Confidence in the dynamic spread of epidemics under biased sampling conditions

TL;DR

This paper introduces a stochastic sampling model to evaluate confidence in epidemic metrics under biased testing conditions, aiding in more accurate disease trend and spread estimations during outbreaks like COVID-19.

Contribution

It presents a novel stochastic model for assessing confidence in epidemic metrics using Monte-Carlo simulations under biased sampling conditions.

Findings

Trend detection possible with under 10,000 biased samples daily

Disease spread estimation requires 1,000-2,000 unbiased samples daily

Model can evaluate precision and recall of peak detection strategies

Abstract

The interpretation of sampling data plays a crucial role in policy response to the spread of a disease during an epidemic, such as the COVID-19 epidemic of 2020. However, this is a non-trivial endeavor due to the complexity of real world conditions and limits to the availability of diagnostic tests, which necessitate a bias in testing favoring symptomatic individuals. A thorough understanding of sampling confidence and bias is necessary in order make accurate conclusions. In this manuscript, we provide a stochastic model of sampling for assessing confidence in disease metrics such as trend detection, peak detection, and disease spread estimation. Our model simulates testing for a disease in an epidemic with known dynamics, allowing us to use Monte-Carlo sampling to assess metric confidence. This model can provide realistic simulated data which can be used in the design and calibration of data analysis and prediction methods. As an example, we use this method to show that trends in the disease may be identified using under $10000$ biased samples each day, and an estimate of disease spread can be made with additional $1000-2000$ unbiased samples each day. We also demonstrate that the model can be used to assess more advanced metrics by finding the precision and recall of a strategy for finding peaks in the dynamics.