COVID-19 airborne transmission risk calculation using CO2 concentrations in a 3D office environment

The ongoing COVID-19 pandemic has caused millions of deaths worldwide along with detrimental socioeconomic consequences. Existing evidence suggests that the rate of indoor transmission is directly linked with the Indoor Air Quality (IAQ) conditions. Most of the existing methodologies for virus transmissibility risk estimation are based on the well-known Wells-Riley equation and assume well-mixed, uniform conditions;so spatiotemporal variations within the indoor space are not captured. In this work, a novel fine-grained methodology for real-time virus transmission risk estimation is developed using a 3D model of a real office room with 31 occupants. CONTAM-CFD0 software is used to compute the airflow vectors and the resulting 3D CO2 concentration map (attributed to the exhalations from the occupants). Simulation results are also provided that demonstrate the efficacy of using CO2 sensors for estimating the infection risk in real-time in the 3D office environment. © 2022 17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022. All rights reserved.

PRISC: Privacy-Preserved Pandemic Infection Risk Computation through Cellular Enabled IoT Devices

The pandemics such as COVID-19 are worldwide health risks and result in catastrophic impacts on the global economy. To prevent the spread of pandemics, it is critical to trace the contacts between people to identify the infection chain. Nevertheless, the privacy concern is a great challenge to contact tracing. Moreover, existing contact tracing apps cannot obtain the macro-level infection risk information, e.g., the hotspots where the infection occurs, which, however, is critical to optimize healthcare planning to better control and prevent the outbreak of pandemics. In this paper, we develop a novel privacy-preserved pandemic tracing system, PRISC, to compute the infection risk through cellular-enabled IoT devices. In the PRISC system, there are three parties: a mobile network operator, a social network provider, and the health department. The physical contact records between users are obtained by the mobile network operator from the users’cellular-enabled IoT devices. The social contacts are obtained by the social network provider, while the health department has the records of pandemic patients. The three parties work together to compute a heatmap of pandemic infection risk in a region, while fully protecting the data privacy of each other. The heatmap provides both macro and micro level infection risk information to help control pandemics. The experiment results indicate that PRISC can compute an infection risk score within a couple of seconds and a few mega-bytes (MBs) communication cost, for datasets with 100,000 users. IEEE

System for Evaluation of COVID-19 Prevention Guidelines Compliance

In a catastrophic medical situation caused by an infectious disease, such as COVID-19, it is very important to quickly determine who and where to be tested and supervised. The current COVID-19 screening test is conducted by identifying people with high probability of infection, such as who made direct or indirect contact with the confirmed person, by identifying the moving path of the confirmed person. Currently, various methods are being employed, such as interviewing or location tracking through cell phone forensics, to determine the moving path of the confirmed person. Mostly, however, these methods are time consuming, inaccurate, and easy to invade privacy while promptness, accuracy and anonymity are key values of epidemiological surveillance. There is still no preemptive management methods for a space where infection occurs are possible. Investigation and action on the area where the infection occurred are just carried out only after a confirmed person has been confirmed. In order to solve these problems, it is necessary to develop an automatic system for evaluating space for compliance of infectious disease prevention guidelines, or simply risk estimation system, using artificial intelligence and computer vision technology. In this paper, we discuss the system for evaluation of COVID-19 prevention guidelines compliance which has been researching and developing by ASSIST. © 2022 IEEE.

Real-Time Estimation and Monitoring of COVID-19 Aerosol Transmission Risk in Office Buildings.

A healthy and safe indoor environment is an important part of containing the coronavirus disease 2019 (COVID-19) pandemic. Therefore, this work presents a real-time Internet of things (IoT) software architecture to automatically calculate and visualize a COVID-19 aerosol transmission risk estimation. This risk estimation is based on indoor climate sensor data, such as carbon dioxide (CO2) and temperature, which is fed into Streaming MASSIF, a semantic stream processing platform, to perform the computations. The results are visualized on a dynamic dashboard that automatically suggests appropriate visualizations based on the semantics of the data. To evaluate the complete architecture, the indoor climate during the student examination periods of January 2020 (pre-COVID) and January 2021 (mid-COVID) was analyzed. When compared to each other, we observe that the COVID-19 measures in 2021 resulted in a safer indoor environment.

A SIMULATION-BASED APPROACH TO MITIGATE DISEASE TRANSMISSION RISK FROM AEROSOL PARTICLES IN BUILDINGS

Understanding the role of architectural design in identifying the risk of disease transmission is essential for creating resilience in buildings. Here we used a Grasshopper simulation workflow to execute aerosol disease transmission risk estimation coupled with EnergyPlus simulation inputs to assess the impact of architectural factors on the risk of COVID-19 transmission. We simulated the risk for a simple geometry with different window configurations and geographic locations. We observed that increasing the fractional opening of a single window as well as cross ventilation design can increase the outdoor air exchange, which corresponds to substantially reduced risk of disease transmission. Furthermore, indoor relative humidity in cold climates can be significantly lower in winter due to the impacts of increased mechanical heating which translates to an increased risk of infection. We demonstrate that early architectural design decisions implicate the resultant risk of disease transmission indoors that should be prioritized in the future. © 2022 Society for Modeling & Simulation International (SCS)

COVID-19 Infection Risk Assessment for Shoppers in Retail Stores

Over the last few years, a large number of smartphone apps have been developed to 'flatten the curve' of the rising number of COVID-19 infections. Knowledge of potential symptoms and their distribution enables the early identification of infected individuals. We developed a mobile app-based crowdsourcing methodology to assess the COVID-19 infection risk through shopping habits at indoor retail stores. The app's goal is to instil trust in customers to visit stores, which will assist small and medium businesses to survive their operations in the near term. According to the literature, there are several implementations for COVID-19 infection risk estimations for such scenarios. A mobile app prototype was developed, and the risk was calculated using the COVID-19 Aerosol Transmission Estimator model established by the University of Colorado Boulder. The developed prototype mobile app was tested with end users to gather their feedback through a questionnaire. In comparison to the complex implementation associated with AI-based alternatives, this solution could be delivered at a lower cost with adequate accuracy of COVID-19 infection risk assessments. © 2022 IEEE.

A Simulation-Based Approach To Mitigate Disease Transmission Risk From Aerosol Particles In Buildings

Understanding the role of architectural design in identifying the risk of disease transmission is essential for creating resilience in buildings. Here we used a Grasshopper simulation workflow to execute aerosol disease transmission risk estimation coupled with EnergyPlus simulation inputs to assess the impact of architectural factors on the risk of COVID-19 transmission. We simulated the risk for a simple geometry with different window configurations and geographic locations. We observed that increasing the fractional opening of a single window as well as cross ventilation design can increase the outdoor air exchange, which corresponds to substantially reduced risk of disease transmission. Furthermore, indoor relative humidity in cold climates can be significantly lower in winter due to the impacts of increased mechanical heating which translates to an increased risk of infection. We demonstrate that early architectural design decisions implicate the resultant risk of disease transmission indoors that should be prioritized in the future. © 2022 SCS.

Comparative Optimism, Self-Superiority, Egocentric Impact Perception and Health Information Seeking: A COVID-19 Study.

We examined perceived self-other differences (self-uniqueness) in appraisals of one's risk of an infectious disease (COVID-19), one's adherence to behavioural precautionary measures against the disease, and the impact of these measures on one's life. We also examined the relationship of self-uniqueness with information seeking and trust in sources of information about the disease. We administered an online survey to a community sample (N = 8696) of Dutch-speaking individuals, mainly in Belgium and The Netherlands, during the first lockdown (late April-Mid June 2020). As a group, participants reported that they were less likely to get infected or infect others or to suffer severe outcomes than average (unrealistic optimism) and that they adhered better than average to behavioural precautionary measures (illusory superiority). Except for participants below 25, who reported that they were affected more than average by these measures (egocentric impact bias), participants also generally reported that they were less affected than average (allocentric impact bias). Individual differences in self-uniqueness were associated with differences in the number of information sources being used and trust on these sources. Higher comparative optimism for infection, self-superiority, and allocentric impact perception were associated with information being sought from fewer sources; higher self-superiority and egocentric impact perception were associated with lower trust. We discuss implications for health communication.

The COVID-19 Pandemic, a Risk Management Approach

The coronavirus disease-2019 pandemic, one of many global threats to human health, provides an opportunity to analyze how to detect, minimize, and even prevent the spread of future viral zoonotic agents with pandemic potential. Such analysis can utilize existing risk assessment techniques that seek formally to define the hazard, assess the health risk, characterize the health threat, and estimate the probability of occurrence. © 2021 by Turkish Neurological Society.

An internally validated prediction model for critical COVID-19 infection and intensive care unit admission in symptomatic pregnant women.

BACKGROUND: Pregnant women are at an increased risk of mortality and morbidity owing to COVID-19. Many studies have reported on the association of COVID-19 with pregnancy-specific adverse outcomes, but prediction models utilizing large cohorts of pregnant women are still lacking for estimating the risk of maternal morbidity and other adverse events. OBJECTIVE: The main aim of this study was to develop a prediction model to quantify the risk of progression to critical COVID-19 and intensive care unit admission in pregnant women with symptomatic infection. STUDY DESIGN: This was a multicenter retrospective cohort study including 8 hospitals from 4 countries (the United Kingdom, Austria, Greece, and Turkey). The data extraction was from February 2020 until May 2021. Included were consecutive pregnant and early postpartum women (within 10 days of birth); reverse transcriptase polymerase chain reaction confirmed SARS-CoV-2 infection. The primary outcome was progression to critical illness requiring intensive care. The secondary outcomes included maternal death, preeclampsia, and stillbirth. The association between the primary outcome and 12 candidate predictors having a known association with severe COVID-19 in pregnancy was analyzed with log-binomial mixed-effects regression and reported as adjusted risk ratios. All the potential predictors were evaluated in 1 model and only the baseline factors in another. The predictive accuracy was assessed by the area under the receiver operating characteristic curves. RESULTS: Of the 793 pregnant women who were positive for SARS-CoV-2 and were symptomatic, 44 (5.5%) were admitted to intensive care, of whom 10 died (1.3%). The 'mini-COvid Maternal Intensive Therapy' model included the following demographic and clinical variables available at disease onset: maternal age (adjusted risk ratio, 1.45; 95% confidence interval, 1.07-1.95; P=.015); body mass index (adjusted risk ratio, 1.34; 95% confidence interval, 1.06-1.66; P=.010); and diagnosis in the third trimester of pregnancy (adjusted risk ratio, 3.64; 95% confidence interval, 1.78-8.46; P=.001). The optimism-adjusted area under the receiver operating characteristic curve was 0.73. The 'full-COvid Maternal Intensive Therapy' model included body mass index (adjusted risk ratio, 1.39; 95% confidence interval, 1.07-1.95; P=.015), lower respiratory symptoms (adjusted risk ratio, 5.11; 95% confidence interval, 1.81-21.4; P=.007), neutrophil to lymphocyte ratio (adjusted risk ratio, 1.62; 95% confidence interval, 1.36-1.89; P<.001); and serum C-reactive protein (adjusted risk ratio, 1.30; 95% confidence interval, 1.15-1.44; P<.001), with an optimism-adjusted area under the receiver operating characteristic curve of 0.85. Neither model showed signs of a poor fit. Categorization as high-risk by either model was associated with a shorter diagnosis to intensive care unit admission interval (log-rank test P<.001, both), higher maternal death (5.2% vs 0.2%; P<.001), and preeclampsia (5.7% vs 1.0%; P<.001). A spreadsheet calculator is available for risk estimation. CONCLUSION: At presentation with symptomatic COVID-19, pregnant and recently postpartum women can be stratified into high- and low-risk for progression to critical disease, even where resources are limited. This can support the nature and place of care. These models also highlight the independent risk for severe disease associated with obesity and should further emphasize that even in the absence of other comorbidities, vaccination is particularly important for these women. Finally, the model also provides useful information for policy makers when prioritizing national vaccination programs to quickly protect those at the highest risk of critical and fatal COVID-19.

Using QMRA to understand possible exposure risks of SARS-CoV-2 from the water environment.

This study investigated the human risk of infection due to inadvertent ingestion of water during swimming in a river that receives SARS-CoV-2-containing effluent from a wastewater treatment plant (WWTP). A quantitative microbial risk assessment (QMRA) approach was applied for risk estimation using dose-response models (DRM) of different surrogate coronaviruses (SARS-CoV-1, MERS-CoV) and the virus responsible for most infectious respiratory illnesses (i.e., influenza A H5N1) due to the unavailability of DRM for SARS-CoV-2. The ratio of infectious concentration to genomic copies of SARS-CoV-2 is unknown and also unavailable for other coronaviruses. Therefore, literature-based information on enteric viruses was used for formulating the ratio used for QMRA, although it is acknowledged that identifying this information for SARS-CoV-2 is a priority, and in the absence of information specific to SARS-CoV-2, another coronavirus would be a preferable surrogate to the enteric viruses used here. The calculated concentration of ingested SARS-CoV-2 ranged between 4.6 × 10-7 and 80.5 genomic copies/dip (one swim = 32 mL). The risk of infection (> 9 × 10-12 to 5.8 × 10-1) was found to be > 1/10,000 annual risk of infection. Moreover, the study revealed that the risk estimation was largely dependent on the value of the molecular concentration of SARS-CoV-2 (gc/mL). Overall immediate attention is required for obtaining information on the (i) ratio of infectious virus to genomic copies, (ii) DRM for SARS-CoV-2, and (iii) virus reduction rate after treatment in the WWTPs. The QMRA structure used in present findings is helpful in analyzing and prioritizing upcoming health risks due to swimming performed in contaminated rivers during the COVID-19 outbreak.

Evidence against risk as a motivating driver of COVID-19 preventive behaviors in the United States.

Does an individual's risk profile predict their social distancing and mask wearing in the U.S. during the COVID-19 pandemic? Common sense and some health behavior theories suggest that as a perceived threat increases, an individual should be more likely to take preventive measures. We explore this hypothesis using survey responses collected from 1114 U.S. adults during April and October 2020, and find that neither perceived nor actual risk predicted these preventive behaviors. Instead, being an essential worker, partisanship, and believing compliance was important were more reliable predictors. These results provide guidance for better pandemic response policies and challenge models of health behavior.

Can pharmaceutical drugs used to treat Covid-19 infection leads to human health risk? A hypothetical study to identify potential risk.

This is the first study to assess human health risks due to the exposure of 'repurposed' pharmaceutical drugs used to treat Covid-19 infection. The study used a six-step approach to determine health risk estimates. For this, consumption of pharmaceuticals under normal circumstances and in Covid-19 infection was compiled to calculate the predicted environmental concentrations (PECs) in river water and in fishes. Risk estimates of pharmaceutical drugs were evaluated for adults as they are most affected by Covid-19 pandemic. Acceptable daily intakes (ADIs) are estimated using the no-observed-adverse-effect-level (NOAEL) or no observable effect level (NOEL) values in rats. The estimated ADI values are then used to calculate predicted no-effect concentrations (PNECs) for three different exposure routes (i) through the accidental ingestion of contaminated surface water during recreational activities only, (ii) through fish consumption only, and (iii) through combined accidental ingestion of contaminated surface water during recreational activities and fish consumption. Higher risk values (hazard quotient, HQ: 337.68, maximum; 11.83, minimum) were obtained for the combined ingestion of contaminated water during recreational activities and fish consumption exposure under the assumptions used in this study indicating possible effects to human health. Amongst the pharmaceutical drugs, ritonavir emerged as main drug, and is expected to pose adverse effects on r human health through fish consumption. Mixture toxicity analysis showed major risk effects of exposure of pharmaceutical drugs (interaction-based hazard index, HIint: from 295.42 (for lopinavir + ritonavir) to 1.20 for chloroquine + rapamycin) demonstrating possible risks due to the co-existence of pharmaceutical in water. The presence of background contaminants in contaminated water does not show any influence on the observed risk estimates as indicated by low HQadd values (<1). Regular monitoring of pharmaceutical drugs in aquatic environment needs to be carried out to reduce the adverse effects of pharmaceutical drugs on human health.

[Fatality and risk factors for severe courses of COVID-19 pneumonia]. / Letalität und Risikofaktoren für einen schweren Verlauf der COVID-19-Pneumonie.

The severity of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pneumonia, the course, fatality and mortality are multifactorial and attributable to the immediate parenchymal damage in the region of the lungs (including pulmonary vessels), pre-existing comorbidities, extrapulmonary complications, secondary infections and the quality of the available medical care. In this respect, coronavirus disease 2019 (COVID-19) is comparable with other severe community-acquired forms of pneumonia caused by conventional pathogens, even if the pathogenesis is different. The fatality of hospitalized COVID-19 patients is approximately 20% (and therefore higher than for other pneumonia pathogens), in intensive care patients 30-40% and in invasively ventilated patients ca. 50%. Risk factors that are decisive for the fatality are old age, overweight, male gender and typical age-related cardiopulmonary underlying diseases. The clinical risk estimation in hospital should essentially be carried out in accordance with the valid guidelines on pneumonia. The value of laboratory surrogate markers specific for COVID-19 for risk estimation and treatment optimization cannot yet be adequately assessed.

Risk estimation and prediction of the transmission of coronavirus disease-2019 (COVID-19) in the mainland of China excluding Hubei province.

BACKGROUND: In December 2019, an outbreak of coronavirus disease (later named as COVID-19) was identified in Wuhan, China and, later on, detected in other parts of China. Our aim is to evaluate the effectiveness of the evolution of interventions and self-protection measures, estimate the risk of partial lifting control measures and predict the epidemic trend of the virus in the mainland of China excluding Hubei province based on the published data and a novel mathematical model. METHODS: A novel COVID-19 transmission dynamic model incorporating the intervention measures implemented in China is proposed. COVID-19 daily data of the mainland of China excluding Hubei province, including the cumulative confirmed cases, the cumulative deaths, newly confirmed cases and the cumulative recovered cases between 20 January and 3 March 2020, were archived from the National Health Commission of China (NHCC). We parameterize the model by using the Markov Chain Monte Carlo (MCMC) method and estimate the control reproduction number (Rc), as well as the effective daily reproduction ratio- Re(t), of the disease transmission in the mainland of China excluding Hubei province. RESULTS: The estimation outcomes indicate that Rc is 3.36 (95% CI: 3.20-3.64) and Re(t) has dropped below 1 since 31 January 2020, which implies that the containment strategies implemented by the Chinese government in the mainland of China are indeed effective and magnificently suppressed COVID-19 transmission. Moreover, our results show that relieving personal protection too early may lead to a prolonged disease transmission period and more people would be infected, and may even cause a second wave of epidemic or outbreaks. By calculating the effective reproduction ratio, we prove that the contact rate should be kept at least less than 30% of the normal level by April, 2020. CONCLUSIONS: To ensure the pandemic ending rapidly, it is necessary to maintain the current integrated restrict interventions and self-protection measures, including travel restriction, quarantine of entry, contact tracing followed by quarantine and isolation and reduction of contact, like wearing masks, keeping social distance, etc. People should be fully aware of the real-time epidemic situation and keep sufficient personal protection until April. If all the above conditions are met, the outbreak is expected to be ended by April in the mainland of China apart from Hubei province.

Risk estimation of the SARS-CoV-2 acute respiratory disease outbreak outside China.

BACKGROUND: On December 31, 2019, the World Health Organization was alerted to the occurrence of cases of pneumonia in Wuhan, Hubei Province, China, that were caused by an unknown virus, which was later identified as a coronavirus and named the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We aimed to estimate the reproductive number of SARS-CoV-2 in the Hubei Province and evaluate the risk of an acute respiratory coronavirus disease (COVID-19) outbreak outside China by using a mathematical model and stochastic simulations. RESULTS: We constructed a mathematical model of SARS-CoV-2 transmission dynamics, estimated the rate of transmission, and calculated the reproductive number in Hubei Province by using case-report data from January 11 to February 6, 2020. The possible number of secondary cases outside China was estimated by stochastic simulations in various scenarios of reductions in the duration to quarantine and rate of transmission. The rate of transmission was estimated as 0.8238 (95% confidence interval [CI] 0.8095-0.8382), and the basic reproductive number as 4.1192 (95% CI 4.0473-4.1912). Assuming the same rate of transmission as in Hubei Province, the possibility of no local transmission is 54.9% with a 24-h quarantine strategy, and the possibility of more than 20 local transmission cases is 7% outside of China. CONCLUSION: The reproductive number for SARS-CoV-2 transmission dynamics is significantly higher compared to that of the previous SARS epidemic in China. This implies that human-to-human transmission is a significant factor for contagion in Hubei Province. Results of the stochastic simulation emphasize the role of quarantine implementation, which is critical to prevent and control the SARS-CoV-2 outbreak outside China.