Fundamental limits on inferring epidemic resurgence in real time using effective reproduction numbers
PLoS Computational Biology
; 18(4), 2022.
Article
in English
| ProQuest Central | ID: covidwho-1842903
ABSTRACT
We find that epidemic resurgence, defined as an upswing in the effective reproduction number (R) of the contagion from subcritical to supercritical values, is fundamentally difficult to detect in real time. Inherent latencies in pathogen transmission, coupled with smaller and intrinsically noisier case incidence across periods of subcritical spread, mean that resurgence cannot be reliably detected without significant delays of the order of the generation time of the disease, even when case reporting is perfect. In contrast, epidemic suppression (where R falls from supercritical to subcritical values) may be ascertained 5–10 times faster due to the naturally larger incidence at which control actions are generally applied. We prove that these innate limits on detecting resurgence only worsen when spatial or demographic heterogeneities are incorporated. Consequently, we argue that resurgence is more effectively handled proactively, potentially at the expense of false alarms. Timely responses to recrudescent infections or emerging variants of concern are more likely to be possible when policy is informed by a greater quality and diversity of surveillance data than by further optimisation of the statistical models used to process routine outbreak data.
Biology--Computer Applications; Epidemiology; Infectious disease control; COVID 19; Infectious disease epidemiology; Infectious disease surveillance; Pathogens; Infectious diseases; Signal to noise ratio; Epidemics; Case reports; Reproduction; False alarms; Real time; Optimization; Noise; Statistical models; Surveillance; Statistical analysis; Time series; Performance evaluation; Mathematical models; COVID-19
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Collection:
Databases of international organizations
Database:
ProQuest Central
Type of study:
Experimental Studies
Language:
English
Journal:
PLoS Computational Biology
Year:
2022
Document Type:
Article
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