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. 2022 May 5;14(5):967.
doi: 10.3390/v14050967.

Modelling the Impact of Mass Testing to Transition from Pandemic Mitigation to Endemic COVID-19

Joel R Koo  1 , Alex R Cook  1 , Jue Tao Lim  1 , Ken Wei Tan  1 , Borame L Dickens  1
Affiliations

Modelling the Impact of Mass Testing to Transition from Pandemic Mitigation to Endemic COVID-19

Joel R Koo et al. Viruses. .

Abstract

As countries transition from pandemic mitigation to endemic COVID-19, mass testing may blunt the impact on the healthcare system of the liminal wave. We used GeoDEMOS-R, an agent-based model of Singapore's population with demographic distributions and vaccination status. A 250-day COVID-19 Delta variant model was run at varying maximal rapid antigen test sensitivities and frequencies. Without testing, the number of infections reached 1,021,000 (899,400-1,147,000) at 250 days. When conducting fortnightly and weekly mass routine rapid antigen testing 30 days into the outbreak at a maximal test sensitivity of 0.6, this was reduced by 12.8% (11.3-14.5%) and 25.2% (22.5-28.5%). An increase in maximal test sensitivity of 0.2 results a corresponding reduction of 17.5% (15.5-20.2%) and 34.4% (30.5-39.1%). Within the maximal test sensitivity range of 0.6-0.8, test frequency has a greater impact than maximal test sensitivity with an average reduction of 2.2% in infections for each day removed between tests in comparison to a 0.43% average reduction per 1% increase in test frequency. Our findings highlight that mass testing using rapid diagnostic tests can be used as an effective intervention for countries transitioning from pandemic mitigation to endemic COVID-19.

Keywords: SARS-CoV-2; agent-based model; endemicity; mass testing; rapid antigen test.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Figures of some parameters used within GeoDEMOS-R. (A) Infectiousness of an infected individual relative to the day of symptom onset. (B) RT-PCR and Ag-RDT test sensitivity profiles. (C) Proportion of traced contacts of detected cases, which diminishes as the cases increase.
Figure 2
Figure 2
GeoDEMOS-R baseline outputs from 200 simulations. (A) Daily and (B) cumulative detected cases for the initial 90 days of baseline simulations. (C) Daily and (D) cumulative infections and detected cases for the entire 250 days of baseline simulations. Dashed lines represent the 95% interval.
Figure 3
Figure 3
Effects of RRT on the COVID-19 transmission by measuring the effective reproduction number Rt of the baseline simulation, fortnightly, weekly and tridaily; RRT interventions implemented at (A) early implementation and (B) peak implementation.
Figure 4
Figure 4
Percentage reduction in COVID-19 infections with various RRTs. Infection reduction values derived from the generalized additive model, with reductions at various test frequencies and maximal Ag-RDT sensitivities. (A) Infection reductions from the baseline with RRT at early implementation. (B) Infection reductions from the baseline with RRT at peak implementation.
Figure 5
Figure 5
Baseline ICU cases and dampening of ICU case trajectories with RRT intervention at early implementation at various maximal test sensitivity (0.2, 0.4, 0.6, 0.8), with (A) fortnightly testing, (B) weekly testing and (C) tridaily testing.
See this image and copyright information in PMC

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