Susceptibility and vulnerability to health effects of air pollution: The case of nitrogen dioxide

Epidemiological and toxicological studies have reported adverse health effects in response to exposure to air pollution including nitrogen dioxide (NO2). Some of these studies have indicated that specific populations may be at different risk of NO2 related health effects that others. Adverse health effects from air pollution are not equally distributed among populations and individuals. Differences in vulnerability and susceptibility may affect the risk of developing a health effect and its severity. A description and characterization of factors associated with vulnerability and susceptibility to health effects of ambient air pollution with a focus on NO2 exposure, a common air pollutant which has been associated with human morbidity and mortality, is presented based on a scoping review for the period 2011-2015. We identified epidemiological studies of factors that may play the role of effect modifiers of the association between exposure to NO2 and related health effects. Studies that may influence risk were critically reviewed. Population groups and characteristics were identified and health effects described and put into the context of risk assessment of air pollution. Population characteristics that can modify the health effects related to NO2 and confer susceptibility are predominantly age, underlying disease, and potentially genetics and gender. These population characteristics don’t differ from those identified for other air pollutants. Understanding about the latter two characteristics has been limited also in association with other air pollutants. Differential vulnerability has been shown due to socioeconomic factors. Insufficient attention in terms of exploration has been paid to the effects of other vulnerability factors. Understanding how NO2 may differently affect individuals or population subgroups is of major relevance for evidence-based policy making in emission reduction strategies and in health protection of those populations most vulnerable and susceptible.JRC.H.2-Air and Climat

Risk Mapping and Mathematical Modelling:Assessment Tools for the Impact of Climate Change on Infectious Diseases

There is now near undisputed scientific consensus that the rise in atmospheric concentration of greenhouse gases causes warming at the Earth¿s surface. Global warming will also have impacts on human health. We focus here on vector-borne infectious diseases because climatic variables are major determinants of the geographical distribution of the cold-blooded insect and tick species that can transmit viruses, bacteria and other microparasites to humans. The distribution of vectors is thus one important component of infection risk. We review the methods that have been developed in the past few years to determine and to model the distribution of species under actual and hypothetical environmental conditions and show how mathematical models have been used in this context. Remote sensing technology offers progressively better environmental and climatic data which can be employed in conjunction with Geographic Information Systems (GIS) and spatial statistical techniques to determine the distribution of vector species under different scenarios. Mathematical models can help to elucidate many aspects of infectious disease dynamics. The available studies lead to the expectation that climate change affects the transmission dynamics of vector-borne infectious diseases. However, the details and the degree of these effects are very uncertain. In order to predict more reliably the effects of extreme climate variability or climate change on infectious disease dynamics more data on the interaction between ecological, epidemiological, economical and social processes are needed.JRC.G.2-Support to external securit

Operations research in disaster preparedness and response: The public health perspective

Operations research is the scientific study of operations for the purpose of better decision making and management. Disasters are defined as events whose consequences exceed the capability of civil protection and public health systems to provide necessary responses in a timely manner. Public health science is applied to the design of operations of public health services and therefore operations research principles and techniques can be applied in public health. Disaster response quantitative methods such as operations research addressing public health are important tools for planning effective responses to disasters. Models address a variety of decision makers (e.g. first responders, public health officials), geographic settings, strategies modelled (e.g. dispensing, supply chain network design, prevention or mitigation of disaster effects, treatment) and outcomes evaluated (costs, morbidity, mortality, logistical outcomes) and use a range of modelling methodologies. Regarding natural disasters the modelling approaches have been rather limited. Response logistics related to public health impact of disasters have been modelled more intensively since decisions about procurement, transport, stockpiling, and maintenance of needed supplies but also mass vaccination, prophylaxis, and treatment are essential in the emergency management. Major issues at all levels of disaster response decision making, including long-range strategic planning, tactical response planning, and real-time operational support are still unresolved and operations research can provide useful techniques for decision management.-JRC.G.2-Global security and crisis managemen

On modeling airborne infection risk

Airborne infection risk analysis is usually performed for enclosed spaces where susceptible individuals are exposed to infectious airborne respiratory droplets by inhalation. It is usually based on exponential, dose-response models of which a widely used variant is the Wells-Riley (WR) model. We employ a population-based Susceptible-Exposed-Droplet-Infected-Recovered (SEDIR) model to revisit the infection-risk estimate at the population level during an epidemic. We demonstrate the link between epidemiological models and the WR model, including its Gammaitoni-Nucci (GN) generalization. This connection shows how infection quanta are related to the number of infectious airborne droplets. For long latent periods, the SEDIR model reduces to the GN model with parameters that depend on biological properties of the pathogen (size-dependent pathogen droplet concentration, infection probability of a deposited infectious droplet), physical droplet properties (lung-deposition probability), and individual behavioral properties (exposure time). In two scenarios we calculate the probability of infection during the epidemic. The WR and GN limits of the SEDIR model reproduce accurately the SEDIR-calculated infection risk.Comment: 14 pages, 3 figure

Migratory birds, the H5N1 influenza virus and the scientific method

<p>Abstract</p> <p>Background</p> <p>The role of migratory birds and of poultry trade in the dispersal of highly pathogenic H5N1 is still the topic of intense and controversial debate. In a recent contribution to this journal, Flint argues that the strict application of the scientific method can help to resolve this issue.</p> <p>Discussion</p> <p>We argue that Flint's identification of the scientific method with null hypothesis testing is misleading and counterproductive. There is far more to science than the testing of hypotheses; not only the justification, bur also the discovery of hypotheses belong to science. We also show why null hypothesis testing is weak and that Bayesian methods are a preferable approach to statistical inference. Furthermore, we criticize the analogy put forward by Flint between involuntary transport of poultry and long-distance migration.</p> <p>Summary</p> <p>To expect ultimate answers and unequivocal policy guidance from null hypothesis testing puts unrealistic expectations on a flawed approach to statistical inference and on science in general.</p

The H1N1 (2009) influenza pandemic: insights into its dynamics from different types of epidemiological data

For the assessment of the transmission potential and the severity of the recent H1N1 (2009) influenza pandemic, a series of different types of epidemiological data were used. We describe the way these data have been employed to estimate some key epidemiological parameters of the pandemic. A preliminary statistical analysis of European data related to Severe Acute Respiratory Infections (SARI) provided interesting insights into the severity of the pandemic as this was manifested in Europe.JRC.G.2-Global security and crisis managemen

Spatial dynamics of airborne infectious diseases

Disease outbreaks, such as those of Severe Acute Respiratory Syndrome in 2003 and the 2009 pandemic A(H1N1) influenza, have highlighted the potential for airborne transmission in indoor environments. Respirable pathogen-carrying droplets provide a vector for the spatial spread of infection with droplet transport determined by diffusive and convective processes. An epidemiological model describing the spatial dynamics of disease transmission is presented. The effects of an ambient airflow, as an infection control, are incorporated leading to a delay equation, with droplet density dependent on the infectious density at a previous time. It is found that small droplets ($\sim 0.4\ \mu$m) generate a negligible infectious force due to the small viral load and the associated duration they require to transmit infection. In contrast, larger droplets ($\sim 4\ \mu$m) can lead to an infectious wave propagating through a fully susceptible population or a secondary infection outbreak for a localised susceptible population. Droplet diffusion is found to be an inefficient mode of droplet transport leading to minimal spatial spread of infection. A threshold air velocity is derived, above which disease transmission is impaired even when the basic reproduction number $R_{0}$ exceeds unity.Comment: 31 pages, 6 figures, to appear in the Journal of Theoretical Biolog

Temporal Variability of Urinary Phthalate Metabolite Levels in Men of Reproductive Age

Phthalates are a family of multifunctional chemicals widely used in personal care and other consumer products. The ubiquitous use of phthalates results in human exposure through multiple sources and routes, including dietary ingestion, dermal absorption, inhalation, and parenteral exposure from medical devices containing phthalates. We explored the temporal variability over 3 months in urinary phthalate metabolite levels among 11 men who collected up to nine urine samples each during this time period. Eight phthalate metabolites were measured by solid-phase extraction–high-performance liquid chromatography–tandem mass spectrometry. Statistical analyses were performed to determine the between- and within-subject variance apportionment, and the sensitivity and specificity of a single urine sample to classify a subject’s 3-month average exposure. Five of the eight phthalates were frequently detected. Monoethyl phthalate (MEP) was detected in 100% of samples; monobutyl phthalate, monobenzyl phthalate, mono-2-ethylhexyl phthalate (MEHP), and monomethyl phthalate were detected in > 90% of samples. Although we found both substantial day-to-day and month-to-month variability in each individual’s urinary phthalate metabolite levels, a single urine sample was moderately predictive of each subject’s exposure over 3 months. The sensitivities ranged from 0.56 to 0.74. Both the degree of between- and within-subject variance and the predictive ability of a single urine sample differed among phthalate metabolites. In particular, a single urine sample was most predictive for MEP and least predictive for MEHP. These results suggest that the most efficient exposure assessment strategy for a particular study may depend on the phthalates of interest

On the Transmission Dynamics of SARS-CoV-2 in a Temperate Climate

An epidemiological model, which describes the transmission dynamics of SARS-CoV-2 under specific consideration of the incubation period including the population with subclinical infections and being infective is presented. The COVID-19 epidemic in Greece was explored through a Monte Carlo uncertainty analysis framework, and the optimal values for the parameters that determined the transmission dynamics could be obtained before, during, and after the interventions to control the epidemic. The dynamic change of the fraction of asymptomatic individuals was shown. The analysis of the modelling results at the intra-annual climatic scale allowed for in depth investigation of the transmission dynamics of SARS-CoV-2 and the significance and relative importance of the model parameters. Moreover, the analysis at this scale incorporated the exploration of the forecast horizon and its variability. Three discrete peaks were found in the transmission rates throughout the investigated period (15 February–15 December 2020). Two of them corresponded to the timing of the spring and autumn epidemic waves while the third one occurred in mid-summer, implying that relaxation of social distancing and increased mobility may have a strong effect on rekindling the epidemic dynamics offsetting positive effects from factors such as decreased household crowding and increased environmental ultraviolet radiation. In addition, the epidemiological state was found to constitute a significant indicator of the forecast reliability horizon, spanning from as low as few days to more than four weeks. Embedding the model in an ensemble framework may extend the predictability horizon. Therefore, it may contribute to the accuracy of health risk assessment and inform public health decision making of more efficient control measures