Viral kinetics and exhaled droplet size affect indoor transmission dynamics of influenza infection

A mechanistic modeling and estimation framework for environmental pathogen surveillance

Environmental pathogen surveillance is a promising disease surveillance modality that has been widely adopted for SARS-CoV-2 monitoring. The highly variable nature of environmental pathogen data is a challenge for integrating these data into public health response. One source of this variability is heterogeneous infection both within an individual over the course of infection as well as between individuals in their pathogen shedding over time. We present a mechanistic modeling and estimation framework for connecting environmental pathogen data to the number of infected individuals. Infected individuals are modeled as shedding pathogen into the environment via a Poisson process whose rate parameter λt varies over the course of their infection. These shedding curves λt are themselves random, allowing for variation between individuals. We show that this results in a Poisson process for environmental pathogen levels with rate parameter a function of the number of infected individuals, total shedding over the course of infection, and pathogen removal from the environment. Theoretical results include determination of identifiable parameters for the model from environmental pathogen data and simple, explicit formulas for the likelihood for particular choices of individual shedding curves. We give a two step Bayesian inference framework, where the first step corresponds to calibration from data where the number of infected individuals is known, followed by an estimation step from environmental surveillance data when the number of infected individuals is unknown. We apply this modeling and estimation framework to synthetic data, as well as to an empirical case study of SARS-CoV-2 in environmental dust collected from isolation rooms housing university students. Both the synthetic data and empirical case study indicate high inter-individual variation in shedding, leading to wide credible intervals for the number of infected individuals. We examine how uncertainty in estimates of the number of infected individuals from environmental pathogen levels scales with the true number of infected individuals and model misspecification. While credible intervals for the number of infected individuals are wide, our results suggest that distinguishing between no infection and small-to-moderate levels of infection (≈10 infected individuals) may be possible, and that it is broadly possible to differentiate between moderate (≈40) and high (≈200) numbers of infected individuals.

Development of a standard evaluation method for microbial UV sensitivity using light-emitting diodes

Ultraviolet (UV) light is an effective disinfection method. In particular, UV light-emitting diodes (UV-LEDs) are expected to have many applications as light sources owing to their compact form factor and wide range of choices of wavelengths. However, the UV sensitivity of microorganisms for each UV wavelength has not been evaluated comprehensively because standard experimental conditions based on LED characteristics have not been established. Therefore, it is necessary to establish a standard evaluation method based on LED characteristics. Here, we developed a new UV-LED device based on strictly controlled irradiation conditions using LEDs for each wavelength (250-365 nm), checked the validity of the device characteristics and evaluated the UV sensitivity of Escherichia coli using this new evaluation method. For this new device, we considered accurate irradiance, accurate spectra, irradiance uniformity, accurate dose, beam angle, surrounding material reflections, and sample condition. From our results, the following UV irradiation conditions were established as standard: 1 mW/cm2 irradiance, bacterial solution with absorbance value of A600 = 0.5 diluted 10 times solution, solution volume of 1 mL, working distance (WD) of 100 mm. In order to compare the effects of irradiation under uniform conditions on inactivation of microorganisms, we assessed inactivation effect of E. coli by LED irradiation at each wavelength using the U280 LED as a standard wavelength. The inactivation effect for U280 LED irradiation was -0.95 ± 0.21 log at a dose of 4 mJ/cm2. Under this condition of dose, our results showed a high wavelength dependence of the inactivation effect at each UV wavelength peaking at 267 nm. Our study showed that this irradiation system was validated for the standard UV irradiation system and could be contributed to the establishment of food and water hygiene control methods and the development of equipment for the prevention of infectious diseases.

Measuring the effects of respiratory protective equipment and other protectors in preventing the scattering of vocalization droplets

This study was conducted to quantitatively examine the effects of respiratory protective equipment (respirators) and various other types of protectors in preventing the scattering of vocalization droplets. Each of 12 adult male volunteers was asked to vocalize intermittently for 1 min at a target intensity of approximately 100 dBA in an experimental room adjusted to a humidity of approximately 60-70%. The subjects vocalized while wearing respirators, other types of protectors, or no protectors at all. The droplet concentration in a particle size range of 0.3 to 10 μm was measured under each experimental condition, and the transmitted particle concentration and penetration were calculated. The concentration and penetration of particles transmitted from the respirators were lower than those transmitted from the other protectors examined. The probability of infection reduction through the use of the protectors was estimated from the data obtained on the effectiveness of the protectors in preventing the scattering of droplets. We concluded that there is no need for additional droplet scattering prevention in various work settings when appropriate respirators are used under optimal conditions.

Research on Airflow Optimization and Infection Risk Assessment of Medical Cabin of Negative-Pressure Ambulance

Medical cabins within negative-pressure ambulances currently only use the front air supply, which causes poor emission of infectious disease droplets. For this problem, based on the classification and design methods of airflow organization, the side and top supply airflow organization model has been designed to study the influence of these airflow organization models on the spread of droplet particles. The distribution of droplet particles within airflow organization models, under conditions in which the patient is coughing and sneezing, is analyzed. According to the comparison and analysis of this distribution, the state of droplet particles, the emission efficiency, and the security coefficient are studied. The response surface method is used to optimize the emission efficiency and security coefficient of the airflow organization. According to the characteristics of the medical cabin within negative-pressure ambulances, a dose-response model is used to evaluate the infection risk of medical personnel and then the infection probability is obtained. These research results can be used to improve the ability of negative-pressure ambulances to prevent cross-infection.

Effect of Train-Induced Wind on the Transmission of COVID-19: A New Insight into Potential Infectious Risks

The unprecedented COVID-19 pandemic has caused a traffic tie-up across the world. In addition to home quarantine orders and travel bans, the social distance guideline of about six feet was enacted to reduce the risk of contagion. However, with recent life gradually returning to normal, the crisis is not over. In this research, a moving train test and a Gaussian puff model were employed to investigate the impact of wind raised by a train running on the transmission and dispersion of SARS-CoV-2 from infected individuals. Our findings suggest that the 2 m social distance guideline may not be enough; under train-induced wind action, human respiratory disease-carrier droplets may travel to unexpected places. However, there are deficiencies in passenger safety guidelines and it is necessary to improve the quantitative research in the relationship between train-induced wind and virus transmission. All these findings could provide a fresh insight to contain the spread of COVID-19 and provide a basis for preventing and controlling the pandemic virus, and probe into strategies for control of the disease in the future.

The transmission of SARS-CoV-2 is likely comodulated by temperature and by relative humidity

Inferring the impact of climate upon the transmission of SARS-CoV-2 has been confounded by variability in testing, unknown disease introduction rates, and changing weather. Here we present a data model that accounts for dynamic testing rates and variations in disease introduction rates. We apply this model to data from Colombia, whose varied and seasonless climate, central port of entry, and swift, centralized response to the COVID-19 pandemic present an opportune environment for assessing the impact of climate factors on the spread of COVID-19. We observe strong attenuation of transmission in climates with sustained daily temperatures above 30 degrees Celsius and simultaneous mean relative humidity below 78%, with outbreaks occurring at high humidity even where the temperature is high. We hypothesize that temperature and relative humidity comodulate the infectivity of SARS-CoV-2 within respiratory droplets.

Theoretical investigation of pre-symptomatic SARS-CoV-2 person-to-person transmission in households

Since its emergence, the phenomenon of SARS-CoV-2 transmission by seemingly healthy individuals has become a major challenge in the effort to achieve control of the pandemic. Identifying the modes of transmission that drive this phenomenon is a perquisite in devising effective control measures, but to date it is still under debate. To address this problem, we have formulated a detailed mathematical model of discrete human actions (such as coughs, sneezes, and touching) and the continuous decay of the virus in the environment. To take into account those discrete and continuous events we have extended the common modelling approach and employed a hybrid stochastic mathematical framework. This allowed us to calculate higher order statistics which are crucial for the reconstruction of the observed distributions. We focused on transmission within a household, the venue with the highest risk of infection and validated the model results against the observed secondary attack rate and the serial interval distribution. Detailed analysis of the model results identified the dominant driver of pre-symptomatic transmission as the contact route via hand-face transfer and showed that wearing masks and avoiding physical contact are an effective prevention strategy. These results provide a sound scientific basis to the present recommendations of the WHO and the CDC.

COVID-19 Transmission, Current Treatment, and Future Therapeutic Strategies

At the stroke of the New Year 2020, COVID-19, a zoonotic disease that would turn into a global pandemic, was identified in the Chinese city of Wuhan. Although unique in its transmission and virulence, COVID-19 is similar to zoonotic diseases, including other SARS variants (e.g., SARS-CoV) and MERS, in exhibiting severe flu-like symptoms and acute respiratory distress. Even at the molecular level, many parallels have been identified between SARS and COVID-19 so much so that the COVID-19 virus has been named SARS-CoV-2. These similarities have provided several opportunities to treat COVID-19 patients using clinical approaches that were proven to be effective against SARS. Importantly, the identification of similarities in how SARS-CoV and SARS-CoV-2 access the host, replicate, and trigger life-threatening pathological conditions have revealed opportunities to repurpose drugs that were proven to be effective against SARS. In this article, we first provided an overview of COVID-19 etiology vis-à-vis other zoonotic diseases, particularly SARS and MERS. Then, we summarized the characteristics of droplets/aerosols emitted by COVID-19 patients and how they aid in the transmission of the virus among people. Moreover, we discussed the molecular mechanisms that enable SARS-CoV-2 to access the host and become more contagious than other betacoronaviruses such as SARS-CoV. Further, we outlined various approaches that are currently being employed to diagnose and symptomatically treat COVID-19 in the clinic. Finally, we reviewed various approaches and technologies employed to develop vaccines against COVID-19 and summarized the attempts to repurpose various classes of drugs and novel therapeutic approaches.

Health, work performance, and risk of infection in office-like environments: The role of indoor temperature, air humidity, and ventilation

Epidemiological and experimental studies have revealed the effects of the room temperature, indoor air humidity, and ventilation on human health, work and cognitive performance, and risk of infection. In this overview, we integrate the influence of these important microclimatic parameters and assess their influence in offices based on literature searches. The dose-effect curves of the temperature describe a concave shape. Low temperature increases the risk of cardiovascular and respiratory diseases and elevated temperature increases the risk of acute non-specific symptoms, e.g., dry eyes, and respiratory symptoms. Cognitive and work performance is optimal between 22 °C and 24 °C for regions with temperate or cold climate, but both higher and lower temperatures may deteriorate the performances and learning efficiency. Low temperature may favor virus viability, however, depending on the status of the physiological tissue in the airways. Low indoor air humidity causes vulnerable eyes and airways from desiccation and less efficient mucociliary clearance. This causes elevation of the most common mucous membrane-related symptoms, like dry and tired eyes, which deteriorates the work performance. Epidemiological, experimental, and clinical studies support that intervention of dry indoor air conditions by humidification alleviates symptoms of dry eyes and airways, fatigue symptoms, less complaints about perceived dry air, and less compromised work performance. Intervention of dry air conditions by elevation of the indoor air humidity may be a non-pharmaceutical treatment of the risk of infection by reduced viability and transport of influenza virus. Relative humidity between 40 and 60% appears optimal for health, work performance, and lower risk of infection. Ventilation can reduce both acute and chronic health outcomes and improve work performance, because the exposure is reduced by the dilution of the indoor air pollutants (including pathogens, e.g., as virus droplets), and in addition to general emission source control strategies. Personal control of ventilation appears an important factor that influences the satisfaction of the thermal comfort due to its physical and positive psychological impact. However, natural ventilation or mechanical ventilation can become sources of air pollutants, allergens, and pathogens of outdoor or indoor origin and cause an increase in exposure. The "health-based ventilation rate" in a building should meet WHO's air quality guidelines and dilute human bio-effluent emissions to reach an acceptable perceived indoor air quality. Ventilation is a modifying factor that should be integrated with both the indoor air humidity and the room temperature in a strategic joint control to satisfy the perceived indoor air quality, health, working performance, and minimize the risk of infection.

The transmission of SARS-CoV-2 is likely comodulated by temperature and by relative humidity

Inferring the impact of climate upon the transmission of SARS-CoV-2 has been confounded by variability in testing, unknown disease introduction rates, and changing weather. Here we present a data model that accounts for dynamic testing rates and variations in disease introduction rates. We apply this model to data from Colombia, whose varied and seasonless climate, central port of entry, and swift, centralized response to the COVID-19 pandemic present an opportune environment for assessing the impact of climate factors on the spread of COVID-19. We observe strong attenuation of transmission in climates with sustained daily temperatures above 30 degrees Celsius and simultaneous mean relative humidity below 78%, with outbreaks occurring at high humidity even where the temperature is high. We hypothesize that temperature and relative humidity comodulate the infectivity of SARS-CoV-2 within respiratory droplets.

Environmental impacts on the transmission and evolution of COVID-19 combing the knowledge of pathogenic respiratory coronaviruses

The emergence of a novel coronavirus named SARS-CoV-2 during December 2019, has caused the global outbreak of coronavirus disease 2019 (COVID-19), which is officially announced to be a pandemic by the World Health Organization (WHO). The increasing burden from this pandemic is seriously affecting everyone's life, and threating the global public health. Understanding the transmission, survival, and evolution of the virus in the environment will assist in the prevention, control, treatment, and eradication of its infection. Herein, we aimed to elucidate the environmental impacts on the transmission and evolution of SARS-CoV-2, based on briefly introducing this respiratory virus. Future research objectives for the prevention and control of these contagious viruses and their related diseases are highlighted from the perspective of environmental science. This review should be of great help to prevent and control the epidemics caused by emerging respiratory coronaviruses (CoVs).

Prevention and control of COVID-19 in public transportation: Experience from China

Due to continuous spread of coronavirus disease 2019 (COVID-19) worldwide, long-term effective prevention and control measures should be adopted for public transport facilities, as they are increasing in popularity and serve as the principal modes for travel of many people. The human infection risk could be extremely high due to length of exposure time window, transmission routes and structural characteristics during travel or work. This can result in the rapid spread of the infection. Based on the transmission characteristics of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) and the nature of public transport sites, we identified comprehensive countermeasures toward the prevention and control of COVID-19, including the strengthening of personnel management, personal protection, environmental cleaning and disinfection, and health education. Multi-pronged strategies can enhance safety of public transportation. The prevention and control of the disease during the use of public transportation will be particularly important when all countries in the world resume production. The aim of this study is to introduce experience of the prevention and control measures for public transportation in China to promote the global response to COVID-19.

Evaluation of Regeneration Processes for Filtering Facepiece Respirators in Terms of the Bacteria Inactivation Efficiency and Influences on Filtration Performance

The regeneration of filtering facepiece respirators (FFRs) is of critical importance because of the severe shortage of FFRs during large-scale outbreaks of respiratory epidemics, such as COVID-19. Comprehensive experiments regarding FFR regeneration were performed in this study with model bacteria to illustrate the decontamination performance of the regeneration processes. The results showed that it is dangerous to use a contaminated FFR without any microbe inactivation treatment because the bacteria can live for more than 8 h. The filtration efficiency and surface electrostatic potential of 75% ethanol-treated FFRs were significantly reduced, and a most penetrating particle size of 200 nm was observed. Steam and microwave irradiation (MWI) showed promising decontamination performances, achieving 100% inactivation in 90 and 30 min, respectively. The filtration efficiencies of steam-treated FFRs for 50 and 100 nm particles decreased from 98.86% and 99.51% to 97.58% and 98.79%, respectively. Ultraviolet irradiation (UVI) effectively inactivated the surface bacteria with a short treatment of 5 min and did not affect the filtration performance. However, the UV dose reaching different layers of the FFP2 mask sample gradually decreased from the outermost layer to the innermost layer, while the model bacteria on the second and third layers could not be killed completely. UVI+MWI and steam were recommended to effectively decontaminate the used respirators and still maintain the respirators' filtration efficiency. The present work provides a comprehensive evaluation for FFR regeneration in terms of the filtration efficiencies for 50-500 nm particles, the electrostatic properties, mechanical properties, and decontamination effects.

Comparison of methods to Estimate Basic Reproduction Number (R 0) of influenza, Using Canada 2009 and 2017-18 A (H1N1) Data

Background

The basic reproduction number (R ₀) has a key role in epidemics and can be utilized for preventing epidemics. In this study, different methods are used for estimating R ₀'s and their vaccination coverage to find the formula with the best performance.

Materials and Methods

We estimated R ₀ for cumulative cases count data from April 18 to July 6, 2009 and 35-2017 to 34-2018 weeks in Canada: maximum likelihood (ML), exponential growth rate (EG), time-dependent reproduction numbers (TD), attack rate (AR), gamma-distributed generation time (GT), and the final size of the epidemic. Gamma distribution with mean and standard deviation 3.6 ± 1.4 is used as GT.

Results

The AR method obtained a R _{0 (}95% confidence interval [CI]) value of 1.116 (1.1163, 1.1165) and an EG (95%CI) value of 1.46 (1.41, 1.52). The R ₀ (95%CI) estimate was 1.42 (1.27, 1.57) for the obtained ML, 1.71 (1.12, 2.03) for the obtained TD, 1.49 (1.0, 1.97) for the gamma-distributed GT, and 1.00 (0.91, 1.09) for the final size of the epidemic. The minimum and maximum vaccination coverage were related to AR and TD methods, respectively, where the TD method has minimum mean squared error (MSE). Finally, the R ₀ (95%CI) for 2018 data was 1.52 (1.11, 1.94) by TD method, and vaccination coverage was estimated as 34.2%.

Conclusion

For the purposes of our study, the estimation of TD was the most useful tool for computing the R ₀, because it has the minimum MSE. The estimation R ₀ > 1 indicating that the epidemic has occurred. Thus, it is required to vaccinate at least 41.5% to prevent and control the next epidemic.

Protection of Upper Respiratory Tract, Mouth and Eyes

Pathogenic bacteria and viruses may invade via upper and lower respiratory tract and via eye mucosa. When an infected person coughs or sneezes heavily, small, invisible droplets with the infective agent may reach a good distance from the source. By using the right form of protection at the right time, infection and disease are prevented. The present chapter is focused on the protection against airborne infections.

High-resolution epidemic simulation using within-host infection and contact data

BACKGROUND

Recent epidemics have entailed global discussions on revamping epidemic control and prevention approaches. A general consensus is that all sources of data should be embraced to improve epidemic preparedness. As a disease transmission is inherently governed by individual-level responses, pathogen dynamics within infected hosts posit high potentials to inform population-level phenomena. We propose a multiscale approach showing that individual dynamics were able to reproduce population-level observations.

METHODS

Using experimental data, we formulated mathematical models of pathogen infection dynamics from which we simulated mechanistically its transmission parameters. The models were then embedded in our implementation of an age-specific contact network that allows to express individual differences relevant to the transmission processes. This approach is illustrated with an example of Ebola virus (EBOV).

RESULTS

The results showed that a within-host infection model can reproduce EBOV's transmission parameters obtained from population data. At the same time, population age-structure, contact distribution and patterns can be expressed using network generating algorithm. This framework opens a vast opportunity to investigate individual roles of factors involved in the epidemic processes. Estimating EBOV's reproduction number revealed a heterogeneous pattern among age-groups, prompting cautions on estimates unadjusted for contact pattern. Assessments of mass vaccination strategies showed that vaccination conducted in a time window from five months before to one week after the start of an epidemic appeared to strongly reduce epidemic size. Noticeably, compared to a non-intervention scenario, a low critical vaccination coverage of 33% cannot ensure epidemic extinction but could reduce the number of cases by ten to hundred times as well as lessen the case-fatality rate.

CONCLUSIONS

Experimental data on the within-host infection have been able to capture upfront key transmission parameters of a pathogen; the applications of this approach will give us more time to prepare for potential epidemics. The population of interest in epidemic assessments could be modelled with an age-specific contact network without exhaustive amount of data. Further assessments and adaptations for different pathogens and scenarios to explore multilevel aspects in infectious diseases epidemics are underway.

Heterogeneous shedding of influenza by human subjects and its implications for epidemiology and control

Heterogeneity of infectiousness is an important feature of the spread of many infections, with implications for disease dynamics and control, but its relevance to human influenza virus is still unclear. For a transmission event to occur, an infected individual needs to release infectious particles via respiratory symptoms. Key factors to take into account are virus dynamics, particle release in relation to respiratory symptoms, the amount of virus shed and, importantly, how these vary between infected individuals. A quantitative understanding of the process of influenza transmission is relevant to designing effective mitigation measures. Here we develop an influenza infection dynamics model fitted to virological, systemic and respiratory symptoms to investigate how within-host dynamics relates to infectiousness. We show that influenza virus shedding is highly heterogeneous between subjects. From analysis of data on experimental infections, we find that a small proportion (<20%) of influenza infected individuals are responsible for the production of 95% of infectious particles. Our work supports targeting mitigation measures at most infectious subjects to efficiently reduce transmission. The effectiveness of public health interventions targeted at highly infectious individuals would depend on accurate identification of these subjects and on how quickly control measures can be applied.

Crossing the scale from within-host infection dynamics to between-host transmission fitness: a discussion of current assumptions and knowledge

The progression of an infection within a host determines the ability of a pathogen to transmit to new hosts and to maintain itself in the population. While the general connection between the infection dynamics within a host and the population-level transmission dynamics of pathogens is widely acknowledged, a comprehensive and quantitative understanding that would allow full integration of the two scales is still lacking. Here, we provide a brief discussion of both models and data that have attempted to provide quantitative mappings from within-host infection dynamics to transmission fitness. We present a conceptual framework and provide examples of studies that have taken first steps towards development of a quantitative framework that scales from within-host infections to population-level fitness of different pathogens. We hope to illustrate some general themes, summarize some of the recent advances and-maybe most importantly-discuss gaps in our ability to bridge these scales, and to stimulate future research on this important topic.

How sticky should a virus be? The impact of virus binding and release on transmission fitness using influenza as an example

Budding viruses face a trade-off: virions need to efficiently attach to and enter uninfected cells while newly generated virions need to efficiently detach from infected cells. The right balance between attachment and detachment-the right amount of stickiness-is needed for maximum fitness. Here, we design and analyse a mathematical model to study in detail the impact of attachment and detachment rates on virus fitness. We apply our model to influenza, where stickiness is determined by a balance of the haemagglutinin (HA) and neuraminidase (NA) proteins. We investigate how drugs, the adaptive immune response and vaccines impact influenza stickiness and fitness. Our model suggests that the location in the 'stickiness landscape' of the virus determines how well interventions such as drugs or vaccines are expected to work. We discuss why hypothetical NA enhancer drugs might occasionally perform better than the currently available NA inhibitors in reducing virus fitness. We show that an increased antibody or T-cell-mediated immune response leads to maximum fitness at higher stickiness. We further show that antibody-based vaccines targeting mainly HA or NA, which leads to a shift in stickiness, might reduce virus fitness above what can be achieved by the direct immunological action of the vaccine. Overall, our findings provide potentially useful conceptual insights for future vaccine and drug development and can be applied to other budding viruses beyond influenza.

A multi-scale analysis of influenza A virus fitness trade-offs due to temperature-dependent virus persistence

Successful replication within an infected host and successful transmission between hosts are key to the continued spread of most pathogens. Competing selection pressures exerted at these different scales can lead to evolutionary trade-offs between the determinants of fitness within and between hosts. Here, we examine such a trade-off in the context of influenza A viruses and the differential pressures exerted by temperature-dependent virus persistence. For a panel of avian influenza A virus strains, we find evidence for a trade-off between the persistence at high versus low temperatures. Combining a within-host model of influenza infection dynamics with a between-host transmission model, we study how such a trade-off affects virus fitness on the host population level. We show that conclusions regarding overall fitness are affected by the type of link assumed between the within- and between-host levels and the main route of transmission (direct or environmental). The relative importance of virulence and immune response mediated virus clearance are also found to influence the fitness impacts of virus persistence at low versus high temperatures. Based on our results, we predict that if transmission occurs mainly directly and scales linearly with virus load, and virulence or immune responses are negligible, the evolutionary pressure for influenza viruses to evolve toward good persistence at high within-host temperatures dominates. For all other scenarios, influenza viruses with good environmental persistence at low temperatures seem to be favored.

It has recently been suggested that for avian influenza viruses, prolonged persistence in the environment plays an important role in the transmission between birds. In such situations, influenza virus strains may face a trade-off: they need to persist well in the environment at low temperatures, but they also need to do well inside an infected bird at higher temperatures. Here, we analyze how potential trade-offs on these two scales interact to determine overall fitness of the virus. We find that the link between infection dynamics within a host and virus shedding and transmission is crucial in determining the relative advantage of good low-temperature versus high-temperature persistence. We also find that the role of virus-induced mortality, the immune response and the route of transmission affect the balance between optimal low-temperature and high-temperature persistence.

Assessment the protection performance of different level personal respiratory protection masks against viral aerosol

New viral disease such as SARS and H1N1 highlighted the vulnerability of healthcare workers to aerosol-transmitted viral infections. This paper was to assess the protection performance of different level personal respiratory protection equipments against viral aerosol. Surgical masks, N95 masks and N99 masks were purchased from the market. The masks were sealed onto the manikin in the aerosol testing chamber. Viral aerosol was generated and then sampled simultaneously before and after the tested mask using biosamplers. This allows a percentage efficiency value to be calculated against test phage SM702 aerosols which surrogates of viral pathogens aerosol. At the same time, the masks face fit factor was determined by TSI8020. The viral aerosol particles aerodynamic diameter was 0.744 μm, and GSD was 1.29. The protection performance of the material of all the tested masks against viral aerosol was all >95 %. All the five surgical masks face fit factor were <8. F model N95 mask and H model N99 mask face fit factor were all >160. G model N95 mask face fit factor was 8.2. The protection performances of N95 or N99 masks were many times higher than surgical mask when considering the face fit factor. Surgical masks cannot offer sufficient protection against the inhalation of viral aerosol because they cannot provide a close face seal.

Survival of influenza virus on hands and fomites in community and laboratory settings

BACKGROUND

Transmission dynamics modeling provides a practical method for virtual evaluation of the impact of public health interventions in response to prospective influenza pandemics and also may help determine the relative contribution of different modes of transmission to overall infection rates. Accurate estimates of longevity for all forms of viral particles are needed for such models to be useful.

METHODS

We conducted a time course study to determine the viability and longevity of H1N1 virus on naturally contaminated hands and household surfaces of 20 individuals with laboratory-confirmed infection. Participants coughed or sneezed into their hands, which were sampled immediately and again after 5, 10, and 30 minutes. Samples also were obtained from household surfaces handled by the participants immediately after coughing/sneezing. Clinically obtained H1N1 isolates were used to assess the viability and longevity of the virus on various artificially inoculated common household surfaces and human hands in a controlled laboratory setting. Viral detection was achieved by culture and real-time reverse-transcriptase polymerase chain reaction.

RESULTS

The results suggest that H1N1 does not survive long on naturally contaminated skin and fomites, and that secretions deposited on hands by coughing or sneezing have a concentration of <2.15 × 10 to 2.94 × 10 TCID(50)/mL.

CONCLUSIONS

These data can be used to estimate the relative contribution of direct and indirect contact transmission on overall infection rates.

Close encounters of the infectious kind: methods to measure social mixing behaviour

A central tenet of close-contact or respiratory infection epidemiology is that infection patterns within human populations are related to underlying patterns of social interaction. Until recently, few researchers had attempted to quantify potentially infectious encounters made between people. Now, however, several studies have quantified social mixing behaviour, using a variety of methods. Here, we review the methodologies employed, suggest other appropriate methods and technologies, and outline future research challenges for this rapidly advancing field of research.

A comprehensive breath plume model for disease transmission via expiratory aerosols

The peak in influenza incidence during wintertime in temperate regions represents a longstanding, unresolved scientific question. One hypothesis is that the efficacy of airborne transmission via aerosols is increased at lower humidities and temperatures, conditions that prevail in wintertime. Recent work with a guinea pig model by Lowen et al. indicated that humidity and temperature do modulate airborne influenza virus transmission, and several investigators have interpreted the observed humidity dependence in terms of airborne virus survivability. This interpretation, however, neglects two key observations: the effect of ambient temperature on the viral growth kinetics within the animals, and the strong influence of the background airflow on transmission. Here we provide a comprehensive theoretical framework for assessing the probability of disease transmission via expiratory aerosols between test animals in laboratory conditions. The spread of aerosols emitted from an infected animal is modeled using dispersion theory for a homogeneous turbulent airflow. The concentration and size distribution of the evaporating droplets in the resulting “Gaussian breath plume” are calculated as functions of position, humidity, and temperature. The overall transmission probability is modeled with a combination of the time-dependent viral concentration in the infected animal and the probability of droplet inhalation by the exposed animal downstream. We demonstrate that the breath plume model is broadly consistent with the results of Lowen et al., without invoking airborne virus survivability. The results also suggest that, at least for guinea pigs, variation in viral kinetics within the infected animals is the dominant factor explaining the increased transmission probability observed at lower temperatures.

Preventing airborne disease transmission: review of methods for ventilation design in health care facilities

Health care facility ventilation design greatly affects disease transmission by aerosols. The desire to control infection in hospitals and at the same time to reduce their carbon footprint motivates the use of unconventional solutions for building design and associated control measures. This paper considers indoor sources and types of infectious aerosols, and pathogen viability and infectivity behaviors in response to environmental conditions. Aerosol dispersion, heat and mass transfer, deposition in the respiratory tract, and infection mechanisms are discussed, with an emphasis on experimental and modeling approaches. Key building design parameters are described that include types of ventilation systems (mixing, displacement, natural and hybrid), air exchange rate, temperature and relative humidity, air flow distribution structure, occupancy, engineered disinfection of air (filtration and UV radiation), and architectural programming (source and activity management) for health care facilities. The paper describes major findings and suggests future research needs in methods for ventilation design of health care facilities to prevent airborne infection risk.

Using experimental human influenza infections to validate a viral dynamic model and the implications for prediction

The aim of this work was to use experimental infection data of human influenza to assess a simple viral dynamics model in epithelial cells and better understand the underlying complex factors governing the infection process. The developed study model expands on previous reports of a target cell-limited model with delayed virus production. Data from 10 published experimental infection studies of human influenza was used to validate the model. Our results elucidate, mechanistically, the associations between epithelial cells, human immune responses, and viral titres and were supported by the experimental infection data. We report that the maximum total number of free virions following infection is 10(3)-fold higher than the initial introduced titre. Our results indicated that the infection rates of unprotected epithelial cells probably play an important role in affecting viral dynamics. By simulating an advanced model of viral dynamics and applying it to experimental infection data of human influenza, we obtained important estimates of the infection rate. This work provides epidemiologically meaningful results, meriting further efforts to understand the causes and consequences of influenza A infection.

Use of risk assessment and likelihood estimation to analyze spatial distribution pattern of respiratory infection cases

Obvious spatial infection patterns are often observed in cases associated with airborne transmissible diseases. Existing quantitative infection risk assessment models analyze the observed cases by assuming a homogeneous infectious particle concentration and ignore the spatial infection pattern, which may cause errors. This study aims at developing an approach to analyze spatial infection patterns associated with infectious respiratory diseases or other airborne transmissible diseases using infection risk assessment and likelihood estimation. Mathematical likelihood, based on binomial probability, was used to formulate the retrospective component with some additional mathematical treatments. Together with an infection risk assessment model that can address spatial heterogeneity, the method can be used to analyze the spatial infection pattern and retrospectively estimate the influencing parameters causing the cases, such as the infectious source strength of the pathogen. A Varicella outbreak was selected to demonstrate the use of the new approach. The infectious source strength estimated by the Wells-Riley concept using the likelihood estimation was compared with the estimation using the existing method. It was found that the maximum likelihood estimation matches the epidemiological observation of the outbreak case much better than the estimation under the assumption of homogeneous infectious particle concentration. Influencing parameters retrospectively estimated using the new approach can be used as input parameters in quantitative infection risk assessment of the disease under other scenarios. The approach developed in this study can also serve as an epidemiological tool in outbreak investigation. Limitations and further developments are also discussed.

A review of mathematical models of influenza A infections within a host or cell culture: lessons learned and challenges ahead

Most mathematical models used to study the dynamics of influenza A have thus far focused on the between-host population level, with the aim to inform public health decisions regarding issues such as drug and social distancing intervention strategies, antiviral stockpiling or vaccine distribution. Here, we investigate mathematical modeling of influenza infection spread at a different scale; namely that occurring within an individual host or a cell culture. We review the models that have been developed in the last decades and discuss their contributions to our understanding of the dynamics of influenza infections. We review kinetic parameters (e.g., viral clearance rate, lifespan of infected cells) and values obtained through fitting mathematical models, and contrast them with values obtained directly from experiments. We explore the symbiotic role of mathematical models and experimental assays in improving our quantitative understanding of influenza infection dynamics. We also discuss the challenges in developing better, more comprehensive models for the course of influenza infections within a host or cell culture. Finally, we explain the contributions of such modeling efforts to important public health issues, and suggest future modeling studies that can help to address additional questions relevant to public health.

Influenza A virus transmission: contributing factors and clinical implications

Efficient human-to-human transmission is a necessary property for the generation of a pandemic influenza virus. To date, only influenza A viruses within the H1-H3 subtypes have achieved this capacity. However, sporadic cases of severe disease in individuals following infection with avian influenza A viruses over the past decade, and the emergence of a pandemic H1N1 swine-origin virus in 2009, underscore the need to better understand how influenza viruses acquire the ability to transmit efficiently. In this review, we discuss the biological constraints and molecular features known to affect virus transmissibility to and among humans. Factors influencing the behaviour of aerosols in the environment are described, and the mammalian models used to study virus transmission are presented. Recent progress in understanding the molecular determinants that confer efficient transmission has identified crucial roles for the haemagglutinin and polymerase proteins; nevertheless, influenza virus transmission remains a polygenic trait that is not completely understood. The clinical implications of this research, including methods currently under investigation to mitigate influenza virus human-to-human transmission, are discussed. A better understanding of the viral determinants necessary for efficient transmission will allow us to identify avian influenza viruses with pandemic potential.

High infectivity and pathogenicity of influenza A virus via aerosol and droplet transmission

Influenza virus may be transmitted through the respiratory route by inhalation of an aerosol of non-sedimenting droplets, or by deposition of sedimenting droplets in the upper respiratory tract. Whichever of these is the predominant route for infection with influenza virus has been subject of continuing debate, resulting in detailed studies of aerosol versus droplet exposure. A decisive knowledge gap preventing a satisfying conclusion is absence of a well defined human dose response model for influenza virus. This study uses a hierarchical approach generalizing over twelve human challenge studies collected in a literature search. Distinction is made between aerosol and intranasal inoculation. The results indicate high infectivity via either route, but intranasal inoculation leads to about 20 times lower infectivity than when the virus is delivered in an inhalable aerosol. A scenario study characterizing exposure to airborne virus near a coughing infected person in a room with little ventilation demonstrates that with these dose response models the probabilities of infection by either aerosol or sedimenting droplets are approximately equal. Droplet transmission results in a slightly higher illness risk due to the higher doses involved. Establishing a dose response model for influenza provides a firm basis for studies of interventions reducing exposure to different classes of infectious particles. More studies are needed to clarify the role of different modes of transmission in other settings.

Mathematical models for assessing the role of airflow on the risk of airborne infection in hospital wards

Understanding the risk of airborne transmission can provide important information for designing safe healthcare environments with an appropriate level of environmental control for mitigating risks. The most common approach for assessing risk is to use the Wells-Riley equation to relate infectious cases to human and environmental parameters. While it is a simple model that can yield valuable information, the model used as in its original presentation has a number of limitations. This paper reviews recent developments addressing some of the limitations including coupling with epidemic models to evaluate the wider impact of control measures on disease progression, linking with zonal ventilation or computational fluid dynamics simulations to deal with imperfect mixing in real environments and recent work on dose-response modelling to simulate the interaction between pathogens and the host. A stochastic version of the Wells-Riley model is presented that allows consideration of the effects of small populations relevant in healthcare settings and it is demonstrated how this can be linked to a simple zonal ventilation model to simulate the influence of proximity to an infector. The results show how neglecting the stochastic effects present in a real situation could underestimate the risk by 15 per cent or more and that the number and rate of new infections between connected spaces is strongly dependent on the airflow. Results also indicate the potential danger of using fully mixed models for future risk assessments, with quanta values derived from such cases less than half the actual source value.