FAIR data pipeline: provenance-driven data management for traceable scientific workflows.

Modern epidemiological analyses to understand and combat the spread of disease depend critically on access to, and use of, data. Rapidly evolving data, such as data streams changing during a disease outbreak, are particularly challenging. Data management is further complicated by data being imprecisely identified when used. Public trust in policy decisions resulting from such analyses is easily damaged and is often low, with cynicism arising where claims of 'following the science' are made without accompanying evidence. Tracing the provenance of such decisions back through open software to primary data would clarify this evidence, enhancing the transparency of the decision-making process. Here, we demonstrate a Findable, Accessible, Interoperable and Reusable (FAIR) data pipeline. Although developed during the COVID-19 pandemic, it allows easy annotation of any data as they are consumed by analyses, or conversely traces the provenance of scientific outputs back through the analytical or modelling source code to primary data. Such a tool provides a mechanism for the public, and fellow scientists, to better assess scientific evidence by inspecting its provenance, while allowing scientists to support policymakers in openly justifying their decisions. We believe that such tools should be promoted for use across all areas of policy-facing research. This article is part of the theme issue 'Technical challenges of modelling real-life epidemics and examples of overcoming these'.

Visualization for epidemiological modelling: challenges, solutions, reflections and recommendations.

We report on an ongoing collaboration between epidemiological modellers and visualization researchers by documenting and reflecting upon knowledge constructs-a series of ideas, approaches and methods taken from existing visualization research and practice-deployed and developed to support modelling of the COVID-19 pandemic. Structured independent commentary on these efforts is synthesized through iterative reflection to develop: evidence of the effectiveness and value of visualization in this context; open problems upon which the research communities may focus; guidance for future activity of this type and recommendations to safeguard the achievements and promote, advance, secure and prepare for future collaborations of this kind. In describing and comparing a series of related projects that were undertaken in unprecedented conditions, our hope is that this unique report, and its rich interactive supplementary materials, will guide the scientific community in embracing visualization in its observation, analysis and modelling of data as well as in disseminating findings. Equally we hope to encourage the visualization community to engage with impactful science in addressing its emerging data challenges. If we are successful, this showcase of activity may stimulate mutually beneficial engagement between communities with complementary expertise to address problems of significance in epidemiology and beyond. See https://ramp-vis.github.io/RAMPVIS-PhilTransA-Supplement/. This article is part of the theme issue 'Technical challenges of modelling real-life epidemics and examples of overcoming these'.

How public health crises expose systemic, day-to-day health inequalities in low- and-middle income countries: an example from East Africa.

BACKGROUND: The current Coronavirus disease pandemic reveals political and structural inequities of the world's poorest people who have little or no access to health care and yet the largest burdens of poor health. This is in parallel to a more persistent but silent global health crisis, antimicrobial resistance (AMR). We explore the fundamental challenges of health care in humans and animals in relation to AMR in Tanzania. METHODS: We conducted 57 individual interviews and focus groups with providers and patients in high, middle and lower tier health care facilities and communities across three regions of Tanzania between April 2019 and February 2020. We covered topics from health infrastructure and prescribing practices to health communication and patient experiences. RESULTS: Three interconnected themes emerged about systemic issues impacting health. First, there are challenges around infrastructure and availability of vital resources such as healthcare staff and supplies. Second, health outcomes are predicated on patient and provider access to services as well as social determinants of health. Third, health communication is critical in defining trusted sources of information, and narratives of blame emerge around health outcomes with the onus of responsibility for action falling on individuals. CONCLUSION: Entanglements between infrastructure, access and communication exist while constraints in the health system lead to poor health outcomes even in 'normal' circumstances. These are likely to be relevant across the globe and highly topical for addressing pressing global health challenges. Redressing structural health inequities can better equip countries and their citizens to not only face pandemics but also day-to-day health challenges.