Impact of Government Measures on COVID-19 Incidence of Reported New Cases: An Analysis of 9 Countries
Author(s)
Shah S*1;Ray B2;Holy C3;Sakthivel M1;Elangovanraaj N1;Krishnan D4;Gupta S5;Trivedi P1;Devulapally M1;Mohapatra A1, Coplan P6
1Mu Sigma, Bangalore, KA, India, 2Mu Sigma, Bangalore , India, 3Johnson & Johnson, Somerville, MA, USA, 4Mu-Sigma, Bangalore, India, 5Mu Sigma, Bangalore, India, 6Johnson & Johnson, New Brunswick, NJ, USA
OBJECTIVES: Many governments have adopted strict social distancing and stay-at-home orders to slow the spread of the Coronavirus-19 (COVID-19) in early 2020. The resulting growth in new infection cases has varied between geographies. This study was designed to measure the association between government action, mobility responses and new daily reported infection cases.
METHODS: This is a retrospective data analysis using the Worldwide WHO situation reports along with population density information and family size information (from Worldometer.com), government response data (7 policies: school closure (SC), workplace closure (WC), public event cancellation (PEC), restriction on gatherings (RG), public transport closure (PTC), stay-at-home (SAH), internal movement restrictions (IMR) - Oxford University) and mobility reports (Apple). Poisson regression models were developed to evaluate associations between new reported daily cases of COVID-19 and all other available variables, for a list of 9 countries that had more than 50K tests per million people. A 42-day lag was applied to mobility and government response variables to evaluate the impact thereof on new COVID-19 cases.
RESULTS: Nine countries were included in the analysis (Australia, Belgium, Chile, Italy, Peru, Saudi Arabia, Spain, UK, USA). From all the government measures, highest association was observed for RG (Incidence proportion ratio (IPR): 0.30 (95%CI: 0.19-0.47), followed by PTC (IPR: 0.38 (95%CI: 0.31-0.45)) and WC (IPR: 0.58 (95%CI: 0.46-0.72)). Reduction in mobility due to transit was only slightly protective, and only for the top quartile of transit mobility reduction (IPR: 0.91 (95%CI: 0.84-98)). Walking and driving mobility restriction were not associated with new cases.
CONCLUSIONS: Unprecedented government responses have been deployed to try and contain the COVID-19 pandemic. Whereas the actual effectiveness of each measure is unknown, this study highlights key metrics that might have proven useful in containing the COVID-19 pandemic.
METHODS: This is a retrospective data analysis using the Worldwide WHO situation reports along with population density information and family size information (from Worldometer.com), government response data (7 policies: school closure (SC), workplace closure (WC), public event cancellation (PEC), restriction on gatherings (RG), public transport closure (PTC), stay-at-home (SAH), internal movement restrictions (IMR) - Oxford University) and mobility reports (Apple). Poisson regression models were developed to evaluate associations between new reported daily cases of COVID-19 and all other available variables, for a list of 9 countries that had more than 50K tests per million people. A 42-day lag was applied to mobility and government response variables to evaluate the impact thereof on new COVID-19 cases.
RESULTS: Nine countries were included in the analysis (Australia, Belgium, Chile, Italy, Peru, Saudi Arabia, Spain, UK, USA). From all the government measures, highest association was observed for RG (Incidence proportion ratio (IPR): 0.30 (95%CI: 0.19-0.47), followed by PTC (IPR: 0.38 (95%CI: 0.31-0.45)) and WC (IPR: 0.58 (95%CI: 0.46-0.72)). Reduction in mobility due to transit was only slightly protective, and only for the top quartile of transit mobility reduction (IPR: 0.91 (95%CI: 0.84-98)). Walking and driving mobility restriction were not associated with new cases.
CONCLUSIONS: Unprecedented government responses have been deployed to try and contain the COVID-19 pandemic. Whereas the actual effectiveness of each measure is unknown, this study highlights key metrics that might have proven useful in containing the COVID-19 pandemic.
Conference/Value in Health Info
2020-11, ISPOR Europe 2020, Milan, Italy
Value in Health, Volume 23, Issue S2 (December 2020)
Code
IN3
Topic
Epidemiology & Public Health
Topic Subcategory
Prevalence, Incidence & Disease Risk Factors, Public Health
Disease
Infectious Disease (non-vaccine)