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[This corrects the article DOI: 10.1038/s41567-022-01715-8.].

FLAMINGO: calibrating large cosmological hydrodynamical simulations with machine learning.

To fully take advantage of the data provided by large-scale structure surveys, we need to quantify the potential impact of baryonic effects, such as feedback from active galactic nuclei (AGN) and star formation, on cosmological observables. In simulations, feedback processes originate on scales that remain unresolved. Therefore, they need to be sourced via subgrid models that contain free parameters.

First observation of O.

Subjecting a physical system to extreme conditions is one of the means often used to obtain a better understanding and deeper insight into its organization and structure. In the case of the atomic nucleus, one such approach is to investigate isotopes that have very different neutron-to-proton (N/Z) ratios than in stable nuclei. Light, neutron-rich isotopes exhibit the most asymmetric N/Z ratios and those lying beyond the limits of binding, which undergo spontaneous neutron emission and exist only as very short-lived resonances (about 10 s), provide the most stringent tests of modern nuclear-structure theories.

Demonstrating multi-country calibration of a tuberculosis model using new history matching and emulation package - hmer.

Infectious disease models are widely used by epidemiologists to improve the understanding of transmission dynamics and disease natural history, and to predict the possible effects of interventions. As the complexity of such models increases, however, it becomes increasingly challenging to robustly calibrate them to empirical data. History matching with emulation is a calibration method that has been successfully applied to such models, but has not been widely used in epidemiology partly due to the lack of available software.

Ab initio predictions link the neutron skin of Pb to nuclear forces.

Heavy atomic nuclei have an excess of neutrons over protons, which leads to the formation of a neutron skin whose thickness is sensitive to details of the nuclear force. This links atomic nuclei to properties of neutron stars, thereby relating objects that differ in size by orders of magnitude. The nucleus Pb is of particular interest because it exhibits a simple structure and is experimentally accessible.

Bayesian emulation and history matching of JUNE.

We analyze JUNE: a detailed model of COVID-19 transmission with high spatial and demographic resolution, developed as part of the RAMP initiative. JUNE requires substantial computational resources to evaluate, making model calibration and general uncertainty analysis extremely challenging. We describe and employ the uncertainty quantification approaches of Bayes linear emulation and history matching to mimic JUNE and to perform a global parameter search, hence identifying regions of parameter space that produce acceptable matches to observed data, and demonstrating the capability of such methods.

Complex model calibration through emulation, a worked example for a stochastic epidemic model.

Uncertainty quantification is a formal paradigm of statistical estimation that aims to account for all uncertainties inherent in the modelling process of real-world complex systems. The methods are directly applicable to stochastic models in epidemiology, however they have thus far not been widely used in this context. In this paper, we provide a tutorial on uncertainty quantification of stochastic epidemic models, aiming to facilitate the use of the uncertainty quantification paradigm for practitioners with other complex stochastic simulators of applied systems.

Challenges in estimation, uncertainty quantification and elicitation for pandemic modelling.

The estimation of parameters and model structure for informing infectious disease response has become a focal point of the recent pandemic. However, it has also highlighted a plethora of challenges remaining in the fast and robust extraction of information using data and models to help inform policy. In this paper, we identify and discuss four broad challenges in the estimation paradigm relating to infectious disease modelling, namely the Uncertainty Quantification framework, data challenges in estimation, model-based inference and prediction, and expert judgement.

June: open-source individual-based epidemiology simulation.

We introduce June, an open-source framework for the detailed simulation of epidemics on the basis of social interactions in a virtual population constructed from geographically granular census data, reflecting age, sex, ethnicity and socio-economic indicators. Interactions between individuals are modelled in groups of various sizes and properties, such as households, schools and workplaces, and other social activities using social mixing matrices. June provides a suite of flexible parametrizations that describe infectious diseases, how they are transmitted and affect contaminated individuals.

Primary motor cortical activity during unimanual movements with increasing demand on precision.

In functional magnetic resonance imaging (fMRI) studies, performance of unilateral hand movements is associated with primary motor cortex activity ipsilateral to the moving hand (M1), in addition to contralateral activity (M1). The magnitude of M1 activity increases with the demand on precision of the task. However, it is unclear how demand-dependent increases in M1 recruitment relate to the control of hand movements.

Understanding hormonal crosstalk in Arabidopsis root development via emulation and history matching.

A major challenge in plant developmental biology is to understand how plant growth is coordinated by interacting hormones and genes. To meet this challenge, it is important to not only use experimental data, but also formulate a mathematical model. For the mathematical model to best describe the true biological system, it is necessary to understand the parameter space of the model, along with the links between the model, the parameter space and experimental observations.

Predicting badger visits to farm yards and making predictions available to farmers.

The use of agricultural resources or environments by wildlife may result in opportunities for transmission of infections amongst wild animals, livestock and humans. Targeted use of biosecurity measures may therefore reduce disease risks, although this requires practical knowledge of where such measures would be most effective, and effective means of communicating risks so that stakeholders can make informed decisions about such investment. In parts of Europe, the European badger Meles meles may act as a wildlife reservoir for Mycobacterium bovis, the causative agent of bovine tuberculosis, and badger visits to farmyards may provide potential opportunities for transmission of M.

Choice of time horizon critical in estimating costs and effects of changes to HIV programmes.

Background: Uganda changed its antiretroviral therapy guidelines in 2014, increasing the CD4 threshold for antiretroviral therapy initiation from 350 cells/μl to 500 cells/μl. We investigate what effect this change in policy is likely to have on HIV incidence, morbidity, and programme costs, and estimate the cost-effectiveness of the change over different time horizons.

Methods: We used a complex individual-based model of HIV transmission and antiretroviral therapy scale-up in Uganda.

Bayesian uncertainty analysis for complex systems biology models: emulation, global parameter searches and evaluation of gene functions.

Background: Many mathematical models have now been employed across every area of systems biology. These models increasingly involve large numbers of unknown parameters, have complex structure which can result in substantial evaluation time relative to the needs of the analysis, and need to be compared to observed data of various forms. The correct analysis of such models usually requires a global parameter search, over a high dimensional parameter space, that incorporates and respects the most important sources of uncertainty.

Improving ART programme retention and viral suppression are key to maximising impact of treatment as prevention - a modelling study.

Background: UNAIDS calls for fewer than 500,000 new HIV infections/year by 2020, with treatment-as-prevention being a key part of their strategy for achieving the target. A better understanding of the contribution to transmission of people at different stages of the care pathway can help focus intervention services at populations where they may have the greatest effect. We investigate this using Uganda as a case study.

History matching of a complex epidemiological model of human immunodeficiency virus transmission by using variance emulation.

Complex stochastic models are commonplace in epidemiology, but their utility depends on their calibration to empirical data. History matching is a (pre)calibration method that has been applied successfully to complex deterministic models. In this work, we adapt history matching to stochastic models, by emulating the variance in the model outputs, and therefore accounting for its dependence on the model's input values.

Universal test, treat, and keep: improving ART retention is key in cost-effective HIV control in Uganda.

Background: With ambitious new UNAIDS targets to end AIDS by 2030, and new WHO treatment guidelines, there is increased interest in the best way to scale-up ART coverage. We investigate the cost-effectiveness of various ART scale-up options in Uganda.

Methods: Individual-based HIV/ART model of Uganda, calibrated using history matching.

How well do farmers know their badgers? Relating farmer knowledge to ecological survey data.

Knowledge of badger distribution is important for the management of bovine tuberculosis. At the farm level, typically the only information on badger activity available is from the farmers themselves. This study compares how well farmer perceptions of badger activity match data obtained from ecological surveys.

Bayesian history matching of complex infectious disease models using emulation: a tutorial and a case study on HIV in Uganda.

Advances in scientific computing have allowed the development of complex models that are being routinely applied to problems in disease epidemiology, public health and decision making. The utility of these models depends in part on how well they can reproduce empirical data. However, fitting such models to real world data is greatly hindered both by large numbers of input and output parameters, and by long run times, such that many modelling studies lack a formal calibration methodology.

American Indian women, HIV/AIDS, and health disparity.

Data are presented regarding the prevalence of HIV/AIDS among American Indian women. Health disparities found among American Indians are discussed and biological, economic, social, and behavioral risk factors associated with HIV are detailed. Recommendations are suggested to alleviate the spread of HIV among American Indian women and, in the process, to diminish a culture of treatment malpractice and a weakening of treatment ethics, racism, and genderism.

Advancing HIV/AIDS prevention among American Indians through capacity building and the community readiness model.

Although HIV/AIDS prevention has presented challenges over the past 25 years, prevention does work! To be most effective, however, prevention must be specific to the culture and the nature of the community. Building the capacity of a community for prevention efforts is not an easy process. If capacity is to be sustained, it must be practical and utilize the resources that already exist in the community.

The changing face of HIV/AIDS among Native populations.

AIDS has steadily increased in recent years, becoming the ninth leading killer of Native people between the ages of 15 and 44. In 2003, the Centers for Disease Control and Prevention (CDC) reported that ethnic minorities account for more than 71% of all reported AIDS cases and that there are still increases in AIDS cases in the American Indian population. Despite the work that has been done related to HIV/AIDS, there remain some major challenges in the prevention of HIV/AIDS in Native communities.

Prevention of HIV/AIDS in Native American communities: promising interventions.

Objective: This article presents the latest data on trends in AIDS prevalence among Native American men and women and discusses problems of classification, data collection, factors that contribute to high risk, and factors that affect prevention and intervention. It presents a model for building effective prevention and intervention strategies.

Observations: The number of people in the United States diagnosed with AIDS has risen by less than 5% per year since 1992, and the slowdown is estimated to continue in coming years.