Sharing, synthesis and sustainability of data analysis for epidemic preparedness in Europe

Data integration and synthesis for pandemic and epidemic intelligence

The COVID-19 pandemic highlighted substantial obstacles in real-time data generation and management needed for clinical research and epidemiological analysis. Three years after the pandemic, reflection on the difficulties of data integration offers potential to improve emergency preparedness. The fourth session of the WHO Pandemic and Epidemic Intelligence Forum sought to report the experiences of key global institutions in data integration and synthesis, with the aim of identifying solutions for effective integration. Data integration, defined as the combination of heterogeneous sources into a cohesive system, allows for combining epidemiological data with contextual elements such as socioeconomic determinants to create a more complete picture of disease patterns. The approach is critical for predicting outbreaks, determining disease burden, and evaluating interventions. The use of contextual information improves real-time intelligence and risk assessments, allowing for faster outbreak responses. This report captures the growing acknowledgment of data integration importance in boosting public health intelligence and readiness and show examples of how global institutions are strengthening initiatives to respond to this need. However, obstacles persist, including interoperability, data standardization, and ethical considerations. The success of future data integration efforts will be determined by the development of a common technical and legal framework, the promotion of global collaboration, and the protection of sensitive data. Ultimately, effective data integration can potentially transform public health intelligence and our way to successfully respond to future pandemics.

Epidemic history and evolution of an emerging threat of international concern, the severe acute respiratory syndrome coronavirus 2

This comprehensive review focuses on the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its impact as the cause of the COVID-19 pandemic. Its objective is to provide a cohesive overview of the epidemic history and evolutionary aspects of the virus, with a particular emphasis on its emergence, global spread, and implications for public health. The review delves into the timelines and key milestones of SARS-CoV-2's epidemiological progression, shedding light on the challenges encountered during early containment efforts and subsequent waves of transmission. Understanding the evolutionary dynamics of the virus is crucial in monitoring its potential for adaptation and future outbreaks. Genetic characterization of SARS-CoV-2 is discussed, with a focus on the emergence of new variants and their implications for transmissibility, severity, and immune evasion. The review highlights the important role of genomic surveillance in tracking viral mutations linked to establishing public health interventions. By analyzing the origins, global spread, and genetic evolution of SARS-CoV-2, valuable insights can be gained for the development of effective control measures, improvement of pandemic preparedness, and addressing future emerging infectious diseases of international concern.

On the role of data, statistics and decisions in a pandemic

A pandemic poses particular challenges to decision-making because of the need to continuously adapt decisions to rapidly changing evidence and available data. For example, which countermeasures are appropriate at a particular stage of the pandemic? How can the severity of the pandemic be measured? What is the effect of vaccination in the population and which groups should be vaccinated first? The process of decision-making starts with data collection and modeling and continues to the dissemination of results and the subsequent decisions taken. The goal of this paper is to give an overview of this process and to provide recommendations for the different steps from a statistical perspective. In particular, we discuss a range of modeling techniques including mathematical, statistical and decision-analytic models along with their applications in the COVID-19 context. With this overview, we aim to foster the understanding of the goals of these modeling approaches and the specific data requirements that are essential for the interpretation of results and for successful interdisciplinary collaborations. A special focus is on the role played by data in these different models, and we incorporate into the discussion the importance of statistical literacy and of effective dissemination and communication of findings.

Maximum likelihood pandemic-scale phylogenetics.. [PMID: 35350209 PMCID: PMC8963701 DOI: 10.1101/2022.03.22.485312]

Genomic data plays an essential role in the study of transmissible disease, as exemplified by its current use in identifying and tracking the spread of novel SARS-CoV-2 variants. However, with the increase in size of genomic epidemiological datasets, their phylogenetic analyses become increasingly impractical due to high computational demand. In particular, while maximum likelihood methods are go-to tools for phylogenetic inference, the scale of datasets from the ongoing pandemic has made apparent the urgent need for more computationally efficient approaches. Here we propose a new likelihood-based phylogenetic framework that greatly reduces both the memory and time demand of popular maximum likelihood approaches when analysing many closely related genomes, as in the scenario of SARS-CoV-2 genome data and more generally throughout genomic epidemiology. To achieve this, we rewrite the classical Felsenstein pruning algorithm so that we can infer phylogenetic trees on at least 10 times larger datasets with higher accuracy than existing maximum likelihood methods. Our algorithms provide a powerful framework for maximum-likelihood genomic epidemiology and could facilitate similarly groundbreaking applications in Bayesian phylogenomic analyses as well.