Stochastic Modelling of Air Pollution Impacts on Respiratory Infection Risk

The effect of particulate matter 2.5 on seasonal influenza transmission in 1,330 counties, China: A Bayesian spatial analysis based on Köppen Geiger climate zones classifications

Previous research has linked seasonal influenza transmission with particulate matters (PM2.5). However, the effect of PM2.5 on seasonal influenza transmission varied by region. This study aims explore how PM2.5 influenced seasonal influenza transmission in the elderly across 1330 counties in two Köppen Geiger climate zones in China, incorporating the socio-economic factors to enhance climate-driven early warning systems (EWS) for influenza. Data included weekly 2015-2019 influenza cases in those aged >65 from China's national influenza surveillance system for 1330 counties in two Köppen Geiger climate zones: Temperate, Hot Summer with Dry Winter (Cwa) and No Dry Season (Cfa). PM2.5 data from 2015 to 2019 were sourced from Copernicus Atmosphere Monitoring Services. Additional data on floating population, population density and Gross Domestic Product (GDP) per capita were collected from pertinent departments. A Bayesian spatial autoregressive model assessed the association of PM2.5 and influenza transmission after adjustment of socio-economic factors. Our research results showed PM2.5 (per 1 μg/m³ increase) was linked to increased influenza transmission in the Cwa zone during winter season (Relative Risk (RR) = 1.023, 95% Credible Interval (CI):1.008-1.040) but not in the Cfa winter (RR = 1.003, 95% CI: 0.992-1.015). Floating population significantly enhanced transmission in both zones (highest RR = 1.362, 95% CI:1.181-1.583), while GDP per capita growth was associated with reduced transmission risk (highest RR = 0.619, 95% CI: 0.445-0.861). The study identifies PM2.5 as a significant factor influencing influenza transmission in the elderly, with effects varying by climate zone, suggesting the need to incorporate PM2.5 and socio-economic factors into seasonal influenza EWS.

Statistical inference and neural network training based on stochastic difference model for air pollution and associated disease transmission

A polluted air environment can potentially provoke infections of diverse respiratory diseases. The development of mathematical models can study the mechanism of air pollution and its effect on the spread of diseases. The key is to characterize the intrinsic correlation between the disease infection and the change in air pollutant concentration. In this paper, we establish a coupled discrete susceptible-exposed-infectious-susceptible (SEIS) model with demography to characterize the transmission of disease, and the change in the concentration of air pollutants is described in the form of the Beverton-Holt (BH) model with a time-varying inflow rate of air pollutants. Considering the periodic variation characteristics of data, time-varying parameters are defined as specific functional forms. We estimate the change point at which the parameters switch and the parameter values within the switching interval based on Bayesian statistical theory. The data fitting of the model can reflect the seasonal peaks and annual growth trends of values of air quality index (AQI) and the number of influenza-like illnesses (ILI) cases. However, the bias in data fitting indicates a more complex correlation pattern between disease and pollutant concentration changes. To explore unknown mechanisms, we propose the extended transmission-dynamics-informed neural network (TDINN) algorithm by combining deep learning with difference equations and obtain the curves of the transmission rate and inflow rate functions over time. The results show that neural network models can help us determine time-varying parameters in the model, thereby better reflecting the trend of data changes.

Forecasting PM 2.5 concentration based on integrating of CEEMDAN decomposition method with SVM and LSTM

With urbanization and increasing consumption, there is a growing need to prioritize sustainable development across various industries. Particularly, sustainable development is hindered by air pollution, which poses a threat to both living organisms and the environment. The emission of combustion gases containing particulate matter (PM 2.5) during human and social activities is a major cause of air pollution. To mitigate health risks, it is crucial to have accurate and reliable methods for forecasting PM 2.5 levels. In this study, we propose a novel approach that combines support vector machine (SVM) and long short-term memory (LSTM) with complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to forecast PM 2.5 concentrations. The methodology involves extracting Intrinsic mode function (IMF) components through CEEMDAN and subsequently applying different regression models (SVM and LSTM) to forecast each component. The Naive Evolution algorithm is employed to determine the optimal parameters for combining CEEMDAN, SVM, and LSTM. Daily PM 2.5 concentrations in Kaohsiung, Taiwan from 2019 to 2021 were collected to train models and evaluate their performance. The performance of the proposed model is evaluated using metrics such as mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), and coefficient of determination (R2) for each district. Overall, our proposed model demonstrates superior performance in terms of MAE (1.858), MSE (7.2449), RMSE (2.6682), and (0.9169) values compared to other methods for 1-day ahead PM 2.5 forecasting. Furthermore, our proposed model also achieves the best performance in forecasting PM 2.5 for 3- and 7-day ahead predictions.

Randomness accelerates the dynamic clearing process of the COVID-19 outbreaks in China

During the implementation of strong non-pharmaceutical interventions (NPIs), more than one hundred COVID-19 outbreaks induced by different strains in China were dynamically cleared in about 40 days, which presented the characteristics of small scale clustered outbreaks with low peak levels. To address how did randomness affect the dynamic clearing process, we derived an iterative stochastic difference equation for the number of newly reported cases based on the classical stochastic SIR model and calculate the stochastic control reproduction number (SCRN). Further, by employing the Bayesian technique, the change points of SCRNs have been estimated, which is an important prerequisite for determining the lengths of the exponential growth and decline phases. To reveal the influence of randomness on the dynamic zeroing process, we calculated the explicit expression of the mean first passage time (MFPT) during the decreasing phase using the relevant theory of first passage time (FPT), and the main results indicate that random noise can accelerate the dynamic zeroing process. This demonstrates that powerful NPI measures can rapidly reduce the number of infected people during the exponential decline phase, and enhanced randomness is conducive to dynamic zeroing, i.e. the greater the random noise, the shorter the average clearing time is. To confirm this, we chose 26 COVID-19 outbreaks in various provinces in China and fitted the data by estimating the parameters and change points. We then calculated the MFPTs, which were consistent with the actual duration of dynamic zeroing interventions.

The influence of ambient air pollution on the transmission of tuberculosis in Jiangsu, China

In this paper, based on the statistical data, we investigate the effects of long-term exposure to ambient particulate air pollution on the transmission dynamics of tuberculosis (TB) in Jiangsu, China by studying the threshold dynamics of the TB epidemic model via the statistical data analytically and numerically. The basic reproduction number R 0 > 1 reveals that TB in Jiangsu, China is an endemic disease and will persist for a long time. And the numerical results show that, in order to control the TB in Jiangsu effectively, we must decrease the depuration coefficient of PM₁₀ in the body, the proportion of TB symptomatic infectious by direct transmission, the reactivation rate of the pre-symptomatic infectious and the effect coefficient of PM₁₀ and MTB inhaled of TB transmission, and increase the uptake coefficient, the recovery rate of the symptomatic/pre-symptomatic infectious and the influence coefficient of PM₁₀ on the body of mortality. Our study shows that PM₁₀ is closely related to the incidence of TB, and the effective control efforts are suggested to focus on increasing close-contact distance and wearing protective mask to decrease the influence of PM₁₀ on the TB transmission, which may shed a new light on understanding the environmental drivers to TB.

Dynamic analysis and optimal control of a class of SISP respiratory diseases

In this paper, the actual background of the susceptible population being directly patients after inhaling a certain amount of PM2.5 is taken into account. The concentration response function of PM2.5 is introduced, and the SISP respiratory disease model is proposed. Qualitative theoretical analysis proves that the existence, local stability and global stability of the equilibria are all related to the daily emission P0 of PM2.5 and PM2.5 pathogenic threshold K. Based on the sensitivity factor analysis and time-varying sensitivity analysis of parameters on the number of patients, it is found that the conversion rate β and the inhalation rate η has the largest positive correlation. The cure rate γ of infected persons has the greatest negative correlation on the number of patients. The control strategy formulated by the analysis results of optimal control theory is as follows: The first step is to improve the clearance rate of PM2.5 by reducing the PM2.5 emissions and increasing the intensity of dust removal. Moreover, such removal work must be maintained for a long time. The second step is to improve the cure rate of patients by being treated in time. After that, people should be reminded to wear masks and go out less so as to reduce the conversion rate of susceptible people becoming patients.

A cross-sectional analysis of associations between environmental indices and asthma in U.S. counties from 2003 to 2012

BACKGROUND

To capture the impacts of environmental stressors, environmental indices like the Air Quality Index, Toxic Release Inventory, and Environmental Quality Index have been used to investigate the environmental quality and its association with public health issues. However, past studies often rely on relatively small sample sizes, and they have typically not adjusted for important individual-level disease risk factors.

OBJECTIVE

We aim to estimate associations between existing environmental indices and asthma prevalence over a large population and multiple years.

METHODS

Based on data availability, we assessed the predictive capability of these indices for prevalent asthma across U.S. counties from 2003 to 2012. We gathered asthma data from the U.S. CDC Behavioral Risk Factor Surveillance System by county and used multivariable weighted logistic regression models to estimate the associations between the environmental indices and asthma, adjusting for individual factors such as smoking, income level, and obesity.

RESULTS

Environmental indices showed little to no correlation with one another and with prevalent asthma over time. Associations of environmental indices with prevalent asthma were very weak; whereas individual factors were more substantially associated with prevalent asthma.

SIGNIFICANCE

Our study suggests that an improved environmental index is needed to predict population-level asthma prevalence.

THE EFFECT OF STOCHASTIC VARIABILITY ON TRANSMISSION DYNAMICS OF ECHINOCOCCOSIS

Echinococcosis, one of the most serious zoonotic diseases, has a severe impact on the human health and economic development. This paper mainly focuses on the effect of stochastic variability on transmission dynamics of echinococcosis. A stochastic model describing the transmission of echinococcus granulosus in dog-livestock-human is proposed. By using the Itô formula, strong law of large numbers for martingale and the inequality analysis, the criteria on the extinction with probability one for the disease are obtained. In addition, by constructing an appropriate stochastic Lyapunov function, the existence of a unique ergodic stationary distribution is established. Furthermore, numerical simulations have been performed to not only support our analytical results but also display that noise intensities would have significant impact on the speed of the extinction for the echinococcosis disease, and as well as the shape for the probability density curve of the stationary distribution of the solution for this model.

The assessment of two different pollutants dispersion from a coal-fired power plant for various thermal regimes

In this study, numerical simulations of the movement and emissions dispersion of two pollutants (sulfur dioxide(SO₂) and carbon dioxide(CO₂)) into the atmospheric boundary layer were considered under natural atmospheric conditions. To test the numerical algorithm and to select the optimal turbulent model, the test problem was solved numerically. The obtained computational data were compared with measurement data and values from the computation of other authors and the SST k-omega model illustrated the closest values to the data from the experiment, this is achieved by modifying the boundary condition for turbulent kinetic energy. The tested computational algorithm was used to characterize the emissions process of two pollutants from two chimneys of the Ekibastuz SDPP and the distribution of CO₂ and SO₂ in the air flow field in natural air condition. For this task, four various velocity variations were considered, as well as several various thermal variations (temperature inversion, constant temperature and decreasing temperature by the height). From the obtained computational results, it should be noticed that different environmental temperature conditions extremely impact the distribution of pollutants CO₂ and SO₂ in the atmospheric surface layer, so at constant temperature conditions, the species for all velocity variations have nearly identical species profile.

Macro-Impacts of Air Quality on Property Values in China—A Meta-Regression Analysis of the Literature

Air pollution has received increasing attention in recent years, particularly in China, due to the rapid industrialisation that has wrought intense levels of air pollution. A number of studies, therefore, have been devoted to quantifying the impacts of air pollution on property value in China. However, the empirical results are somewhat mixed. This naturally raises questions of whether there is a significant relationship between air quality and housing prices and the plausible reasons for the mixed results in previous studies. This study aims to fill this gap by explaining the variations in the findings by a meta-regression analysis. To control for heterogeneity, a weighted least square model was used to explore the factors influencing the magnitude and significance of the air quality effect based on empirical estimates from 117 observations. This study confirms that air quality does have a discernible impact on housing prices beyond the publication bias. Besides, the types of air quality indicator and the air data source do significantly influence estimates through affecting both the magnitude of the elasticity and the partial correlation coefficient (PCC). Further, the selections of control variables and estimation approaches also have significant impacts on estimates. This study also finds that published papers tend to be biased towards more economically significant estimates. The implications of the findings have also been discussed.

Ambient air pollution and respiratory bacterial infections, a troubling association: epidemiology, underlying mechanisms, and future challenges

The World Health Organization attributed more than four million premature deaths to ambient air pollution in 2016. Numerous epidemiologic studies demonstrate that acute respiratory tract infections and exacerbations of pre-existing chronic airway diseases can result from exposure to ambient (outdoor) air pollution. In this context, the atmosphere contains both chemical and microbial pollutants (bioaerosols), whose impact on human health remains unclear. Therefore, this review: summarises the findings from recent studies on the association between exposure to air pollutants-especially particulate matter and ozone-and onset or exacerbation of respiratory infections (e.g. pneumonia, cystic fibrosis lung infection, and tuberculosis); discusses the mechanisms underlying the relationship between air pollution and respiratory bacterial infections, which is necessary to define prevention and treatment strategies; demonstrates the relevance of air pollution modelling in investigating and preventing the impact of exposure to air pollutants on human health; and outlines future actions required to improve air quality and reduce morbidity and mortality related to air pollution.

The impact of air pollution on the transmission of pulmonary tuberculosis

In this paper, we investigate the relationship between the air pollution and tuberculosis cases and its prediction in Jiangsu, China by using the time-series analysis method, and find that the seasonal ARIMA(1, 1, 0)×(0, 1, 1)₁₂ model is the preferred model for predicting the TB cases in Jiangsu, China. Furthermore, we evaluate the relationship between AQI, PM2.5, PM10 and the number of TB cases, and find that the prediction accuracy of the ARIMA model is improved by adding monthly PM2.5 with 0-month lag as an external variable, i.e., ARIMA(1, 1, 0)×(0, 1, 1)₁₂+PM2.5. The results show that ARIMAX model can be a useful tool for predicting TB cases in Jiangsu, China, and it can provide a scientific basis for the prevention and treatment of TB.