Vaccines alone are no silver bullets: a modeling study on the impact of efficient contact tracing on COVID-19 infection and transmission (preprint)

BackgroundThe computer simulation presented in this study aimed to investigate the effect of contact tracing on COVID-19 transmission and infection in the context of rising vaccination rates. MethodsThis study proposed a deterministic SEIRV model with contact tracing and vaccination components. We initialized some parameters using the Malaysian COVID-19 data to inform the model. We defined contact tracing effectiveness as the proportion of contacts of a positive case that was successfully traced and vaccination rate as the proportion of daily doses administered per population in Malaysia. Sensitivity analyses on the untraced and infectious populations were conducted. The study presented in silico findings on multiple scenarios by varying the contact tracing effectiveness and daily vaccination rates. ResultsAt a vaccination rate of 1.4%, a contact tracing with the effectiveness of 70% could delay the peak of untraced asymptomatic cases by 17 days and reduce the highest number of daily cases by 70% compared with a 30% contact tracing effectiveness. A similar trend was observed for symptomatic cases when a similar experiment setting was used. We also performed sensitivity analyses by using different combinations of contact tracing effectiveness and vaccination rates. In all scenarios, the effect of contact tracing on COVID-19 incidence persisted for both asymptomatic and symptomatic cases. ConclusionDespite testing only on two public health and social measures (PHSMs), we observed the scenario with low contact tracing and increasing vaccination rates successfully mimicked the current transmission trend in Malaysia. Hence, while vaccines are progressively rolled out, efficient contact tracing must be rapidly implemented concurrently to reach, find, test, isolate, and support the affected populations to bring the pandemic under control.

Time-variant strategies for optimizing the performance of non-pharmaceutical interventions (NPIs) in protecting lives and livelihoods during the COVID-19 pandemic (preprint)

A stochastic Individual Contact Model (ICM) using SIR compartments allowing for time-variant parameters was used to simulate 100 non-pharmaceutical intervention (NPI) strategies and exit trajectories for a hypothetical population, and to collect epidemiological and non-epidemiological outcomes to measure the performance of these strategies over the course of a period of intervention (up to six months) for a total duration of one-year to allow the full implications of the strategy and endgame to manifest. We find that variations in the time dimension and intensity of various strategies can have vastly different performance outcomes: (i) the timing of NPIs can 'shrink the area under the curve' (cumulative infections) not just 'flatten the curve'; (ii) prolonged lockdowns have diminishing margins of returns; (iii) smooth, submaximal lockdowns perform better than pulsatile lockdowns; and (iv) the efficiency of various strategies incorporating both epidemiological and non-epidemiological outcomes vary substantially. Most sobering, none of the simulated strategies allow for an 'acceptable' path to exit within six months due to very large gaps in health system capacity.