Some Challenges in Monitoring Epidemics

TL;DR

This paper highlights the limitations of traditional epidemic models by incorporating measurement errors and network effects, emphasizing the need for scientific corrections in epidemic monitoring and forecasting.

Contribution

It extends standard models with a measurement process component, analyzing how false positives, negatives, and sampling bias affect epidemic data interpretation.

Findings

Measurement errors significantly impact epidemic assessment.

Incorporating measurement models improves understanding of epidemic dynamics.

Correcting for measurement biases enhances forecasting accuracy.

Abstract

Epidemic models often reflect characteristic features of infectious spreading processes by coupled non-linear differential equations considering different states of health (such as Susceptible, Infected, or Recovered). This compartmental modeling approach, however, delivers an incomplete picture of the dynamics of epidemics, as it neglects stochastic and network effects, and also the role of the measurement process, on which the estimation of epidemiological parameters and incidence values relies. In order to study the related issues, we extend established epidemiological spreading models with a model of the measurement (i.e. testing) process, considering the problems of false positives and false negatives as well as biased sampling. Studying a model-generated ground truth in conjunction with simulated observation processes (virtual measurements) allows one to gain insights into the limitations of purely data-driven methods to assess the epidemic situation. We conclude that epidemic monitoring, simulation, and forecasting are wicked problems, as applying a conventional data-driven approach to a complex system with non-linear dynamics, network effects, and uncertainty can be misleading. Nevertheless, some of the errors can be corrected for, using scientific knowledge of the spreading dynamics and the measurement process. We conclude that such corrections should generally be part of epidemic monitoring, modeling, and forecasting efforts.