Relative Role of Age Groups and Indoor Environments in Influenza Transmission Under Different Urbanization Rates in China.

Exploring the relative role of different indoor environments in respiratory infections transmission remains unclear, which is crucial for developing targeted nonpharmaceutical interventions. In this study, a total of 2,583,441 influenza-like illness cases tested from 2010 to 2017 in China were identified. An agent-based model was built and calibrated with the surveillance data, to assess the roles of 3 age groups (children <19 years, younger adults 19-60 years, older adults >60 years) and 4 types of indoor environments (home, schools, workplaces, and community areas) in influenza transmission by province with varying urbanization rates. When the urbanization rates increased from 35% to 90%, the proportion of children aged <19 years among influenza cases decreased from 76% to 45%. Additionally, we estimated that infections originating from children decreased from 95.1% (95% confidence interval (CI): 92.7, 97.5) to 59.3% (95% CI: 49.8, 68.7). Influenza transmission in schools decreased from 80.4% (95% CI: 76.5, 84.3) to 36.6% (95% CI: 20.6, 52.5), while transmission in the community increased from 2.4% (95% CI: 1.9, 2.8) to 45.4% (95% CI: 35.9, 54.8). With increasing urbanization rates, community areas and younger adults contributed more to infection transmission. These findings could help the development of targeted public health policies. This article is part of a Special Collection on Environmental Epidemiology. This article is part of a Special Collection on Environmental Epidemiology.

Effect of Rapid Urbanization in Mainland China on the Seasonal Influenza Epidemic: Spatiotemporal Analysis of Surveillance Data From 2010 to 2017.

BACKGROUND: The world is undergoing an unprecedented wave of urbanization. However, the effect of rapid urbanization during the early or middle stages of urbanization on seasonal influenza transmission remains unknown. Since about 70% of the world population live in low-income countries, exploring the impact of urbanization on influenza transmission in urbanized countries is significant for global infection prediction and prevention. OBJECTIVE: The aim of this study was to explore the effect of rapid urbanization on influenza transmission in China. METHODS: We performed spatiotemporal analyses of province-level influenza surveillance data collected in Mainland China from April 1, 2010, to March 31, 2017. An agent-based model based on hourly human contact-related behaviors was built to simulate the influenza transmission dynamics and to explore the potential mechanism of the impact of urbanization on influenza transmission. RESULTS: We observed persistent differences in the influenza epidemic attack rates among the provinces of Mainland China across the 7-year study period, and the attack rate in the winter waves exhibited a U-shaped relationship with the urbanization rates, with a turning point at 50%-60% urbanization across Mainland China. Rapid Chinese urbanization has led to increases in the urban population density and percentage of the workforce but decreases in household size and the percentage of student population. The net effect of increased influenza transmission in the community and workplaces but decreased transmission in households and schools yielded the observed U-shaped relationship. CONCLUSIONS: Our results highlight the complicated effects of urbanization on the seasonal influenza epidemic in China. As the current urbanization rate in China is approximately 59%, further urbanization with no relevant interventions suggests a worrisome increasing future trend in the influenza epidemic attack rate.

Comparative analysis of inflight transmission of SARS-CoV-2, influenza, and SARS-CoV-1.

The aim of this study is to evaluate the infection risk of aircraft passengers seated within and beyond two rows of the index case(s) of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), influenza A(H1N1)pdm09 virus, and SARS-CoV-1. PubMed databases were searched for articles containing information on air travel-related transmission of SARS-CoV-2, influenza A(H1N1)pdm09 virus, and SARS-CoV-1 infections. We performed a meta-analysis of inflight infection data. In the eight flights where the attack rate could be calculated, the inflight SARS-CoV-2 attack rates ranged from 2.6% to 16.1%. The risk ratios of infection for passengers seated within and outside the two rows of the index cases were 5.64 (95% confidence interval (CI):1.94-16.40) in SARS-CoV-2 outbreaks, 4.26 (95% CI:1.08-16.81) in the influenza A(H1N1)pdm09 virus outbreaks, and 1.91 (95% CI:0.80-4.55) in SARS-CoV-1 outbreaks. Furthermore, we found no significant difference between the attack rates of SARS-CoV-2 in flights where the passengers were wearing masks and those where they were not (p = 0.22). The spatial distribution of inflight SARS-CoV-2 outbreaks was more similar to that of the influenza A(H1N1)pdm09 virus outbreaks than to that of SARS-CoV-1. Given the high proportion of asymptomatic or pre-symptomatic infection in SARS-CoV-2 transmission, we hypothesised that the proximity transmission, especially short-range airborne route, might play an important role in the inflight SARS-CoV-2 transmission.

Association between Temperature and Influenza Activity across Different Regions of China during 2010-2017.

Influenza causes a significant disease burden as an acute respiratory infection. Evidence suggests that meteorological factors can influence the spread of influenza; however, the association between these factors and influenza activity remains controversial. In this study, we investigated the impact of temperature on influenza across different regions of China based on the meteorological data and influenza data from 554 sentinel hospitals in 30 provinces and municipalities in China from 2010 to 2017. A distributed lag nonlinear model (DLNM) was used to analyze the exposure lag response of daily mean temperatures to the risk of influenza-like illness (ILI), influenza A (Flu A), and influenza B (Flu B). We found that in northern China, low temperatures increased the risk of ILI, Flu A, and Flu B, while in central and southern China, both low and high temperatures increased the risk of ILI and Flu A, and only low temperatures increased the risk of Flu B. This study suggests that temperature is closely associated with the influenza activity in China. Temperature should be integrated into the current public health surveillance system for highly accurate influenza warnings and the timely implementation of disease prevention and control measures.

Wearable Sensor-Based Monitoring of Environmental Exposures and the Associated Health Effects: A Review.

Recent advances in sensor technology have facilitated the development and use of personalized sensors in monitoring environmental factors and the associated health effects. No studies have reviewed the research advancement in examining population-based health responses to environmental exposure via portable sensors/instruments. This study aims to review studies that use portable sensors to measure environmental factors and health responses while exploring the environmental effects on health. With a thorough literature review using two major English databases (Web of Science and PubMed), 24 eligible studies were included and analyzed out of 16,751 total records. The 24 studies include 5 on physical factors, 19 on chemical factors, and none on biological factors. The results show that particles were the most considered environmental factor among all of the physical, chemical, and biological factors, followed by total volatile organic compounds and carbon monoxide. Heart rate and heart rate variability were the most considered health indicators among all cardiopulmonary outcomes, followed by respiratory function. The studies mostly had a sample size of fewer than 100 participants and a study period of less than a week due to the challenges in accessing low-cost, small, and light wearable sensors. This review guides future sensor-based environmental health studies on project design and sensor selection.

Increasing contributions of airborne route in SARS-CoV-2 omicron variant transmission compared with the ancestral strain.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron variant has become the dominant lineage worldwide. Experimental studies have shown that SARS-CoV-2 Omicron variant is more stable on various environmental surfaces than the ancestral strains of SARS-CoV-2. However, the influences on the role of the contact route in SARS-CoV-2 transmission are still unknown. In this study, we built a Markov chain model to simulate the transmission of the Omicron and ancestral strains of SARS-CoV-2 within a household over a 1-day period from multiple pathways; that is, airborne, droplet, and contact routes. We assumed that there were two adults and one child in the household, and that one of the adults was infected with SARS-CoV-2. We assumed two scenarios. (1) Asymptomatic/presymptomatic infection, and (2) symptomatic infection. During asymptomatic/presymptomatic infection, the contact route contributing the most (37%-45%), followed by the airborne (34%-38%) and droplet routes (21%-28%). During symptomatic infection, the droplet route was the dominant pathway (48%-71%), followed by the contact route (25%-42%), with the airborne route playing a negligible role (<10%). Compared to the ancestral strain, although the contribution of the contact route increased in Omicron variant transmission, the increase was slight, from 25%-41% to 30%-45%. With the growing concern about the increase in the proportion of asymptomatic/presymptomatic infection in Omicron strain transmissions, the airborne route, rather than the fomite route, should be of focus. Our findings suggest the importance of ventilation in the SARS-CoV-2 Omicron variant prevention in building environment.

Probable cross-corridor transmission of SARS-CoV-2 due to cross airflows and its control.

A COVID-19 outbreak occurred in May 2020 in a public housing building in Hong Kong - Luk Chuen House, located in Lek Yuen Estate. The horizontal cluster linked to the index case' flat (flat 812) remains to be explained. Computational fluid dynamics simulations were conducted to obtain the wind-pressure coefficients of each external opening on the eighth floor of the building. The data were then used in a multi-zone airflow model to estimate the airflow rate and aerosol concentration in the flats and corridors on that floor. Apart from flat 812 and corridors, the virus-laden aerosol concentrations in flats 811, 813, 815, 817 and 819 (opposite to flat 812, across the corridor) were the highest on the eighth floor. When the doors of flats 813 and 817 were opened by 20%, the hourly-averaged aerosol concentrations in these two flats were at least four times as high as those in flats 811, 815 and 819 during the index case's home hours or the suspected exposure period of secondary cases. Thus, the flats across the corridor that were immediately downstream from flat 812 were at the highest exposure risk under a prevailing easterly wind, especially when their doors or windows that connected to the corridor were open. Given that the floorplan and dimension of Luk Chuen House are similar to those of many hotels, our findings provide a probable explanation for COVID-19 outbreaks in quarantine hotels. Positive pressure and sufficient ventilation in the corridor would help to minimise such cross-corridor infections.

Vaccination strategy for preventing the spread of SARS-CoV-2 in the limited supply condition: A mathematical modeling study.

To mitigate SARS-CoV-2 transmission, vaccines have been urgently approved. With their limited availability, it is critical to distribute the vaccines reasonably. We simulated the SARS-CoV-2 transmission for 365 days over four intervention periods: free transmission, structural mitigation, personal mitigation, and vaccination. Sensitivity analyses were performed to obtain robust results. We further evaluated two proposed vaccination allocations, including one-dose-high-coverage and two-doses-low-coverage, when the supply was low. 33.35% (infection rate, 2.68 in 10 million people) and 40.54% (2.36) of confirmed cases could be avoided as the nonpharmaceutical interventions (NPIs) adherence rate rose from 50% to 70%. As the vaccination coverage reached 60% and 80%, the total infections could be reduced by 32.72% and 41.19%, compared to the number without vaccination. When the durations of immunity were 90 and 120 days, the infection rates were 2.67 and 2.38. As the asymptomatic infection rate rose from 30% to 50%, the infection rate increased 0.92 (SD, 0.16) times. Conditioned on 70% adherence rate, with the same amount of limited available vaccines, the 20% and 40% vaccination coverage of one-dose-high-coverage, the infection rates were 2.70 and 2.35; corresponding to the two-doses-low-coverage with 10% and 20% vaccination coverage, the infection rates were 3.22 and 2.92. Our results indicated as the duration of immunity prolonged, the second wave of SARS-CoV-2 would be delayed and the scale would be declined. On average, the total infections in two-doses-low-coverage was 1.48 times (SD, 0.24) as high as that in one-dose-high-coverage. It is crucial to encourage people in order to improve vaccination coverage and establish immune barriers. Particularly when the supply is limited, a wiser strategy to prevent SARS-CoV-2 is equally distributing doses to the same number of individuals. Besides vaccination, NPIs are equally critical to the prevention of widespread of SARS-CoV-2.

Drivers and forecasts of multiple waves of the coronavirus disease 2019 pandemic: A systematic analysis based on an interpretable machine learning framework.

Coronavirus disease 2019 (COVID-19) has become a global pandemic and continues to prevail with multiple rebound waves in many countries. The driving factors for the spread of COVID-19 and their quantitative contributions, especially to rebound waves, are not well studied. Multidimensional time-series data, including policy, travel, medical, socioeconomic, environmental, mutant and vaccine-related data, were collected from 39 countries up to 30 June 2021, and an interpretable machine learning framework (XGBoost model with Shapley Additive explanation interpretation) was used to systematically analyze the effect of multiple factors on the spread of COVID-19, using the daily effective reproduction number as an indicator. Based on a model of the pre-vaccine era, policy-related factors were shown to be the main drivers of the spread of COVID-19, with a contribution of 60.81%. In the post-vaccine era, the contribution of policy-related factors decreased to 28.34%, accompanied by an increase in the contribution of travel-related factors, such as domestic flights, and contributions emerged for mutant-related (16.49%) and vaccine-related (7.06%) factors. For single-peak countries, the dominant ones were policy-related factors during both the rising and fading stages, with overall contributions of 33.7% and 37.7%, respectively. For double-peak countries, factors from the rebound stage contributed 45.8% and policy-related factors showed the greatest contribution in both the rebound (32.6%) and fading (25.0%) stages. For multiple-peak countries, the Delta variant, domestic flights (current month) and the daily vaccination population are the three greatest contributors (8.12%, 7.59% and 7.26%, respectively). Forecasting models to predict the rebound risk were built based on these findings, with accuracies of 0.78 and 0.81 for the pre- and post-vaccine eras, respectively. These findings quantitatively demonstrate the systematic drivers of the spread of COVID-19, and the framework proposed in this study will facilitate the targeted prevention and control of the ongoing COVID-19 pandemic.

Insufficient ventilation led to a probable long-range airborne transmission of SARS-CoV-2 on two buses.

Uncertainty remains on the threshold of ventilation rate in airborne transmission of SARS-CoV-2. We analyzed a COVID-19 outbreak in January 2020 in Hunan Province, China, involving an infected 24-year-old man, Mr. X, taking two subsequent buses, B1 and B2, in the same afternoon. We investigated the possibility of airborne transmission and the ventilation conditions for its occurrence. The ventilation rates on the buses were measured using a tracer-concentration decay method with the original driver on the original route. We measured and calculated the spread of the exhaled virus-laden droplet tracer from the suspected index case. Ten additional passengers were found to be infected, with seven of them (including one asymptomatic) on B1 and two on B2 when Mr. X was present, and one passenger infected on the subsequent B1 trip. B1 and B2 had time-averaged ventilation rates of approximately 1.7 and 3.2 L/s per person, respectively. The difference in ventilation rates and exposure time could explain why B1 had a higher attack rate than B2. Airborne transmission due to poor ventilation below 3.2 L/s played a role in this two-bus outbreak of COVID-19.

Influence of network structure on contaminant spreading efficiency.

Contaminants, such as pathogens or non-living substances, can spread through the interaction of their carriers (e.g., air and surfaces), which constitute a network. The structure of such networks plays an important role in the contaminant spread. We measured the contaminant spreading efficiency in different networks using a newly defined parameter. We analyzed basic networks to identify the effect of the network structure on the contaminant spread. The spreading efficiency was highly related to some network parameters, such as the source node's average path length and degree, and considerably varied with the transfer rate per inter-node interaction. We compared the contaminant spreading efficiencies in some complex networks, namely scale-free, random, regular-lattice, and bipartite networks, with centralized, linear, and fractal networks. The contaminant spreading was particularly efficient in the fractal network when the transfer rate was ~0.5. Two categories of experiments were performed to validate the effect of the network structure on contaminant spreading in practical cases: (I) gas diffusion in multi-compartment cabins (II) bacteria transfer in multi-finger networks. The gas diffusion could be well estimated based on the diffusion between two compartments, and it was considerably affected by the network structure. Meanwhile, the bacteria spread was generally less efficient than expected.

Predominant airborne transmission and insignificant fomite transmission of SARS-CoV-2 in a two-bus COVID-19 outbreak originating from the same pre-symptomatic index case.

The number of people infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continues to increase worldwide, but despite extensive research, there remains significant uncertainty about the predominant routes of SARS-CoV-2 transmission. We conducted a mechanistic modeling and calculated the exposure dose and infection risk of each passenger in a two-bus COVID-19 outbreak in Hunan province, China. This outbreak originated from a single pre-symptomatic index case. Some human behavioral data related to exposure including boarding and alighting time of some passengers and seating position and mask wearing of all passengers were obtained from the available closed-circuit television images/clips and/or questionnaire survey. Least-squares fitting was performed to explore the effect of effective viral load on transmission risk, and the most likely quanta generation rate was also estimated. This study reveals the leading role of airborne SARS-CoV-2 transmission and negligible role of fomite transmission in a poorly ventilated indoor environment, highlighting the need for more targeted interventions in such environments. The quanta generation rate of the index case differed by a factor of 1.8 on the two buses and transmission occurred in the afternoon of the same day, indicating a time-varying effective viral load within a short period of five hours.

Improved plant bioconcentration modeling of pesticides: The role of periderm dynamics.

BACKGROUND: There is a continuous need to advance pesticide plant uptake models in support of improving pest control and reducing human exposure to pesticide residues. The periderm of harvested root and tuber crops may affect pesticide uptake, but is usually not considered in plant uptake models. To quantify the influence of the periderm on pesticide uptake from soil into potatoes, we propose a model that includes an explicit periderm compartment in the soil-plant mass balance for pesticides. RESULTS: Our model shows that the potato periderm acts as an active barrier to the uptake of lipophilic pesticides with high KOW , while it lets more lipophobic pesticides accumulate in the medulla (pulp). We estimated bioconcentration factors (BCFs) for over 700 pesticides and proposed parameterizations for including the effects of the periderm into a full plant uptake modeling framework. A sensitivity analysis shows that both the degradation half-life inside the tuber and the lipophilicity drive the contributions of other aspects to the variability of BCFs, while highlighting distinct dynamics in the periderm and medulla compartments. Finally, we compare model estimates with measured data, showing that predictions agree with field observations for current-use pesticides and some legacy pesticides frequently found in potatoes. CONCLUSION: Considering the periderm improves the accuracy of quantifying pesticide uptake and bioconcentration in potatoes as input for optimizing pest control and minimizing human exposure to pesticide residues in edible crops.

Evidence for lack of transmission by close contact and surface touch in a restaurant outbreak of COVID-19.

BACKGROUND: Coronavirus disease 2019 (COVID-19) is primarily a respiratory disease that has become a global pandemic. Close contact plays an important role in infection spread, while fomite may also be a possible transmission route. Research during the COVID-19 pandemic has identified long-range airborne transmission as one of the important transmission routes although lack solid evidence. METHODS: We examined video data related to a restaurant associated COVID-19 outbreak in Guangzhou. We observed more than 40,000 surface touches and 13,000 episodes of close contacts in the restaurant during the entire lunch duration. These data allowed us to analyse infection risk via both the fomite and close contact routes. RESULTS: There is no significant correlation between the infection risk via both fomite and close contact routes among those who were not family members of the index case. We can thus rule out virus transmission via fomite contact and interpersonal close contact routes in the Guangzhou restaurant outbreak. The absence of a fomite route agrees with the COVID-19 literature. CONCLUSIONS: These results provide indirect evidence for the long-range airborne route dominating SARS-CoV-2 transmission in the restaurant. We note that the restaurant was poorly ventilated, allowing for increasing airborne SARS-CoV-2 concentration.

Lack of cross-transmission of SARS-CoV-2 between passenger's cabins on the Diamond Princess cruise ship.

An outbreak of COVID-19 occurred on the Diamond Princess cruise ship in January and February 2020 in Japan. We analysed information on the cases of infection to infer whether airborne transmission of SARS-CoV-2, the causative agent of COVID-19, had occurred between cabins. We infer from our analysis that most infections in passengers started on 28 January and were completed by 6 February, except in those who shared a cabin with another infected passenger. The distribution of the infected cabins was random, and no spatial cluster of the infected can be identified. We infer that the ship's central air-conditioning system for passenger's cabins did not play a role in SARS-CoV-2 transmission, i.e. airborne transmission did not occur between cabins during the outbreak, suggesting that the sufficient ventilation was provided. We also infer that the ship's cabin drainage system did not play a role. Most transmission appears to have occurred in the public areas of the cruise ship, likely due to crowding and insufficient ventilation in some of these areas.

Improving Pesticide Uptake Modeling into Potatoes: Considering Tuber Growth Dynamics.

To explore pesticide uptake from soil into a growing potato, a moving-boundary dynamic model is proposed on the basis of the radical diffusion process of a chemical to a sphere. This model, which considers the logistic growth of the potato tuber, describes two hypothetical processes of chemical diffusion within a growing tuber. The model was tested in an illustrative case study for an application of chlorpyrifos. Results indicate that the distribution of chlorpyrifos concentrations along the potato radius is significantly affected by the tuber development. In comparison of our results to results from a classic model using a fixed boundary, the proposed dynamic model yields a quick and big jump for both the average concentration and bioconcentration factor (BCF) of chlorpyrifos in the potato as a result of the sigmoid expansion boundary. Overall, the dynamic model predicts that chlorpyrifos BCFs in the potato at harvest are higher than those using the classical model. In comparison of model results to measured uptake of chlorpyrifos into potato at harvest, the dynamic model shows better performance than the classical model. Our results provide a new perspective on pesticide uptake into potatoes and inform human health risk assessment for pesticides applied at different tuber growth stages.

Probable airborne transmission of SARS-CoV-2 in a poorly ventilated restaurant.

Although airborne transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been recognized, the condition of ventilation for its occurrence is still being debated. We analyzed a coronavirus disease 2019 (COVID-19) outbreak involving three families in a restaurant in Guangzhou, China, assessed the possibility of airborne transmission, and characterized the associated environmental conditions. We collected epidemiological data, obtained a full video recording and seating records from the restaurant, and measured the dispersion of a warm tracer gas as a surrogate for exhaled droplets from the index case. Computer simulations were performed to simulate the spread of fine exhaled droplets. We compared the in-room location of subsequently infected cases and spread of the simulated virus-laden aerosol tracer. The ventilation rate was measured using the tracer gas concentration decay method. This outbreak involved ten infected persons in three families (A, B, C). All ten persons ate lunch at three neighboring tables at the same restaurant on January 24, 2020. None of the restaurant staff or the 68 patrons at the other 15 tables became infected. During this occasion, the measured ventilation rate was 0.9 L/s per person. No close contact or fomite contact was identified, aside from back-to-back sitting in some cases. Analysis of the airflow dynamics indicates that the infection distribution is consistent with a spread pattern representative of long-range transmission of exhaled virus-laden aerosols. Airborne transmission of the SARS-CoV-2 virus is possible in crowded space with a ventilation rate of 1 L/s per person.

Toilets dominate environmental detection of severe acute respiratory syndrome coronavirus 2 in a hospital.

Respiratory and fecal aerosols play confirmed and suspected roles, respectively, in transmitting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). An extensive environmental sampling campaign of both toilet and non-toilet environments was performed in a dedicated hospital building for patients with coronavirus disease 2019 (COVID-19), and the associated environmental factors were analyzed. In total, 107 surface samples, 46 air samples, two exhaled condensate samples, and two expired air samples were collected within and beyond four three-bed isolation rooms. The data of the COVID-19 patients were collected. The building environmental design and the cleaning routines were reviewed. Field measurements of airflow and CO2 concentrations were conducted. The 107 surface samples comprised 37 from toilets, 34 from other surfaces in isolation rooms, and 36 from other surfaces outside the isolation rooms in the hospital. Four of these samples were positive, namely two ward door handles, one bathroom toilet seat cover, and one bathroom door handle. Three were weakly positive, namely one bathroom toilet seat, one bathroom washbasin tap lever, and one bathroom ceiling exhaust louver. Of the 46 air samples, one collected from a corridor was weakly positive. The two exhaled condensate samples and the two expired air samples were negative. The fecal-derived aerosols in patients' toilets contained most of the detected SARS-CoV-2 in the hospital, highlighting the importance of surface and hand hygiene for intervention.