally, in the event of insufficient medication and fast testkits, the use of slower, conventional testkits together with proper isolation policies while waiting for the diagnostic results can be an equally effective substitute.     

一种针对新型冠状病毒肺炎的准确、快速、便携式双功能电检测仪

新型冠状病毒肺炎(COVID-19)正在多个国家快速传播,已经导致了严重的全球大流行.由于目前没有针对此类病人的特效药和针对此病毒的疫苗,准确、快速地进行新冠病人检测成为了控制大流行最有效的措施.本文中我们开发了一种基于石墨烯场效应晶体管的便携式双功能电检测仪,其通过核酸互补杂交或者抗原-抗体特异性结合作用,能分别进行...     

Technology Landscape for Epidemiological Prediction and Diagnosis of COVID-19

The COVID-19 outbreak initiated from the Chinese city of Wuhan and eventually affected almost every nation around the globe. From China, the disease started spreading to the rest of the world. After China, Italy became the next epicentre of the virus and witnessed a very high death toll. Soon nations like the USA became severely hit by SARS-CoV-2 virus. The World Health Organisation, on 11th March 2020, declared COVID-19 a pandemic. To combat the epidemic, the nations from every corner of the world has instituted various policies like physical distancing, isolation of infected population and researching on the potential vaccine of SARS-CoV-2. To identify the impact of various policies implemented by the affected countries on the pandemic spread, a myriad of AI-based models have been presented to analyse and predict the epidemiological trends of COVID-19. In this work, the authors present a detailed study of different artificial intelligence frameworks applied for predictive analysis of COVID-19 patient record. The forecasting models acquire information from records to detect the pandemic spreading and thus enabling an opportunity to take immediate actions to reduce the spread of the virus. This paper addresses the research issues and corresponding solutions associated with the prediction and detection of infectious diseases like COVID-19. It further focuses on the study of vaccinations to cope with the pandemic. Finally, the research challenges in terms of data availability, reliability, the accuracy of the existing prediction models and other open issues are discussed to outline the future course of this study.     

Multi-scale modelling reveals that early super-spreader events are a likely contributor to novel variant predominance

The emergence of new SARS-CoV-2 variants of concern (VOC) has hampered international efforts to contain the COVID-19 pandemic. VOCs have been characterized to varying degrees by higher transmissibility, worse infection outcomes and evasion of vaccine and infection-induced immunologic memory. VOCs are hypothesized to have originated from animal reservoirs, communities in regions with low surveillance and/or single individuals with poor immunologic control of the virus. Yet, the factors dictating which variants ultimately predominate remain incompletely characterized. Here we present a multi-scale model of SARS-CoV-2 dynamics that describes population spread through individuals whose viral loads and numbers of contacts (drawn from an over-dispersed distribution) are both time-varying. This framework allows us to explore how super-spreader events (SSE) (defined as greater than five secondary infections per day) contribute to variant emergence. We find stochasticity remains a powerful determinant of predominance. Variants that predominate are more likely to be associated with higher infectiousness, an SSE early after variant emergence and ongoing decline of the current dominant variant. Additionally, our simulations reveal that most new highly infectious variants that infect one or a few individuals do not achieve permanence in the population. Consequently, interventions that reduce super-spreading may delay or mitigate emergence of VOCs.     

Effectiveness of non-pharmaceutical interventions to contain COVID-19: a case study of the 2020 spring pandemic wave in New York City

As COVID-19 continues to pose significant public health threats, quantifying the effectiveness of different public health interventions is crucial to inform intervention strategies. Using detailed epidemiological and mobility data available for New York City and comprehensive modelling accounting for under-detection, we reconstruct the COVID-19 transmission dynamics therein during the 2020 spring pandemic wave and estimate the effectiveness of two major non-pharmaceutical interventions—lockdown-like measures that reduce contact rates and universal masking. Lockdown-like measures were associated with greater than 50% transmission reduction for all age groups. Universal masking was associated with an approximately 7% transmission reduction overall and up to 20% reduction for 65+ year olds during the first month of implementation. This result suggests that face covering can substantially reduce transmission when lockdown-like measures are lifted but by itself may be insufficient to control SARS-CoV-2 transmission. Overall, findings support the need to implement multiple interventions simultaneously to effectively mitigate COVID-19 spread before the majority of population can be protected through mass-vaccination.     

Comparison between one and two dose SARS-CoV-2 vaccine prioritization for a fixed number of vaccine doses

The swift development of SARS-CoV-2 vaccines has been met with worldwide commendation. However, in the context of an ongoing pandemic there is an interplay between infection and vaccination. While infection can grow exponentially, vaccination rates are generally limited by supply and logistics. With the first SARS-CoV-2 vaccines receiving medical approval requiring two doses, there has been scrutiny on the spacing between doses; an elongated period between doses allows more of the population to receive a first vaccine dose in the short-term generating wide-spread partial immunity. Focusing on data from England, we investigated prioritization of a one dose or two dose vaccination schedule given a fixed number of vaccine doses and with respect to a measure of maximizing averted deaths. We optimized outcomes for two different estimates of population size and relative risk of mortality for at-risk groups within the Phase 1 vaccine priority order. Vaccines offering relatively high protection from the first dose favour strategies that prioritize giving more people one dose, although with increasing vaccine supply eventually those eligible and accepting vaccination will receive two doses. While optimal dose timing can substantially reduce the overall mortality risk, there needs to be careful consideration of the logistics of vaccine delivery.     

Natural Host–Environmental Media–Human: A New Potential Pathway of COVID-19 Outbreak

Identifying the first infected case (patient zero) is key in tracing the origin of a virus; however, doing so is extremely challenging. Patient zero for coronavirus disease 2019 (COVID-19) is likely to be permanently unknown. Here, we propose a new viral transmission route by focusing on the environmental media containing viruses of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) or RaTG3-related bat-borne coronavirus (Bat-CoV), which we term the “environmental quasi-host.” We reason that the environmental quasi-host is likely to be a key node in helping recognize the origin of SARS-CoV-2; thus, SARS-CoV-2 might be transmitted along the route of natural host–environmental media–human. Reflecting upon viral outbreaks in the history of humanity, we realize that many epidemic events are caused by direct contact between humans and environmental media containing infectious viruses. Indeed, contacts between humans and environmental quasi-hosts are greatly increasing as the space of human activity incrementally overlaps with animals’ living spaces, due to the rapid development and population growth of human society. Moreover, viruses can survive for a long time in environmental media. Therefore, we propose a new potential mechanism to trace the origin of the COVID-19 outbreak.     

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 economy. 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.     

Publication patterns’ changes due to the COVID-19 pandemic: a longitudinal and short-term scientometric analysis

Scientometrics - In recent months the COVID-19 (also known as SARS-CoV-2 and Coronavirus) pandemic has spread throughout the world. In parallel, extensive scholarly research regarding various...     

Effect of Weather on the Spread of COVID-19 Using Eigenspace Decomposition

Since the end of 2019, the world has suffered from a pandemic of the disease called COVID-19. WHO reports show approximately 113 M confirmed cases of infection and 2.5 M deaths. All nations are affected by this nightmare that continues to spread. Widespread fear of this pandemic arose not only from the speed of its transmission: a rapidly changing “normal life” became a fear for everyone. Studies have mainly focused on the spread of the virus, which showed a relative decrease in high temperature, low humidity, and other environmental conditions. Therefore, this study targets the effect of weather in considering the spread of the novel coronavirus SARS-CoV-2 for some confirmed cases in Iraq. The eigenspace decomposition technique was used to analyze the effect of weather conditions on the spread of the disease. Our theoretical findings showed that the average number of confirmed COVID-19 cases has cyclic trends related to temperature, humidity, wind speed, and pressure. We supposed that the dynamic spread of COVID-19 exists at a temperature of 130 F. The minimum transmission is at 120 F, while steady behavior occurs at 160 F. On the other hand, during the spread of COVID-19, an increase in the rate of infection was seen at 125% humidity, where the minimum spread was achieved at 200%. Furthermore, wind speed showed the most significant effect on the spread of the virus. The spread decreases with a wind speed of 45 KPH, while an increase in the infectious spread appears at 50 KPH.     

The importance of the generation interval in investigating dynamics and control of new SARS-CoV-2 variants

Inferring the relative strength (i.e. the ratio of reproduction numbers) and relative speed (i.e. the difference between growth rates) of new SARS-CoV-2 variants is critical to predicting and controlling the course of the current pandemic. Analyses of new variants have primarily focused on characterizing changes in the proportion of new variants, implicitly or explicitly assuming that the relative speed remains fixed over the course of an invasion. We use a generation-interval-based framework to challenge this assumption and illustrate how relative strength and speed change over time under two idealized interventions: a constant-strength intervention like idealized vaccination or social distancing, which reduces transmission rates by a constant proportion, and a constant-speed intervention like idealized contact tracing, which isolates infected individuals at a constant rate. In general, constant-strength interventions change the relative speed of a new variant, while constant-speed interventions change its relative strength. Differences in the generation-interval distributions between variants can exaggerate these changes and modify the effectiveness of interventions. Finally, neglecting differences in generation-interval distributions can bias estimates of relative strength.     

Inferring the reproduction number using the renewal equation in heterogeneous epidemics

Real-time estimation of the reproduction number has become the focus of modelling groups around the world as the SARS-CoV-2 pandemic unfolds. One of the most widely adopted means of inference of the reproduction number is via the renewal equation, which uses the incidence of infection and the generation time distribution. In this paper, we derive a multi-type equivalent to the renewal equation to estimate a reproduction number which accounts for heterogeneity in transmissibility including through asymptomatic transmission, symptomatic isolation and vaccination. We demonstrate how use of the renewal equation that misses these heterogeneities can result in biased estimates of the reproduction number. While the bias is small with symptomatic isolation, it can be much larger with asymptomatic transmission or transmission from vaccinated individuals if these groups exhibit substantially different generation time distributions to unvaccinated symptomatic transmitters, whose generation time distribution is often well defined. The bias in estimate becomes larger with greater population size or transmissibility of the poorly characterized group. We apply our methodology to Ebola in West Africa in 2014 and the SARS-CoV-2 in the UK in 2020–2021.     

Misinformation can prevent the suppression of epidemics

The effectiveness of non-pharmaceutical interventions, such as mask-wearing and social distancing, as control measures for pandemic disease relies upon a conscientious and well-informed public who are aware of and prepared to follow advice. Unfortunately, public health messages can be undermined by competing misinformation and conspiracy theories, spread virally through communities that are already distrustful of expert opinion. In this article, we propose and analyse a simple model of the interaction between disease spread and awareness dynamics in a heterogeneous population composed of both trusting individuals who seek better quality information and will take precautionary measures, and distrusting individuals who reject better quality information and have overall riskier behaviour. We show that, as the density of the distrusting population increases, the model passes through a phase transition to a state in which major outbreaks cannot be suppressed. Our work highlights the urgent need for effective interventions to increase trust and inform the public.     

Nanoparticle approaches against SARS-CoV-2 infection

Coronavirus disease 2019 (COVID-19), caused by the highly contagious severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has become the worst pandemic disease of the current millennium. To address this crisis, therapeutic nanoparticles, including inorganic nanoparticles, lipid nanoparticles, polymeric nanoparticles, virus-like nanoparticles, and cell membrane-coated nanoparticles, have all offered compelling antiviral strategies. This article reviews these strategies in three categories: (1) nanoparticle-enabled detection of SARS-CoV-2, (2) nanoparticle-based treatment for COVID-19, and (3) nanoparticle vaccines against SARS-CoV-2. We discuss how nanoparticles are tailor-made to biointerface with the host and the virus in each category. For each nanoparticle design, we highlight its structure–function relationship that enables effective antiviral activity. Overall, nanoparticles bring numerous new opportunities to improve our response to the current COVID-19 pandemic and enhance our preparedness for future viral outbreaks.     

Impact of contact data resolution on the evaluation of interventions in mathematical models of infectious diseases

Computational models offer a unique setting to test strategies to mitigate the spread of infectious diseases, providing useful insights to applied public health. To be actionable, models need to be informed by data, which can be available at different levels of detail. While high-resolution data describing contacts between individuals are increasingly available, data gathering remains challenging, especially during a health emergency. Many models thus use synthetic data or coarse information to evaluate intervention protocols. Here, we evaluate how the representation of contact data might affect the impact of various strategies in models, in the realm of COVID-19 transmission in educational and work contexts. Starting from high-resolution contact data, we use detailed to coarse data representations to inform a model of SARS-CoV-2 transmission and simulate different mitigation strategies. We find that coarse data representations estimate a lower risk of superspreading events. However, the rankings of protocols according to their efficiency or cost remain coherent across representations, ensuring the consistency of model findings to inform public health advice. Caution should be taken, however, on the quantitative estimations of those benefits and costs triggering the adoption of protocols, as these may depend on data representation.     

Beyond R0: heterogeneity in secondary infections and probabilistic epidemic forecasting

The basic reproductive number, R₀, is one of the most common and most commonly misapplied numbers in public health. Often used to compare outbreaks and forecast pandemic risk, this single number belies the complexity that different epidemics can exhibit, even when they have the same R₀. Here, we reformulate and extend a classic result from random network theory to forecast the size of an epidemic using estimates of the distribution of secondary infections, leveraging both its average R₀ and the underlying heterogeneity. Importantly, epidemics with lower R₀ can be larger if they spread more homogeneously (and are therefore more robust to stochastic fluctuations). We illustrate the potential of this approach using different real epidemics with known estimates for R₀, heterogeneity and epidemic size in the absence of significant intervention. Further, we discuss the different ways in which this framework can be implemented in the data-scarce reality of emerging pathogens. Lastly, we demonstrate that without data on the heterogeneity in secondary infections for emerging infectious diseases like COVID-19 the uncertainty in outbreak size ranges dramatically. Taken together, our work highlights the critical need for contact tracing during emerging infectious disease outbreaks and the need to look beyond R₀.     

Inferring pandemic growth rates from sequence data

Using sequence data to infer population dynamics is playing an increasing role in the analysis of outbreaks. The most common methods in use, based on coalescent inference, have been widely used but not extensively tested against simulated epidemics. Here, we use simulated data to test the ability of both parametric and non-parametric methods for inference of effective population size (coded in the popular BEAST package) to reconstruct epidemic dynamics. We consider a range of simulations centred on scenarios considered plausible for pandemic influenza, but our conclusions are generic for any exponentially growing epidemic. We highlight systematic biases in non-parametric effective population size estimation. The most prominent such bias leads to the false inference of slowing of epidemic spread in the recent past even when the real epidemic is growing exponentially. We suggest some sampling strategies that could reduce (but not eliminate) some of the biases. Parametric methods can correct for these biases if the infected population size is large. We also explore how some poor sampling strategies (e.g. that over-represent epidemiologically linked clusters of cases) could dramatically exacerbate bias in an uncontrolled manner. Finally, we present a simple diagnostic indicator, based on coalescent density and which can easily be applied to reconstructed phylogenies, that identifies time-periods for which effective population size estimates are less likely to be biased. We illustrate this with an application to the 2009 H1N1 pandemic.     

Agent-based simulations for protecting nursing homes with prevention and vaccination strategies

Due to its high lethality among older people, the safety of nursing homes has been of central importance during the COVID-19 pandemic. With test procedures and vaccines becoming available at scale, nursing homes might relax prohibitory measures while controlling the spread of infections. By control we mean that each index case infects less than one other person on average. Here, we develop an agent-based epidemiological model for the spread of SARS-CoV-2 calibrated to Austrian nursing homes to identify optimal prevention strategies. We find that the effectiveness of mitigation testing depends critically on test turnover time (time until test result), the detection threshold of tests and mitigation testing frequencies. Under realistic conditions and in absence of vaccinations, we find that mitigation testing of employees only might be sufficient to control outbreaks if tests have low turnover times and detection thresholds. If vaccines that are 60% effective against high viral load and transmission are available, control is achieved if 80% or more of the residents are vaccinated, even without mitigation testing and if residents are allowed to have visitors. Since these results strongly depend on vaccine efficacy against infection, retention of testing infrastructures, regular testing and sequencing of virus genomes is advised to enable early identification of new variants of concern.