Abstract
SummaryBackgroundThe ‘third wave’ of COVID-19 in Hong Kong, China was suppressed by non-pharmaceutical interventions (NPIs). Although social distancing regulations were quickly strengthened, the outbreak continued to grow, causing increasing delays in tracing and testing. Further regulations were introduced, plus ‘targeted testing’ services for at-risk groups. Estimating the impact of individual NPIs could provide lessons about how outbreaks can be controlled without radical lockdown. However, the changing delays in confirmation time challenge current modelling methods. We used a novel approach aimed at disentangling and quantifying the effects of individual interventions.MethodsWe incorporated the causes of delays in tracing and testing (i.e. load-efficiency relationship) and the consequences from such delays (i.e. the proportion of un-traced cases and the proportion of traced-cases with confirmation delay) into a deterministic transmission model, which was fitted to the daily number of cases with and without an epi‑link (an indication of being contact-traced). The effect of each NPI was then calculated.FindingsThe model estimated that after earlier relaxation of regulations, Re rose from 0.7 to 3.2. Restoration of social distancing to the previous state only reduced Re to 1.3, because of increased delay in confirmation caused by load on the contact-tracing system. However, Re decreased by 20.3% after the introduction of targeted testing and by 17.5% after extension of face-mask rules, reducing Re to 0.9 and suppressing the outbreak. The output of the model without incorporation of delay failed to capture important features of transmission and Re.InterpretationChanging delay in confirmation has a significant impact on disease transmission and estimation of transmissibility. This leads to a clear recommendation that delay should be monitored and mitigated during outbreaks, and that delay dynamics should be incorporated into models to assess the effects of NPIs.FundingCity University of Hong Kong and Health and Medical Research Fund.
Highlights
An important objective of epidemic modelling is to assess the impact of interventions and provide valuable insights for public health decision-makers.[2]
Our intention was to quantify the effect of nonpharmaceutical interventions (NPIs) deployed during Hong Kong’s ‘third wave’ of COVID-19, which was successfully suppressed without a lockdown.[1]
Recent theoretical studies of COVID-19 have estimated what proportion of people have to be traced to reduce the effective reproduction number (Re) below 1, www.thelancet.com Vol xx Month xx, 2021
Summary
An important objective of epidemic modelling is to assess the impact of interventions and provide valuable insights for public health decision-makers.[2] In this study, our intention was to quantify the effect of nonpharmaceutical interventions (NPIs) deployed during Hong Kong’s ‘third wave’ of COVID-19, which was successfully suppressed without a lockdown.[1] Hong Kong has adopted a ‘suppress and lift’ strategy,[3] in which interventions are progressively strengthened when infections rise, and relaxed when they decline These interventions include social distancing measures and contact tracing, which aims to identify the close contacts of cases, who are immediately quarantined or self-isolated, and tested. Recent theoretical studies of COVID-19 have estimated what proportion of people have to be traced to reduce the effective reproduction number (Re) below 1, www.thelancet.com Vol xx Month xx, 2021
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