The COVID-19 Parenting project will reach 57 million families in DAC countries during the COVID epidemic, with evidence-based resources to prevent violence against children and reduce parenting stress. DAC countries are facing far-reaching COVID epidemics, with cyclical periods of lockdowns and school closures (Mahler, 2020). Parents and caregivers globally are caring for children under exceptionally stressful conditions. Even the most secure families are struggling to manage children within extended lockdowns. Shouting and physical violence are worsened by stress, poverty, alcohol use, confined and crowded conditions (Meinck, 2017), all heightened under COVID-19. UNICEF reports global escalation in child abuse, with severe health, social and economic impacts. We will work with the World Health Organisation, UNICEF, the Global Partnership to End Violence, UNODC, USAID, the US Centers for Disease Control and other NGOs including the Special Olympics, World Without Orphans and local DAC country community organisations to: 1. Adapt parenting programs with demonstrated effectiveness into scalable resources for DAC countries, using the best evidence. This will include text message-based systems and low-data ir or offline app support for families. 2. Deliver parenting support programs and resources to 57 million families in an initial 14 DAC countries, through partnerships with UN agencies, NGOs and faith-based organisations. Translate resources into relevant DAC country languages to facilitate uptake. 3. Evaluate mechanisms of delivery, costs and their impact on reduction in violence, parenting and stress through online pre-post repeated surveys and in-depth qualitative research with families in DAC settings. We will achieve a rare outcome for research translation: direct delivery of support to 57 million families in DAC countries. It is exceptional value for money, with a cost to UKRI of less than one penny (£0.008) per DAC family receiving evidence-based violence prevention support during COVID-19.

The fungus Aspergillus fumigatus is globally ubiquitous in the environment, being present on decaying vegetation and in soils, where it performs a valuable role in nutrient recycling. The fungus is a minimal health threat to healthy individuals. However, patients that suffer from cystic fibrosis, cancer or have received organ transplants and are undergoing corticosteroid therapy, are at risk from 'invasive aspergillosis'. Current estimates indicate that over 63,000 patients develop this fungal disease annually across Europe. The primary method for controlling infections is by administering azole antifungal drugs. However, we and others have shown a sharp increase in the resistance of A. fumigatus to frontline azole antifungals, with unacceptably high mortality rates in these at-risk patient groups. The mutations that confer resistance of A. fumigatus to these drugs appear to have evolved in the environment, rather than in the patient. Azole compounds are also used as fungicides to control crop diseases. This has led to the hypothesis that the widespread use in agricultural crops of azole antifungal sprays is leading to the environmental selection for resistance in A. fumigatus, which is then resulting in decreased patient survival following infection. Our project aims to examine this hypothesis by determining the relative proportions of azole-resistant and azole-sensitive A. fumigatus in the UK by sampling environmental populations using growth media containing antifungal drugs. This environmental exposure assessment approach will target a range of environments that have had high to low applications of crop-antifungals and will enable us to statistically examine whether there are links between the intensive use of these azole-based compounds in the environment and the occurrence of drug-resistant A. fumigatus. We will then use powerful technologies to sequence the genomes of many hundreds of A. fumigatus that are sensitive, or resistant, to azole antifungals. We already have numerous isolates pre-collected from around the world though a broad network of project partners, and we now know that there are two main azole-resistance mutations that widely occur. Our plan is to use our genome sequences and cutting-edge statistical genetic methods in order to determine when and where these mutations originated globally, use our newly isolated samples to test whether they occur within the UK environment and patient populations, whether they are spreading to invade new environments here and elsewhere, and whether novel undescribed resistance mutations exist. A. fumigatus is capable of sexual, as well as asexual, reproduction. In this case, the rate at which a newly-evolved resistance mutation can be integrated into new genetic backgrounds depends on the fertility of the A. fumigatus populations. In order to directly measure the 'sexiness' of the A. fumigatus populations, we will perform sexual crosses using sequenced isolates that represent not only the range of genetic diversity that we encounter, but also the range of azole-resistance mutations. By measuring the number and fitness of progeny, we will be able to determine the rate at which resistance mutations can recombine into new genetic backgrounds, and also discover unknown drug-resistance mechanisms. By addressing these questions, we will directly measure the risk that the use of antifungal compounds has on evolving resistance in non-target fungal species, and also answer important questions on the distance that these airborne fungi are able to spread and share genes with one another. Our findings will not only be of high relevance to health care professionals, directly informing diagnostic protocols and disease management in intensive-care settings, but will also inform current debates on the costs of widespread use of antimicrobial compounds in the environment. These goals all directly feed into NERCs new strategic direction 'The Business of the Environment'.

The CDT will train the next generation of leaders in statistics and statistical machine learning, who will be able to develop widely-applicable novel methodology and theory, as well as create application-specific methods, leading to breakthroughs in real-world problems in government, medicine, industry and science. The research will focus on the development of applicable modern statistical theory and methods as well as on the underpinnings of statistical machine learning. The research will be strongly linked to applications. There is an urgent national need for graduates from this CDT. Large volumes of complicated data are now routinely collected in all sectors of society, encompassing electronic health records, massive scientific datasets, governmental data, and data collected through the advent of the digital economy. The underpinning techniques for exploiting these data come from statistics and machine learning. Exploiting such data is crucial for future UK prosperity. However, several reports from government and learned societies have identified a lack of individuals able to exploit this data. In many situations, existing methodology is insufficient. Off-the-shelf approaches may be misleading due to a lack of reproducibility or sampling biases which they do not correct. Furthermore, understanding the underlying mechanisms is often desired: scientifically valid, interpretable and reproducible results are needed to understand scientific phenomena and to justify decisions, particularly those affecting individuals. Bespoke, model-based statistical methods are needed, that may need to be blended with statistical machine learning approaches to deal with large data. Individuals that can fulfill these more sophisticated demands are doctoral level graduates in statistics who are well versed in the foundations of machine learning. Yet the UK only graduates a small number of statistics PhDs per year, and many of these graduates will not have been exposed to machine learning. The Centre will bring together Imperial and Oxford, two top statistics groups, as equal partners, offering an exceptional training environment and the direct involvement of absolute research leaders in their fields. The supervisor pool will include outstanding researchers in statistical methodology and theory as well as in statistical machine learning. We will use innovative and student-led teaching, focussing on PhD-level training. Teaching cuts across years and thus creates strong cohort cohesion not just within a year group but also between year groups. We will link theoretical advances to application areas through partner interactions as well as through a placement of students with users of statistics. The CDT has a large number of high profile partners that helped shape our application priority areas (digital economy, medicine, engineering, public health, science) and that will co-fund and co-supervise PhD students, as well as co-deliver teaching elements.