Harnessing COVID-19 data to support public health and economic decision-making in Kenya and Malawi – COVID AI
Programs and partnerships
Lead institution(s)
Summary
The emergence of the COVID-19 global pandemic presents serious health and livelihood threats to people in low- and middle-income countries (LMICs) all over the world. There is an urgent need for accurate, real-time data for health policy and planning to combat the threats.Read more
The emergence of the COVID-19 global pandemic presents serious health and livelihood threats to people in low- and middle-income countries (LMICs) all over the world. There is an urgent need for accurate, real-time data for health policy and planning to combat the threats. In many countries there are methodological gaps in data integration and a lack of information and research capacity to make informed decisions to guide public health policy. The absence of data also makes it difficult to identify vulnerable populations and provide appropriate information to protect and improve people’s health. Moreover, obtaining information can be especially difficult under the restrictions of pandemic lockdowns.
Current innovations in AI and data science can support the collection and analysis of real-time, accurate data from multiple data sources. This project proposes to develop the key elements of a coordinated pan-African COVID-19 data ecosystem with a robust suite of data standards, technologies, and data integration methods that leverage AI and data science for analysis and oversight. It will focus on data from Kenya and Malawi because both countries adopted different strategies to combat the COVID-19 pandemic. The goal is to scale the dissemination of information to enhance decision-making and guide the development and implementation of strategies to reduce morbidity and mortality from COVID-19.
The project is developing a network of stakeholders to exchange information, experiences, and knowledge that will support data acquisition, management, governance, and reporting; a data tracking system; a common data model; and a comprehensive data hub for COVID-19 data to demonstrate how COVID-19 is affecting transmission dynamics, its impact on health, education, work, transport, and effective interventions. It will also enhance the methodological capacity of data analysts and develop communication strategies for the public, policymakers, and decision-makers.
Research outputs
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The COVID-19 pandemic has spurred the use of AI and DS innovations in data collection and aggregation. Extensive data on many aspects of the COVID-19 has been collected and used to optimize public health response to the pandemic and to manage the recovery of patients in Sub-Saharan Africa. However, there is no standard mechanism for collecting, documenting, and disseminating COVID19 related data or metadata, which makes the use and reuse a challenge. INSPIRE utilizes the Observational Medical Outcomes Partnership (OMOP) as the Common Data Model (CDM) implemented in the cloud as a Platform as a Service (PaaS) for COVID-19 data. The INSPIRE PaaS for COVID-19 data leverages the cloud gateway for both individual research organizations and for data networks. Individual research institutions may choose to use the PaaS to access the FAIR data management, data analysis and data sharing capabilities which come with the OMOP CDM. Network data hubs may be interested in harmonizing data across localities using the CDM conditioned by the data ownership and data sharing agreements available under OMOP’s federated model. The INSPIRE platform for evaluation of COVID-19 Harmonized data (PEACH) harmonizes data from Kenya and Malawi. Data sharing platforms must remain trusted digital spaces that protect human rights and foster citizens’ participation is vital in an era where information overload from the internet exists. The channel for sharing data between localities is included in the PaaS and is based on data sharing agreements provided by the data producer. This allows the data producers to retain control over how their data are used, which can be further protected through the use of the federated CDM. Federated regional OMOP-CDM are based on the PaaS instances and analysis workbenches in INSPIRE-PEACH with harmonized analysis powered by the AI technologies in OMOP. These AI technologies can be used to discover and evaluate pathways that COVID-19 cohorts take through public health interventions and treatments. By using both the data mapping and terminology mapping, we construct ETLs that populate the data and/or metadata elements of the CDM, making the hub both a central model and a distributed model.
About the partnership
