OECD:

An analysis of past crises, in particular the global financial crisis of 2008-2009, points to potential upcoming challenges with regard to STI funding. Government funding for STIs may change in the coming years due to high levels of public debt, as well as declining demand and revenue. 

In general, the reduction in funding for universities and research institutes will have long-term consequences if it causes a leak of researchers from the countries that have most reduced funding. Data from the financial crisis of 2008-2009 show that it was not easy to return to the pre-crisis level of business R & D and innovation after the crisis. In some countries, such as Canada, Japan and Spain, it took several years to return to the pre-crisis level of R & D investment, while in others the level of investment remained fairly stable (for example, Australia, Germany, France, the United Kingdom) or increased significantly (for example, the People's Republic of China, Estonia, Hungary, Ireland, and Korea).

In contrast to previous crises, the emerging demand for a range of medical and digital tools and services since the beginning of the pandemic shows uneven dynamics across sectors. 

As a result of the COVID-19 pandemic, the demand for digital tools may increase, which may cause a new wave of technological progress in this area. The need to quickly find solutions to the health emergency has sparked an unprecedented number of open science initiatives.  The key challenge is to make the data fairly easily discoverable, accessible, interoperable, and re-usable (FAIR). Access to other countries is difficult due to the data protection system in the OECD countries, which creates the need for data exchange standards. 

The financial difficulties resulting from the crisis adversely affect vulnerable segments of the population, as well as the families of students from disadvantaged families. The pandemic may exacerbate inequalities in participation in STI ecosystems. These students may not receive a degree or receive lower grades, and therefore will not be able to receive a higher level of education and training to participate in the STI field. Looking at a scenario in which public investment in education will fall by 25% in 2020 and 2021, and parental income by 5%, researchers at the US Federal Reserve have estimated that in 2045 there will be 2.7% fewer workers with higher education and 3.8% more workers without education. Existing evidence suggests that closing schools for only one-third of the school year could reduce long-term GDP by 2.6% by 2100.

A new emphasis may be placed on supporting specific technologies and innovations (for example, those that are crucial for the production of "essential goods" or for the transition to green energy and clean transport). 

A key policy priority is to improve skills, capacity and basic infrastructure, ensuring that all sectors of society have the opportunity to develop, to contribute to and benefit from innovation. An example of accelerated change in the context of COVID-19 was the need for digital adoption by government, industry, workers, and citizens. Another policy priority is to increase the flexibility of the public sector to respond to future shocks. 

Special attention can be paid to the analysis of R & D funding allocation processes in emergency situations to ensure both quality and diversity of approaches. International cooperation can speed up the response to negative events. 

More detailed, behavioral, and timely data were used to study the wide range of impacts of the pandemic.

For example, data from users of the Google Android platform showed how the blocking measures affected mobility in different countries. Metrics are interesting because they provide information about actual behavior, as they track movement based on mobile phones, rather than (as in the case of the survey) people's statements about their mobility. 

The COVID-19 crisis has also led to more experiments with rapid surveys of companies and citizens, using the power of digital tools to collect and analyze responses. Pulse surveys (or rapid response surveys) with a reduced number of targeted questions have become more common as a means of collecting near-real-time snapshots of the impact of a crisis. For example, the U.S. Census Bureau has launched a small business legume survey to collect weekly information on the challenges small businesses face during a pandemic, with a high level of geographic and industry detail.

A team of researchers at Stanford University and Stony Brook University applied a large-scale analysis of linguistic patterns in Twitter to track the impact of social distancing measures on mental health and emotional well-being. In Japan, NTT Data and Citibeats use artificial intelligence algorithms to analyze real-time data about the concerns expressed by citizens on Twitter about the COVID-19 pandemic.

In addition, new tools from data science, computer science, and machine learning were used. For example, Bank of England researchers use machine learning to search for new data sources (restaurant reservations, public transport apps, flights, electricity consumption, job ads) to better understand the interaction between macroeconomics and the pandemic. Using agent-based modeling frameworks, they model how people's economic choices can change their risks of exposure to the virus. 

In Austria, Invenium, an enterprise of the Graz University of Technology, in partnership with A1 Telekom Austria Group, has developed a traffic analysis application based on anonymous mobile phone data to inform about traffic congestion. In the context of COVID-19, the application was used to assess the effectiveness of social distancing and mobility restrictions imposed by the Government.

Big data and digital tools can enable the collection of more accurate data to monitor politically relevant factors. However, the use of new data and tools requires significant investment. This includes investments in the infrastructure of data management systems and information services. It must also be technically and scientifically sound to guarantee its legitimacy. Ensuring the confidentiality and security of your data is also an important aspect. Public officials need to be trained on how to use this data and tools.

Creating such a policy infrastructure requires collaboration between government, research institutions, and industry. In addition, effective public - private partnerships are needed in terms of harnessing the power of the private sector to optimize tools for policy purposes and to use data collected by the private sector. This may include developing clear, sustainable business models that create incentives for the private sector to generate reliable data and indicators (for example, by offering tax incentives to share indicators) relevant to the public sector.

It is important to understand that the use of such technologies for political purposes also requires building trust and public acceptance. Decisions made with the help of artificial intelligence tools should be clear and transparent to citizens. Such tools should not replace, but support, evidence-based decision-making, and should be constantly monitored to ensure that they do not pose risks to data privacy and security.

The duration of the pandemic or its longer-term effects on people's consumption patterns and preferences remain highly uncertain, and the STI systems of the future will largely be determined by the policy choices made today and in the coming months. In this context, STI policy development should be based on a comprehensive vision of the complexity of events and their interrelated consequences.