Abstract
AbstractWhen novel human diseases emerge into naive populations, identification and isolation of infected individuals forms the first line of defense against the invading pathogens1,2. Diagnostic testing plays a critical role3,4, but health agencies unprepared for a novel disease invasion may struggle to meet the massive testing capacities demanded by an epidemic outbreak5, potentially resulting in a failure of epidemic containment as with COVID-196. What factors make a disease controllable versus uncontrollable with limited testing supplies remains unclear. Specifically, is the failure of testing-isolation unique to COVID-19, or is this a likely outcome across the spectrum of disease traits that may constitute future epidemics? Here, using a generalized mathematical disease model parameterized for each of seven different human diseases, we show that testing-isolation strategies will typically fail to contain epidemic outbreaks at practicably achievable testing capacities. From this analysis, we identify three key disease characteristics that govern controllability under resource constraints; the basic reproduction number, mean latent period, and non-symptomatic transmission index. Interactions among these characteristics play prominent roles in both explaining controllability differences among diseases and enhancing the efficacy of testing-isolation in combination with transmission-reduction measures. This study provides broad guidelines for managing controllability expectations during future novel disease invasions, describing which classes of diseases are most amenable to testing-isolation strategies alone and which will necessitate additional transmission-reduction measures like social distancing.
Publisher
Cold Spring Harbor Laboratory