The effectiveness of public health interventions against COVID-19: Lessons from the Singapore experience.

dc.contributor.author

Ansah, John P

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Matchar, David Bruce

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Shao Wei, Sean Lam

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Low, Jenny G

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Pourghaderi, Ahmad Reza

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Siddiqui, Fahad Javaid

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Min, Tessa Lui Shi

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Wei-Yan, Aloysius Chia

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Ong, Marcus Eng Hock

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2021-05-03T03:00:22Z

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2021-05-03T03:00:22Z

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2021-01

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2021-05-03T03:00:20Z

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Background

In dealing with community spread of COVID-19, two active interventions have been attempted or advocated-containment, and mitigation. Given the extensive impact of COVID-19 globally, there is international interest to learn from best practices that have been shown to work in controlling community spread to inform future outbreaks. This study explores the trajectory of COVID-19 infection in Singapore had the government intervention not focused on containment, but rather on mitigation. In addition, we estimate the actual COVID-19 infection cases in Singapore, given that confirmed cases are publicly available.

Methods and findings

We developed a COVID-19 infection model, which is a modified SIR model that differentiate between detected (diagnosed) and undetected (undiagnosed) individuals and segments total population into seven health states: susceptible (S), infected asymptomatic undiagnosed (A), infected asymptomatic diagnosed (I), infected symptomatic undiagnosed (U), infected symptomatic diagnosed (E), recovered (R), and dead (D). To account for the infection stages of the asymptomatic and symptomatic infected individuals, the asymptomatic infected individuals were further disaggregated into three infection stages: (a) latent (b) infectious and (c) non-infectious; while the symptomatic infected were disaggregated into two stages: (a) infectious and (b) non-infectious. The simulation result shows that by the end of the current epidemic cycle without considering the possibility of a second wave, under the containment intervention implemented in Singapore, the confirmed number of Singaporeans infected with COVID-19 (diagnosed asymptomatic and symptomatic cases) is projected to be 52,053 (with 95% confidence range of 49,370-54,735) representing 0.87% (0.83%-0.92%) of the total population; while the actual number of Singaporeans infected with COVID-19 (diagnosed and undiagnosed asymptomatic and symptomatic infected cases) is projected to be 86,041 (81,097-90,986), which is 1.65 times the confirmed cases and represents 1.45% (1.36%-1.53%) of the total population. A peak in infected cases is projected to have occurred on around day 125 (27/05/2020) for the confirmed infected cases and around day 115 (17/05/2020) for the actual infected cases. The number of deaths is estimated to be 37 (34-39) among those infected with COVID-19 by the end of the epidemic cycle; consequently, the perceived case fatality rate is projected to be 0.07%, while the actual case fatality rate is estimated to be 0.043%. Importantly, our simulation model results suggest that there about 65% more COVID-19 infection cases in Singapore that have not been captured in the official reported numbers which could be uncovered via a serological study. Compared to the containment intervention, a mitigation intervention would have resulted in early peak infection, and increase both the cumulative confirmed and actual infection cases and deaths.

Conclusion

Early public health measures in the context of targeted, aggressive containment including swift and effective contact tracing and quarantine, was likely responsible for suppressing the number of COVID-19 infections in Singapore.
dc.identifier

PONE-D-20-19492

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1932-6203

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1932-6203

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https://hdl.handle.net/10161/22734

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eng

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Public Library of Science (PLoS)

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PloS one

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10.1371/journal.pone.0248742

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Humans

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Contact Tracing

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Models, Statistical

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Public Health

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Quarantine

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Singapore

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Outcome Assessment, Health Care

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COVID-19

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The effectiveness of public health interventions against COVID-19: Lessons from the Singapore experience.

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Journal article

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Matchar, David Bruce|0000-0003-3020-2108

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e0248742

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3

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School of Medicine

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Duke Clinical Research Institute

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Duke Global Health Institute

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Pathology

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Medicine, General Internal Medicine

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Duke

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Institutes and Centers

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University Institutes and Centers

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Institutes and Provost's Academic Units

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Clinical Science Departments

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Medicine

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Published

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16

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