Impact of containment measures on community mobility, daily confirmed cases, and mortality in the third wave of COVID-19 epidemic in Myanmar

Abstract

In Myanmar, third wave of COVID-19 epidemic began with a surge of confirmed cases in the last week of May 2021. The laboratory-confirmed cases and deaths distinctly increased within 9 weeks. The government and the Ministry of Health adopted containment measures to flatten the peak of the epidemic and to suppress the disease transmission. The strictly containment measures: stay-at-home restrictions, school closure, and office closure have reduced the community mobility, confirmed cases and mortality. Therefore, the timely containment measures implemented by the government were important to reduce the transmission as observed in the third wave of COVID-19 epidemic in Myanmar.

Statistical model for factors correlating with COVID-19 deaths

“…One approach explores how countries were prepared for crises such as COVID-19 [ [2] , [3] , [4] ] or how to design strategies to cope with future pandemics in society [ [5] , [6] , [7] ]. Researchers have also explored the side effects of interventions, such as changes in mobility [ 8 ] or air pollution [ 9 , 10 ], and compared the first and second waves of COVID-19 in terms of deaths, confirmed cases and hospitalization [ 11 , 12 ]. Furthermore, several researchers have published models and analyses of impact of different factors or interventions on deaths and incidences [ 8 , [13] , [14] , [15] , [16] , [17] , [18] , [19] ].…”

“…Researchers have also explored the side effects of interventions, such as changes in mobility [ 8 ] or air pollution [ 9 , 10 ], and compared the first and second waves of COVID-19 in terms of deaths, confirmed cases and hospitalization [ 11 , 12 ]. Furthermore, several researchers have published models and analyses of impact of different factors or interventions on deaths and incidences [ 8 , [13] , [14] , [15] , [16] , [17] , [18] , [19] ]. In previous studies, several country specific factors, such as healthcare system specific, environmental and cultural features, as well non-pharmaceutical measures, such as school and workplace closing, public events closing, restrictions on gatherings, stay at home orders, restrictions on internal movement, international travel controls and public info campaigns, are studied as predictive factors for modelling epidemic impact and country differences [ 8 , [14] , [15] , [16] , [20] , [21] , [22] , [23] , [24] , [25] , [26] ].…”

“…Furthermore, several researchers have published models and analyses of impact of different factors or interventions on deaths and incidences [ 8 , [13] , [14] , [15] , [16] , [17] , [18] , [19] ]. In previous studies, several country specific factors, such as healthcare system specific, environmental and cultural features, as well non-pharmaceutical measures, such as school and workplace closing, public events closing, restrictions on gatherings, stay at home orders, restrictions on internal movement, international travel controls and public info campaigns, are studied as predictive factors for modelling epidemic impact and country differences [ 8 , [14] , [15] , [16] , [20] , [21] , [22] , [23] , [24] , [25] , [26] ]. Many studies have concluded that the interventions and containment measures slow down the pandemic and seem to explain country differences in COVID-19 mortality [ 8 , [14] , [15] , [16] , [17] , 25 , 26 ].…”

“…In previous studies, several country specific factors, such as healthcare system specific, environmental and cultural features, as well non-pharmaceutical measures, such as school and workplace closing, public events closing, restrictions on gatherings, stay at home orders, restrictions on internal movement, international travel controls and public info campaigns, are studied as predictive factors for modelling epidemic impact and country differences [ 8 , [14] , [15] , [16] , [20] , [21] , [22] , [23] , [24] , [25] , [26] ]. Many studies have concluded that the interventions and containment measures slow down the pandemic and seem to explain country differences in COVID-19 mortality [ 8 , [14] , [15] , [16] , [17] , 25 , 26 ]. Furthermore, Sorokowska et al [ 22 ], found that interpersonal distances due to social norms and culture can affect the behaviour of the citizens which can influence the outcome of an epidemic.…”

Correction: Impact of containment measures on community mobility, daily confirmed cases, and mortality in the third wave of COVID-19 epidemic in Myanmar

“…Following publication of the original article [ 1 ], it was brought to our attention that the Competing Interests section was incorrect. It should read as follows:…”