Global COVID-19 Pandemic Waves: Limited Lessons Learned Worldwide over the Past Year

Abstract

The occurrence of coronavirus disease 2019 (COVID-19) was followed by a small burst of cases around the world; afterward, due to a series of emergency non-pharmaceutical interventions (NPIs), the increasing number of confirmed cases slowed down in many countries. However, the subsequent gradual easing of governance led to a sudden increase in cases in late July 2020, arousing deep concern across the globe. This study evaluates the situation of the COVID-19 pandemic in countries and territories worldwide from January 2020 to February 2021. According to the time-varying reproduction number ( R ( t )) of each country or territory, the results show that almost half of the countries and territories in the world have never controlled the epidemic. Among the countries and territories that had once contained the occurrence, nearly half failed to maintain their prevention and control, causing the COVID-19 pandemic to rebound across the world—resulting in even higher waves in half of the rebounding countries or territories. This work also proposes and uses a time-varying country-level transmission risk score (CTRS), which takes into account both R ( t ) and daily new cases, to demonstrate country-level or territory-level transmission potential and trends. Time-varying hierarchical clustering of time-varying CTRS values was used to successfully reveal the countries and territories that contributed to the recent aggravation of the global pandemic in the last quarter of 2020 and the beginning of 2021, and to identify countries and territories with an increasing risk of COVID-19 transmission in the near future. Furthermore, a regression analysis indicated that the introduction and relaxation of NPIs, including workplace closure policies and stay-at-home requirements, appear to be associated with recent global transmission changes. In conclusion, a systematic evaluation of the global COVID-19 pandemic over the past year indicates that the world is now in an unexpected situation, with limited lessons learned. Summarizing the lessons learned could help in designing effective public responses for constraining future waves of COVID-19 worldwide.

Improvements throughout the Three Waves of COVID-19 Pandemic: Results from 4 Million Inhabitants of North-West Italy

“…Many authors described the pandemic trends in editorials or opinion papers [ 18 , 22 , 23 ], and registries have collected data from different COVID-19 waves in 2020 [ 12 , 13 , 24 , 25 ], but this is the first observational study that collected clinical data on a region-wide cohort in Italy, evaluating about 245,000 SARS-CoV-2 infected subjects in the first three waves since the beginning of the pandemic. While in Italy, the first wave overwhelmed an unprepared health care system until strict restrictions were imposed at the national level [ 2 ], organizational improvement was achieved over time, and policies were targeted at the regional level throughout the following waves [ 26 ].…”

Changes in SARS-CoV-2 seroprevalence and population immunity in Finland, 2020–2022

“…The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), responsible for the coronavirus disease 2019 (COVID-19) pandemic 1 , has spread rapidly in successive waves and caused significant morbidity and mortality throughout the world 2 . However, the epidemic waves and different SARS-CoV-2 variants have progressed at varying pace and volume in different countries [3][4][5] .…”

A novel resilience analysis methodology for airport networks system from the perspective of different epidemic prevention and control policy responses

“…Different epidemic prevention and control measures would lead to different levels of confirmed cases and deaths, and some countries and territories in the world failed to maintain their prevention and control, causing the COVID-19 pandemic to rebound in even higher waves [ 37 ]. Therefore, we assessed the different strengths of prevention and control policy responses in each country and region using the stringency index.…”