Mathematical modelling has played an important role in offering informed advice during the COVID-19 pandemic. In England, a cross government and academia collaboration generated medium-term projections (MTPs) of possible epidemic trajectories over the future 4-6 weeks from a collection of epidemiological models. In this article, we outline this collaborative modelling approach and evaluate the accuracy of the combined and individual model projections against the data over the period November 2021-December 2022 when various Omicron subvariants were spreading across England. Using a number of statistical methods, we quantify the predictive performance of the model projections for both the combined and individual MTPs, by evaluating the point and probabilistic accuracy. Our results illustrate that the combined MTPs, produced from an ensemble of heterogeneous epidemiological models, were a closer fit to the data than the individual models during the periods of epidemic growth or decline, with the 90% confidence intervals widest around the epidemic peaks. We also show that the combined MTPs increase the robustness and reduce the biases associated with a single model projection. Learning from our experience of ensemble modelling during the COVID-19 epidemic, our findings highlight the importance of developing cross-institutional multi-model infectious disease hubs for future outbreak control.

Brain cell structure and function reflect neurodevelopment, plasticity, and aging; and changes can help flag pathological processes such as neurodegeneration and neuroinflammation. Accurate and quantitative methods to noninvasively disentangle cellular structural features are needed and are a substantial focus of brain research. Diffusion-weighted MRS (dMRS) gives access to diffusion properties of endogenous intracellular brain metabolites that are preferentially located inside specific brain cell populations. Despite its great potential, dMRS remains a challenging technique on all levels: from the data acquisition to the analysis, quantification, modeling, and interpretation of results. These challenges were the motivation behind the organization of the Lorentz Center workshop on \"Best Practices & Tools for Diffusion MR Spectroscopy\" held in Leiden, the Netherlands, in September 2021. During the workshop, the dMRS community established a set of recommendations to execute robust dMRS studies. This paper provides a description of the steps needed for acquiring, processing, fitting, and modeling dMRS data, and provides links to useful resources.

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We aimed to use a high-fidelity computational model that captures key interactions between the cardiovascular and pulmonary systems to investigate whether current CPR protocols could potentially be improved.
We developed and validated the computational model against available human data. We used a global optimisation algorithm to find CPR protocol parameters that optimise the outputs associated with return of spontaneous circulation in a cohort of 10 virtual subjects.
Compared with current protocols, myocardial tissue oxygen volume was more than 5 times higher, and cerebral tissue oxygen volume was nearly doubled, during optimised CPR. While the optimal maximal sternal displacement (5.5 cm) and compression ratio (51%) found using our model agreed with the current American Heart Association guidelines, the optimal chest compression rate was lower (67 compressions min-1). Similarly, the optimal ventilation strategy was more conservative than current guidelines, with an optimal minute ventilation of 1500 ml min-1 and inspired fraction of oxygen of 80%. The end compression force was the parameter with the largest impact on CO, followed by PEEP, the compression ratio and the CC rate.
Our results indicate that current CPR protocols could potentially be improved. Excessive ventilation could be detrimental to organ oxygenation during CPR, due to the negative haemodynamic effect of increased pulmonary vascular resistance. Particular attention should be given to the chest compression force to achieve satisfactory CO. Future clinical trials aimed at developing improved CPR protocols should explicitly consider interactions between chest compression and ventilation parameters.

BACKGROUND: International strategies to reduce chronic diseases have called for a reduction in the amounts of saturated fat (SAFA), trans fat (TFA), salt and sugars in the global food supply. This paper describes the development approach and potential impact of a set of standards for these nutrients to drive food (re)formulation.
METHODS: To set the standards, WHO nutrient guidelines for daily intake were translated into product group specific standards. The impact of reformulation towards these standards on population nutrient intakes was modelled using the food consumption data of five countries: UK, France, US, Brazil and China. The impact of the TFA standards could not be modelled due to lack of data.
RESULTS: (Re)formulation of foods and beverages towards these standards would substantially decrease mean population intakes of energy, sodium, SAFA and sugars, with reductions up to 30%.
CONCLUSIONS: These science-based standards for nutrients to limit could drive impactful reductions in energy, sodium, SAFA and sugars in food and beverage products, enabling mean population intakes to move closer to WHO nutrient guidelines.

The COVID-19 pandemic has brought the combined disciplines of public health, infectious disease and policy modelling squarely into the spotlight. Never before have decisions regarding public health measures and their impacts been such a topic of international deliberation, from the level of individuals and communities through to global leaders. Nor have models-developed at rapid pace and often in the absence of complete information-ever been so central to the decision-making process. However, after nearly 3 years of experience with modelling, policy-makers need to be more confident about which models will be most helpful to support them when taking public health decisions, and modellers need to better understand the factors that will lead to successful model adoption and utilization. We present a three-stage framework for achieving these ends.

COVID-19 has highlighted the need for credible epidemiological models to inform pandemic policy. Traditional mechanisms of commissioning research are ill-suited to guide policy during a rapidly evolving pandemic. At the same time, contracting with a single centre of expertise has been criticized for failing to reflect challenges inherent in specific modelling approaches.
This report describes an alternative approach to mobilizing scientific expertise. Ontario\'s COVID-19 Modelling Consensus Table (MCT) was created in March 2020 to enable rapid communication of credible estimates of the impact of COVID-19 and to accelerate learning on how the disease is spreading and what could slow its transmission. The MCT is a partnership between the province and academic modellers and consists of multiple groups of experts, health system leaders, and senior decision-makers. Armed with Ministry of Health data, the MCT meets once per week to share results from modelling exercises, generate consensus judgements of the likely future impact of COVID-19, and discuss decision-makers\' priorities.
The MCT has enabled swift access to data for participants, a structure for developing consensus estimates and communicating these to decision-makers, credible models to inform health system planning, and increased transparency in public reporting of COVID-19 data. It has also facilitated the rapid publication of research findings and its incorporation into government policy.
The MCT approach is one way to quickly draw on scientific advice outside of government and public health agencies. Beyond speed, this approach allows for nimbleness as experts from different organizations can be added as needed. It also shows how universities and research institutes have a role to play in crisis situations, and how this expertise can be marshalled to inform policy while respecting academic freedom and confidentiality.
RéSUMé: LIEU: La COVID-19 a mis en évidence le besoin de modèles épidémiologiques crédibles pour éclairer la politique pandémique. Les mécanismes habituels pour commander des travaux de recherche sont peu propices à orienter les politiques lors d’une pandémie qui évolue rapidement. En même temps, la passation de contrats avec un seul centre d’expertise est critiquée, car elle ne tient pas compte des difficultés inhérentes de certaines approches de modélisation. INTERVENTION: Le présent rapport décrit une approche de rechange pour mobiliser le savoir scientifique. L’Ontario a créé en mars 2020 une Table de concertation sur la modélisation (TCM) qui permet de communiquer de façon rapide et fiable les estimations des effets de la COVID-19 et d’apprendre plus vite comment la maladie se propage et ce qui pourrait en ralentir la transmission. La TCM, un partenariat entre les modélisateurs de la province et des milieux universitaires, est composée de plusieurs groupes d’experts, de dirigeants du système de santé et de décideurs de haut niveau. Armée des données du ministère de la Santé, la TCM se réunit une fois par semaine pour partager les résultats d’exercices de modélisation, générer des jugements consensuels sur les futurs effets probables de la COVID-19 et discuter des priorités des décideurs. RéSULTATS: La TCM rend possible un accès rapide aux données pour les participants, une structure pour élaborer des estimations consensuelles et les communiquer aux décideurs, des modèles fiables pour éclairer la planification du système de santé, ainsi qu’une transparence accrue dans la communication des données sur la COVID-19 au public. Elle facilite aussi la publication rapide des résultats de recherche et leur intégration dans la politique gouvernementale. CONSéQUENCES: L’approche de la TCM est un moyen d’obtenir rapidement des conseils scientifiques à l’extérieur du gouvernement et des organismes de santé publique. Au-delà de sa rapidité, cette approche offre une grande souplesse, car des experts de différents organismes peuvent être ajoutés au besoin. Elle montre aussi que les universités et les établissements de recherche ont un rôle à jouer dans les situations de crise, et qu’il est possible de mobiliser leurs compétences pour éclairer les politiques tout en respectant la liberté et la confidentialité des milieux de la recherche et de l’enseignement.

Advances in molecular and synthetic biology are enabling the engineering of gene drives in insects for disease vector/pest control. Engineered gene drives (that bias their own inheritance) can be designed either to suppress interbreeding target populations or modify them with a new genotype. Depending on the engineered gene drive system, theoretically, a genetic modification of interest could spread through target populations and persist indefinitely, or be restricted in its spread or persistence. While research on engineered gene drives and their applications in insects is advancing at a fast pace, it will take several years for technological developments to move to practical applications for deliberate release into the environment. Some gene drive modified insects (GDMIs) have been tested experimentally in the laboratory, but none has been assessed in small-scale confined field trials or in open release trials as yet. There is concern that the deliberate release of GDMIs in the environment may have possible irreversible and unintended consequences. As a proactive measure, the European Food Safety Authority (EFSA) has been requested by the European Commission to review whether its previously published guidelines for the risk assessment of genetically modified animals (EFSA, 2012 and 2013), including insects (GMIs), are adequate and sufficient for GDMIs, primarily disease vectors, agricultural pests and invasive species, for deliberate release into the environment. Under this mandate, EFSA was not requested to develop risk assessment guidelines for GDMIs. In this Scientific Opinion, the Panel on Genetically Modified Organisms (GMO) concludes that EFSA\'s guidelines are adequate, but insufficient for the molecular characterisation (MC), environmental risk assessment (ERA) and post-market environmental monitoring (PMEM) of GDMIs. While the MC,ERA and PMEM of GDMIs can build on the existing risk assessment framework for GMIs that do not contain engineered gene drives, there are specific areas where further guidance is needed for GDMIs.

Intense research activity in HPV modelling over this decade has prompted the development of additional guidelines to those for general modelling. A specific framework is required to address different policy questions and unique complexities of HPV modelling. HPV-FRAME is an initiative to develop a consensus statement and quality-based framework for epidemiologic and economic HPV models. Its development involved an established process. Reporting standards have been structured according to seven domains reflecting distinct policy questions in HPV and cancer prevention and categorised by relevance to a population or evaluation. Population-relevant domains are: 1) HPV vaccination in pre-adolescent and young adolescent individuals; 2) HPV vaccination in older individuals; 3) targeted vaccination in men who have sex with men; 4) considerations for individuals living with HIV and 5) considerations for low- and middle-income countries. Additional considerations applicable to specific evaluations are: 6) cervical screening or integrated cervical screening and HPV vaccination approaches and 7) alternative vaccine types and alternative dosing schedules. HPV-FRAME aims to promote the development of models in accordance with an explicit framework, to better enable target audiences to understand a model\'s strength and weaknesses in relation to a specific policy question and ultimately improve the model\'s contribution to informed decision-making.

The origin of population-scale coordination has puzzled philosophers and scientists for centuries. Recently, game theory, evolutionary approaches and complex systems science have provided quantitative insights on the mechanisms of social consensus. However, the literature is vast and widely scattered across fields, making it hard for the single researcher to navigate it. This short review aims to provide a compact overview of the main dimensions over which the debate has unfolded and to discuss some representative examples. It focuses on those situations in which consensus emerges \'spontaneously\' in the absence of centralized institutions and covers topics that include the macroscopic consequences of the different microscopic rules of behavioural contagion, the role of social networks and the mechanisms that prevent the formation of a consensus or alter it after it has emerged. Special attention is devoted to the recent wave of experiments on the emergence of consensus in social systems.