Computationally efficient estimation of high-dimension autoregressive models : with application to air pollution in Malta

The modelling and analysis of spatiotemporal behaviour is receiving wide-spread attention due to its applicability to various scientific fields such as the mapping of the electrical activity in the human brain, the spatial spread of pandemics and the diffusion of hazardous pollutants. Nevertheless, due to the complexity of the dynamics describing these systems and the vast datasets of the measurements involved, efficient computational methods are required to obtain representative mathematical descriptions of such behaviour. In this work, a computationally efficient method for the estimation of heterogeneous spatio-temporal autoregressive models is proposed and tested on a dataset of air pollutants measured over the Maltese islands. Results will highlight the computation advantages of the proposed methodology and the accuracy of the predictions obtained through the estimated model.peer-reviewe

The multidisciplinary nature of COVID-19 research

Objective. We analyzed the scientific output after COVID-19 and contrasted it with studies published in the aftermath of seven epidemics/pandemics: Severe Acute Respiratory Syndrome (SARS), Influenza A virus H5N1 and Influenza A virus H1N1 human infections, Middle East Respiratory Syndrome (MERS), Ebola virus disease, Zika virus disease, and Dengue. Design/Methodology/Approach. We examined bibliometric measures for COVID-19 and the rest of the studied epidemics/pandemics. Data were extracted from Web of Science, using its journal classification scheme as a proxy to quantify the multidisciplinary coverage of scientific output. We proposed a novel Thematic Dispersion Index (TDI) for the analysis of pandemic early stages.  Results/Discussion. The literature on the seven epidemics/pandemics before COVID-19 has shown explosive growth of the scientific production and continuous impact during the first three years following each emergence or re-emergence of the specific infectious disease. A subsequent decline was observed with the progressive control of each health emergency. We observed an unprecedented growth in COVID-19 scientific production. TDI measured for COVID-19 (29,4) in just six months, was higher than TDI of the rest (7,5 to 21) during the first three years after epidemic initiation. Conclusions. COVID-19 literature showed the broadest subject coverage, which is clearly a consequence of its social, economic, and political impact. The proposed indicator (TDI), allowed the study of multidisciplinarity, differentiating the thematic complexity of COVID-19 from the previous seven epidemics/pandemics. Originality/Value. The multidisciplinary nature and thematic complexity of COVID-19 research were successfully analyzed through a scientometric perspective

What country, university or research institute, performed the best on COVID-19? Bibliometric analysis of scientific literature

In this article, we conduct data mining to discover the countries, universities and companies, produced or collaborated the most research on Covid-19 since the pandemic started. We present some interesting findings, but despite analysing all available records on COVID-19 from the Web of Science Core Collection, we failed to reach any significant conclusions on how the world responded to the COVID-19 pandemic. Therefore, we increased our analysis to include all available data records on pandemics and epidemics from 1900 to 2020. We discover some interesting results on countries, universities and companies, that produced collaborated most the most in research on pandemic and epidemics. Then we compared the results with the analysing on COVID-19 data records. This has created some interesting findings that are explained and graphically visualised in the article

Evolutionary biology and anthropology suggest biome reconstitution as a necessary approach toward dealing with immune disorders

Industrialized society currently faces a wide range of non-infectious, immune-related pandemics. These pandemics include a variety of autoimmune, inflammatory and allergic diseases that are often associated with common environmental triggers and with genetic predisposition, but that do not occur in developing societies. In this review, we briefly present the idea that these pandemics are due to a limited number of evolutionary mismatches, the most damaging being ‘biome depletion’. This particular mismatch involves the loss of species from the ecosystem of the human body, the human biome, many of which have traditionally been classified as parasites, although some may actually be commensal or even mutualistic. This view, evolved from the ‘hygiene hypothesis’, encompasses a broad ecological and evolutionary perspective that considers host-symbiont relations as plastic, changing through ecological space and evolutionary time. Fortunately, this perspective provides a blueprint, termed 'biome reconstitution', for disease treatment and especially for disease prevention. Biome reconstitution includes the controlled and population-wide reintroduction (i.e. domestication) of selected species that have been all but eradicated from the human biome in industrialized society and holds great promise for the elimination of pandemics of allergic, inflammatory and autoimmune diseases

Human Disease - Unintended Globalization

Before man was exchanging goods and ideas, he was exchanging germs. As such, the spread of infectious disease constitutes the first truly global phenomenon and, therefore, marks the beginnings—primitive though they may have been—of what today we have finally termed ‘globalization.’ The global spread of disease, then, proves that globalization is not new and that its origins were the result of a different narrative than the ones we read from globalization theorists; it further demonstrates that the modern conception of the phenomenon is only now so well recognized because the accelerated and efficient processes that inform its daily activities have heightened our conscious acknowledgement of its existence

Bayesian Best-Arm Identification for Selecting Influenza Mitigation Strategies

Pandemic influenza has the epidemic potential to kill millions of people. While various preventive measures exist (i.a., vaccination and school closures), deciding on strategies that lead to their most effective and efficient use remains challenging. To this end, individual-based epidemiological models are essential to assist decision makers in determining the best strategy to curb epidemic spread. However, individual-based models are computationally intensive and it is therefore pivotal to identify the optimal strategy using a minimal amount of model evaluations. Additionally, as epidemiological modeling experiments need to be planned, a computational budget needs to be specified a priori. Consequently, we present a new sampling technique to optimize the evaluation of preventive strategies using fixed budget best-arm identification algorithms. We use epidemiological modeling theory to derive knowledge about the reward distribution which we exploit using Bayesian best-arm identification algorithms (i.e., Top-two Thompson sampling and BayesGap). We evaluate these algorithms in a realistic experimental setting and demonstrate that it is possible to identify the optimal strategy using only a limited number of model evaluations, i.e., 2-to-3 times faster compared to the uniform sampling method, the predominant technique used for epidemiological decision making in the literature. Finally, we contribute and evaluate a statistic for Top-two Thompson sampling to inform the decision makers about the confidence of an arm recommendation

INDEMICS: An Interactive High-Performance Computing Framework for Data Intensive Epidemic Modeling

We describe the design and prototype implementation of Indemics (_Interactive; Epi_demic; _Simulation;)—a modeling environment utilizing high-performance computing technologies for supporting complex epidemic simulations. Indemics can support policy analysts and epidemiologists interested in planning and control of pandemics. Indemics goes beyond traditional epidemic simulations by providing a simple and powerful way to represent and analyze policy-based as well as individual-based adaptive interventions. Users can also stop the simulation at any point, assess the state of the simulated system, and add additional interventions. Indemics is available to end-users via a web-based interface. Detailed performance analysis shows that Indemics greatly enhances the capability and productivity of simulating complex intervention strategies with a marginal decrease in performance. We also demonstrate how Indemics was applied in some real case studies where complex interventions were implemented

Equation-Free Multiscale Computational Analysis of Individual-Based Epidemic Dynamics on Networks

The surveillance, analysis and ultimately the efficient long-term prediction and control of epidemic dynamics appear to be one of the major challenges nowadays. Detailed atomistic mathematical models play an important role towards this aim. In this work it is shown how one can exploit the Equation Free approach and optimization methods such as Simulated Annealing to bridge detailed individual-based epidemic simulation with coarse-grained, systems-level, analysis. The methodology provides a systematic approach for analyzing the parametric behavior of complex/ multi-scale epidemic simulators much more efficiently than simply simulating forward in time. It is shown how steady state and (if required) time-dependent computations, stability computations, as well as continuation and numerical bifurcation analysis can be performed in a straightforward manner. The approach is illustrated through a simple individual-based epidemic model deploying on a random regular connected graph. Using the individual-based microscopic simulator as a black box coarse-grained timestepper and with the aid of Simulated Annealing I compute the coarse-grained equilibrium bifurcation diagram and analyze the stability of the stationary states sidestepping the necessity of obtaining explicit closures at the macroscopic level under a pairwise representation perspective

Some Remarks about the Complexity of Epidemics Management

Recent outbreaks of Ebola, H1N1 and other infectious diseases have shown that the assumptions underlying the established theory of epidemics management are too idealistic. For an improvement of procedures and organizations involved in fighting epidemics, extended models of epidemics management are required. The necessary extensions consist in a representation of the management loop and the potential frictions influencing the loop. The effects of the non-deterministic frictions can be taken into account by including the measures of robustness and risk in the assessment of management options. Thus, besides of the increased structural complexity resulting from the model extensions, the computational complexity of the task of epidemics management - interpreted as an optimization problem - is increased as well. This is a serious obstacle for analyzing the model and may require an additional pre-processing enabling a simplification of the analysis process. The paper closes with an outlook discussing some forthcoming problems

Innovative in silico approaches to address avian flu using grid technology

The recent years have seen the emergence of diseases which have spread very quickly all around the world either through human travels like SARS or animal migration like avian flu. Among the biggest challenges raised by infectious emerging diseases, one is related to the constant mutation of the viruses which turns them into continuously moving targets for drug and vaccine discovery. Another challenge is related to the early detection and surveillance of the diseases as new cases can appear just anywhere due to the globalization of exchanges and the circulation of people and animals around the earth, as recently demonstrated by the avian flu epidemics. For 3 years now, a collaboration of teams in Europe and Asia has been exploring some innovative in silico approaches to better tackle avian flu taking advantage of the very large computing resources available on international grid infrastructures. Grids were used to study the impact of mutations on the effectiveness of existing drugs against H5N1 and to find potentially new leads active on mutated strains. Grids allow also the integration of distributed data in a completely secured way. The paper presents how we are currently exploring how to integrate the existing data sources towards a global surveillance network for molecular epidemiology.Comment: 7 pages, submitted to Infectious Disorders - Drug Target