tetano
Editor, Senior Moderator
Sci Rep
. 2022 Jan 18;12(1):944.
doi: 10.1038/s41598-021-04731-5.
Harnessing Artificial Intelligence to assess the impact of nonpharmaceutical interventions on the second wave of the Coronavirus Disease 2019 pandemic across the world
Sile Tao[SUP] #[/SUP][SUP] 1 [/SUP], Nicola Luigi Bragazzi[SUP] #[/SUP][SUP] 2 [/SUP], Jianhong Wu[SUP] 2 [/SUP], Bruce Mellado[SUP] 3 4 [/SUP], Jude Dzevela Kong[SUP] 5 [/SUP]
Affiliations
Abstract
In the present paper, we aimed to determine the influence of various non-pharmaceutical interventions (NPIs) enforced during the first wave of COVID-19 across countries on the spreading rate of COVID-19 during the second wave. For this purpose, we took into account national-level climatic, environmental, clinical, health, economic, pollution, social, and demographic factors. We estimated the growth of the first and second wave across countries by fitting a logistic model to daily-reported case numbers, up to the first and second epidemic peaks. We estimated the basic and effective (second wave) reproduction numbers across countries. Next, we used a random forest algorithm to study the association between the growth rate of the second wave and NPIs as well as pre-existing country-specific characteristics. Lastly, we compared the growth rate of the first and second waves of COVID-19. The top three factors associated with the growth of the second wave were body mass index, the number of days that the government sets restrictions on requiring facial coverings outside the home at all times, and restrictions on gatherings of 10 people or less. Artificial intelligence techniques can help scholars as well as decision and policy-makers estimate the effectiveness of public health policies, and implement "smart" interventions, which are as efficacious as stringent ones.
. 2022 Jan 18;12(1):944.
doi: 10.1038/s41598-021-04731-5.
Harnessing Artificial Intelligence to assess the impact of nonpharmaceutical interventions on the second wave of the Coronavirus Disease 2019 pandemic across the world
Sile Tao[SUP] #[/SUP][SUP] 1 [/SUP], Nicola Luigi Bragazzi[SUP] #[/SUP][SUP] 2 [/SUP], Jianhong Wu[SUP] 2 [/SUP], Bruce Mellado[SUP] 3 4 [/SUP], Jude Dzevela Kong[SUP] 5 [/SUP]
Affiliations
- PMID: 35042945
- DOI: 10.1038/s41598-021-04731-5
Abstract
In the present paper, we aimed to determine the influence of various non-pharmaceutical interventions (NPIs) enforced during the first wave of COVID-19 across countries on the spreading rate of COVID-19 during the second wave. For this purpose, we took into account national-level climatic, environmental, clinical, health, economic, pollution, social, and demographic factors. We estimated the growth of the first and second wave across countries by fitting a logistic model to daily-reported case numbers, up to the first and second epidemic peaks. We estimated the basic and effective (second wave) reproduction numbers across countries. Next, we used a random forest algorithm to study the association between the growth rate of the second wave and NPIs as well as pre-existing country-specific characteristics. Lastly, we compared the growth rate of the first and second waves of COVID-19. The top three factors associated with the growth of the second wave were body mass index, the number of days that the government sets restrictions on requiring facial coverings outside the home at all times, and restrictions on gatherings of 10 people or less. Artificial intelligence techniques can help scholars as well as decision and policy-makers estimate the effectiveness of public health policies, and implement "smart" interventions, which are as efficacious as stringent ones.