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

Data-driven mathematical modeling approaches for COVID-19: A survey

In this review, we successively present the methods for phenomenological modeling of the evolution of reported and unreported cases of COVID-19, both in the exponential phase of growth and then in a complete epidemic wave. After the case of an isolated wave, we present the modeling of several successive waves separated by endemic stationary periods. Then, we treat the case of multi-compartmental models without or with age structure. Eventually, we review the literature, based on 260 articles selected in 11 sections, ranging from the medical survey of hospital cases to forecasting the dynamics of new cases in the general population. This review favors the phenomenological approach over the mechanistic approach in the choice of references and provides simulations of the evolution of the number of observed cases of COVID-19 for 10 states (California, China, France, India, Israel, Japan, New York, Peru, Spain and United Kingdom).

The role of digital tools and emerging devices in COVID-19 contact tracing during the first 18 months of the pandemic: a systematic review

BACKGROUND

Contact tracing is a public health intervention implemented in synergy with other preventive measures to curb epidemics, like the coronavirus pandemic. The development and use of digital devices have increased worldwide to enhance the contact tracing process. The aim of the study was to evaluate the effectiveness and impact of tracking coronavirus disease 2019 (COVID-19) patients using digital solutions.

METHODS

Observational studies on digital contact tracing (DCT), published 2020-21, in English were identified through a systematic literature review performed on nine online databases. An ad hoc form was used for data extraction of relevant information. Quality assessment of the included studies was performed with validated tools. A qualitative synthesis of the findings is reported.

RESULTS

Over 8000 records were identified and 37 were included in the study: 24 modelling and 13 population-based studies. DCT improved the identification of close contacts of COVID-19 cases and reduced the effective reproduction number of COVID-19-related infections and deaths by over 60%. It impacted positively on societal and economic costs, in terms of lockdowns and use of resources, including staffing. Privacy and security issues were reported in 27 studies.

CONCLUSIONS

DCT contributed to curbing the COVID-19 pandemic, especially with the high uptake rate of the devices and in combination with other public health measures, especially conventional contact tracing. The main barriers to the implementation of the devices are uptake rate, security and privacy issues. Public health digitalization and contact tracing are the keys to countries' emergency preparedness for future health crises.

Effects of User Experience in Automated Information Processing on Perceived Usefulness of Digital Contact-Tracing Apps: Cross-Sectional Survey Study

BACKGROUND

In pandemic situations, digital contact tracing (DCT) can be an effective way to assess one's risk of infection and inform others in case of infection. DCT apps can support the information gathering and analysis processes of users aiming to trace contacts. However, users' use intention and use of DCT information may depend on the perceived benefits of contact tracing. While existing research has examined acceptance in DCT, automation-related user experience factors have been overlooked.

OBJECTIVE

We pursued three goals: (1) to analyze how automation-related user experience (ie, perceived trustworthiness, traceability, and usefulness) relates to user behavior toward a DCT app, (2) to contextualize these effects with health behavior factors (ie, threat appraisal and moral obligation), and (3) to collect qualitative data on user demands for improved DCT communication.

METHODS

Survey data were collected from 317 users of a nationwide-distributed DCT app during the COVID-19 pandemic after it had been in app stores for >1 year using a web-based convenience sample. We assessed automation-related user experience. In addition, we assessed threat appraisal and moral obligation regarding DCT use to estimate a partial least squares structural equation model predicting use intention. To provide practical steps to improve the user experience, we surveyed users' needs for improved communication of information via the app and analyzed their responses using thematic analysis.

RESULTS

Data validity and perceived usefulness showed a significant correlation of r=0.38 (P<.001), goal congruity and perceived usefulness correlated at r=0.47 (P<.001), and result diagnosticity and perceived usefulness had a strong correlation of r=0.56 (P<.001). In addition, a correlation of r=0.35 (P<.001) was observed between Subjective Information Processing Awareness and perceived usefulness, suggesting that automation-related changes might influence the perceived utility of DCT. Finally, a moderate positive correlation of r=0.47 (P<.001) was found between perceived usefulness and use intention, highlighting the connection between user experience variables and use intention. Partial least squares structural equation modeling explained 55.6% of the variance in use intention, with the strongest direct predictor being perceived trustworthiness (β=.54; P<.001) followed by moral obligation (β=.22; P<.001). Based on the qualitative data, users mainly demanded more detailed information about contacts (eg, place and time of contact). They also wanted to share information (eg, whether they wore a mask) to improve the accuracy and diagnosticity of risk calculation.

CONCLUSIONS

The perceived result diagnosticity of DCT apps is crucial for perceived trustworthiness and use intention. By designing for high diagnosticity for the user, DCT apps could improve their support in the action regulation of users, resulting in higher perceived trustworthiness and use in pandemic situations. In general, automation-related user experience has greater importance for use intention than general health behavior or experience.

Mobility census for monitoring rapid urban development

Monitoring urban structure and development requires high-quality data at high spatio-temporal resolution. While traditional censuses have provided foundational insights into demographic and socio-economic aspects of urban life, their pace may not always align with the pace of urban development. To complement these traditional methods, we explore the potential of analysing alternative big-data sources, such as human mobility data. However, these often noisy and unstructured big data pose new challenges. Here, we propose a method to extract meaningful explanatory variables and classifications from such data. Using movement data from Beijing, which are produced as a by-product of mobile communication, we show that meaningful features can be extracted, revealing, for example, the emergence and absorption of subcentres. This method allows the analysis of urban dynamics at a high-spatial resolution (here 500 m) and near real-time frequency, and high computational efficiency, which is especially suitable for tracing event-driven mobility changes and their impact on urban structures.

Privacy and security concerns with passively collected location data for digital contact tracing among U.S. college students

People continue to use technology in new ways, and how governments harness digital information should consider privacy and security concerns. During COVID19, numerous countries deployed digital contact tracing that collect location data from user's smartphones. However, these apps had low adoption rates and faced opposition. We launched an interdisciplinary study to evaluate smartphone location data concerns among college students in the US. Using interviews and a large survey, we find that college students have higher concerns regarding privacy, and place greater trust in local government with their location data. We discuss policy recommendations for implementing improved contact tracing efforts.

Community-Based Digital Contact Tracing of Emerging Infectious Diseases: Design and Implementation Study With Empirical COVID-19 Cases

BACKGROUND

Contact tracing for containing emerging infectious diseases such as COVID-19 is resource intensive and requires digital transformation to enable timely decision-making.

OBJECTIVE

This study demonstrates the design and implementation of digital contact tracing using multimodal health informatics to efficiently collect personal information and contain community outbreaks. The implementation of digital contact tracing was further illustrated by 3 empirical SARS-CoV-2 infection clusters.

METHODS

The implementation in Changhua, Taiwan, served as a demonstration of the multisectoral informatics and connectivity between electronic health systems needed for digital contact tracing. The framework incorporates traditional travel, occupation, contact, and cluster approaches and a dynamic contact process enabled by digital technology. A centralized registry system, accessible only to authorized health personnel, ensures privacy and data security. The efficiency of the digital contact tracing system was evaluated through a field study in Changhua.

RESULTS

The digital contact tracing system integrates the immigration registry, communicable disease report system, and national health records to provide real-time information about travel, occupation, contact, and clusters for potential contacts and to facilitate a timely assessment of the risk of COVID-19 transmission. The digitalized system allows for informed decision-making regarding quarantine, isolation, and treatment, with a focus on personal privacy. In the first cluster infection, the system monitored 665 contacts and isolated 4 (0.6%) cases; none of the contacts (0/665, 0%) were infected during quarantine. The estimated reproduction number of 0.92 suggests an effective containment strategy for preventing community-acquired outbreak. The system was also used in a cluster investigation involving foreign workers, where none of the 462 contacts (0/462, 0%) tested positive for SARS-CoV-2.

CONCLUSIONS

By integrating the multisectoral database, the contact tracing process can be digitalized to provide the information required for risk assessment and decision-making in a timely manner to contain a community-acquired outbreak when facing the outbreak of emerging infectious disease.

Characterizing the role of human behavior in the effectiveness of contact-tracing applications

Introduction

Although numerous countries relied on contact-tracing (CT) applications as an epidemic control measure against the COVID-19 pandemic, the debate around their effectiveness is still open. Most studies indicate that very high levels of adoption are required to stop disease progression, placing the main interest of policymakers in promoting app adherence. However, other factors of human behavior, like delays in adherence or heterogeneous compliance, are often disregarded.

Methods

To characterize the impact of human behavior on the effectiveness of CT apps we propose a multilayer network model reflecting the co-evolution of an epidemic outbreak and the app adoption dynamics over a synthetic population generated from survey data. The model was initialized to produce epidemic outbreaks resembling the first wave of the COVID-19 pandemic and was used to explore the impact of different changes in behavioral features in peak incidence and maximal prevalence.

Results

The results corroborate the relevance of the number of users for the effectiveness of CT apps but also highlight the need for early adoption and, at least, moderate levels of compliance, which are factors often not considered by most policymakers.

Discussion

The insight obtained was used to identify a bottleneck in the implementation of several apps, such as the Spanish CT app, where we hypothesize that a simplification of the reporting system could result in increased effectiveness through a rise in the levels of compliance.

Transformative practice and its interactional challenges in COVID-19 telephone contact tracing in Flanders

This article focuses on transformative interactional practice in COVID-19 contact tracing telephone calls in Flanders (Belgium). It is based on a large corpus of recorded telephone conversations conducted by COVID-19 contact tracers with index patients in the period mid-2020 to mid-2022. The calls were conducted through government-contracted commercial call centers. For nearly 2 years and applied country-wide, this was the most prominent strategy in Belgium for breaking transmission chains. COVID-19 telephone contact tracing with infected patients counts as transformative professional work in two ways. First, in addition to the registration of recent contacts in a relevant time window, the work is oriented to awareness-raising about how patients and their co-dwellers can and should adjust their behavior by attending actively to critical aspects of the pandemic during an individual period of (potential) infection. This is the terrain of advice, interdictions and recommendations about quarantine, isolation, personal hygiene, etc. In addition, the focus on interactional attention indexes patients' affect and emotions (e.g., anxiety, worry, or anger) in a period of health uncertainty and social isolation. The transformative work thus depends on successfully established rapport and empathetic, responsive behavior. Our analysis of the recorded conversational sequences focuses on the complexities of client-sensitive and responsive transformative sequences and highlights the constraints and affordances which surround the interactional task of 'instructional awareness raising' which is central to telephone contact tracing. Specifically, we detail the following dimensions of transformative sequences: (i) how do contact tracers deal with the knowledge status of clients, (ii) their use of upgrading/downgrading formulations, (iii) the use of humor and other mitigating strategies, and (iv) how contact tracers attend to interactional displays of affect and emotion. In a final section, we tie together our observations about the communication of particularized advice in a context of general measures through the twin notions of categorization/particularization-work. The findings in this paper are limited to the first step in the chain of contact tracing, i.e., telephone calls with tested and infected citizens.

A Survey on harnessing the Applications of Mobile Computing in Healthcare during the COVID-19 Pandemic: Challenges and Solutions

The COVID-19 pandemic ravaged almost every walk of life but it triggered many challenges for the healthcare system, globally. Different cutting-edge technologies such as Internet of things (IoT), machine learning, Virtual Reality (VR), Big data, Blockchain etc. have been adopted to cope with this menace. In this regard, various surveys have been conducted to highlight the importance of these technologies. However, among these technologies, the role of mobile computing is of paramount importance which is not found in the existing literature. Hence, this survey in mainly targeted to highlight the significant role of mobile computing in alleviating the impacts of COVID-19 in healthcare sector. The major applications of mobile computing such as software-based solutions, hardware-based solutions and wireless communication-based support for diagnosis, prevention, self-symptom reporting, contact tracing, social distancing, telemedicine and treatment related to coronavirus are discussed in detailed and comprehensive fashion. A state-of-the-art work is presented to identify the challenges along with possible solutions in adoption of mobile computing with respect to COVID-19 pandemic. Hopefully, this research will help the researchers, policymakers and healthcare professionals to understand the current research gaps and future research directions in this domain. To the best level of our knowledge, this is the first survey of its type to address the COVID-19 pandemic by exploring the holistic contribution of mobile computing technologies in healthcare area.

Comparative effectiveness of contact tracing interventions in the context of the COVID-19 pandemic: a systematic review

Contact tracing is a non-pharmaceutical intervention (NPI) widely used in the control of the COVID-19 pandemic. Its effectiveness may depend on a number of factors including the proportion of contacts traced, delays in tracing, the mode of contact tracing (e.g. forward, backward or bidirectional contact training), the types of contacts who are traced (e.g. contacts of index cases or contacts of contacts of index cases), or the setting where contacts are traced (e.g. the household or the workplace). We performed a systematic review of the evidence regarding the comparative effectiveness of contact tracing interventions. 78 studies were included in the review, 12 observational (ten ecological studies, one retrospective cohort study and one pre-post study with two patient cohorts) and 66 mathematical modelling studies. Based on the results from six of the 12 observational studies, contact tracing can be effective at controlling COVID-19. Two high quality ecological studies showed the incremental effectiveness of adding digital contact tracing to manual contact tracing. One ecological study of intermediate quality showed that increases in contact tracing were associated with a drop in COVID-19 mortality, and a pre-post study of acceptable quality showed that prompt contact tracing of contacts of COVID-19 case clusters / symptomatic individuals led to a reduction in the reproduction number R. Within the seven observational studies exploring the effectiveness of contact tracing in the context of the implementation of other non-pharmaceutical interventions, contact tracing was found to have an effect on COVID-19 epidemic control in two studies and not in the remaining five studies. However, a limitation in many of these studies is the lack of description of the extent of implementation of contact tracing interventions. Based on the results from the mathematical modelling studies, we identified the following highly effective policies: (1) manual contact tracing with high tracing coverage and either medium-term immunity, highly efficacious isolation/quarantine and/ or physical distancing (2) hybrid manual and digital contact tracing with high app adoption with highly effective isolation/ quarantine and social distancing, (3) secondary contact tracing, (4) eliminating contact tracing delays, (5) bidirectional contact tracing, (6) contact tracing with high coverage in reopening educational institutions. We also highlighted the role of social distancing to enhance the effectiveness of some of these interventions in the context of 2020 lockdown reopening. While limited, the evidence from observational studies shows a role for manual and digital contact tracing in controlling the COVID-19 epidemic. More empirical studies accounting for the extent of contact tracing implementation are required.

Feasibility of digital contact tracing in low-income settings - pilot trial for a location-based DCT app

BACKGROUND

Data about the effectiveness of digital contact tracing are based on studies conducted in countries with predominantly high- or middle-income settings. Up to now, little research is done to identify specific problems for the implementation of such technique in low-income countries.

METHODS

A Bluetooth-assisted GPS location-based digital contact tracing (DCT) app was tested by 141 participants during 14 days in a hospital in Monrovia, Liberia in February 2020. The DCT app was compared to a paper-based reference system. Hits between participants and 10 designated infected participants were recorded simultaneously by both methods. Additional data about GPS and Bluetooth adherence were gathered and surveys to estimate battery consumption and app adherence were conducted. DCT apps accuracy was evaluated in different settings.

RESULTS

GPS coordinates from 101/141 (71.6%) participants were received. The number of hours recorded by the participants during the study period, true Hours Recorded (tHR), was 496.3 h (1.1% of maximum Hours recordable) during the study period. With the paper-based method 1075 hits and with the DCT app five hits of designated infected participants with other participants have been listed. Differences between true and maximum recording times were due to failed permission settings (45%), data transmission issues (11.3%), of the participants 10.1% switched off GPS and 32.5% experienced other technical or compliance problems. In buildings, use of Bluetooth increased the accuracy of the DCT app (GPS + BT 22.9 m ± 21.6 SD vs. GPS 60.9 m ± 34.7 SD; p = 0.004). GPS accuracy in public transportation was 10.3 m ± 10.05 SD with a significant (p = 0.007) correlation between precision and phone brand. GPS resolution outdoors was 10.4 m ± 4.2 SD.

CONCLUSION

In our study several limitations of the DCT together with the impairment of GPS accuracy in urban settings impede the solely use of a DCT app. It could be feasible as a supplement to traditional manual contact tracing. DKRS, DRKS00029327 . Registered 20 June 2020 - Retrospectively registered.

Visual Analytics Platform for Centralized Covid-19 Digital Contact Tracing

The Covid-19 pandemic and its dramatic worldwide impact have required global multidisciplinary actions to mitigate its effects. Mobile phone activity-based digital contact tracing (DCT) via Bluetooth Low Energy (BLE) technology has been considered a powerful pandemic monitoring tool, yet it sparked a controversial debate about privacy risks for people. In order to explore the potential benefits of a DCT system in the context of Occupational Risk Prevention, this paper presents the potential of Visual Analytics methods to summarize and extract relevant information from complex DCT data collected during a long-term experiment at our research centre. Visual tools were combined with quantitative metrics to provide insights into contact patterns among volunteers. Results showed that crucial actors such as participants acting as bridges between groups could be easily identified - ultimately allowing for making more informed management decisions aimed at containing the potential spread of a disease.

Population-wide measures due to the COVID-19 pandemic and exposome changes in the general population of Cyprus in March-May 2020

Non-pharmacological interventions (e.g., stay-at-home orders, school closures, physical distancing) implemented during the COVID-19 pandemic are expected to have modified routines and lifestyles, eventually impacting key exposome parameters, including, among others, physical activity, diet and cleaning habits. The objectives were to describe the exposomic profile of the general Cypriot population and compliance to the population-wide measures implemented during March-May 2020 to lower the risk of SARS-CoV-2 transmission, and to simulate the population-wide measures' effect on social contacts and SARS-CoV-2 spread. A survey was conducted in March-May 2020 capturing different exposome parameters, e.g., individual characteristics, lifestyle/habits, time spent and contacts at home/work/elsewhere. We described the exposome parameters and their correlations. In an exposome-wide association analysis, we used the number of hours spent at home as an indicator of compliance to the measures. We generated synthetic human proximity networks, before and during the measures using the dynamic-[Formula: see text]¹ model and simulated SARS-CoV-2 transmission (i.e., to identify possible places where higher transmission/number of cases could originate from) on the networks with a dynamic Susceptible-Exposed-Infectious-Recovered model. Overall, 594 respondents were included in the analysis (mean age 45.7 years, > 50% in very good health and communicating daily with friends/family via phone/online). The median number of contacts at home and at work decreased during the measures (from 3 to 2 and from 12 to 0, respectively) and the hours spent at home increased, indicating compliance with the measures. Increased time spent at home during the measures was associated with time spent at work before the measures (β= -0.87, 95% CI [-1.21,-0.53]) as well as with being retired vs employed (β= 2.32, 95% CI [1.70, 2.93]). The temporal network analysis indicated that most cases originated at work, while the synthetic human proximity networks adequately reproduced the observed SARS-CoV-2 spread. Exposome approaches (i.e., holistic characterization of the spatiotemporal variation of multiple exposures) would aid the comprehensive description of population-wide measures' impact and explore how behaviors and networks may shape SARS-CoV-2 transmission.

Understanding the impact of digital contact tracing during the COVID-19 pandemic

Digital contact tracing (DCT) applications have been introduced in many countries to aid the containment of COVID-19 outbreaks. Initially, enthusiasm was high regarding their implementation as a non-pharmaceutical intervention (NPI). However, no country was able to prevent larger outbreaks without falling back to harsher NPIs. Here, we discuss results of a stochastic infectious-disease model that provide insights in how the progression of an outbreak and key parameters such as detection probability, app participation and its distribution, as well as engagement of users impact DCT efficacy informed by results of empirical studies. We further show how contact heterogeneity and local contact clustering impact the intervention's efficacy. We conclude that DCT apps might have prevented cases on the order of single-digit percentages during single outbreaks for empirically plausible ranges of parameters, ignoring that a substantial part of these contacts would have been identified by manual contact tracing. This result is generally robust against changes in network topology with exceptions for homogeneous-degree, locally-clustered contact networks, on which the intervention prevents more infections. An improvement of efficacy is similarly observed when app participation is highly clustered. We find that DCT typically averts more cases during the super-critical phase of an epidemic when case counts are rising and the measured efficacy therefore depends on the time of evaluation.

Dutch public health professionals’ perspectives and needs regarding citizen involvement in COVID-19 contact tracing through digital support tools: an exploratory qualitative study

Background

Contact tracing (CT) is an important, but resource-intensive tool to control outbreaks of communicable diseases. Under pandemic circumstances, public health services may not have sufficient resources at their disposal to effectively facilitate CT. This may be addressed by giving cases and their contact persons more autonomy and responsibility in the execution of CT by public health professionals, through digital contact tracing support tools (DCTS-tools). However, the application of this approach has not yet been systematically investigated from the perspective of public health practice. Therefore, we investigated public health professionals’ perspectives and needs regarding involving cases and contact persons in CT for COVID-19 through DCTS-tools.

Methods

Between October 2020 and February 2021, we conducted online semi-structured interviews (N = 17) with Dutch public health professionals to explore their perspectives and needs regarding the involvement of cases and contact persons in CT for COVID-19 through DCTS-tools, in the contact identification, notification, and monitoring stages of the CT-process. Interviews were audio recorded and transcribed verbatim. A thematic analysis was performed.

Results

Four main themes related to Dutch public health professionals’ perspectives and needs regarding involving cases and contact persons in CT for COVID-19 through DCTS-tools emerged from the data: ‘Distinct characteristics of CT with DCTS-tools’; ‘Anticipated benefits and challenges of CT for COVID-19 with DCTS- tools’; ‘Circumstances in CT for COVID-19 that permit or constrain the application of DCTS-tools’; and ‘Public health professionals’ needs regarding the development and application of DCTS-tools for CT’. Public health professionals seem to have a positive attitude towards involving cases and contact persons through DCTS-tools. Public health professionals’ (positive) attitudes seem conditional on the circumstances under which CT is performed, and the fulfilment of their needs in the development and application of DCTS-tools.

Conclusions

Dutch public health professionals seem positive towards involving cases and contact persons in CT for COVID-19 through DCTS-tools. Through adequate implementation of DCTS-tools in the CT-process, anticipated challenges can be overcome. Future research should investigate the perspectives and needs of cases and contact persons regarding DCTS-tools, and the application of DCTS-tools in practice.

Statistical inference links data and theory in network science

The number of network science applications across many different fields has been rapidly increasing. Surprisingly, the development of theory and domain-specific applications often occur in isolation, risking an effective disconnect between theoretical and methodological advances and the way network science is employed in practice. Here we address this risk constructively, discussing good practices to guarantee more successful applications and reproducible results. We endorse designing statistically grounded methodologies to address challenges in network science. This approach allows one to explain observational data in terms of generative models, naturally deal with intrinsic uncertainties, and strengthen the link between theory and applications.

Theoretical models and structures recovered from measured data serve for analysis of complex networks. The authors discuss here existing gaps between theoretical methods and real-world applied networks, and potential ways to improve the interplay between theory and applications.

Adoption of Covid-19 contact tracing app by extending UTAUT theory: Perceived disease threat as moderator

Objectives

Contact tracing applications are technological solutions that can quickly trace and notify users of their potential exposure to the Covid-19 virus and help contain the spread of the disease. However, extant research delineating the various factors predicting the adoption of contact tracing apps is scant. The study's primary objective is to develop and validate a research model based on the unified theory of acceptance and use of technology (UTAUT), health belief model (HBM), perceived privacy risk and perceived security risk to understand the adoption of contact tracing application.

Methods

An online survey was carried out among users of the 'Aarogya Setu' contact tracing app in India. The partial least squares structural equation modelling (PLS-SEM) tool was employed to analyze data from 307 respondents.

Results

The results showed that performance expectancy, social influence, and facilitating conditions positively influenced users' intention to adopt the app. In contrast, perceived privacy and security risks were significant barriers to app adoption. Perceived disease threat as a moderator mitigated the adverse impact of perceived privacy risk on users' intention to adopt contact tracing apps.

Conclusions

The current study gives insights on both drivers and barriers to the adoption of contract tracing applications. Various theoretical and practical implications of significance are provided for academicians and practitioners to effectively promote app adoption to tackle the Covid-19 pandemic.

Micro-level social structures and the success of COVID-19 national policies

Similar policies in response to the COVID-19 pandemic have resulted in different success rates. Although many factors are responsible for the variances in policy success, our study shows that the micro-level structure of person-to-person interactions-measured by the average household size and in-person social contact rate-can be an important explanatory factor. To create an explainable model, we propose a network transformation algorithm to create a simple and computationally efficient scaled network based on these micro-level parameters, as well as incorporate national-level policy data in the network dynamic for SEIR simulations. The model was validated during the early stages of the COVID-19 pandemic, which demonstrated that it can reproduce the dynamic ordinal ranking and trend of infected cases of various European countries that are sufficiently similar in terms of some socio-cultural factors. We also performed several counterfactual analyses to illustrate how policy-based scenario analysis can be performed rapidly and easily with these explainable models.

Smart Building Technologies in Response to COVID-19

The COVID-19 pandemic has had a huge impact on society. Scientists are working to mitigate the impact in many ways. As a field closely related to human life, building engineering can make a great contribution. In this article, we started with the concept of the smart building as our guide. The impact of COVID-19 on daily energy consumption, information and communication technology, the ventilation of the interior environment of buildings, and the higher demand for new energy technologies such as electric vehicles is an entry point. We discuss how the concept of the smart building and related technologies (refrigeration, measurement, sensor networks, robotics, local energy generation, and storage) could help human society respond to the pandemic. We also analyze the current problems and difficulties that smart buildings face and the possible future directions of this technology.

Extending the Unified Theory of Acceptance and Use of Technology for COVID-19 Contact Tracing Application by Malaysian Users

The Malaysian government has mobilized its strength to confront the current COVID-19 pandemic and has sought to develop and implement a digital contact tracking application, making it an integral part of the exit strategy from the lockdown. These applications record which users have been near one another. When a user is confirmed with COVID-19, app users who have recently been near this person are notified. The effectiveness of these applications is determined by the users’ willingness to install and use them. Therefore, this research aims at identifying the factors that would stimulate or slow down the adoption of a contact-tracing app. It proposes solutions to mitigate the impact of the factors affecting the user’s acceptance of COVID-19 Digital Contact Tracing Apps. A quantitative approach was followed in this research, where an electronic survey was spread in Malaysia, for the objective of data collection, considering the previous discussion of the results. Then, using PLS-SEM, the collected data were analyzed statistically. The findings of this study indicate that the unified theory of acceptance and use of technology (UTAUT) factors (Performance Expectancy, Effort Expectancy, Social Influence, Facilities Condition) were significant predictors of MySejahtera application adoption among citizens in Malaysia. On the other hand, the factors of app-related privacy concern were found to be insignificant for MySejahtera application adoption.

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 COVID-19 Pandemic: Topology-aware optimization of vaccination strategy for minimizing virus spreading

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Paper discusses the relevance of the adoption of ad-hoc vaccination strategies.

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Paper shows how to evaluate the impact of different vaccination strategy by considering network-based models.

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Tailored interventions, e.g., vaccination, applied on central nodes of these networks may efficiently stop the propagation of an infection.

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The way node "centrality" is defined is the key to curb infection spreading.

The mitigation of an infectious disease spreading has recently gained considerable attention from the research community. It may be obtained by adopting sanitary measurements (e.g., vaccination, wearing masks), social rules (e.g., social distancing), together with an extensive vaccination campaign. Vaccination is currently the primary way for mitigating the Coronavirus Disease (COVID-19) outbreak without severe lockdown. Its effectiveness also depends on the number and timeliness of administrations and thus demands strict prioritization criteria. Almost all countries have prioritized similar classes of exposed workers: healthcare professionals and the elderly, obtaining to maximize the survival of patients and years of life saved. Nevertheless, the virus is currently spreading at high rates, and any prioritization criterion so far adopted did not account for the structural organization of the contact networks.

We reckon that a network where nodes are people while the edges represent their social contacts may efficiently model the virus’s spreading. It is known that tailored interventions (e.g., vaccination) on central nodes may efficiently stop the propagation, thereby eliminating the “bridge edges.” We then introduce such a model and consider both synthetic and real datasets. We present the benefits of a topology-aware versus an age-based vaccination strategy to mitigate the spreading of the virus. The code is available at https://github.com/mazzalab/playgrounds.

Epidemic spreading and digital contact tracing: Effects of heterogeneous mixing and quarantine failures

Contact tracing via digital tracking applications installed on mobile phones is an important tool for controlling epidemic spreading. Its effectivity can be quantified by modifying the standard methodology for analyzing percolation and connectivity of contact networks. We apply this framework to networks with varying degree distributions, numbers of application users, and probabilities of quarantine failures. Further, we study structured populations with homophily and heterophily and the possibility of degree-targeted application distribution. Our results are based on a combination of explicit simulations and mean-field analysis. They indicate that there can be major differences in the epidemic size and epidemic probabilities which are equivalent in the normal susceptible-infectious-recovered (SIR) processes. Further, degree heterogeneity is seen to be especially important for the epidemic threshold but not as much for the epidemic size. The probability that tracing leads to quarantines is not as important as the application adoption rate. Finally, both strong homophily and especially heterophily with regard to application adoption can be detrimental. Overall, epidemic dynamics are very sensitive to all of the parameter values we tested out, which makes the problem of estimating the effect of digital contact tracing an inherently multidimensional problem.

Determinants of the acceptance and adoption of a digital contact tracing tool during the COVID-19 pandemic in Singapore

The motivations that govern the adoption of digital contact tracing (DCT) tools are complex and not well understood. Hence, we assessed the factors influencing the acceptance and adoption of Singapore's national DCT tool - TraceTogether - during the COVID-19 pandemic. We surveyed 3943 visitors of Tan Tock Seng Hospital from July 2020 to February 2021 and stratified the analyses into three cohorts. Each cohort was stratified based on the time when significant policy interventions were introduced to increase the adoption of TraceTogether. Binary logistic regression was preceded by principal components analysis to reduce the Likert items. Respondents who 'perceived TraceTogether as useful and necessary' had higher likelihood of accepting it but those with 'Concerns about personal data collected by TraceTogether' had lower likelihood of accepting and adopting the tool. The injunctive and descriptive social norms were also positively associated with both the acceptance and adoption of the tool. Liberal individualism was mixed in the population and negatively associated with the acceptance and adoption of TraceTogether. Policy measures to increase the uptake of a national DCT bridged the digital divide and accelerated its adoption. However, good public communications are crucial to address the barriers of acceptance to improve voluntary uptake widespread adoption.

Individual risk perception and empirical social structures shape the dynamics of infectious disease outbreaks

The dynamics of a spreading disease and individual behavioral changes are entangled processes that have to be addressed together in order to effectively manage an outbreak. Here, we relate individual risk perception to the adoption of a specific set of control measures, as obtained from an extensive large-scale survey performed via Facebook-involving more than 500,000 respondents from 64 countries-showing that there is a "one-to-one" relationship between perceived epidemic risk and compliance with a set of mitigation rules. We then develop a mathematical model for the spreading of a disease-sharing epidemiological features with COVID-19-that explicitly takes into account non-compliant individual behaviors and evaluates the impact of a population fraction of infectious risk-deniers on the epidemic dynamics. Our modeling study grounds on a wide set of structures, including both synthetic and more than 180 real-world contact patterns, to evaluate, in realistic scenarios, how network features typical of human interaction patterns impact the spread of a disease. In both synthetic and real contact patterns we find that epidemic spreading is hindered for decreasing population fractions of risk-denier individuals. From empirical contact patterns we demonstrate that connectivity heterogeneity and group structure significantly affect the peak of hospitalized population: higher modularity and heterogeneity of social contacts are linked to lower peaks at a fixed fraction of risk-denier individuals while, at the same time, such features increase the relative impact on hospitalizations with respect to the case where everyone correctly perceive the risks.

Non-Pharmaceutical Interventions against COVID-19 Pandemic: Review of Contact Tracing and Social Distancing Technologies, Protocols, Apps, Security and Open Research Directions

The COVID-19 Pandemic has punched a devastating blow on the majority of the world's population. Millions of people have been infected while hundreds of thousands have died of the disease throwing many families into mourning and other psychological torments. It has also crippled the economy of many countries of the world leading to job losses, high inflation, and dwindling Gross Domestic Product (GDP). The duo of social distancing and contact tracing are the major technological-based non-pharmaceutical public health intervention strategies adopted for combating the dreaded disease. These technologies have been deployed by different countries around the world to achieve effective and efficient means of maintaining appropriate distance and tracking the transmission pattern of the diseases or identifying those at high risk of infecting others. This paper aims to synthesize the research efforts on contact tracing and social distancing to minimize the spread of COVID-19. The paper critically and comprehensively reviews contact tracing technologies, protocols, and mobile applications (apps) that were recently developed and deployed against the coronavirus disease. Furthermore, the paper discusses social distancing technologies, appropriate methods to maintain distances, regulations, isolation/quarantine, and interaction strategies. In addition, the paper highlights different security/privacy vulnerabilities identified in contact tracing and social distancing technologies and solutions against these vulnerabilities. We also x-rayed the strengths and weaknesses of the various technologies concerning their application in contact tracing and social distancing. Finally, the paper proposed insightful recommendations and open research directions in contact tracing and social distancing that could assist researchers, developers, and governments in implementing new technological methods to combat the menace of COVID-19.

Epidemics with asymptomatic transmission: Subcritical phase from recursive contact tracing

The challenges presented by the COVID-19 epidemic have created a renewed interest in the development of new methods to combat infectious diseases, and it has shown the importance of preparedness for possible future diseases. A prominent property of the SARS-CoV-2 transmission is the significant fraction of asymptomatic transmission. This may influence the effectiveness of the standard contact tracing procedure for quarantining potentially infected individuals. However, the effects of asymptomatic transmission on the epidemic threshold of epidemic spreading on networks have rarely been studied explicitly. Here we study the critical percolation transition for an arbitrary disease with a nonzero asymptomatic rate in a simple epidemic network model in the presence of a recursive contact tracing algorithm for instant quarantining. We find that, above a certain fraction of asymptomatic transmission, standard contact tracing loses its ability to suppress spreading below the epidemic threshold. However, we also find that recursive contact tracing opens a possibility to contain epidemics with a large fraction of asymptomatic or presymptomatic transmission. In particular, we calculate the required fraction of network nodes participating in the contact tracing for networks with arbitrary degree distributions and for varying recursion depths and discuss the influence of recursion depth and asymptomatic rate on the epidemic percolation phase transition. We anticipate recursive contact tracing to provide a basis for digital, app-based contact tracing tools that extend the efficiency of contact tracing to diseases with a large fraction of asymptomatic transmission.

Designing Better Exposure Notification Apps: The Role of Persuasive Design

BACKGROUND

Digital contact tracing apps have been deployed worldwide to limit the spread of COVID-19 during this pandemic and to facilitate the lifting of public health restrictions. However, due to privacy-, trust-, and design-related issues, the apps are yet to be widely adopted. This calls for an intervention to enable a critical mass of users to adopt them.

OBJECTIVE

The aim of this paper is to provide guidelines to design contact tracing apps as persuasive technologies to make them more appealing and effective.

METHODS

We identified the limitations of the current contact tracing apps on the market using the Government of Canada's official exposure notification app (COVID Alert) as a case study. Particularly, we identified three interfaces in the COVID Alert app where the design can be improved. The interfaces include the no exposure status interface, exposure interface, and diagnosis report interface. We propose persuasive technology design guidelines to make them more motivational and effective in eliciting the desired behavior change.

RESULTS

Apart from trust and privacy concerns, we identified the minimalist and nonmotivational design of exposure notification apps as the key design-related factors that contribute to the current low uptake. We proposed persuasive strategies such as self-monitoring of daily contacts and exposure time to make the no exposure and exposure interfaces visually appealing and motivational. Moreover, we proposed social learning, praise, and reward to increase the diagnosis report interface's effectiveness.

CONCLUSIONS

We demonstrated that exposure notification apps can be designed as persuasive technologies by incorporating key persuasive features, which have the potential to improve uptake, use, COVID-19 diagnosis reporting, and compliance with social distancing guidelines.

Privacy and uniqueness of neighborhoods in social networks

The ability to share social network data at the level of individual connections is beneficial to science: not only for reproducing results, but also for researchers who may wish to use it for purposes not foreseen by the data releaser. Sharing such data, however, can lead to serious privacy issues, because individuals could be re-identified, not only based on possible nodes’ attributes, but also from the structure of the network around them. The risk associated with re-identification can be measured and it is more serious in some networks than in others. While various optimization algorithms have been proposed to anonymize networks, there is still only a limited theoretical understanding of which network features are important for the privacy problem. Using network models and real data, we show that the average degree of networks is a crucial parameter for the severity of re-identification risk from nodes’ neighborhoods. Dense networks are more at risk, and, apart from a small band of average degree values, either almost all nodes are uniquely re-identifiable or they are all safe. Our results allow researchers to assess the privacy risk based on a small number of network statistics which are available even before the data is collected. As a rule-of-thumb, the privacy risks are high if the average degree is above 10. Guided by these results, we explore sampling of edges as a strategy to mitigate the re-identification risk of nodes. This approach can be implemented during the data collection phase, and its effect on various network measures can be estimated and corrected using sampling theory. The new understanding of the uniqueness of neighborhoods in networks presented in this work can support the development of privacy-aware ways of designing network data collection procedures, anonymization methods, and sharing network data.

Public perception on the use of digital contact tracing tools post COVID-19 lockdown: Sentiment analysis and opinion mining (Preprint)

Background

Singapore’s national digital contact-tracing (DCT) tool—TraceTogether—attained an above 70% uptake by December 2020 after a slew of measures. Sentiment analysis can help policymakers to assess public sentiments on the implementation of new policy measures in a short time, but there is a paucity of sentiment analysis studies on the usage of DCT tools.

Objective

We sought to understand the public’s knowledge of, concerns with, and sentiments on the use of TraceTogether over time and their preferences for the type of TraceTogether tool.

Methods

We conducted a cross-sectional survey at a large public hospital in Singapore after the COVID-19 lockdown, from July 2020 through February 2021. In total, 4097 respondents aged 21-80 years were sampled proportionately by sex and 4 age groups. The open-ended responses were processed and analyzed using natural language processing tools. We manually corrected the language and logic errors and replaced phrases with words available in the syuzhet sentiment library without altering the original meaning of the phrases. The sentiment scores were computed by summing the scores of all the tokens (phrases split into smaller units) in the phrase. Stopwords (prepositions and connectors) were removed, followed by implementing the bag-of-words model to calculate the bigram and trigram occurrence in the data set. Demographic and time filters were applied to segment the responses.

Results

Respondents’ knowledge of and concerns with TraceTogether changed from a focus on contact tracing and Bluetooth activation in July-August 2020 to QR code scanning and location check-ins in January-February 2021. Younger males had the highest TraceTogether uptake (24/40, 60%), while older females had the lowest uptake (8/34, 24%) in the first half of July 2020. This trend was reversed in mid-October after the announcement on mandatory TraceTogether check-ins at public venues. Although their TraceTogether uptake increased over time, older females continued to have lower sentiment scores. The mean sentiment scores were the lowest in January 2021 when the media reported that data collected by TraceTogether were used for criminal investigations. Smartphone apps were initially preferred over tokens, but the preference for the type of TraceTogether tool equalized over time as tokens became accessible to the whole population. The sentiments on token-related comments became more positive as the preference for tokens increased.

Conclusions

The public’s knowledge of and concerns with the use of a mandatory DCT tool varied with the national regulations and public communications over time with the evolution of the COVID-19 pandemic. Effective communications tailored to subpopulations and greater transparency in data handling will help allay public concerns with data misuse and improve trust in the authorities. Having alternative forms of the DCT tool can increase the uptake of and positive sentiments on DCT.

Concurrency measures in the era of temporal network epidemiology: a review

Diseases spread over temporal networks of interaction events between individuals. Structures of these temporal networks hold the keys to understanding epidemic propagation. One early concept of the literature to aid in discussing these structures is concurrency-quantifying individuals' tendency to form time-overlapping 'partnerships'. Although conflicting evaluations and an overabundance of operational definitions have marred the history of concurrency, it remains important, especially in the area of sexually transmitted infections. Today, much of theoretical epidemiology uses more direct models of contact patterns, and there is an emerging body of literature trying to connect methods to the concurrency literature. In this review, we will cover the development of the concept of concurrency and these new approaches.