A collection curated by the Development Data Partnership of COVID-19 focused projects.
The project team t the World Bank analyzed how COVID-19 cases fuel the economic anxiety in ASEAN countries -Singapore, Malaysia, Thailand, Cambodia, Indonesia, Philippines, and Vietnam. Google Search Trends for distinct categories, including Aid, Assistance, Debt, Government, and Unemployment, were analyzed. The team showed that search trends could give policymakers real-time insights into the growing anxiety due to COVID-19 cases and reactions to fiscal policies.
The exponential growth of Covid-19 cases globally has shown us how vital it is to identify and anticipate the emerging contagion hotspots to limit the spread of the virus. Leveraging Facebook’s Population Density Maps, WorldPop population data, DLR's World Settlement Footprint 3D product, and Open Street Map, the World Bank developed a methodology that identifies hotspots for contagion and vulnerability.
The World Bank Digital Development team used Ookla's Speedtest data to analyze the effect of COVID-19-related lockdown measures on internet speed in a sample of developing countries in Africa. The findings of the study demonstrate the resiliency of digital infrastructure in Africa and highlight the benefits of partnerships between public and private stakeholders to manage traffic surges.
The Inter-American Development Bank’s Covid-19 Labor Observatory used LinkedIn hiring rate data and household surveys to analyze hiring rate fluctuations in different sectors across Latin America and the Caribbean. LinkedIn’s data has been complementing traditional labor dynamics statistics to understand better the Covid-19 impact in the region.
The IMF used household surveys (High-Frequency Phone Surveys on COVID-19) and Ookla's CoverageRight™ data to assess whether households with mobile connectivity withstood COVID-19 shocks better regarding income, employment, food security, and education in Sub-Saharan Africa. The study found that digital connectivity enhanced households' resilience to shocks during the pandemic in three areas: market access, employment, and human capital development.
Using the Mapbox API, Facebook Population Density Maps, and OpenStreetMap POIs, the Development Data Partnership team examined how physically accessible hospitals and clinics are in Indonesia and the Philippines. The team generated maps that show the percentage of a population in a study region that cannot reach a health facility within 1 hour of travel time by mode.
The Inter-American Development Bank levered Veraset’s Movement data to support the Municipality of Lima in its effort to mitigate the spread of Covid-19, while ensuring that markets and public spaces could continue to function. The team analyzed visitors’ movements through market areas, identifying origin districts and activity hotspots throughout the day. By comparing pre-and post-pandemic flows, the team was able to inform a new design for these public spaces, and the method developed by the team will be shared as a public good.
The World Bank South Asia Transport Team conducted an urban mobility diagnostic using data from Unacast, Facebook’s Relative Wealth Index among other sources to understand transport issues in the city, quantify the impacts from Cyclone Nivar and the impacts of COVID-19 on mobility. The team leveraged Mobilkit - a Python Toolkit developed by the World Bank for Urban Resilience and Disaster Risk Management Analytics using High Frequency Human Mobility Data which has been used across many countries and World Bank projects, (e.g., Mexico, Costa Rica, Nepal.