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

Role of artificial intelligence in early diagnosis and treatment of infectious diseases

Infectious diseases remain a global health challenge, necessitating innovative approaches for their early diagnosis and effective treatment. Artificial Intelligence (AI) has emerged as a transformative force in healthcare, offering promising solutions to address this challenge. This review article provides a comprehensive overview of the pivotal role AI can play in the early diagnosis and treatment of infectious diseases. It explores how AI-driven diagnostic tools, including machine learning algorithms, deep learning, and image recognition systems, enhance the accuracy and efficiency of disease detection and surveillance. Furthermore, it delves into the potential of AI to predict disease outbreaks, optimise treatment strategies, and personalise interventions based on individual patient data and how AI can be used to gear up the drug discovery and development (D3) process.The ethical considerations, challenges, and limitations associated with the integration of AI in infectious disease management are also examined. By harnessing the capabilities of AI, healthcare systems can significantly improve their preparedness, responsiveness, and outcomes in the battle against infectious diseases.

Using a Bayesian hierarchical approach to study the association between non-pharmaceutical interventions and the spread of Covid-19 in Germany

Non-Pharmaceutical Interventions (NPIs) are community mitigation strategies, aimed at reducing the spread of illnesses like the coronavirus pandemic, without relying on pharmaceutical drug treatments. This study aims to evaluate the effectiveness of different NPIs across sixteen states of Germany, for a time period of 21 months of the pandemic. We used a Bayesian hierarchical approach that combines different sub-models and merges information from complementary sources, to estimate the true and unknown number of infections. In this framework, we used data on reported cases, hospitalizations, intensive care unit occupancy, and deaths to estimate the effect of NPIs. The list of NPIs includes: "contact restriction (up to 5 people)", "strict contact restriction", "curfew", "events permitted up to 100 people", "mask requirement in shopping malls", "restaurant closure", "restaurants permitted only with test", "school closure" and "general behavioral changes". We found a considerable reduction in the instantaneous reproduction number by "general behavioral changes", "strict contact restriction", "restaurants permitted only with test", "contact restriction (up to 5 people)", "restaurant closure" and "curfew". No association with school closures could be found. This study suggests that some public health measures, including general behavioral changes, strict contact restrictions, and restaurants permitted only with tests are associated with containing the Covid-19 pandemic. Future research is needed to better understand the effectiveness of NPIs in the context of Covid-19 vaccination.

Modeling the effects of Prophylactic behaviors on the spread of SARS-CoV-2 in West Africa

Various general and individual measures have been implemented to limit the spread of SARS-CoV-2 since its emergence in China. Several phenomenological and mechanistic models have been developed to inform and guide health policy. Many of these models ignore opinions about certain control measures, although various opinions and attitudes can influence individual actions. To account for the effects of prophylactic opinions on disease dynamics and to avoid identifiability problems, we expand the SIR-Opinion model of Tyson et al. (2020) to take into account the partial detection of infected individuals in order to provide robust modeling of COVID-19 as well as degrees of adherence to prophylactic treatments, taking into account a hybrid modeling technique using Richard's model and the logistic model. Applying the approach to COVID-19 data from West Africa demonstrates that the more people with a strong prophylactic opinion, the smaller the final COVID-19 pandemic size. The influence of individuals on each other and from the media significantly influences the susceptible population and, thus, the dynamics of the disease. Thus, when considering the opinion of susceptible individuals to the disease, the view of the population at baseline influences its dynamics. The results are expected to inform public policy in the context of emerging and re-emerging infectious diseases.

Coupled System Approach to Healthy Earth Environments and Individual Human Resilience

The SARS-CoV-2 pandemic has stressed our social organizations, health care systems and economies at a level not experienced since WWII or the last “Spanish flu” pandemic of 1918. This shock provides a real-life test of the resilience of human societies and of individuals, challenging our understanding and level of preparation. While hurried coercive non-pharmaceutical measures and vaccinations were the main responses, for the future, we propose a coupled double-system approach linking efforts to improve both human well-being and Earth environmental health. Concretely, this means linking (i) the build-up of individual health resilience using holistic medical system perspectives applied to each person with (ii) efforts to depollute and achieve more healthy Earth environments that are intrinsic pillars of humans’ health and wealth. The push to fight Earth ecological damages towards environmental sustainability should be rethought as being motivated by recovering an ecosystem in which each own personal biological ecosystem (i.e., each person's homeostatic balance) can strive again. We propose to prioritize Human-Environment-Health initiatives for depolluting the environment and of our immune systems, as well as improving individual responsibility and resilience.

Addressing Complexity in the Pandemic Context: How Systems Thinking Can Facilitate Understanding of Design Aspects for Preventive Technologies

The COVID-19 pandemic constitutes a wicked problem that is defined by rapidly evolving and dynamic conditions, where the physical world changes (e.g., pathogens mutate) and, in parallel, our understanding and knowledge rapidly progress. Various preventive measures have been developed or proposed to manage the situation, including digital preventive technologies to support contact tracing or physical distancing. The complexity of the pandemic and the rapidly evolving nature of the situation pose challenges for the design of effective preventive technologies. The aim of this conceptual paper is to apply a systems thinking model, DSRP (distinctions, systems, relations, perspectives) to explain the underlying assumptions, patterns, and connections of the pandemic domain, as well as to identify potential leverage points for design of preventive technologies. Two different design approaches, contact tracing and nudging for distance, are compared, focusing on how their design and preventive logic are related to system complexity. The analysis explains why a contact tracing technology involves more complexity, which can challenge both implementation and user understanding. A system utilizing nudges can operate using a more distinct system boundary, which can benefit understanding and implementation. However, frequent nudges might pose challenges for user experience. This further implies that these technologies have different contextual requirements and are useful at different levels in society. The main contribution of this work is to show how systems thinking can organize our understanding and guide the design of preventive technologies in the context of epidemics and pandemics.

Indoor Spatiotemporal Contact Analytics Using Landmark-Aided Pedestrian Dead Reckoning on Smartphones

Due to the prevalence of COVID-19, providing safe environments and reducing the risks of virus exposure play pivotal roles in our daily lives. Contact tracing is a well-established and widely-used approach to track and suppress the spread of viruses. Most digital contact tracing systems can detect direct face-to-face contact based on estimated proximity, without quantifying the exposed virus concentration. In particular, they rarely allow for quantitative analysis of indirect environmental exposure due to virus survival time in the air and constant airborne transmission. In this work, we propose an indoor spatiotemporal contact awareness framework (iSTCA), which explicitly considers the self-containing quantitative contact analytics approach with spatiotemporal information to provide accurate awareness of the virus quanta concentration in different origins at various times. Smartphone-based pedestrian dead reckoning (PDR) is employed to precisely detect the locations and trajectories for distance estimation and time assessment without the need to deploy extra infrastructure. The PDR technique we employ calibrates the accumulative error by identifying spatial landmarks automatically. We utilized a custom deep learning model composed of bidirectional long short-term memory (Bi-LSTM) and multi-head convolutional neural networks (CNNs) for extracting the local correlation and long-term dependency to recognize landmarks. By considering the spatial distance and time difference in an integrated manner, we can quantify the virus quanta concentration of the entire indoor environment at any time with all contributed virus particles. We conducted an extensive experiment based on practical scenarios to evaluate the performance of the proposed system, showing that the average positioning error is reduced to less than 0.7 m with high confidence and demonstrating the validity of our system for the virus quanta concentration quantification involving virus movement in a complex indoor environment.

Using a Proximity-Detection Technology to Nudge for Physical Distancing in a Swedish Workplace During the COVID-19 Pandemic: Retrospective Case Study

BACKGROUND

The recent COVID-19 pandemic has contributed to the emergence of several technologies for infectious disease management. Although much focus has been placed on contact-tracing apps, another promising new tactic is proximity tracing, which focuses on health-related behavior and can be used for primary prevention. Underpinned by theories on behavioral design, a proximity-detection system can be devised that provides a user with immediate nudges to maintain physical distance from others. However, the practical feasibility of proximity detection during an infectious disease outbreak has not been sufficiently investigated.

OBJECTIVE

We aimed to evaluate the feasibility of using a wearable device to nudge for distance and to gather important insights about how functionality and interaction are experienced by users. The results of this study can guide future research and design efforts in this emerging technology.

METHODS

In this retrospective case study, a wearable proximity-detection technology was used in a workplace for 6 weeks during the production of a music competition. The purpose of the technology was to nudge users to maintain their physical distance using auditory feedback. We used a mixed methods sequential approach, including interviews (n=8) and a survey (n=30), to compile the experiences of using wearable technology in a real-life setting.

RESULTS

We generated themes from qualitative analysis based on data from interviews and open-text survey responses. The quantitative data were subsequently integrated into these themes: feasibility (implementation and acceptance-establishing a shared problem; distance tags in context-strategy, environment, and activities; understanding and learning; and accomplishing the purpose) and design aspects (a purposefully annoying device; timing, tone, and proximity; and additional functions).

CONCLUSIONS

This empirical study reports on the feasibility of using wearable technology based on proximity detection to nudge individuals to maintain physical distance in the workplace. The technology supports attention to distance, but the usability of this approach is dependent on the context and situation. In certain situations, the audio signal is frustrating, but most users agree that it needs to be annoying to ensure sufficient behavioral adaption. We proposed a dual nudge that involves vibration followed by sound. There are indications that the technology also facilitates learning how to maintain a greater distance from others, and that this behavior can persist beyond the context of technology use. This study demonstrates that the key value of this technology is that it places the user in control and enables immediate action when the distance to others is not maintained. This study provides insights into the emerging field of personal and wearable technologies used for primary prevention during infectious disease outbreaks. Future research is needed to evaluate the preventive effect on transmission and investigate behavioral changes in detail and in relation to different forms of feedback.

Predicting and preventing COVID-19 outbreaks in indoor environments: an agent-based modeling study

How to mitigate the spread of infectious diseases like COVID-19 in indoor environments remains an important research question. In this study, we propose an agent-based modeling framework to evaluate facility usage policies that aim to lower the probability of outbreaks. The proposed framework is individual-based, spatially-resolved with time resolution of up to 1 s, and takes into detailed account specific floor layouts, occupant schedules and movement. It enables decision makers to compute realistic contact networks and generate risk profiles of their facilities without relying on wearable devices, smartphone tagging or surveillance cameras. Our demonstrative modeling results indicate that not all facility occupants present the same risk of starting an outbreak, where the driver of outbreaks varies with facility layouts as well as individual occupant schedules. Therefore, generic mitigation strategies applied across all facilities should be considered inferior to tailored policies that take into account individual characteristics of the facilities of interest. The proposed modeling framework, implemented in Python and now available to the public in an open-source platform, enables such strategy evaluation.