Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations

Background: Short-term forecasts of infectious disease contribute to situational awareness and capacity planning. Based on best practice in other fields and recent insights in infectious disease epidemiology, one can maximise forecasts' predictive performance by combining independent models into an ensemble. Here we report the performance of ensemble predictions of COVID-19 cases and deaths across Europe from March 2021 to March 2022.

Methods: We created the European COVID-19 Forecast Hub, an online open-access platform where modellers upload weekly forecasts for 32 countries with results publicly visualised and evaluated. We created a weekly ensemble forecast from the equally-weighted average across individual models' predictive quantiles. We measured forecast accuracy using a baseline and relative Weighted Interval Score (rWIS). We retrospectively explored ensemble methods, including weighting by past performance.

Results: We collected weekly forecasts from 48 models, of which we evaluated 29 models alongside the ensemble model. The ensemble had a consistently strong performance across countries over time, performing better on rWIS than 91% of forecasts for deaths (N=763 predictions from 20 models), and 83% forecasts for cases (N=886 predictions from 23 models). Performance remained stable over a 4-week horizon for death forecasts but declined with longer horizons for cases. Among ensemble methods, the most influential choice came from using a median average instead of the mean, regardless of weighting component models.

Conclusions: Our results support combining independent models into an ensemble forecast to improve epidemiological predictions, and suggest that median averages yield better performance than methods based on means. We highlight that forecast consumers should place more weight on incident death forecasts than case forecasts at horizons greater than two weeks.

Funding: European Commission, Ministerio de Ciencia, Innovación y Universidades, FEDER; Agència de Qualitat i Avaluació Sanitàries de Catalunya; Netzwerk Universitätsmedizin; Health Protection Research Unit; Wellcome Trust; European Centre for Disease Prevention and Control; Ministry of Science and Higher Education of Poland; Federal Ministry of Education and Research; Los Alamos National Laboratory; German Free State of Saxony; NCBiR; FISR 2020 Covid-19 I Fase; Spanish Ministry of Health / REACT-UE (FEDER); National Institutes of General Medical Sciences; Ministerio de Sanidad/ISCIII; PERISCOPE European H2020; PERISCOPE European H2021; InPresa; National Institutes of Health, NSF, US Centers for Disease Control and Prevention, Google, University of Virginia, Defense Threat Reduction Agency.

Light spectra of biophilic LED-sourced system modify essential oils composition and plant morphology of Mentha piperita L. and Ocimum basilicum L

Investigating morphological and molecular mechanisms that plants adopt in response to artificial biophilic lighting is crucial for implementing biophilic approaches in indoor environments. Also, studying the essential oils (EOs) composition in aromatic plants can help unveil the light influence on plant metabolism and open new investigative routes devoted to producing valuable molecules for human health and commercial applications. We assessed the growth performance and the EOs composition of Mentha x piperita and Ocimum basilicum grown under an innovative artificial biophilic lighting system (CoeLux^®), that enables the simulation of natural sunlight with a realistic sun perception, and compared it to high-pressure sodium lamps (control) We found that plants grown under the CoeLux^® light type experienced a general suppression of both above and belowground biomass, a high leaf area, and a lower leaf thickness, which might be related to the shade avoidance syndrome. The secondary metabolites composition in the plants' essential oils was scarcely affected by both light intensity and spectral composition of the CoeLux^® light type, as similarities above 80% were observed with respect to the control light treatments and within both plant species. The major differences were detected with respect to the EOs extracted from plants grown under natural sunlight (52% similarity in M. piperita and 75% in O. basilicum). Overall, it can be speculated that the growth of these two aromatic plants under the CoeLux^® lighting systems is a feasible strategy to improve biophilic approaches in closed environments that include both plants and artificial sunlight. Among the two plant species analyzed, O. basilicum showed an overall better performance in terms of both morphological traits and essential oil composition. To increase biomass production and enhance the EOs quality (e.g., higher menthol concentrations), further studies should focus on technical solutions to raise the light intensity irradiating plants during their growth under the CoeLux^® lighting systems.

Two years of COVID-19 pandemic: The Italian experience of Statgroup-19

The amount and poor quality of available data and the need of appropriate modeling of the main epidemic indicators require specific skills. In this context, the statistician plays a key role in the process that leads to policy decisions, starting with monitoring changes and evaluating risks. The "what" and the "why" of these changes represent fundamental research questions to provide timely and effective tools to manage the evolution of the epidemic. Answers to such questions need appropriate statistical models and visualization tools. Here, we give an overview of the role played by Statgroup-19, an independent Italian research group born in March 2020. The group includes seven statisticians from different Italian universities, each with different backgrounds but with a shared interest in data analysis, statistical modeling, and biostatistics. Since the beginning of the COVID-19 pandemic the group has interacted with authorities and journalists to support policy decisions and inform the general public about the evolution of the epidemic. This collaboration led to several scientific papers and an accrued visibility across various media, all made possible by the continuous interaction across the group members that shared their unique expertise.

Effects of EOs vs. Antibiotics on E. coli Strains Isolated from Drinking Waters of Grazing Animals in the Upper Molise Region, Italy

The health and safety of grazing animals was the subject of microbiological monitoring on natural source of drinking waters in the upper Molise region, Italy. Surface water samples, on spring-summer season, were collected and submitted to analyses using sterile membrane filtration, cultural medium, and incubation. The level of environmental microbial contamination (Total viable microbial count, yeasts and fungi) and faecal presence (Total and faecal coliforms, E. coli, and Salmonellae spp.) were carried out. By the selective microbiological screening, twenty-three E. coli strains from drinking waters were isolated and submitted to further studies to evaluate antibiotic resistance by antibiograms vs. three animal and two diffuse human antibiotics. Furthermore, after a fine chemical characterization by GC and GC-MS, three Essential Oils (EOs) of aromatic plants (Timus vulgaris, Melaleuca alternifolia, Cinnamomun verum) aromatograms were performed and results statistically compared. The effects of EOs vs. antibiotics on E. coli strains isolated from drinking waters showed a total absence of microbial resistance. In our experimental conditions, even if some suggestions will be further adopted for better managements of grazing animals, because the health and safety represent a guarantee for both animals and humans.

Spatio-temporal modelling of COVID-19 incident cases using Richards' curve: An application to the Italian regions

We introduce an extended generalised logistic growth model for discrete outcomes, in which spatial and temporal dependence are dealt with the specification of a network structure within an Auto-Regressive approach. A major challenge concerns the specification of the network structure, crucial to consistently estimate the canonical parameters of the generalised logistic curve, e.g. peak time and height. We compared a network based on geographic proximity and one built on historical data of transport exchanges between regions. Parameters are estimated under the Bayesian framework, using Stan probabilistic programming language. The proposed approach is motivated by the analysis of both the first and the second wave of COVID-19 in Italy, i.e. from February 2020 to July 2020 and from July 2020 to December 2020, respectively. We analyse data at the regional level and, interestingly enough, prove that substantial spatial and temporal dependence occurred in both waves, although strong restrictive measures were implemented during the first wave. Accurate predictions are obtained, improving those of the model where independence across regions is assumed.

Prisoners of variants, or free to act as prisoners of swabs? The case of Italy

To the editor, " To all evils there are two remedies: time and silence [1]." This citation from The Count of Monte Cristo by Alexandre Dumas, written in 1844, in collaboration with Auguste Maquet, appears to be timely nowadays. This showed how novels can often be able to teach us how to lead some wars. The fight against the novel coronavirus (SARS-CoV-2), that overwhelmed the planet for about two years, required and still requires silence to think and time to act, to find the right strategies for the beginning of the "new normal". This article is protected by copyright. All rights reserved.

The unresolved question on COVID-19 virus origin: The three cards game?

The ongoing discussion about the real origin of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) feeds acrimonious debates. Where did SARS-CoV-2 come from? Was SARS-CoV-2 transmitted in the wild from an animal to a person before exploding in Wuhan or was it an engineered virus that escaped from research or a laboratory in Wuhan? Right now, we still don't know enough whether SARS-CoV-2 is human-made or not, and lab-leak theories remain essentially speculative. Many recent studies have pointed out several plausible scenarios. Anyhow, currently, even if suspicions by some about the possibility of lab-leak hypothesis still remain, the consensus view is that the pandemic probably started from a natural source and, to determine the real origin of the SARS-CoV-2 virus, further research is needed.

Nowcasting COVID-19 incidence indicators during the Italian first outbreak

A novel parametric regression model is proposed to fit incidence data typically collected during epidemics. The proposal is motivated by real-time monitoring and short-term forecasting of the main epidemiological indicators within the first outbreak of COVID-19 in Italy. Accurate short-term predictions, including the potential effect of exogenous or external variables are provided. This ensures to accurately predict important characteristics of the epidemic (e.g., peak time and height), allowing for a better allocation of health resources over time. Parameter estimation is carried out in a maximum likelihood framework. All computational details required to reproduce the approach and replicate the results are provided.

An ensemble approach to short-term forecast of COVID-19 intensive care occupancy in Italian regions

The availability of intensive care beds during the COVID‐19 epidemic is crucial to guarantee the best possible treatment to severely affected patients. In this work we show a simple strategy for short‐term prediction of COVID‐19 intensive care unit (ICU) beds, that has proved very effective during the Italian outbreak in February to May 2020. Our approach is based on an optimal ensemble of two simple methods: a generalized linear mixed regression model, which pools information over different areas, and an area‐specific nonstationary integer autoregressive methodology. Optimal weights are estimated using a leave‐last‐out rationale. The approach has been set up and validated during the first epidemic wave in Italy. A report of its performance for predicting ICU occupancy at regional level is included.

Microbial community analysis with a specific statistical approach after a record breaking snowfall in Southern Italy

Purpose

Snow and ice ecosystems present unexpectedly high microbial abundance and diversity. Although arctic and alpine snow environments have been intensively investigated from a microbiological point of view, few studies have been conducted in the Apennines. Accordingly, the main purpose of this research was to analyze the microbial communities of the snow collected in two different locations of Capracotta municipality (Southern Italy) after a snowfall record occurred on March 2015 (256 cm of snow in less than 24 h).

Methods

Bacterial communities were analyzed by the Next-Generation Sequencing techniques. Furthermore, a specific statistical approach for taxonomic hierarchy data was introduced, both for the assessment of diversity within microbial communities and the comparison between different microbiotas. In general, diversity and similarity indices are more informative when computed at the lowest level of the taxonomic hierarchy, the species level. This is not the case with microbial data, for which the species level is not necessarily the most informative. Indeed, the possibility to detect a large number of unclassified records at every level of the hierarchy (even at the top) is very realistic due to both the partial knowledge about the cultivable fraction of microbial communities and limitations to taxonomic assignment connected to the quality and completeness of the 16S rRNA gene reference databases. Thus, a global approach considering information from the whole taxonomic hierarchy was adopted in order to obtain a more consistent assessment of the biodiversity.

Result

The main phyla retrieved in the investigated snow samples were Proteobacteria, Actinobacteria, Bacteroidetes, and Firmicutes. Interestingly, DNA from bacteria adapted to thrive at low temperatures, but also from microorganisms normally associated with other habitats, whose presence in the snow could be justified by wind-transport, was found. Biomolecular investigations and statistical data analysis showed relevant differences in terms of biodiversity, composition, and distribution of bacterial species between the studied snow samples.

Conclusion

The relevance of this research lies in the expansion of knowledge about microorganisms associated with cold environments in contexts poorly investigated such as the Italian Apennines, and in the development of a global statistical approach for the assessment of biological diversity and similarity of microbial communities as an additional tool to be usefully combined with the barcoding methods.

Winter survival of microbial contaminants in soil: an in situ verification

The aim of the research was to evaluate, at site scale, the influence of freezing and freeze/thaw cycles on the survival of faecal coliforms and faecal enterococci in soil, in a climate change perspective. Before the winter period and during grazing, viable cells of faecal coliforms and faecal enterococci were detected only in the first 10 cm below ground, while, after the winter period and before the new seasonal grazing, a lower number of viable cells of both faecal indicators was detected only in some of the investigated soil profiles, and within the first 5 cm. Taking into consideration the results of specific investigations, we hypothesise that the non-uniform spatial distribution of grass roots within the studied soil can play an important role in influencing this phenomenon, while several abiotic factors do not play any significant role. Taking into account the local trend in the increase of air temperature, a different distribution of microbial pollution over time is expected in spring waters, in future climate scenarios. The progressive increase in air temperature will cause a progressive decrease in freeze/thaw cycles at higher altitudes, minimising cold shocks on microbial cells, and causing spring water pollution also during winter.

Predicting the geographical distribution of two invasive termite species from occurrence data

Predicting the potential habitat of species under both current and future climate change scenarios is crucial for monitoring invasive species and understanding a species' response to different environmental conditions. Frequently, the only data available on a species is the location of its occurrence (presence-only data). Using occurrence records only, two models were used to predict the geographical distribution of two destructive invasive termite species, Coptotermes gestroi (Wasmann) and Coptotermes formosanus Shiraki. The first model uses a Bayesian linear logistic regression approach adjusted for presence-only data while the second one is the widely used maximum entropy approach (Maxent). Results show that the predicted distributions of both C. gestroi and C. formosanus are strongly linked to urban development. The impact of future scenarios such as climate warming and population growth on the biotic distribution of both termite species was also assessed. Future climate warming seems to affect their projected probability of presence to a lesser extent than population growth. The Bayesian logistic approach outperformed Maxent consistently in all models according to evaluation criteria such as model sensitivity and ecological realism. The importance of further studies for an explicit treatment of residual spatial autocorrelation and a more comprehensive comparison between both statistical approaches is suggested.

Disease mapping models: an empirical evaluation. Disease Mapping Collaborative Group

The analysis of small area disease incidence has now developed to a degree where many methods have been proposed. However, there are few studies of the relative merits of the methods available. While many Bayesian models have been examined with respect to prior sensitivity, it is clear that wider comparisons of methods are largely missing from the literature. In this paper we present some preliminary results concerning the goodness-of-fit of a variety of disease mapping methods to simulated data for disease incidence derived from a range of models. These simulated models cover simple risk gradients to more complex true risk structures, including spatial correlation. The main general results presented here show that the gamma-Poisson exchangeable model and the Besag, York and Mollie (BYM) model are most robust across a range of diverse models. Mixture models are less robust. Non-parametric smoothing methods perform badly in general. Linear Bayes methods display behaviour similar to that of the gamma-Poisson methods.