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COVID-19 Epidemic Monitoring After Non-pharmaceutical Interventions: The Use of Time-varying Reproduction Number in a Country with a Large Migrant Population

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Publisher Elsevier
Date 2020 Aug 24
PMID 32829052
Citations 20
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Abstract

Background: COVID-19's emergence carries with it many uncertainties and challenges, including strategies to manage the epidemic. Oman has implemented non-pharmaceutical interventions (NPIs) to mitigate the impact of COVID-19. However, responses to NPIs may be different across different populations within a country with a large number of migrants, such as Oman. This study investigated the different responses to NPIs, and assessed the use of the time-varying reproduction number (R) to monitor them.

Methods: Polymerase chain reaction (PCR) laboratory-confirmed COVID-19 data for Oman, from February 24 to June 3, 2020, were used alongside demographic and epidemiological information. Data were arranged into pairs of infector-infectee, and two main libraries of R software were used to estimate reproductive number (R). R was calculated for both Omanis and non-Omanis.

Findings: A total of 13,538 cases were included, 44.9% of which were Omanis. Among all these cases we identified 2769 infector-infectee pairs for calculating R. There was a sharp drop in R from 3.7 (95% confidence interval [CI] 2.8-4.6) in mid-March to 1.4 (95% CI 1.2-1.7) in late March in response to NPIs. R then decreased further to 1.2 (95% CI 1.1-1.3) in late April after which it rose, corresponding to the easing of NPIs. Comparing the two groups, the response to major public health controls was more evident in Omanis in reducing R to 1.09 (95% CI 0.84-1.3) by the end of March.

Interpretation: Use of real-time estimation of R allowed us to follow the effects of NPIs. The migrant population responded differently than the Omani population.

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References
1.
Prem K, Liu Y, Russell T, Kucharski A, Eggo R, Davies N . The effect of control strategies to reduce social mixing on outcomes of the COVID-19 epidemic in Wuhan, China: a modelling study. Lancet Public Health. 2020; 5(5):e261-e270. PMC: 7158905. DOI: 10.1016/S2468-2667(20)30073-6. View

2.
Remuzzi A, Remuzzi G . COVID-19 and Italy: what next?. Lancet. 2020; 395(10231):1225-1228. PMC: 7102589. DOI: 10.1016/S0140-6736(20)30627-9. View

3.
Nagraj V, Randhawa N, Campbell F, Crellen T, Sudre B, Jombart T . epicontacts: Handling, visualisation and analysis of epidemiological contacts. F1000Res. 2019; 7:566. PMC: 6572866. DOI: 10.12688/f1000research.14492.2. View

4.
Leung K, Wu J, Liu D, Leung G . First-wave COVID-19 transmissibility and severity in China outside Hubei after control measures, and second-wave scenario planning: a modelling impact assessment. Lancet. 2020; 395(10233):1382-1393. PMC: 7195331. DOI: 10.1016/S0140-6736(20)30746-7. View

5.
Distante C, Piscitelli P, Miani A . Covid-19 Outbreak Progression in Italian Regions: Approaching the Peak by the End of March in Northern Italy and First Week of April in Southern Italy. Int J Environ Res Public Health. 2020; 17(9). PMC: 7246918. DOI: 10.3390/ijerph17093025. View