A Flexible Data-Driven Framework for COVID-19 Case Forecasting Deployed in a Developing-world Public Health Setting

  1. Sansiddh Jain
  2. Avtansh Tiwari
  3. Nayana Bannur
  4. Ayush Deva
  5. Siddhant Shingi
  6. Vishwa Shah
  7. Mihir Kulkarni
  8. Namrata Deka
  9. Keshav Ramaswami
  10. Vasudha Khare
  11. Harsh Maheshwari
  12. Soma Dhavala
  13. Jithin Sreedharan
  14. Jerome White
  15. Srujana Merugu
  16. Alpan Raval

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Abstract

Forecasting infection case counts and estimating accurate epidemiological parameters are critical components of managing the response to a pandemic. This paper describes a modular, extensible framework for a COVID-19 forecasting system, primarily deployed during the first Covid wave in Mumbai and Jharkhand, India. We employ a variant of the SEIR compartmental model motivated by the nature of the available data and operational constraints. We estimate best fit parameters using Sequential Model-Based Optimization (SMBO), and describe the use of a novel, fast and approximate Bayesian model averaging method (ABMA) for parameter uncertainty estimation that compares well with a more rigorous Markov Chain Monte Carlo (MCMC) approach in practice. We address on-the-ground deployment challenges such as spikes in the reported input data using a novel weighted smoothing method. We describe extensive empirical analyses to evaluate the accuracy of our method on ground truth as well as against other state-of-the-art approaches. Finally, we outline deployment lessons and describe how inferred model parameters were used by government partners to interpret the state of the epidemic and how model forecasts were used to estimate staffing and planning needs essential for addressing COVID-19 hospital burden.

CCS CONCEPTS

  • Applied computing → Health care information systems ; Forecasting;

  • Computing methodologies → Modeling methodologies .

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  2. Version published to 10.1101/2021.11.01.21260020 on medRxiv