Coronavirus Disease 2019 and Airborne Transmission: Science Rejected, Lives Lost. Can Society Do Better?

This is an account that should be heard of an important struggle: the struggle of a large group of experts who came together at the beginning of the COVID-19 pandemic to warn the world about the risk of airborne transmission and the consequences of ignoring it. We alerted the World Health Organization about the potential significance of the airborne transmission of SARS-CoV-2 and the urgent need to control it, but our concerns were dismissed. Here we describe how this happened and the consequences. We hope that by reporting this story we can raise awareness of the importance of interdisciplinary collaboration and the need to be open to new evidence, and to prevent it from happening again. Acknowledgement of an issue, and the emergence of new evidence related to it, is the first necessary step towards finding effective mitigation solutions.

Relational analysis between economic activities and mobility during the COVID

Coronavirus disease (COVID-19) has significantly affected daily lives since its declaration as a Pandemic in March 2020 by the WHO. Studies in Korea to interpret the relationship between COVID-19 and transportation show that there has been a decrease in public transportation use, and a significant decrease when a widespread infection occurs. In addition, COVID-19 has affected the commercial environment, particularly the number of people using commercial districts where the use of retail outlets decreased significantly. This study analyses the disease-vectors (spread factors) of COVID-19 in terms of transportation demand, the revitalisation of commercial districts, living populations, and socioeconomic indicators. It attempts to identify different infection factors for each district in Seoul using a causal analysis methodology PLS-SEM (Partial Least-Squares Structural Equation Modelling) such that COVID-19 can be managed continuously with the aim to provide a foundation for pre-emptive actions by adjusting or controlling specific influencing factors pertaining to infectious diseases. © 2023 ICE Publishing: All rights reserved.

A pedestrian-based model for simulating COVID-19 transmission on college campus

Nowadays the integration of pedestrian dynamics and epidemiology is heating up due to the pandemic of COVID-19. In this paper, we introduce a pedestrian-based epidemic transmission model that combines cellular automata-based pedestrian dynamics with stochastic infection spread dynamics. Based on this model, we simulate COVID-19 transmission in different indoor scenarios on the college campus. We confirm that COVID-19 patients' infectivity during the incubation period and the presence of asymptomatic patients are key reasons for the difficulty in controlling the epidemic. Then, several non-pharmaceutical interventions at different operational levels are proposed and their effectiveness is evaluated by using computational models. We find that indoor-level interventions can slow the speed of disease transmission while quarantine can downsize the scale of disease transmission. And the combination of these two levels of intervention is superior to any single intervention in reducing the number of new infections.

Protecting Older Adult Residents in Care Facilities Against Influenza and COVID-19 Using the Influenza Communication, Advice and Reporting (FluCARE) App: Prospective Cohort Mixed Methods Study.

BACKGROUND: Early detection and response to influenza and COVID-19 outbreaks in aged care facilities (ACFs) are critical to minimizing health impacts. The Sydney Local Health District (SLHD) Public Health Unit (PHU) has developed and implemented a novel web-based app with integrated functions for online line listings, detection algorithms, and automatic notifications to responders, to assist ACFs in outbreak response. The goal of the Influenza Outbreak Communication, Advice and Reporting (FluCARE) app is to reduce time delays to notifications, which we hope will reduce the spread, duration, and health impacts of an influenza or COVID-19 outbreak, as well as ease workload burdens on ACF staff. OBJECTIVE: The specific aims of the study were to (1) evaluate the acceptability and user satisfaction of the implementation and use of FluCARE in helping ACFs recognize, notify, and manage influenza and COVID-19 outbreaks in their facility; (2) identify the safety of FluCARE and any potential adverse outcomes of using the app; and (3) identify any perceived barriers or facilitators to the implementation and use of FluCARE from the ACF user perspective. METHODS: The FluCARE app was piloted from September 2019 to December 2020 in the SLHD. Associated implementation included promotion and engagement, user training, and operational policies. Participating ACF staff were invited to complete a posttraining survey. Staff were also invited to complete a postpilot evaluation survey that included the user Mobile Application Rating Scale (uMARS) measuring app acceptance, utility, and barriers and facilitators to use. An issues log was also prospectively maintained to assess safety. Survey data were analyzed descriptively or via content analysis where appropriate. RESULTS: Surveys were completed by 31 consenting users from 27 ACFs. FluCARE was rated 3.91 of 5 overall on the uMARS. Of the 31 users, 25 (80%) would definitely use FluCARE for future outbreaks, and all users agreed that the app was useful for identifying influenza and COVID-19 outbreaks at their facilities. There were no reported critical issues with incorrect or missed outbreak detection. User training, particularly online training modules, and technical support were identified as key facilitators to FluCARE use. CONCLUSIONS: FluCARE is an acceptable, useful, and safe app to assist ACF staff with early detection and response to influenza and COVID-19 outbreaks. This study supports feasibility for ongoing implementation and efficacy evaluation, followed by scale-up into other health districts in New South Wales.

Comparing Efficiency and Performance of IoT BLE and RFID-Based Systems for Achieving Contract Tracing to Monitor Infection Spread among Hospital and Office Staff.

COVID-19 is highly contagious and spreads rapidly; it can be transmitted through coughing or contact with virus-contaminated hands, surfaces, or objects. The virus spreads faster indoors and in crowded places; therefore, there is a huge demand for contact tracing applications in indoor environments, such as hospitals and offices, in order to measure personnel proximity while placing as little load on them as possible. Contact tracing is a vital step in controlling and restricting pandemic spread; however, traditional contact tracing is time-consuming, exhausting, and ineffective. As a result, more research and application of smart digital contact tracing is necessary. As the Internet of Things (IoT) and wearable sensor device studies have grown in popularity, this work has been based on the practicality and successful implementation of Bluetooth low energy (BLE) and radio frequency identification (RFID) IoT based wireless systems for achieving contact tracing. Our study presents autonomous, low-cost, long-battery-life wireless sensing systems for contact tracing applications in hospital/office environments; these systems are developed with off-the-shelf components and do not rely on end user participation in order to prevent any inconvenience. Performance evaluation of the two implemented systems is carried out under various real practical settings and scenarios; these two implemented centralised IoT contact tracing devices were tested and compared demonstrating their efficiency results.

COVID-19 Spread Modeling Considering Vaccination and Re-Morbidity

To effectively counter the COVID-19 spread, using scientifically based decision-making methods in this area is required. The disease characteristics and the methods applied to stem it are constantly changing, so it is necessary to update existing methods for predicting the COVID-19 spread in light of new trends. The present paper deals with developing a new SVEIRS model from the SEIR class, taking into account the vaccination campaign and the possibility of recurrent morbidity cases. These improvements make it possible to increase the accuracy of the disease spread prediction due to a more direct correspondence to reality. The developed SVEIRS model was verified when predicting the COVID-19 spread in Moscow in July-September of 2022 and showed higher prediction accuracy compared to the SEVIS reference model. Based on the developed model, it is possible to predict the COVID-19 spread in various regions to form an optimal vaccination campaign strategy.

Regional Workshop for Policy Implementation Based on the Stay with Your Community Principles.

A co-author of this paper had previously presented the principle of "Stay with Your Community" as a method of countermeasures against COVID-19 infection spread and have been working on its social implementation. This case study paper presents an example of activities to spread the Stay with Your Community principle to citizens and visitors in Shimoda City, Shizuoka Prefecture, in order to control the spread of COVID-19 infection. As a result, the infection cluster was successfully controlled. The authors discuss the effect of the regional workshop as a key to open the way to Organizational Citizenship Behavior of participants.

Indoor air pollution, occupant health, and building system controls—a COVID-19 perspective

The recent COVID-19 pandemic has taken a serious toll on humanity and mankind, affecting every section of society. Scientists are still trying to find out the possible transmission routes of this deadly virus, with airborne routes cited by many as a possible route of infection spread. Because airborne aerosols, dust particles, and other indoor pollutants aid in virus transmission, it becomes important to assess their roles in affecting human health. The study therefore tries to review indoor air pollution and its sources, how it impacts human health, and the role of built components and technological systems in combating indoor air pollution and in the process control infection spread also. Most of the studies have found out that there exists a need to accurately determine the airflow distribution pattern rather than relying on generic ventilation standards like ventilation rates, air change rates, and CO2 levels. Although increasing outdoor airflow rates and avoiding air recirculation are some of the suggestions given to control indoor pollution levels and infection spread, it can become challenging in areas with high ambient pollution levels. This signifies the need to incorporate additional engineering controls, sensing technologies, artificial intelligence tools, and predictive modeling methods to combat the health hazards of indoor air pollution and potential novel viruses that can emerge in the future. © 2022 Elsevier Inc. All rights reserved.

Comparison of effectiveness of enhanced infection countermeasures in different scenarios, using a dynamic-spread-function model.

When formulating countermeasures to epidemics such as those generated by COVID-19, estimates of the benefits of a given intervention for a specific population are highly beneficial to policy makers. A recently introduced tool, known as the "dynamic-spread" SIR model, can perform population-specific risk assessment. Behavior is quantified by the dynamic-spread function, which includes the mechanisms of droplet reduction using facemasks and transmission control due to social distancing. The spread function is calibrated using infection data from a previous wave of the infection, or other data felt to accurately represent the population behaviors. The model then computes the rate of spread of the infection for different hypothesized interventions, over the time window for the calibration data. The dynamic-spread model was used to assess the benefit of three enhanced intervention strategies - increased mask filtration efficiency, higher mask compliance, and elevated social distancing - in four COVID-19 scenarios occurring in 2020: the first wave (i.e. until the first peak in numbers of new infections) in New York City; the first wave in New York State; the spread aboard the Diamond Princess Cruise Liner; and the peak occurring after re-opening in Harris County, Texas. Differences in the efficacy of the same intervention in the different scenarios were estimated. As an example, when the average outward filtration efficiency for facemasks worn in New York City was increased from an assumed baseline of 67% to a hypothesized 90%, the calculated peak number of new infections per day decreased by 40%. For the same baseline and hypothesized filtration efficiencies aboard the Diamond Princess Cruise liner, the calculated peak number of new infections per day decreased by about 15%. An important factor contributing to the difference between the two scenarios is the lower mask compliance (derivable from the spread function) aboard the Diamond Princess.

AI-Based Chest CT Analysis for Rapid COVID-19 Diagnosis and Prognosis: A Practical Tool to Flag High-Risk Patients and Lower Healthcare Costs.

Early diagnosis of COVID-19 is required to provide the best treatment to our patients, to prevent the epidemic from spreading in the community, and to reduce costs associated with the aggravation of the disease. We developed a decision tree model to evaluate the impact of using an artificial intelligence-based chest computed tomography (CT) analysis software (icolung, icometrix) to analyze CT scans for the detection and prognosis of COVID-19 cases. The model compared routine practice where patients receiving a chest CT scan were not screened for COVID-19, with a scenario where icolung was introduced to enable COVID-19 diagnosis. The primary outcome was to evaluate the impact of icolung on the transmission of COVID-19 infection, and the secondary outcome was the in-hospital length of stay. Using EUR 20000 as a willingness-to-pay threshold, icolung is cost-effective in reducing the risk of transmission, with a low prevalence of COVID-19 infections. Concerning the hospitalization cost, icolung is cost-effective at a higher value of COVID-19 prevalence and risk of hospitalization. This model provides a framework for the evaluation of AI-based tools for the early detection of COVID-19 cases. It allows for making decisions regarding their implementation in routine practice, considering both costs and effects.

Using Timeliness in Tracking Infections.

We consider real-time timely tracking of infection status (e.g., COVID-19) of individuals in a population. In this work, a health care provider wants to detect both infected people and people who have recovered from the disease as quickly as possible. In order to measure the timeliness of the tracking process, we use the long-term average difference between the actual infection status of the people and their real-time estimate by the health care provider based on the most recent test results. We first find an analytical expression for this average difference for given test rates, infection rates and recovery rates of people. Next, we propose an alternating minimization-based algorithm to find the test rates that minimize the average difference. We observe that if the total test rate is limited, instead of testing all members of the population equally, only a portion of the population may be tested in unequal rates calculated based on their infection and recovery rates. Next, we characterize the average difference when the test measurements are erroneous (i.e., noisy). Further, we consider the case where the infection status of individuals may be dependent, which occurs when an infected person spreads the disease to another person if they are not detected and isolated by the health care provider. In addition, we consider an age of incorrect information-based error metric where the staleness metric increases linearly over time as long as the health care provider does not detect the changes in the infection status of the people. Through extensive numerical results, we observe that increasing the total test rate helps track the infection status better. In addition, an increased population size increases diversity of people with different infection and recovery rates, which may be exploited to spend testing capacity more efficiently, thereby improving the system performance. Depending on the health care provider's preferences, test rate allocation can be adjusted to detect either the infected people or the recovered people more quickly. In order to combat any errors in the test, it may be more advantageous for the health care provider to not test everyone, and instead, apply additional tests to a selected portion of the population. In the case of people with dependent infection status, as we increase the total test rate, the health care provider detects the infected people more quickly, and thus, the average time that a person stays infected decreases. Finally, the error metric needs to be chosen carefully to meet the priorities of the health care provider, as the error metric used greatly influences who will be tested and at what test rate.

Bioaerosol Spread of COVID-19 and TB in Air ConditionedSpaces:HowtheCourtSpearheaded the Movement in India

The measures taken in buildings to make them resilient against the spread of airborne diseases have seen a rise during the COVID-19 pandemic. Changes in the heating, ventilation and air conditioning systems are of importance as bioaerosols spread through recirculation based air conditioning systems. This can be tackled by sanitisation or by dilution ventilation caused by increased fresh air supply. In response to a written petition by a lawyer on the issue of air conditioning in the court premises and the spread of COVID-19, the Delhi High Court held the Fundamental Rights of the citizens by extending it to a right to a healthy environment and acknowledging the concerns in the petition. A committee was also set up by the court to relook at the ventilation and air conditioning within the court. The Right to Information Act, 2005 was used to obtain the minutes of the committee meetings. This short communication discusses the decisions which provide insights into the lack of reliable information available in the initial phases of the meetings. This has been hinted to show the possible lack of regulation for infection control through airborne route in public buildings. Design decisions are also looked at. This paper aims at providing a commentary with the aim of linking research and practice in the area of bioaerosol spread of diseases like COVID-19 and tuberculosis in public spaces. Copyright (c) 2022: Author(s).

Intelligent Data Analysis for Infection Spread Prediction

Intelligent data analysis based on artificial intelligence and Big Data tools is widely used by the scientific community to overcome global challenges. One of these challenges is the worldwide coronavirus pandemic, which began in early 2020. Data science not only provides an opportunity to assess the impact caused by a pandemic, but also to predict the infection spread. In addition, the model expansion by economic, social, and infrastructural factors makes it possible to predict changes in all spheres of human activity in competitive epidemiological conditions. This article is devoted to the use of anonymized and personal data in predicting the coronavirus infection spread. The basic “Susceptible–Exposed–Infected–Recovered” model was extended by including a set of demographic, administrative, and social factors. The developed model is more predictive and applicable in assessing future pandemic impact. After a series of simulation experiment results, we concluded that personal data use in high-level modeling of the infection spread is excessive. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

Parameter Space Exploration in Pedestrian Queue Design to Mitigate Infectious Disease Spread.

Reducing the interactions between pedestrians in crowded environments can potentially curb the spread of infectious diseases including COVID-19. The mixing of susceptible and infectious individuals in many high-density man-made environments such as waiting queues involves pedestrian movement, which is generally not taken into account in modeling studies of disease dynamics. In this paper, a social force-based pedestrian-dynamics approach is used to evaluate the contacts among proximate pedestrians which are then integrated with a stochastic epidemiological model to estimate the infectious disease spread in a localized outbreak. Practical application of such multiscale models to real-life scenarios can be limited by the uncertainty in human behavior, lack of data during early stage epidemics, and inherent stochasticity in the problem. We parametrize the sources of uncertainty and explore the associated parameter space using a novel high-efficiency parameter sweep algorithm. We show the effectiveness of a low-discrepancy sequence (LDS) parameter sweep in reducing the number of simulations required for effective parameter space exploration in this multiscale problem. The algorithms are applied to a model problem of infectious disease spread in a pedestrian queue similar to that at an airport security check point. We find that utilizing the low-discrepancy sequence-based parameter sweep, even for one component of the multiscale model, reduces the computational requirement by an order of magnitude.

Algorithm and Results of Short-Term Forecast of Changes in the COVID-19 Spread Coefficient in the Constituent Entities of the Russian Federation

There was a decrease in the number of COVID-19 cases across many entities of the Russian Federation towards the end of summer season-2020. However, the disease remains a relevant threat to the public health and economy and the possibility of a second epidemic wave is not excluded. Rate of infection transmission (Rt) is one of the most important indicators to justify the transition to next stage of removing/introducing restrictive measures on COVID-19. Objective of the work was to describe the algorithm of analysis and short-term forecast of coronavirus spread rate, to assess correspondence between theoretically expected and actual values of this indicator. Materials and methods. Procedure for making a short-term extrapolation forecast of Rt in 10 RF constituent entities, depending on the presence or absence of indicator trends with calculation of a 95 % confidence interval of possible changes in its value is provided. Results and discussion. It is proposed to carry out Rt forecast based on averaged values over a week, combining regression analysis and expert assessment of time series dynamics nature for prompt transition from trend forecasting to extrapolation of stationary observation sequences and vice versa. It has been demonstrated that predicted Rt values are not statistically different from actual values. When making managerial decisions on COVID-19 prevention, special attention should be paid to cases when actual value of Rt exceeds the upper limit of confidence interval. Six (20.0 %) such cases were detected in surveyed entities on calendar weeks 33-35. Three of them were registered in Trans-Baikal Territory, where upward trend was reported during that period of time. The cause of this phenomenon should be analyzed. The put forward algorithm of analysis and forecasting of the Rt value changes, which was tested in 10 entities of Russia, provides a reliable basis for making management decisions on removing/introducing restrictive measures for COVID-19 prevention. © 2021 Russian Research Anti-Plague Institute. All rights reserved.

COVID-19 pandemic control using restrictions and vaccination.

This work deals with the impact of the vaccination in combination with a restriction parameter that represents non-pharmaceutical interventions measures applied to the compartmental SEIR model in order to control the COVID-19 epidemic. This restriction parameter is used as a control parameter, and the univariate autoregressive integrated moving average (ARIMA) is used to forecast the time series of vaccination of all individuals of a specific country. Having in hand the time series of the population fully vaccinated (real data + forecast), the Levenberg-Marquardt algorithm is used to fit an analytic function that models this evolution over time. Here, it is used two time series of real data that refer to a slow vaccination obtained from India and Brazil, and two faster vaccination as observed in Israel and the United States of America. Together with vaccination, two different control approaches are presented in this paper, which enable reduces the infected people successfully: namely, the feedback and nonfeedback control methods. Numerical results predict that vaccination can reduce the peaks of infections and the duration of the pandemic, however, a better result is achieved when the vaccination is combined with any restrictions or prevention policy.

Retrospective analysis of interventions to epidemics using dynamic simulation of population behavior.

Retrospective analyses of interventions to epidemics, in which the effectiveness of strategies implemented are compared to hypothetical alternatives, are valuable for performing the cost-benefit calculations necessary to optimize infection countermeasures. SIR (susceptible-infected-removed) models are useful in this regard but are limited by the challenge of deciding how and when to update the numerous parameters as the epidemic changes in response to population behaviors. Behaviors of particular interest include facemask adoption (at various levels) and social distancing. We present a method that uses a "dynamic spread function" to systematically capture the continuous variation in the population behavior and the gradual change in infection evolution, resulting from interventions. No parameter updates are made by the user. We use the tool to quantify the reduction in infection rate realizable from the population of New York City adopting different facemask strategies during COVID-19. Assuming a baseline facemask of 67% filtration efficiency, calculations show that increasing the efficiency to 80% could have reduced the roughly 5000 new infections per day occurring at the peak of the epidemic to around 4000. Population behavior that may not be varied as part of the retrospective analysis, such as social distancing in a facemask analysis, are automatically captured as part of the calibration of the dynamic spread function.

Evaluating Epidemiological Risk by Using Open Contact Tracing Data: Correlational Study.

BACKGROUND: During the 2020s, there has been extensive debate about the possibility of using contact tracing (CT) to contain the SARS-CoV-2 pandemic, and concerns have been raised about data security and privacy. Little has been said about the effectiveness of CT. In this paper, we present a real data analysis of a CT experiment that was conducted in Italy for 8 months and involved more than 100,000 CT app users. OBJECTIVE: We aimed to discuss the technical and health aspects of using a centralized approach. We also aimed to show the correlation between the acquired contact data and the number of SARS-CoV-2-positive cases. Finally, we aimed to analyze CT data to define population behaviors and show the potential applications of real CT data. METHODS: We collected, analyzed, and evaluated CT data on the duration, persistence, and frequency of contacts over several months of observation. A statistical test was conducted to determine whether there was a correlation between indices of behavior that were calculated from the data and the number of new SARS-CoV-2 infections in the population (new SARS-CoV-2-positive cases). RESULTS: We found evidence of a correlation between a weighted measure of contacts and the number of new SARS-CoV-2-positive cases (Pearson coefficient=0.86), thereby paving the road to better and more accurate data analyses and spread predictions. CONCLUSIONS: Our data have been used to determine the most relevant epidemiological parameters and can be used to develop an agent-based system for simulating the effects of restrictions and vaccinations. Further, we demonstrated our system's ability to identify the physical locations where the probability of infection is the highest. All the data we collected are available to the scientific community for further analysis.

Recovery during a crisis: facing the challenges of risk assessment and resilience management of COVID-19.

The paper offers a disaster risk management perspective to analyze the COVID-19 pandemic and to propose and assess non-pharmaceutical mitigation measures for the recovery phase. Three main aspects are tackled: (i) the need to take a scenario-based approach; (i) the need to propose more fine-tuned and context-sensitive mitigation measures, the effectiveness and the cost-benefit of which must be carefully appraised; (iii) better communication as a fundamental pillar of any mitigation measure. Evidence and ideas from the field of natural disasters and man-made technological incidents are applied to tackle the health risk posed by the SARS-COV 2 virus and its rapid spread according to a multi-disciplinary perspective that addresses the health-related challenges and the need to avoid societal and economic breakdown.

Nonlinear control of infection spread based on a deterministic SEIR model.

In this study, a mathematical model (SEIR model) with a restriction parameter is used to explore the dynamic of the COVID-19 pandemic. This work presents a nonlinear and robust control algorithm based on variable structure control (VSC) to control the transmission of coronavirus disease (COVID-19). The VSC algorithm is a control gain switching technique in which is necessary to define a switching surface. Three switching surfaces are proposed based on rules that depend on: (i) exposed and infected population, (ii) susceptible and infected population, and (iii) susceptible and total population. In case (iii) a model-based state estimator is presented based on the extended Kalman filter (EKF) and the estimator is used in combination with the VSC. Numerical results demonstrate that the proposed control strategies have the ability to flatten the infection curve. In addition, the simulations show that the success of lowering and flattening the epidemic peak is strongly dependent on the chosen switching surfaces. A comparison between the VSC and sliding mode control (SMC) is presented showing that the VSC control can provide better performance taking into account two aspects: time duration of pandemic and the flattened curve peak with respect to SMC.