COVID-19 incidence in border regions: spatiotemporal patterns and border control measures

Public Health. 2022 Jan:202:80-83. doi: 10.1016/j.puhe.2021.11.006. Epub 2021 Nov 15.

Abstract

Objectives: Among the few studies examining patterns of COVID-19 spread in border regions, findings are highly varied and partially contradictory. This study presents empirical results on the spatial and temporal dynamics of incidence in 10 European border regions. We identify geographical differences in incidence between border regions and inland regions, and we provide a heuristic to characterise spillover effects.

Study design: Observational spatiotemporal analysis.

Methods: Using 14-day incidence rates (04/2020 to 25/2021) for border regions around Germany, we delineate three pandemic 'waves' by the dates with the lowest recorded rates between peak incidence. We mapped COVID-19 incidence data at the finest spatial scale available and compared border regions' incidence rates and trends to their nationwide values. The observed spatial and temporal patterns are then compared to the time and duration of border controls in the study area.

Results: We observed both symmetry and asymmetry of incidence rates within border pairs, varying by country. Several asymmetrical border pairs feature temporal convergence, which is a plausible indicator for spillover dynamics. We thus derived a border incidence typology to characterise (1) symmetric border pairs, (2) asymmetric border pairs without spillover effects, and (3) asymmetric with spillover effects. In all groups, border control measures were enacted but appear to have been effective only in certain cases.

Conclusions: The heuristic of border pairs provides a useful typology for highlighting combinations of spillover effects and border controls. We conclude that border control measures may only be effective if the timing and the combination with other non-pharmaceutical measures is appropriate.

Keywords: Borders; COVID-19; Epidemiology; Geography; Public health.

Publication types

  • Observational Study

MeSH terms

  • COVID-19*
  • Humans
  • Incidence
  • Pandemics
  • SARS-CoV-2
  • Spatio-Temporal Analysis