A mechanistic model to assess the effectiveness of test-trace-isolate-and-quarantine under limited capacities

TL;DR

This paper introduces a mechanistic delay differential equation model to evaluate the effectiveness of test-trace-isolate-and-quarantine strategies under capacity limitations, accounting for presymptomatic transmission and early infection dynamics.

Contribution

It presents a novel mechanistic model incorporating capacity constraints and early transmission phases to better assess TTIQ effectiveness during outbreaks.

Findings

TTIQ effectiveness diminishes with increasing epidemic waves.

Capacity limitations significantly reduce containment success.

Early presymptomatic transmission challenges TTIQ strategies.

Abstract

Diagnostic testing followed by isolation of identified cases with subsequent tracing and quarantine of close contacts - often referred to as test-trace-isolate-and-quarantine (TTIQ) strategy - is one of the cornerstone measures of infectious disease control. The COVID-19 pandemic has highlighted that an appropriate response to outbreaks requires us to be aware about the effectiveness of such containment strategies. This can be evaluated using mathematical models. We present a delay differential equation model of TTIQ interventions for infectious disease control. Our model incorporates a detailed mechanistic description of the state-dependent dynamics induced by limited TTIQ capacities. In addition, we account for transmission during the early phase of SARS-CoV-2 infection, including presymptomatic transmission, which may be particularly adverse to a TTIQ based control. Numerical experiments, inspired by the early spread of COVID-19 in Germany, reveal the effectiveness of TTIQ in a scenario where immunity within the population is low and pharmaceutical interventions are absent - representative of a typical situation during the (re-)emergence of infectious diseases for which therapeutic drugs or vaccines are not yet available. Stability and sensitivity analyses emphasize factors, partially related to the specific disease, which impede or enhance the success of TTIQ. Studying the diminishing effectiveness of TTIQ along simulations of an epidemic wave we highlight consequences for intervention strategies.