Border Restriction as a Public Health Measure to Limit Outbreak of Coronavirus Disease 2019 (COVID-19)

  1. Kwok Wang Chun
  2. Wong Chun Ka
  3. Ma Ting Fung
  4. Ho Ka Wai
  5. Fan Wai Tong Louis
  6. Chan King Pui Florence
  7. Chan Shung Kay Samuel
  8. Tam Chi Chun Terence
  9. Ho Pak Leung
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Abstract

Background

Coronavirus Disease 2019 (COVID-19) led to pandemic that affected almost all countries in the world. Many countries have implemented border restriction as a public health measure to limit local outbreak. However, there is inadequate scientific data to support such a practice, especially in the presence of an established local transmission of the disease.

Method

A novel metapopulation Susceptible-Exposed-Infectious-Recovered (SEIR) model with inspected migration was applied to investigate the effect of border restriction between Hong Kong and mainland China on the epidemiological characteristics of COVID-19 in Hong Kong. Isolation facilities occupancy was also studied.

Results

At R 0 of 2·2, the cumulative COVID-19 cases in Hong Kong can be reduced by 13·99% (from 29,163 to 25,084) with complete border closure. At an in-patient mortality of 1·4%, the number of deaths can be reduced from 408 to 351 (57 lives saved). However, border closure alone was insufficient to prevent full occupancy of isolation facilities in Hong Kong; effective public health measures to reduce local R 0 to below 1·6 was necessary.

Conclusion

As a public health measure to tackle COVID-19, border restriction is effective in reducing cumulative cases and mortality.

Article summary

Strengths and limitations of this study

  • A novel metapopulation SEIR model with inspected migration was developed to investigate the epidemiological characteristics of COVID-19 in Hong Kong, Guangdong and the rest of China (excluding Hubei) in the presence or absence of border restriction.

  • The presented model is also suitable for further analysis of other emerging infectious diseases.

  • Border restriction is an effective public health measure in reducing cumulative cases and mortality for COVID-19.

  • Interaction was assumed to be well-mixed within patch. The spatial effect in disease transmission within each patch is ignored, which can have a non-trivial effect on the dynamic of infectious disease.

  • The proposed model is deterministic in nature which ignores the randomness in migration and in the interactions among people; a stochastic model would be more realistic especially early in the disease.

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