Policy responses

In the context of the COVID-19 outbreak, data gaps can be filled through the adaption of existing data collection tools (e.g. survey content or data collection modes), and the use of alternative data sources such as big data (webscraping or the use of satellite imagery).

FAO proposes methods and tools for the use of new/alternative data sources and innovative data science techniques to produce food, agricultural and rural statistics, using in particular big data, Earth Observation data and web-scraping tools.

-          FAO's Big Data Platform on food chains gathers and analyses real time information showing the pandemic’s impact on food prices, value chains, food security and undertaken measures, with the aim of providing countries with facts to build their decisions. The tool generates, amongst others, a map of food chain disruptions highlighted by newspapers' tweets and a map of food prices variations.

-          FAO, in close collaboration with key partners such as the World Food Programme, the global Food Security Cluster and the Global Network Against Food Crises, is mobilizing resources (USD 10 million) for the establishment of a global data facility with the objective to create a common understanding and inform programming in countries and regions already experiencing humanitarian crises.

-          FAO has adapted its standard Food Insecurity Experience Scale (FIES) survey module to meet the urgent challenge of measuring and monitoring food insecurity in the context of the COVID-19 epidemic and to carefully evaluating its impact. An adapted FIES survey module has been developed to respond to the need for timely, reliable food security information. This version adds follow-up questions to capture the extent to which respondents associate food insecurity experiences with the COVID-19 crisis. Guidance on using the FIES to monitor the impact of COVID-19 on food security is available upon request from FAO’s Food Security and Nutrition Statistics team.

FAO is currently working with satellite time series data to identify and monitor risks of the COVID-19 epidemic on crop production and value-chain. The aim is to first identify crop types and after to assess their growth stage as of current state. This is a fundamental step, as of the current limitation to the work of field assessment teams. Once satellite data is gathered and analysed, machine learning models are trained and used to classify crops for the current year (nowcast) and for past years (hinder cast). Subsequently such spatial information and related data(crop type maps, crop acreage and crop yield) are overlaid with COVID-19 related geographical-disaggregated information (e.g. number of government restriction measures, number of COVID-19 cases, etc.) to inform evidence-based decision-making.

Links and references

- FAO's Hand-in-Hand Initiative

- FAO's Big Data Platform on food chains under the COVID-19 pandemic

-  FAO. 2020. Using the Food Insecurity Experience Scale (FIES) to monitor the impact of COVID-19. Rome.

-  FAO. 2020. Addressing the impacts of COVID-19 in food crises. Rome.

-  Digital Impact Alliance. Using Mobile Network Operator Data for COVID-19 Response.

-  European Space Agency (ESA). April 2020. COVID-19: how can satellites help? Paris.

-  ESRI COVID Data Hub (maps, datasets, applications, and more for coronavirus disease 2019) (COVID-19).

-  European Commission. Europe Media Monitor (EMM) website for current news reported by the world’s online media.

-  European Commission. 2020. Medical Information System – MEDISYS: Media monitoring system providing event-based surveillance to rapidly identify potential public health threats using information from media reports. Brussels.

-  European Commission. Recent Disease Incidents for COVID-19 under MEDISYS webpage.

-  European Commission. Monitoring Agricultural Resources (MARS) webpage.

-  FLOWMINDER: Flowminder COVID-19 Resources.

-  Group on Earth Observations (GEOS) GEO Community Response to COVID-19.

-  GRID3 (Geo-Referenced Infrastructure and Demographic Data for Development) GEO Community Response to COVID-19.

-  IFPRI Resources and Analyses of COVID-19 Impact website.

-  IMF. 2020. COVID-19 Special Notes for Statistics.

-  NASA. 2020. COVID-19: NASA Science Keeps the Lights On (Technical note).

-  Numbeo: Database of user contributed data for cost of living, housing indicators, health care, traffic, crime and pollution.

-  OPAL - Open Algorithms.

-  TReNDS – Thematic Research Network on Data and Statistics. April 2020. ‘’Big Data in a Time of Crisis: Maximizing its Value – And Avoiding its Risks – In the Fight Against COVID-19: How can we best use big data to combat Covid-19?’’.

-  TReNDS – Thematic Research Network on Data and Statistics. April 2020. ‘’In Low-Income Countries Fundamental Data Issues Remain for COVID-19 Response: How are LICs responding to Covid-19 and what are some of the issues we need to bear in mind when using new data sources to respond to the pandemic in these contexts?’’.

-  UN Big Data Working Group: COVID-19 Response

-  UNESCAP. 2020. The Impact and Policy Responses for COVID-19 in Asia and the Pacific. Bangkok.

-  UNITAR. UNOSAT Mapping.

-  UNOCHA. COVID-19 Pandemic in Locations with a Humanitarian Response - Dashboard.

Pros - Sound and timely data available to help informing and guiding decision makers.

Cons - Lack of access to finance, technology and know-how to apply new methods.

Minimum conditions - Institutional, financial, technological and digital capacities.

Other Elements - National governments and resources partners are invited to contact the Office of the Chief Statistician ([email protected]) to learn more about FAO data-related initiatives to support real-time information for policy-making.