CoviHawkes: Temporal Point Process and Deep Learning based Covid-19 forecasting for India

TL;DR

CoviHawkes is a deep learning-based model utilizing temporal point processes to forecast Covid-19 case counts in India, aiding targeted lockdown decisions and pandemic progression predictions.

Contribution

The paper introduces CoviHawkes, a novel machine learning tool that combines temporal point processes with deep learning for multi-level Covid-19 forecasting in India.

Findings

Accurately predicts short-term Covid-19 cases for regions in India.

Simulates pandemic progression under different lockdown scenarios.

Validated performance across national, state, and district levels.

Abstract

Lockdowns are one of the most effective measures for containing the spread of a pandemic. Unfortunately, they involve a heavy financial and emotional toll on the population that often outlasts the lockdown itself. This article argues in favor of ``local'' lockdowns, which are lockdowns focused on regions currently experiencing an outbreak. We propose a machine learning tool called CoviHawkes based on temporal point processes, called CoviHawkes that predicts the daily case counts for Covid-19 in India at the national, state, and district levels. Our short-term predictions ($<30$ days) may be helpful for policymakers in identifying regions where a local lockdown must be proactively imposed to arrest the spread of the virus. Our long-term predictions (up to a few months) simulate the progression of the pandemic under various lockdown conditions, thereby providing a noisy indicator for a potential third wave of cases in India. Extensive experimental results validate the performance of our tool at all levels.