Best practices for government agencies to publish data: lessons from COVID-19.

Without data, knowing how to respond to the COVID-19 pandemic would have been impossible. Data were crucial to understanding how the disease spread and which efforts successfully protected people. Yet, national agencies often did not publish their data in an optimal way, which made responding to the pandemic challenging. Therefore, learning from what went well and what did not for the future is crucial. Drawing on our first-hand experience of republishing COVID-19 data, we identify seven best practices for how to publish data in an optimal way: collect the data that are relevant; make them comparable; clearly document the data; share them frequently and promptly; publish data at a stable location; choose a reusable format; and license others to reuse the data. These best practices are straightforward, inexpensive, and achievable, with some countries already having implemented most of them during the COVID-19 pandemic. More government agencies following these best practices will enable others to access their data and address the world's public health challenges-including the next pandemic.

A global database of COVID-19 vaccinations.

An effective rollout of vaccinations against COVID-19 offers the most promising prospect of bringing the pandemic to an end. We present the Our World in Data COVID-19 vaccination dataset, a global public dataset that tracks the scale and rate of the vaccine rollout across the world. This dataset is updated regularly and includes data on the total number of vaccinations administered, first and second doses administered, daily vaccination rates and population-adjusted coverage for all countries for which data are available (169 countries as of 7 April 2021). It will be maintained as the global vaccination campaign continues to progress. This resource aids policymakers and researchers in understanding the rate of current and potential vaccine rollout; the interactions with non-vaccination policy responses; the potential impact of vaccinations on pandemic outcomes such as transmission, morbidity and mortality; and global inequalities in vaccine access.

A cross-country database of COVID-19 testing.

Our understanding of the evolution of the COVID-19 pandemic is built upon data concerning confirmed cases and deaths. This data, however, can only be meaningfully interpreted alongside an accurate understanding of the extent of virus testing in different countries. This new database brings together official data on the extent of PCR testing over time for 94 countries. We provide a time series for the daily number of tests performed, or people tested, together with metadata describing data quality and comparability issues needed for the interpretation of the time series. The database is updated regularly through a combination of automated scraping and manual collection and verification, and is entirely replicable, with sources provided for each observation. In providing accessible cross-country data on testing output, it aims to facilitate the incorporation of this crucial information into epidemiological studies, as well as track a key component of countries' responses to COVID-19.

COVID-19: Identifying countries with indicators of success in responding to the outbreak.

Background: In designing responses to the COVID-19 pandemic, it is critical to understand what has already worked well. We aimed to identify countries with emerging success stories from whom policymakers might draw important lessons.  Methods: We developed a process to first include countries with large enough populations that results were unlikely to be due to chance, that had sufficient cases for response mechanisms to be tested, and that shared the necessary publicly available data. Within these countries, we looked at indicators suggesting success in terms of detecting disease, containing the outbreak, and treating those who were unwell. To support comparability, we measured indicators per capita (per million) and across time. We then used the indicators to identify three countries with emerging success stories to include some diversity in global region, population demographics and form of government. Results: We identified 66 countries that met our inclusion criteria on 18 th May 2020. Several of these countries had indicators of success against the set indicators at different times in the outbreak. Vietnam had high levels of testing and successful containment with no deaths reported. South Korea had high levels of testing early in the outbreak, supporting containment. Germany had high levels of sustained testing and slower increases in cases and deaths than seen in other comparable settings. Conclusions: At the time of our assessment, Vietnam and South Korea were able to contain the outbreak of COVID-19 and avoid the exponential growth in cases seen elsewhere. Germany had more cases and deaths, but was nevertheless able to contain and mitigate the outbreak. Despite the many limitations to the data currently available, looking at comparative data can help identify countries from whom we can draw lessons, so that countries can inform and adapt their strategies for success in response to COVID-19.

Allocation of development assistance for health: is the predominance of national income justified?

Gross national income (GNI) per capita is widely regarded as a key determinant of health outcomes. Major donors heavily rely on GNI per capita to allocate development assistance for health (DAH). This article questions this paradigm by analysing the determinants of health outcomes using cross-sectional data from 99 countries in 2012. We use disability-adjusted life years (Group I) per capita as our main indicator for health outcomes. We consider four primary variables: GNI per capita, institutional capacity, individual poverty and the epidemiological surroundings. Our empirical strategy has two innovations. First, we construct a health poverty line of 10.89 international-$ per day, which measures the minimum level of income an individual needs to have access to basic healthcare. Second, we take the contagious nature of communicable diseases into account, by estimating the extent to which the population health in neighbouring countries (the epidemiological surroundings) affects health outcomes. We apply a spatial two-stage least-squares model to mitigate the risks of reverse causality. Our model captures 92% of the variation in health outcomes. We emphasize four findings. First, GNI per capita is not a significant predictor of health outcomes once other factors are controlled for. Second, the poverty gap below the 10.89 health poverty line is a good measure of universal access to healthcare, as it explains 19% of deviation in health outcomes. Third, the epidemiological surroundings in which countries are embedded capture as much as 47% of deviation in health outcomes. Finally, institutional capacity explains 10% of deviation in health outcomes. Our empirical findings suggest that allocation frameworks for DAH should not only take into account national income, which remains an important indicator of countries' financial capacity, but also individual poverty, governance and epidemiological surroundings to increase impact on health outcomes.

Carbon dioxide emission-intensity in climate projections: Comparing the observational record to socio-economic scenarios.

The wide spread of projected temperature changes in climate projections does not predominately originate from uncertainty across climate models; instead it is the broad range of different global socio-economic scenarios and the implied energy production that results in high uncertainty about future climate change. It is therefore important to assess the observational tracking of these scenarios. Here we compare these socio-economic scenarios created in both 1992 and 2000 against the recent observational record to investigate the coupling of economic growth and fossil-fuel CO2 emissions. We find that global emission intensity (fossil fuel CO2 emissions per GDP) rose in the first part of the 21st century despite all major climate projections foreseeing a decline. Proposing a method to disaggregate differences between scenarios and observations in global growth rates to country-by-country contributions, we find that the relative discrepancy was driven by unanticipated GDP growth in Asia and Eastern Europe, in particular in Russia and China. The growth of emission intensity over the 2000s highlights the relevance of unforeseen local shifts in projections on a global scale.