Building National-Scale COVID-19 Forecasting for Pakistan’s Public Health Response
During the pandemic, policymakers needed localized forecasts to guide lockdowns, resource distribution, and vaccination strategies. Red Buffer built a machine learning forecasting system that predicted daily cases and deaths at national and provincial levels, turning raw epidemiological data into actionable intelligence.
ROLE
Data integration and cleaning, time-series model development (ARIMA, LSTM, FbProphet), model validation and back-testing, and policy-focused visualization design.
TOOL
ARIMA, LSTM Neural Networks, FbProphet, Pandas, Matplotlib
DURATION
Rapid-response public health initiative delivered during the COVID-19 pandemic with continuous data updates and model refinement.
Our Approach
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Multi-Source Data Integration
Cleaned and integrated daily case and fatality data from NIH with global datasets from OurWorldInData and combined them with regional mobility indicators creating a comprehensive input layer for forecasting.
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Ensemble Time-Series Forecasting
Applied complementary models including ARIMA for trend forecasting, LSTM for nonlinear transmission patterns, and FbProphet for seasonality-aware predictions with each model covering a different aspect of pandemic dynamics.
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Continuous Validation and Recalibration
Back-tested forecasts against historical data to assess accuracy and recalibrated models as new data arrived ensuring forecasts remained reliable as conditions shifted.
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Policy-Ready Visualizations
Created clear dashboards showing national and provincial forecasts, trends, and risk hotspots designed for non-technical policymakers who needed to act on insights quickly.
Why It Matters
The combination of multiple time-series methods with real-time data integration and decision-maker-focused visualization applies to any public health surveillance, disease outbreak monitoring, or resource planning scenario where accurate, localized predictions must inform rapid decisions under uncertainty.
Outcome
High Forecast Accuracy
Reliable predictions of daily cases and deaths at national and provincial levels.
Early Hotspot Identification
Enabled policymakers to detect high-risk provinces and emerging outbreaks before they escalated.
Improved Resource Allocation
Supported more effective distribution of testing kits, hospital beds, and critical care resources.
Proactive Policy Support
Provided timely evidence for decisions on lockdowns, mobility restrictions, and vaccination strategies.
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