Empowering pandemic preparedness by leveraging artificial intelligence and data governance
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Robust forecasting of the numbers of future infections, recovered patients, and death cases is essential. It holds the key to effective pandemic management. More precisely, robust forecasts serve as a guiding light for health care systems and policymakers, enabling them to allocate resources efficiently, plan for contingencies, and respond proactively.
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In a recent study published in the Journal of the Operational Research Society, we explored the potential of three cutting-edge AI/ML algorithms: Group Method of Data Handling (GMDH), Bi-directional Long Short-Term Memory (Bi-LSTM), and Genetic Algorithm + Neural Network (GA+NN). We used publicly available data to fuel these algorithms and generate insights that can contribute to enhancing pandemic preparedness and response.
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A key lesson is that the potential of AI/ML algorithms can only be realized if we maintain records carefully. Data plays a central role in forecasting. Policymakers must prioritize data maintenance and allocate resources for it. They need to ensure accurate data recording and focus on data quality, all guided by robust data governance policies.
Reflecting on the past, we learn that forecasting is possible. Looking forward, we understand that this relies on diligent record-keeping. The development of policies for continuous data recording and attention to data granularity are vital components of our journey forward.
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