Improved Estimation of Daily COVID-19 Rate from Incomplete Data

Abstract

Novel diseases such as COVID-19 present challenges for identifying and assessing the impact of public health interventions due to incomplete and inaccurate data. Many infected persons may be asymptomatic, pre-symptomatic, or may choose to not seek medical treatment. Insufficient testing and reporting standards coupled with reporting delays may also affect the accuracy of case count, recovery rate, fatalities and other key metrics used to model the disease. High error in these metrics are propagated to all aspects of public health response including estimates of daily transmission rates. We propose a method that integrates Monte Carlo simulation based on clinical studies, linear noise approximation (LNA), and Hidden Markov Models (HMMs) to estimate daily reproductive number. Results are validated against known state population behavior, such as social distancing and stay-At-home orders. The proposed approach provides improved model initial conditions resulting in reduced error and superior modeling of COVID-19 disease dynamics, notably including the effective reproduction rate \mathrm{R}_{\mathrm{t}}.