Monitoring the pandemic: A fractional filter for the COVID-19 contact rate

TL;DR

This paper introduces a fractional filter method to accurately estimate the COVID-19 contact rate from noisy data, capturing social behavior persistence and aiding real-time pandemic monitoring.

Contribution

It develops a novel fractional filter for unobserved components models, improving estimation of long-term social behavior dynamics in COVID-19 contact rates.

Findings

Contact rate estimates align with pandemic chronology

Method detects policy intervention effects

Provides precise real-time contact rate monitoring

Abstract

This paper aims to provide reliable estimates for the COVID-19 contact rate of a Susceptible-Infected-Recovered (SIR) model. From observable data on confirmed, recovered, and deceased cases, a noisy measurement for the contact rate can be constructed. To filter out measurement errors and seasonality, a novel unobserved components (UC) model is set up. It specifies the log contact rate as a latent, fractionally integrated process of unknown integration order. The fractional specification reflects key characteristics of aggregate social behavior such as strong persistence and gradual adjustments to new information. A computationally simple modification of the Kalman filter is introduced and is termed the fractional filter. It allows to estimate UC models with richer long-run dynamics, and provides a closed-form expression for the prediction error of UC models. Based on the latter, a conditional-sum-of-squares (CSS) estimator for the model parameters is set up that is shown to be consistent and asymptotically normally distributed. The resulting contact rate estimates for several countries are well in line with the chronology of the pandemic, and allow to identify different contact regimes generated by policy interventions. As the fractional filter is shown to provide precise contact rate estimates at the end of the sample, it bears great potential for monitoring the pandemic in real time.