Spread Of Respiratory Infectious Diseases Research Articles

Evaluating Seasonal Variations in Human Contact Patterns and Their Impact on the Transmission of Respiratory Infectious Diseases.

Human contact patterns are a key determinant driving the spread of respiratory infectious diseases. However, the relationship between contact patterns and seasonality as well as their possible association with the seasonality of respiratory diseases is yet to be clarified. We investigated the association between temperature and human contact patterns using data collected through a cross-sectional diary-based contact survey in Shanghai, China, between December 24, 2017, and May 30, 2018. We then developed a compartmental model of influenza transmission informed by the derived seasonal trends in the number of contacts and validated it against A(H1N1)pdm09 influenza data collected in Shanghai during the same period. We identified a significant inverse relationship between the number of contacts and the seasonal temperature trend defined as a spline interpolation of temperature data (p = 0.003). We estimated an average of 16.4 (95% PrI: 15.1-17.5) contacts per day in December 2017 that increased to an average of 17.6 contacts (95% PrI: 16.5-19.3) in January 2018 and then declined to an average of 10.3 (95% PrI: 9.4-10.8) in May 2018. Estimates of influenza incidence obtained by the compartmental model comply with the observed epidemiological data. The reproduction number was estimated to increase from 1.24 (95% CI: 1.21-1.27) in December to a peak of 1.34 (95% CI: 1.31-1.37) in January. The estimated median infection attack rate at the end of the season was 27.4% (95% CI: 23.7-30.5%). Our findings support a relationship between temperature and contact patterns, which can contribute to deepen the understanding of the relationship between social interactions and the epidemiology of respiratory infectious diseases.

Dynamics and optimal control for a spatial heterogeneity model describing respiratory infectious diseases affected by air pollution

Air pollution has a serious impact on the spread of respiratory infectious diseases. However, the concentration of air pollution is not the same in different places. In order to characterize the spatial heterogeneity and the diffusion of pollutants, this paper proposes a spatial heterogeneity model, where the transmission rate is closely related to air pollution. The dynamics are established in terms of the basic reproduction number R0: if R0≤1, the disease-free equilibrium is globally asymptotically stable; if R0>1, there exists at least one endemic equilibrium and the disease is uniformly persistent. Our findings suggest that spatial heterogeneity, increased pollutants emission, and limited movement of infected individuals will all contribute to the spread of the respiratory infectious diseases. Subsequently, the corresponding strategies are introduced into the model to control the prevalence of the diseases. Under the minimization of the objective function, the necessary condition for the optimal control is acquired with the help of Pontryagin’s maximum principle. Numerical simulations are applied to validate the theoretical results of threshold dynamics and to demonstrate the influences of inflow and clearance rates of pollutants on threshold dynamics and optimal control.

Numerical evaluation of space form design for outpatient waiting space to reduce airborne infection risk in hospitals

The outpatient waiting space (OWS), with high density and frequent variations of occupants, represents a high-risk space for the spread of respiratory infectious diseases (RIDs) in hospitals. While previous studies have proposed strategies related to occupant behavior and environmental quality, there is a limited exploration of the impact of space form design on infection risk in OWSs. This study developed a numerical method for assessing the airborne infection risk, considering the non-uniform distribution of infectious particles and variations in occupant distributions. Validated computational fluid dynamics (CFD) simulations were employed to calculate the particle distributions. The intake fraction, accumulated throughout the waiting process, was then integrated into the Wells-Riley model for risk evaluation. Using the typical ventilation form of ceiling supply and ceiling return, with an air change per hour (ACH) set to 6 h−1 as the baseline, 12 typical space forms, including four space shapes (L shape, T shape, U shape, and O shape) and three entrance layouts (single-side entrance, middle entrance, and double-side entrances), were selected for comparisons. The results showed significant variations in infectious particle distributions across the 12 space forms, with average infection probabilities (IPs) ranging from 3.47 % to 19.75 %, suggesting that appropriate space form design has the potential to reduce IP by 16.28 %. These findings offer a novel perspective on mitigating airborne infection risk in hospitals by emphasizing the significance of architectural design. Moreover, precise space design guidance is proposed for OWS to safeguard human health against the spread of RIDs.

A novel approach for predicting the concentration of exhaled aerosols exposure among healthcare workers in the operating room

The spread of respiratory infectious diseases in close proximity environments due to poor indoor air quality not only adds to the unease that exists globally, but it also presents a perplexing dilemma when assessing the harmful effects on healthcare providers performing complex surgical interventions. Knowing the exposure concentrations under different conditions is a prerequisite for healthcare providers' safety. This study aimed to develop a methodology by merging experimental data with machine learning techniques. The experiments simulated patients' breathing patterns, examining three independent variables: breathing rate, ceiling air change velocity, and breathing height. The researchers monitored aerosol concentration in six locations within healthcare workers' breathing zone. Experimental data assessed the machine learning models, indicating that the decision tree regression (DTR) was more effective than the support vector machine (SVM) in most positions. Four integrated algorithms, random forest regression (RFR), adaptive boosting (Adaboost), gradient boosting decision tree (GBDT), and extreme gradient boosting (XGboost), utilizing decision tree regression as a weak learner were studied, with RFR model showing the best overall performance. To improve results and enhance credibility, two techniques, multi-feature input and interval prediction, were utilized. The study's outcomes shed light on the potential use of machine learning in predicting aerosol concentrations and ensuring the safety of medical personnel in the operating room.

Multi-objective optimization of mechanical ventilation with the aid of purifiers in two scenarios: Regular operation and mitigating the spread of respiratory infectious diseases.

The Appendix is available in the online version of this article at 10.1007/s12273-023-0999-z.

Optimization of Passenger Transport Organization for Effectively Alleviating the Spread of Respiratory Infectious Diseases in Underground Stations

Optimization of Passenger Transport Organization for Effectively Alleviating the Spread of Respiratory Infectious Diseases in Underground Stations

A hybrid ANN-CNN model for predicting non-linear relationship of covid-19 cases based on weather factors

With the global increase in the emergence of viral diseases, the most recent being the Coronavirus Disease 2019 (COVID- 19) in 2020-2021, it has decimated the world with little understanding of its history and the factors that influence its transmission dynamics. Weather significantly influences the spread of respiratory infectious diseases like influenza, yet the impact of weather on COVID-19 transmission in Nigeria remains unexamined and necessitates further clarification. This study presents and compares the results of six machine learning models, the developed Hybrid ANN-CNN, ANN, CNN, LSTM, LASSO, and Multiple Linear Regression models, aiming to predict the impact of weather factors on COVID-19 cases. The dataset used in this study includes daily datasets of Nigerian COVID-19 cases and seven weather variables collected from May 1, 2020, to April 30, 2021. The results indicate that the developed Hybrid ANN-CNN outperforms the remaining five models based on Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) for all cases. Specifically, for confirmed cases, the Hybrid ANN-CNN had an MAE of 0.0274, for recovery cases 0.0257, and for death cases 0.0425. Similarly, for RMSE, the developed Hybrid ANN-CNN had values of 0.0469 for confirmed cases, 0.0813 for recovery cases, and 0.0840 for deaths. This was followed by LASSO with an MAE of 0.01384 and CNN and LSTM with 0.1384 and 0.1385, respectively.

Identifying built environment solutions, in Thai community hospital outpatient clinics, to prevent the spread of COVID-19

Due to the unprecedented outbreak of COVID-19 (Corona Virus Disease - 2019) healthcare facilities, including Thailand community hospitals were mandated to adjust their facilities to both medical and non-clinical areas to prevent the spread of the disease. The study aimed to identify problems focusing on the physical settings of out-patient departments (OPD) in community hospitals during the first Thailand COVID-19 outbreak in March 2020. Furthermore, the study focused on providing design suggestions for preventing the spread of respiratory infectious diseases. The study employed a two-step approach to data collection. First, physical setting observations of the OPD of the two hospital buildings. Second, interviews with 22 healthcare employees; 11 participants from the first hospital and 11 participants from the second hospital. Interviews were transcribed verbatim and analyzed using content analysis. Five main themes were identified: (1) factors leading to accumulation of pathogens, (2) measures for preventing the spread of respiratory diseases, (3) current services and physical settings of outpatient clinics, (4) guidelines for improving the physical setting of outpatient clinics and (5) prevention of accumulation of pathogens in OPD areas. Results indicate that not only improving the physical setting of the OPD areas is required, but a strict patient screening process and an organized building management programme should also be considered. Such actions with workflow management would have played important roles in preventing the spread of respiratory diseases during the first COVID-19 outbreak. The findings from the study contribute to both research and industry. For the research, the study contributes to three literature topics including hospital building evaluation and assessment techniques, hospital design, and design prevention for the spread of COVID-19. For industry, the study contributes to the future development of layout design that can prevent the spread of COVID-19 throughout Thai community hospitals.

Preventing the spread of COVID-19 through environmental design in Thai community hospitals

The unprecedented outbreak of COVID-19 has had a tremendous negative impact on healthcare facilities, especially public hospitals. Thai community hospitals serve as primary care for COVID-19 patients. However, many hospital buildings were constructed in the 1990s and are now becoming outdated. Community hospitals faced many difficulties during the Thailand COVID-19 outbreak. To identify these difficulties this study focused on the physical settings of the community hospital’s outpatient departments (OPD) between 2020 and 2021. Furthermore, the study provided design suggestions for preventing the spread of respiratory infectious diseases. The study used a three-step process of data collection. The first was to observe the physical settings of the OPD buildings of the three hospitals. Secondly, interviews were conducted with thirty healthcare employees: 11 from the first hospital, 11 from the second hospital and 8 from the third hospital. Interviews were transcribed and analyzed using content analysis. Subsequently, the architectural design was produced based on observation and interview analysis. In the third step, the same thirty participants evaluated the architectural design through a focus group. Findings from the interviews led to four themes: 1) factors leading to the accumulation of pathogens, 2) measures for preventing the spread of respiratory diseases, 3) the effect of patient numbers on virus transmission, and 4) suggestions for improving the physical setting of OPDs. The analysis of the four themes led to the preliminary design recommendation, which was evaluated through a focus group, leading to suggestions for the improvement of thirteen areas. The analysis results showed that the participants were satisfied with the architectural design with additional minor recommendations. This study provided originality for the implementation and future development of hospital layout designs that can prevent the spread of COVID-19 throughout Thai community hospitals.

Quantifying human mixing patterns in Chinese provinces outside Hubei after the 2020 lockdown was lifted

BackgroundContact patterns play a key role in the spread of respiratory infectious diseases in human populations. During the COVID-19 pandemic, the regular contact patterns of the population have been disrupted due to social distancing both imposed by the authorities and individual choices. Many studies have focused on age-mixing patterns before the COVID-19 pandemic, but they provide very little information about the mixing patterns in the COVID-19 era. In this study, we aim at quantifying human heterogeneous mixing patterns immediately after lockdowns implemented to contain COVID-19 spread in China were lifted. We also provide an illustrative example of how the collected mixing patterns can be used in a simulation study of SARS-CoV-2 transmission.Methods and resultsIn this work, a contact survey was conducted in Chinese provinces outside Hubei in March 2020, right after lockdowns were lifted. We then leveraged the estimated mixing patterns to calibrate a mathematical model of SARS-CoV-2 transmission. Study participants reported 2.3 contacts per day (IQR: 1.0–3.0) and the mean per-contact duration was 7.0 h (IQR: 1.0–10.0). No significant differences in average contact number and contact duration were observed between provinces, the number of recorded contacts did not show a clear trend by age, and most of the recorded contacts occurred with family members (about 78%). The simulation study highlights the importance of considering age-specific contact patterns to estimate the COVID-19 burden.ConclusionsOur findings suggest that, despite lockdowns were no longer in place at the time of the survey, people were still heavily limiting their contacts as compared to the pre-pandemic situation.

A Review on Research and Development of Healthy Building in China

Healthy buildings are a deep-level development of green buildings, which can effectively help relieve stress and improve occupants’ physical and mental health. In addition, they are is likely to play an important role in preventing the spread of respiratory infectious diseases. Therefore, healthy buildings have attracted worldwide attention. This article reviews the research and development of healthy buildings in China. First, it briefly introduces the definition of healthy buildings, the key elements of evaluation standards, energy conservation measures and new technology applications for healthy buildings, and lessons learned from the global outbreak of SARS-CoV-2. Secondly, it presents the milestones of healthy building development and healthy building projects in China, and the benefits of healthy buildings were also discussed. Finally, the differences in the evaluation systems of healthy buildings between China and other countries were analyzed, the problems of the current policy system of healthy buildings in China were identified, and suggestions for future development were provided.

The COVID-19 epidemic and other notifiable infectious diseases in China

Many infection control measures have been implemented to prevent the spread of SARS-CoV-2 during COVID-19 pandemic. We aimed to investigate the impact of COVID-19 epidemic on the other notifiable infectious diseases in China, including respiratory infectious diseases, diseases transmitted through the digestive tract and animal-borne diseases. Compared with 2019, the overall decline rate of respiratory infectious diseases in 2020 is the highest (60–90%), and the diseases transmitted by the digestive tract and animal-borne diseases are similar at 20–30%. Both hepatitis and sexually transmitted diseases decreased significantly in February, and there were basically no significant changes in other months compared with previous years. The series of measures taken by China government to prevent the spread of SARS-CoV-2 are also very effective in preventing the spread of respiratory infectious diseases. But they also have a certain degree of prevention against notifiable infectious diseases spread by other routes.

Bumpy structured nanofibrous membrane as a highly efficient air filter with antibacterial and antiviral property.

Recently, the pandemic infectious diseases caused by coronavirus have prompted the development of air filter membranes to against infectious agents and protect human health. This research focuses on air filter membrane with antibacterial and antiviral property for high-efficiency particulate matter (PM) removal. Herein, polyamide-6 electrospun nanofibers were anchored with silver nanoparticles through hydrogen-bond. Bumpy nanorough surface and multilevel structure contribute to improve capture capacity, and silver nanoparticles provide a strong ability to inactivate bacteria and virus. In conclusion, this membrane exhibits high PM2.5 filtration efficiency of 99.99% and low pressure drop of 31 Pa; simultaneous removal of multiple aerosol pollutants, e.g., SOx, NOx, methylbenzene, L-Nicotine; superior antibacterial performance against Escherichia coli (Gram negative bacteria) and Staphylococcus aureus (Gram positive bacteria), antiviral property against Porcine Deltacoronavirus and not significant cytotoxicity. Research of air filtration material is important to remove air pollutants and to prevent infection and spread of respiratory infectious diseases.

Spatial Distribution of Exhalation Droplets in the Bus in Different Seasons

ABSTRACT In closed buses, the spread of droplets with viruses/bacteria may cause the spread of respiratory infectious diseases. Discrete phase modeling is used to simulate the diffusion characteristics and concentration distribution of droplets at different temperatures and different exhalation positions by ANSYS FLUENT software. The integral concentration of droplets at different locations can be quantified, which leads to identification of low-risk areas and high-risk areas in the bus. Results show that a higher outdoor temperature leads to lower droplets’ diffusion speed and longer time until the droplets reach the driver. In addition, based on the integral concentration of droplets at the seats, regardless of whether a passenger exhales droplets in the front row of the bus, the position of the rear door or the last row of the bus, the seats in the last row of the bus away from the door belong to the low-risk area. In contrast, the seats near the door and the middle seat in the bus are higher risk areas. Consequently, this study proposed sitting on a seat in the low-risk area as a means to reduce the risk of passengers. Moreover, safety protection facilities around the driver should be modified to improve the isolation of the upper area of the driver’s location, so as to effectively prevent the droplet diffusion towards the driver, thereby effectively reducing the driver’s risk of infection.

Public transportation and transmission of viral respiratory disease: Evidence from influenza deaths in 121 cities in the United States.

One important concern around the spread of respiratory infectious diseases has been the contribution of public transportation, a space where people are in close contact with one another and with high-use surfaces. While disease clearly spreads along transportation routes, there is limited evidence about whether public transportation use itself is associated with the overall prevalence of contagious respiratory illnesses at the local level. We examine the extent of the association between public transportation and influenza mortality, a proxy for disease prevalence, using city-level data on influenza and pneumonia mortality and public transit use from 121 large cities in the United States (US) between 2006 and 2015. We find no evidence of a positive relationship between city-level transit ridership and influenza/pneumonia mortality rates, suggesting that population level rates of transit use are not a singularly important factor in the transmission of influenza.

Social contact patterns relevant to the spread of respiratory infectious diseases in Hong Kong

The spread of many respiratory infections is determined by contact patterns between infectious and susceptible individuals in the population. There are no published data for quantifying social contact patterns relevant to the spread of respiratory infectious diseases in Hong Kong which is a hotspot for emerging infectious diseases due to its high population density and connectivity in the air transportation network. We adopted a commonly used diary-based design to conduct a social contact survey in Hong Kong in 2015/16 using both paper and online questionnaires. Participants using paper questionnaires reported more contacts and longer contact duration than those using online questionnaires. Participants reported 13 person-hours of contact and 8 contacts per day on average, which decreased over age but increased with household size, years of education and income level. Prolonged and frequent contacts, and contacts at home, school and work were more likely to involve physical contacts. Strong age-assortativity was observed in all age groups. We evaluated the characteristics of social contact patterns relevant to the spread of respiratory infectious diseases in Hong Kong. Our findings could help to improve the design of future social contact surveys, parameterize transmission models of respiratory infectious diseases, and inform intervention strategies based on model outputs.

Preparation and Characteristic of Antibacterial Facemasks with Chinese Herbal Microcapsules

ABSTRACTAlong with the current increases in human activities, air pollution, especially that generated from bioaerosols, has begun to receive more extensive attention. Among the bioaerosols, bacteria and fungi can result in organisms that are pathogenic to animals and humans. Therefore, a biological protective respirator with an antibacterial function would be a significant method by which to prevent epidemic spread of respiratory infectious diseases and protect human health. In this study, a type of facemask with an antibacterial function was prepared. The structure of the respirator included a respirator body and an inner filter layer that was assembled with herbal microcapsules. A kind of Chinese herb microcapsule developed through a cross-linking reaction method was successfully prepared to serve as an important component of the respirator. Scutellaria baicalensis (SB), a typical Chinese medicine, was employed to prepare the Chinese herb microcapsule. Three kinds of filter cloth (cotton, polyester fiber, and non-woven fabric, respectively) were employed to assemble with Chinese herb microcapsule. Meanwhile, the antibacterial performance and adhesion of the Chinese herb microcapsule for E. coli and S. aureus were investigated. The measurement results revealed that (i) the antibacterial efficiency of the Chinese herb microcapsule and the respirator filter was above approximately 99.999 and 96.000%, respectively; (ii) the adhesion of the herb microcapsules was still excellent after the filter was washed 100 times; (iii) the as-prepared antibacterial filter modified by an O2 plasma-surface (PSM-O2) treatment exhibited excellent antibacterial performance. Finally, a chamber simulation utilizing an Anderson first order sampler was designed to simulate the effect of wearing the antibacterial filter in a real environment. Overall, the respirator exhibited excellent biological protection properties compared with general masks, which will satisfy a vast number of application prospects.

Non-pharmaceutical interventions for the prevention of respiratory tract infections during Hajj pilgrimage.

SummaryOvercrowding during the yearly Hajj mass gatherings is associated with increased risk of spreading infectious diseases, particularly respiratory diseases. Non-pharmaceutical interventions (e.g., hand hygiene, wearing face masks, social distancing) are known to reduce the spread of respiratory viruses from person to person and are therefore recommended to pilgrims by public health agencies. The implementation of effective public health policies and recommendations involves evaluating the adherence to and effectiveness of these measures in the specific context of the Hajj. This review summarizes the evidence related to the effectiveness of non-pharmaceutical interventions in preventing the spread of respiratory infectious diseases during the Hajj. Overall, although hand hygiene compliance is high among pilgrims, face mask use and social distancing remain difficult challenges. Data about the effectiveness of these measures at the Hajj are limited, and results are contradictory, highlighting the need for future large-scale studies.