Women are affected more than men by the social and economic effects of infectious-disease outbreaks. They bear the brunt of care responsibilities as schools close and family members fall ill 1,2 . They are at greater risk of domestic violence 3 and are disproportionately disadvantaged by reduced access to sexual-and reproductive-health services. Because women are more likely than men to have fewer hours of employed work and be on insecure or zero-hour contracts, they are more affected by job losses in times of economic instability 2 .There has been a "horrifying global surge in domestic violence" since the start of the COVID-19 lockdowns, said United Nations secretary-general António Guterres in early April. Malaysia, for example, reported 57% more calls to domestic-abuse helplines between 18 March and 26 March. Moreover, sexualand reproductive-health clinics are closing worldwide. Some US states have restricted access to abortions 4 .It is all too familiar. During outbreaks of Ebola and Zika viruses in the past few years,The social and economic impacts of COVID-19 fall harder on women than on men. Governments need to gather data and target policy to keep all citizens equally safe, sheltered and secure.

Gender norms, roles and relations differentially affect women, men, and non-binary individuals' vulnerability to disease. Outbreak response measures also have immediate and long-term gendered effects. However, gender-based analysis of outbreaks and responses is limited by lack of data and little integration of feminist analysis within global health scholarship. Recognising these barriers, this paper applies a gender matrix methodology, grounded in feminist political economy approaches, to evaluate the gendered effects of the COVID-19 pandemic and response in four case studies: China, Hong Kong, Canada, and the UK. Through a rapid scoping of documentation of the gendered effects of the outbreak, it applies the matrix framework to analyse findings, identifying common themes across the case studies: financial discrimination, crisis in care, and unequal risks and secondary effects. Results point to transnational structural conditions which put women on the front lines of the pandemic at work and at home while denying them health, economic and personal securityeffects that are exacerbated where racism and other forms of discrimination intersect with gender inequities. Given that women and people living at the intersections of multiple inequities are made additionally vulnerable by pandemic responses, intersectional feminist responses should be prioritised at the beginning of any crises.

This article introduces the Variome Annotation Schema, a schema that aims to capture the core concepts and relations relevant to cataloguing and interpreting human genetic variation and its relationship to disease, as described in the published literature. The schema was inspired by the needs of the database curators of the International Society for Gastrointestinal Hereditary Tumours (InSiGHT) database, but is intended to have application to genetic variation information in a range of diseases. The schema has been applied to a small corpus of full text journal publications on the subject of inherited colorectal cancer. We show that the inter-annotator agreement on annotation of this corpus ranges from 0.78 to 0.95 F-score across different entity types when exact matching is measured, and improves to a minimum F-score of 0.87 when boundary matching is relaxed. Relations show more variability in agreement, but several are reliable, with the highest, cohort-has-size, reaching 0.90 F-score. We also explore the relevance of the schema to the InSiGHT database curation process. The schema and the corpus represent an important new resource for the development of text mining solutions that address relationships among patient cohorts, disease and genetic variation, and therefore, we also discuss the role text mining might play in the curation of information related to the human variome. The corpus is available at http://opennicta.com/home/health/variome.