Analytical Tools for FFX Studies and the Go.Data Framework

At the start of an outbreak of a respiratory pathogen with pandemic potential, countries typically have plans to run "First Few X" (FFX) studies that capture key epidemiological features of the first X cases detected as well as potentially infectious contacts. Typically the cases involved will number in the hundreds or thousands, although this will depend on factors such as available resources for case ascertainment and policy prioritisation of the outbreak in question. Important answers to obtain from these data include estimation of: overall severity and transmissibility of the pathogen; timescales associated with duration of symptoms and infectiousness; timescales associated with transmission and population-level exponential growth; and specific risk-factors (e.g. age, sex) as well as co-morbidities (e.g. enhanced risk for sufferers from chronic conditions such as diabetes). In well-resourced contexts, particularly larger, richer countries, there will typically be significant computational resources available to run any available algorithm, as well as large teams of analysts from government, academia and industry with highly specialised skills available to create bespoke analysis plans and code. Neither of these can be guaranteed in resource-poor contexts, for example smaller or less wealthy countries. The main aim of this project is to develop a suite of analytical methods, together with suitable documentation, that can provide the required answers in the hands of analysts with generalist epidemiology and / or public health training and without access to significant computational resources. As an additional benefit of developing such methods, it will become easier for all countries to pool data from their respective FFX studies. In general, well-resourced jurisdictions will have their own priorities for analyses to carry out to inform their specific policy objectives, but these are not guaranteed to permit shared analyses. Indeed, even relatively simple meta-analyses such as those of household transmission studies often do not find much consistency in analysis protocols and reporting, making it hard to improve statistical power through combination of studies. Having a standard, quick methods suite will mean that every FFX study can be run through comparable analyses with negligible extra computational and staff cost. We will therefore also provide tools for meta-analysis of FFX studies analysed using the methods developed. The software packages released will be designed to interface naturally with the World Health Organisation’s Go.Data framework for management of complex outbreak data (see https://www.who.int/tools/godata). The open-ness of this framework will enable other data management tools to be developed to interface with the analytical methods. Another important question relates to design and protocols for real-time adaptation of FFX studies, for example the relative balance of serological, swabbing, and symptomatic data collection. This project will contribute to the ongoing process of design and refinement of design of these studies through providing simulation tools and insights from new analyses of existing FFX studies including those from Albania, the UK, and Kazakhstan.