Modeling and Analysis of Infectious Disease Transmission Considering Protection and Isolation

Based on the SEIR model, two compartments for self-protection and isolation are introduced, and a more general infectious disease transmission model is proposed.Through qualitative analysis of the model, the basic reproduction number of the model is calculated, and the local asymptotic stability of the disease-free equilibrium point and the endemic equilibrium point of the model is analyzed through eigenvalue theory and Routh-Hurwitz criterion.The numerical simulation and fitting results of COVID-19 virus show that the proposed SEIQRP model can effectively describe the dynamic transmission process of the infectious disease.In the model, the three parameters, i.e.protection rate, incubation period isolation rate, and infected person isolation rate play a very critical role in the spread of the disease.Raising people's awareness of self-protection, focusing on screening for patients in the incubation period, and isolating and treating infected people can effectively reduce the spread of infectious diseases. © 2023 Northeastern University.All rights reserved.

REDUCED TRANSMISSION RISKS FOR PASSENGER OPERATIONS DURING COVID-19 PANDEMIC

In the aircraft cabin, passengers must share a confined environment with other passengers during boarding, flight, and disembarkation, which poses a risk for virus transmission and requires risk-appropriate mitigation strategies. Spacing between passenger groups during boarding and disembarkation reduces the risk of transmission, and optimized sequencing of passenger groups helps to significantly reduce boarding and disembarkation time. We considered passenger groups to be an important factor in overall operational efficiency. The basic idea of our concept is that the members of a group should not be separated, since they were already traveling as a group before entering the aircraft. However, to comply with COVID-19 regulations, different passenger groups should be separated spatially. For the particular challenge of disembarkation, we assume that passenger groups will be informed directly when they are allowed to leave for disembarkation. Today, cabin lighting could be used for this information process, but in a future digitally connected cabin, passengers could be informed directly via their personal devices. These devices could also be used to check the required distances between passengers. The implementation of optimized group sequencing has the potential to significantly reduce boarding and disembarkation times, taking into account COVID-19 constraints. © 2022 ICAS. All Rights Reserved.

IoT Based on Comfort and Hygiene Common Restroom with A Touchless Locking Mechanism

Public restrooms can be a breeding ground for germs and viruses, especially in light of the current COVID-19 pandemic. Touching surfaces like door handles can have a lot of harmful bacteria and microorganisms that increase the risk of transmission of infectious diseases. Additionally, ensuring the cleanliness of public restrooms can be a challenge as its being used by a lot of people on a day-to-day basis. To overcome this, we propose a model that provides a touchless door-locking mechanism with self-sanitization capabilities, thereby reducing the risk of transmission and ensuring a safer and cleaner environment for users. As the Internet of Things is an evolving technology and is providing modern solutions for various problems, the proposed system uses touchless doors that are incorporated with Node Microcontroller Unit and automatic Ultraviolet C sanitization. UVC light radiation is used for disinfecting purposes. The overall invention combines various features to provide a hygienic, secure, and safe restroom experience, ensuring that the restroom is always clean, secure, and accessible to those who need it. © 2023 IEEE.

Projection of COVID-19 Jakarta using CHIME Technology (Covid-19 Hospital Impact Model for Epidemics) Application

After the coronavirus outbreak, the disease known as COVID-19 has been infecting millions of people, and the number of deaths is pilling up to hundreds of thousands. In Indonesia, especially Jakarta, some of the deaths are caused by pandemic-related surges that strain hospital capacity. Besides, people had many obstacles in this pandemic condition because of the lack of knowledge about COVID-19. On that matter, several models emerged worldwide to help inform public decision making in this pandemic situation. With today's technological advances the CHIME (COVID-19 Hospital Impact Model for Epidemics) application is designed to assist hospitals and public health officials with understanding hospital capacity needs as they relate to the COVID pandemic. This paper aims to help inform public health decision making regarding the transmission of COVID-19 in Jakarta using CHIME. This work uses Jakarta COVID-19 data from November 24th, 2021 and its accumulation from 14 days before (November 10th, 2021) to predict the course of COVID-19 in 30 days. With ArcGIS Pro and ArcGIS Experience, this work successfully made a map that uses CHIME to inform about peak demand of each city in DKI Jakarta and the daily new admissions and hospitalization graph. In addition, a Jakarta COVID-19 dashboard is also made to inform more about the transmission of COVID-19. © 2022 IEEE.

3D-Printed Face Mask with Integrated Sensors as Protective and Monitoring Tool

The outbreak of the recent Covid-19 pandemic changed many aspects of our daily life, such as the constant wearing of face masks as protection from virus transmission risks. Furthermore, it exposed the healthcare system's fragilities, showing the urgent need to design a more inclusive model that takes into account possible future emergencies, together with population's aging and new severe pathologies. In this framework, face masks can be both a physical barrier against viruses and, at the same time, a telemedical diagnostic tool. In this paper, we propose a low-cost, 3D-printed face mask able to protect the wearer from virus transmission, thanks to internal FFP2 filters, and to monitor the air quality (temperature, humidity, CO2) inside the mask. Acquired data are automatically transmitted to a web terminal, thanks to sensors and electronics embedded in the mask. Our preliminary results encourage more efforts in these regards, towards rapid, inexpensive and smart ways to integrate more sensors into the mask's breathing zone in order to use the patient's breath as a fingerprint for various diseases. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

Effect of Elevator Design on the Risk Associated with COVID Transmission – A Comparative Study between a Sliding Door and a Collapsible Gate Elevator

The ominous spread of the COVID-19 pandemic is attributed to the droplets respired during coughing, sneezing or speaking. These droplets undergo evaporation to become aerosols, which, along with the larger droplets, are believed to ultimately spread the virus. In this current work, a small, enclosed region like an elevator (containing a COVID infected passenger) is considered where the risk of infection is high as the commonly practiced norm of social distancing is not possible. Numerical simulations are performed using OpenFOAM. Two different types of elevators – one equipped with a sliding door and the other with a collapsible gate, are considered and the change in droplet behavior is examined. Certain parameters pertaining to the risk of virus transmission have been quantified and assessed thoroughly, such as the percentage of droplets floating in the height range from a person's waist height to his mouth height, the radial span of the floating droplets from the infected passenger's mouth. From these parameters, the safety measures to be adopted by other copassengers can be determined. After an extensive study, it has been found that the collapsible gate elevator is safer than the sliding door elevator along with added advantages in the context of disease transmission. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Effects of various respiratory activities on human aerosol emissions: experimental design and preliminary results

This study combines particle measurements and acoustic measurements to study aerosols generated in breathing, speaking, singing and coughing. Particle measurements are carried out using a portable measurement chamber designed specially for the study. Acoustic measurements of voice production are conduced to standardize measurements in human aerosol emission and to reveal possible reasons for the individual differences in particle generation. Understanding mechanisms of human aerosol generation is important in trying to understand how the airborne transmission of pathogens takes place and furthermore in assessing how to minimize the risk of transmission. The results can be used in the context of all airborne diseases. © 2022 17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022. All rights reserved.

Ventilation solutions for healthcare patient rooms to control respiratory infections

Building ventilation significantly impacts healthy and safe indoor conditions preventing airborne virus spread between people. Therefore, ventilation strategy is a globally essential and health-promoting research topic. Previous studies showed the importance of sufficient ventilation for diluting the virus concentration and reducing the infection risk. The present study investigates the probability of coronavirus infection in the typical room calculated with the Wells Riley proposes recommendations for further research of indoor airflow effect on the virus transmission. © 2022 17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022. All rights reserved.

The effect of ventilation performance on risk of indoor airborne transmission of SARS-CoV-2

Ventilation performance plays a significant role in distributing contaminants and airborne infections indoors. Thus, poorly ventilated public spaces may be at high risk due to the presence of both infectious and susceptible people. Adapting HVAC ventilation systems to mitigate virus transmission requires considering ventilation rate, airflow patterns, air balancing, occupancy, and feature placement. The study aims to identify poorly ventilated spaces where airborne transmission of pathogens such as SARS-CoV-2 could be critical. This study is focused on evaluating the ventilation performance of the building stock and the safety of using the facilities based on measured indoor CO2. The results revealed the spaces with the potential risk of indoor airborne transmission of COVID-19. The study proposes recommendations for utilising air ventilation systems in different use cases. © 2022 17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022. All rights reserved.

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.

A simple dilution model of the expired jet

Short-range exposure to expired aerosols or droplet nuclei has been considered as the predominant route for SARS-CoV-2. The observed effect of mask wearing, and social distancing suggests the importance of expired jet in the spread of COVID-19. The well-known steady-state dilution model is no longer valid for the interrupted expiratory jet. We reanalysed the existing interrupted jet data and proposed a simple dilution model of expired jet using the two-stage jet model. The interrupted jet consists of two stages, i.e., the jet-like and puff-like stage. Results show dilution factor grows linearly with the distance at the jet-like stage but increases with the cubic of the increasing distance in the puff-like stage. Dilution factor at any distance for the puff-like stage decreases as the activity intensifies, which is still much larger than that estimated via the steady jet model. The findings can be further applied into the short-range airborne exposure assessment. © 2022 17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022. All rights reserved.

Under what circumstances would airborne transmission of SARS-CoV-2 occur?

To find out the circumstances under which airborne transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) would happen, we conducted mechanistic and systematic modelling of two Coronavirus disease 2019 (COVID-19) outbreaks, i.e., Hunan 2-bus outbreak and Luk Chuen House outbreak (the horizontal cluster). Computational fluid dynamics (CFD) simulations, multi-zone airflow modelling, multi-route mechanistic modelling, and dose-response estimation were carried out selectively according to the transmission characteristics in each outbreak. Our results revealed that poorly ventilated bus indoor environments bred the Hunan 2-bus outbreak in which airborne transmission predominates;prevailing easterly background wind and probable door opening behaviour led to the secondary infections across the corridor in Luk Chuen House outbreak. Measures to facilitate sufficient ventilation indoors and positive pressure in the housing building corridor may help minimise infection risk. © 2022 17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022. All rights reserved.

Template for Extended Abstract contributions to INDOOR AIR 2022

This investigation presents results of Computational Fluid Dynamics (CFD) modelling of aerosol behaviour within an arbitrary 'realistic' 100m2 office environment, with dynamic and variable respiratory droplet release profile applied based on published findings (Morawska et al., 2009). A multitude of ventilation strategies and configurations have been applied to the base model to compare the effectiveness of reducing the concentration of suspended aerosols over time. A key finding of the investigation indicates a relatively low sensitivity to increasing outside air percentage, and that the benefit from this strategy is heavily dependent on the in-duct droplet decay factor. The application of local recirculating air filtration systems with MERV-13 filters mounted on occupant desks proved significantly more effectiveness than increasing outside air concentration from 25% to 100% in reducing the quantity of suspended aerosols. This highlights that the ventilation industry should perhaps focus on opportunities to integrate filtration systems into furniture, partitions, cabinetry etc., and that an appliance-based solution may be more beneficial for reducing COVID-19 transmission in buildings (and likely more straightforward) than modifications to central ventilation systems, particularly in the application of refurbishments and retrofits. © 2022 17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022. All rights reserved.

Machine Learning Techniques on X-ray Images for Covid-19 Classification

The Public Health Commission of Hubei Province, China, at the end of 2019reported cases of severe and unknown pneumonia, marked by fever, malaise, dry cough, dyspnea, and respiratory failure, that occurred in the urban area of Wuhan, according to the World Health Organization (WHO). The lung infection, SARS-CoV-2, also known as COVID-19, was caused by a brand-new coronavirus (coronavirus disease 2019). Since then, infections have increased exponentially, and the WHO labeled the outbreak a worldwide emergency at the beginning of March 2020. Infected and asymptomatic individuals who can spread the virus are the main sources of it. The transmission occurs mainly by airthrough the air through the droplets, however indirect transmission is also possible, such as through contact with infected surfaces. It becomes essential to identify viral carriers as soon as possible in order to stop the spread of the disease and reduce morbidity and mortality. Imaging examinations, which are among the specific tests used to make the definite diagnosis, are crucial in the patient's management when COVID-19 is suspected. Numerous papers that use machine learning techniques discuss the use of X-ray chest radiographs as a component that aids in diagnosis and permits disease follow-up. The goal of this work is to supply the scientific community with information on the most widely used Machine Learning algorithms applied to chest X-ray images. © 2022 IEEE.

A Contact Matrix-Based Approach for Predicting COVID-19 Using Influenza Data

The global pandemic Corona Virus Disease 2019 (COVID-19) has become one of the deadliest epidemics in human history, bringing enormous harm to human society. To help health policymakers respond to the threat of COVID-19, prediction of outbreaks is needed. Research on COVID-19 prediction usually uses data-driven models and mechanism models. However, in the early stages of the epidemic, there were not enough data to establish a data-driven model. The inadequate understanding of the virus that causes COVID-19, SARS-COV-2, has also led to the inaccuracies of the mechanism model. This has left the government with the toughest Non-pharmaceutical interventions (NPIs) to curb the spread of the virus, such as the lockdown of Wuhan in 2020. Yet man is a social animal, and social relations and interactions are necessary for his existence. The novel coronavirus and containment measures have challenged human and community interactions, affecting the lives of individuals and collective societies. To help governments take appropriate and necessary actions in the early stages of an epidemic, and to mitigate its impact on people's psychology and lives, we used the COVID-19 pandemic as an example to develop a model that uses surveillance data from one epidemic to predict the development trend of another. Based on the fact that both influenza and COVID-19 are transmitted through infectious respiratory droplets, we hypothesized that they may have the same underlying contact structure, and we proposed the influenza data-based COVID-19 prediction (ICP) model. In this model, the underlying contact pattern is firstly inferred by using a singular value decomposition method from influenza surveillance data. Then the contact matrix was used to simulate the influenza virus transmission through close contact of people, and the influenza virus transmission model was established. In order to be able to simulate the spread of COVID-19 virus using influenza transmission models, we used influenza contact matrix and COVID-19 infection data to estimate the risk of a population contracting COVID-19, i.e. force of infection of COVID-19. Finally, we used force of infection and influenza virus transmission model to simulate and predict the spread of COVID-19 in the population. We obtained age-disaggregated influenza and COVID-19 infection data for the United States in 2020, as well as data for Europe, which was not disaggregated by age. We use correlation coefficients as an evaluation indicator, and the final results prove that the predicted value and the actual value are positively correlated. So, the development trend of COVID-19 can be predicted using influenza surveillance data. © 2022 IEEE.

Optimal control analysis of Monkeypox disease with the impact of environmental transmission

Monkeypox is an emerging zoonotic viral disease resembling that of smallpox, although it is clinically less severe. Following the COVID-19 outbreak, monkeypox is an additional global health concern. The present study aims to formulate a novel mathematical model to examine various epidemiological aspects and to suggest optimized control strategies for the ongoing outbreak. The environmental viral concentration plays an important role in disease incidence. Therefore, in this study, we consider the impact of the environmental viral concentration on disease dynamics and control. The model is first constructed with constant control measures.The basic mathematical properties including equilibria, stability, and reproduction number of the monkeypox model are presented. Furthermore, using the nonlinear least square method, we estimate the model parameters from the actual cases reported in the USA during a recent outbreak in 2022. Normalized sensitivity analysis is performed to develop the optimal control problem. Based on the sensitivity indices of the model parameters, the model is reformulated by introducing six control variables. Based on theoretical and simulation results, we conclude that considering all suggested control measures simultaneously is the effective and optimal strategy to curtail the infection. We believe that the outcomes of this study will be helpful in understanding the dynamics and prevention of upcoming monkeypox outbreaks. © 2023 the Author(s), licensee AIMS Press.

Carbon Dioxide Monitoring in Refuse Collection Vehicle Cabins to Reduce the Risk of SARS-CoV-2 Airborne Transmission

During the COVID-19 pandemic, essential workers such as waste collection crews continued to provide services in the UK, but due to their small size, maintaining social distancing inside waste collection vehicle cabins is impossible. Ventilation in cabins of 11 vehicles operating in London was assessed by measuring air supply flow rates and carbon dioxide (CO2) in the driver's cabin, a proxy for exhaled breath. The indoor CO2 indicated that air quality in the cabins was mostly good throughout a working day. However, short episodes of high CO2 levels above 1500 ppm did occur, mainly at the beginning of a shift when driving towards the start of their collection routes. This data indicated that the ventilation systems on the vehicles were primarily recirculating air and the fresh air supply made up only 10-20 % of the total airflow. Following recommendations to partly open windows during shifts and to maintain ventilation systems, a second monitoring campaign was carried out, finding on average, an improvement in ventilation on board the vehicles. © 2022 17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022. All rights reserved.

Dynamic linkages between shipping and commodity markets: Evidence from a novel asymmetric time-frequency method

This study extends the existing literature in this area by examining the connectedness and shock spillover between commodity and shipping markets using a new novel time-varying frequency and quantile connectedness method developed by Chatziantoniou et al (2022) based on B&K (2018) and Ando et al (2018). Connectedness and shock transmission between the markets were analysed with daily data covering July 4, 2012 to July 20, 2022. A major value added of this study to the existing literature is the examination of the asymmetric effect of commodity price changes (or return) on the connectedness of the markets. Mean-frequency total connectedness analysis indicates that, the overall shipping market (BDI) is both the transmitter (to) and receiver of the highest shock from the entire market connectedness system. In the short-term, the agricultural markets dominate as both the transmitters and receivers of the major shocks to and from the entire market system, while in the medium-term, the shipping markets dominate as both the transmitters and receivers of the largest shocks to the entire market system. However, in the long-term, connectedness and shock propagation were very low. The time-varying quantile analysis reveals that, connectedness was very strong before, during and after COVID-19 at the bearish and bullish market conditions. Further, the time-varying frequency connectedness analysis shows that, although total connectedness is relatively high overtime, it was propelled by short-term dynamics. Metal markets are connected among themselves, and with both agricultural and shipping markets. Agricultural markets are connected among themselves, and with shipping markets, which are only connected among themselves. There is evidence of the asymmetric effect of commodity return dynamics on the connectedness of the markets. Some important policy recommendations were drawn from the findings.

Transient zonal modeling of SARS-CoV-2 airborne infection risk for incomplete mixing air distribution methods

A widely used analytical model to quantitatively assess airborne infection risk is the Wells-Riley model based on the assumption of complete air mixing in a single zone. This study aimed to extend the Wells-Riley model so that the infection risk can be calculated in spaces where complete mixing is not present. This is done by evaluating the time-dependent distribution of infectious quanta in each zone and by solving the coupled system of differential equations based on the zonal quanta concentrations. In conclusion, this study shows that using the Wells-Riley model based on the assumption of completely mixing air may overestimate the long-range airborne infection risk compared to some high-efficiency ventilation systems such as displacement ventilation, but also underestimate the infection risk in a room heated with warm air supplied from the ceiling. © 2022 17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022. All rights reserved.

Evaluation of airborne infection risk with different window airing strategies in classroom

The coronavirus disease may spread by airborne aerosols, especially in a poorly ventilated enclosure. Natural ventilation can reduce the transmission of infection. The WHO suggested the minimum ventilation rate of 10 L/s/person in non-residential settings. The objective was to evaluate risk of airborne infection with different settings in natural ventilated classroom. The risk was evaluated by using the modified Wells-Riley equation associated with the variation of contaminant concentration simulated by a multi-zone airflow model. The results provide the guidance of natural ventilation strategy in the classroom to reduce the transmission of airborne infection disease. © 2022 17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022. All rights reserved.