Predicted Impact of the Lockdown Measure in Response to Coronavirus Disease 2019 (COVID-19) in Greater Bangkok, Thailand, 2021

Abstract

In mid-2021, Thailand faced a fourth wave of Coronavirus Disease 2019 (COVID-19) predominantly fueled by the Delta and Alpha variants. The number of cases and deaths rose exponentially, alongside a sharp increase in hospitalizations and intubated patients. The Thai Government then implemented a lockdown to mitigate the outbreak magnitude and prevent cases from overwhelming the healthcare system. This study aimed to model the severity of the outbreak over the following months by different levels of lockdown effectiveness. Secondary analysis was performed on data primarily obtained from the Ministry of Health; the data were analyzed using both the deterministic compartmental model and the system dynamics model. The model was calibrated against the number of daily cases in Greater Bangkok during June–July 2021. We then assessed the outcomes (daily cases, daily deaths, and intubated patients) according to hypothetical lockdowns of varying effectiveness and duration. The findings revealed that lockdown measures could reduce and delay the peak of COVID-19 cases and deaths. A two-month lockdown with 60% effectiveness in the reduction in reproduction number caused the lowest number of cases, deaths, and intubated patients, with a peak about one-fifth of the size of a no-lockdown peak. The two-month lockdown policy also delayed the peak until after December, while in the context of a one-month lockdown, cases peaked during the end of September to early December (depending on the varying degrees of lockdown effectiveness in the reduction in reproduction number). In other words, the implementation of a lockdown policy did not mean the end of the outbreak, but it helped delay the peak. In this sense, implementing a lockdown helped to buy time for the healthcare system to recover and better prepare for any future outbreaks. We recommend further studies that explore the impact of lockdown measures at a sub-provincial level, and examine the impact of lockdowns on parameters not directly related to the spread of disease, such as quality of life and economic implications for individuals and society.

Real-World Effectiveness of Mix-and-Match Vaccine Regimens against SARS-CoV-2 Delta Variant in Thailand: A Nationwide Test-Negative Matched Case-Control Study

“…Thailand is among numerous countries severely suffering from the Delta wave. The first COVID-19 wave in Thailand was caused by the original SARS-CoV-2 strain during March–May 2020, followed by the second Alpha wave originated from a cluster of cases in the inner city of a vicinity province of Bangkok [ 6 , 7 ]. The third wave in April 2021 was still attributed to the Alpha variant.…”

“…The third wave in April 2021 was still attributed to the Alpha variant. Then, the country was hardest hit by the fourth wave, caused by the Delta variant, in the second half of 2021 [ 7 ]. During that time, the Thai government implemented a lockdown policy to reduce the case and death tolls.…”

Forecasted Trends of the New COVID-19 Epidemic Due to the Omicron Variant in Thailand, 2022

“…The first COVID-19 wave in Thailand occurred during March–May 2020 due to super-spreading events from a boxing stadium and a nightlife hotspot in Bangkok downtown [ 12 ]. The second wave originated from a cluster of cases in a shrimp market in the inner city of Samut Sakhon and lasted between December 2020 and February 2021 [ 12 , 13 ]. The third wave was mostly caused by the Alpha variant in April 2021, followed by the fourth wave beginning in June 2021 due to extensive local transmission of the Delta variant [ 13 ].…”

“…The second wave originated from a cluster of cases in a shrimp market in the inner city of Samut Sakhon and lasted between December 2020 and February 2021 [ 12 , 13 ]. The third wave was mostly caused by the Alpha variant in April 2021, followed by the fourth wave beginning in June 2021 due to extensive local transmission of the Delta variant [ 13 ]. During that time, the Thai government implemented a lockdown policy as a pre-emptive measure to avoid the collapse of the health care system, and COVID-19 vaccines were rapidly rolled out.…”

Using simulation modelling and systems science to help contain COVID‐19: A systematic review

“…Twenty‐nine papers focused on COVID‐19 epidemic prediction, of which seven tried to estimate the R 0 in different regions (Müller et al, 2021 ; Rypdal et al, 2021 ; Yang et al, 2020 ) and countries (Guo & Xiao, 2020 ; Hoertel, Blachier, Blanco, Olfson, Massetti, Rico, et al, 2020 ; Kolokolnikov & Iron, 2021 ; Krivorotko et al, 2022 ). Most studies made prediction regarding cumulative infections (Hunter & Kelleher, 2021 ; Latkowski & Dunin‐Kȩplicz, 2021 ) and deaths (Ghaffarzadegan & Rahmandad, 2020 ), mortality (Benneyan et al, 2021 ; Lu, Guan, et al, 2021 ), daily testing capacity required (Fiore et al, 2021 ), hospital admissions (Warde et al, 2021 ) and demand for intensive care unit (ICU) beds (Bartz‐Beielstein et al, 2021 ; Garcia‐Vicuña et al, 2021 ; Irvine et al, 2021 ) and so forth as different interventions, such as physical distancing (Aghaei & Lohrasebi, 2021 ), various lockdown (Hoertel, Blachier, Blanco, Olfson, Massetti, Rico, et al, 2020 ; Uansri et al, 2021 ) and vaccination strategy (Suphanchaimat, Nittayasoot, et al, 2021 ; Suphanchaimat, Tuangratananon, et al, 2021 ). The rest predicted the future spread under school reopening (España et al, 2021 ; Rypdal et al, 2021 ; Son & RISEWIDs Team, 2020 ), city reopening (Yin et al, 2021 ), society activities reopening (Cremonini & Maghool, 2020 ) and international borders reopening (Pham et al, 2021 ).…”