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

Satellite Earth Observation Data in Epidemiological Modeling of Malaria, Dengue and West Nile Virus: A Scoping Review

Earth Observation (EO) data can be leveraged to estimate environmental variables that influence the transmission cycle of the pathogens that lead to mosquito-borne diseases (MBDs). The aim of this scoping review is to examine the state-of-the-art and identify knowledge gaps on the latest methods that used satellite EO data in their epidemiological models focusing on malaria, dengue and West Nile Virus (WNV). In total, 43 scientific papers met the inclusion criteria and were considered in this review. Researchers have examined a wide variety of methodologies ranging from statistical to machine learning algorithms. A number of studies used models and EO data that seemed promising and claimed to be easily replicated in different geographic contexts, enabling the realization of systems on regional and national scales. The need has emerged to leverage furthermore new powerful modeling approaches, like artificial intelligence and ensemble modeling and explore new and enhanced EO sensors towards the analysis of big satellite data, in order to develop accurate epidemiological models and contribute to the reduction of the burden of MBDs

Improving the deterministic skill of air quality ensembles

<p><strong>Abstract.</strong> Forecasts from chemical weather models are subject to uncertainties in the input data (e.g. emission inventory, initial and boundary conditions) as well as the model itself (e.g. physical parameterization, chemical mechanism). Multi-model ensemble forecasts can improve the forecast skill provided that certain mathematical conditions are fulfilled. We demonstrate through an intercomparison of two dissimilar air quality ensembles that unconditional raw forecast averaging, although generally successful, is far from optimum. One way to achieve an optimum ensemble is also presented. The basic idea is to either add optimum weights to members or constrain the ensemble to those members that meet certain conditions in time or frequency domain. The methods are evaluated against ground level observations collected from the EMEP and Airbase databases.<br><br> The two ensembles were created for the first and second phase of the Air Quality Model Evaluation International Initiative (AQMEII). Verification statistics shows that the deterministic models simulate better O<sub>3</sub> than NO<sub>2</sub> and PM<sub>10</sub>, linked to different levels of complexity in the represented processes. The ensemble mean achieves higher skill compared to each station's best deterministic model at 39 %–63 % of the sites. The skill gained from the favourable ensemble averaging has at least double the forecast skill compared to using the full ensemble. The method proved robust for the 3-monthly examined time-series if the training phase comprises 60 days. Further development of the method is discussed in the conclusion.</p&gt

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

Development of numerical models for air quality studies

Uncertainties in the input parameters of an air quality model affect the prognostic skills of the system. However, comprehensive sensitivity analysis using traditional statistical methods like Monte Carlo are not effective for 3D models mainly due to their computational demands. Hence, in depth sensitivity analysis of 3D air quality models remains the exception and not the rule. The present work studies an alternative and computationall efficient sensitivity analysis method. The uncertainties in the input parameters are evaluated through a senses of partial derivatives. For this reason, an enhanced region of the 3D air quality model CAMx has been generated (S-CAMx) by utilising the automatic differentation tool ADIFOR. The validation of S-CAM and the interpretation of the sensitivity information is performed for domains with fundamental differences in topography, emissions and photochemistry, namely in Athens, Milan and London (AUTO - OIL II data). In every case, the S-CAMx needed one simulation in order to calculate the sensitivity indices for perturbations in all the input parameters. Additionally, the highest non-lincarity was found for perturbations at the VOC emissions.Η αβεβαιότητα των παραμέτρων εισόδου στα μοντέλα ποιότητας αέρα περιορίζει τις προγνωστικές ικανότητες του συστήματος. Ωστόσο, μελέτες ευαισθησίας χρησιμοποιώντας παραδοσιακές στατιστικές μεθόδους όπως η Monte Carlo δεν είναι εφικτές για τρισδιάστατα μοντέλα κυρίως εξαιτίας των υπολογιστικών τους απαιτήσεων. Ως εκ τούτου, διεξοδική και εκτενής μελέτη ανάλυσης ευαισθησίας τρισδιάστατων μοντέλων ποιότητας αέρα αποτελεί την εξαίρεση και όχι τον κανόνα. Η παρούσα μελέτη εισάγει μια εναλλακτική και υπολογιστικά εφικτή μέθοδο ανάλυσης ευαισθησίας. Με αφετηρία τη θεωρία διαταραχών, οι αβεβαιότητες των παραμέτρων εισόδου εκτιμώνται μέσω μιας σειράς μερικών παραγώγων. Η ενσωμάτωση των μερικών παραγώγων και η ταυτόχρονη δημιουργία του ενισχυμένου κώδικα (S - CAMx = ~ 50000 γραμμές κώδικα) για το τρισδιάστατο μοντέλο ποιότητας αέρα CAMx (~35000 γραμμές κώδικα) πραγματοποιείτε με το εργαλείο αυτόματης διαφοροποίησης ADIFOR. Η εξακρίβωση του S-CAMx πραγματοποιείται σε επικράτειες με θεμελιώδεις διαφορές στην τοπογραφία, τις εκπομπές και τη φωτοχημεία και συγκεκριμένα στην Αθήνα, το Μιλάνο και το Λονδίνο. Σε κα΄θε περίπτωση το S-CAMx χρειάστηκε μιά και μόνο προσομοίωση ώστε να παρέχει πληροφορίες για τη διάδοση τωνδιαταραχών σε όλες τις παραμέτρους εισόδου. Επιπρόσθετα, η μεγαλύτερη μη - γραμμικότητα εντοπίστηκε στις διαταραχές των εκπομπών VOC

Assessment of West Nile virus transmission risk from a weather-dependent epidemiological model and a global sensitivity analysis framework

West Nile virus (WNV )transmission risk is strongly related to weather conditions due to the sensitivity of the mosquitoes to climatic factors. We assess the WNV transmission risk of humans to seasonal weather conditions and the relative effects of parameters affecting the transmission dynamics. The assessment involves a known epidemiological model we extend to account for temperature and precipitation and a global uncertainty and sensitivity analysis framework. We focus on three relevant quantities, the basic reproduction number (R0), the minimum infection rate (MIR), and the number of infected individuals. The highest-priority weather-related WNV transmission risks can be attributed to the birth and death rate of mosquitoes, the biting rate of mosquitoes to birds, and the probability of transmission from birds to mosquitoes. Global sensitivity analysis indicates that these parameters make up a big part of the explained variance in R0 and MIR. The analysis allows for a dynamic assessment over time capturing the period parameters are more relevant than others. Global uncertainty and sensitivity analysis of WNV transmission risk to humans enable insights into the relative importance of individual parameters of the transmission cycle of the virus facilitating the understanding of the dynamics and the implementation of tailored control strategies.JRC.F.7-Knowledge for Health and Consumer Safet

De praeceptis ferendis: good practice in multi-model ensembles

Ensembles of air quality models have been formally and empirically shown to outperform single models in many cases. Evidence suggests that ensemble error is reduced when the members form a diverse and accurate ensemble. Diversity and accuracy are hence two factors that should be taken care of while designing ensembles in order for them to provide better predictions. Theoretical aspects like the bias–variance–covariance decomposition and the accuracy–diversity decomposition are linked together and support the importance of creating ensemble that incorporates both these elements. Hence, the common practice of unconditional averaging of models without prior manipulation limits the advantages of ensemble averaging. We demonstrate the importance of ensemble accuracy and diversity through an inter-comparison of ensemble products for which a sound mathematical framework exists, and provide specific recommendations for model selection and weighting for multi-model ensembles. The sophisticated ensemble averaging techniques, following proper training, were shown to have higher skill across all distribution bins compared to solely ensemble averaging forecasts.JRC.C.5-Air and Climat

E pluribus unum: Ensemble Air Quality Predictions

In this study we present a novel approach for improving the air quality predictions using an ensemble of air quality models generated in the context of AQMEII (Air Quality Model Evaluation International Initiative). . The development of the forecasting method makes use of modeled and observed time series (either spatially aggregated or relative to single monitoring stations) of ozone concentrations over different areas of Europe and North America. The technique considers the underlying forcing mechanisms on ozone by means of the spectrally decomposed previsions. By means of diverse applications we demonstrate how the approach screens the ensemble members, extracts the best components and generates bias-free forecasts with improved accuracy over the candidate models.JRC.H.2-Air and Climat

Uncertainty and Global Sensitivity Analysis of Road Transport Emission Estimates.

Abstract not availableJRC.G-Institute for the Protection and the Security of the Citizen (Ispra

Pauci ex tanto numero: reducing redundancy in multi-model ensembles

We explicitly address the fundamental issue of member diversity in multi-model ensembles. To date, no attempts in this direction have been documented within the air quality (AQ) community despite the extensive use of ensembles in this field. Common biases and redundancy are the two issues directly deriving from lack of independence, undermining the significance of a multi-model ensemble, and are the subject of this study. Shared, dependant biases among models do not cancel out but will instead determine a biased ensemble. Redundancy derives from having too large a portion of common variance among the members of the ensemble, producing overconfidence in the predictions and underestimation of the uncertainty. The two issues of common biases and redundancy are analysed in detail using the AQMEII ensemble of AQ model results for four air pollutants in two European regions. We show that models share large portions of bias and variance, extending well beyond those induced by common inputs. We make use of several techniques to further show that subsets of models can explain the same amount of variance as the full ensemble with the advantage of being poorly correlated. Selecting the members for generating skilful, non-redundant ensembles from such subsets proved, however, non-trivial. We propose and discuss various methods of member selection and rate the ensemble performance they produce. In most cases, the full ensemble is outscored by the reduced ones. We conclude that, although independence of outputs may not always guarantee enhancement of scores (but this depends upon the skill being investigated), we discourage selecting the members of the ensemble simply on the basis of scores; that is, independence and skills need to be considered disjointly.JRC.C.5-Air and Climat