Network meta-analysis of interventions to prevent person-to-person transmission of SARS-CoV-2 and COVID-19

The evidence on the relative ranking of effectiveness measures to prevent the SARS-CoV-2 spread from person to person is crucial for policy decision making. We have conducted the network meta-analysis by extending recent meta-analysis to delineate the relative effectiveness of different preventive measures. We observed properly used N95 respirator provides the best protection against SARS-CoV-2 in overall as well as healthcare set-up. In community settings, social distancing offers the best risk reduction against SARS-CoV-2.

Download Full-text

Relative Eco-Efficiency Recognition Based on DEA Neural Network

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.622-623.1456 ◽

2012 ◽

Vol 622-623 ◽

pp. 1456-1461

Author(s):

Feng Rong Zhang ◽

Yong Hong Cao ◽

Zhuo Li ◽

Xin Wei Xiao

Keyword(s):

Neural Network ◽

Decision Making ◽

Life Cycle ◽

Local Governments ◽

Policy Decision ◽

Life Cycle Stage ◽

Accurate Identification ◽

Policy Decision Making ◽

Set Up ◽

Relative Concept

Regional eco-efficiency is regarded as a relative concept. It could be presented the relative eco-efficiency with the change of economy and environment. That means it could not the optimal compared with the predicted optimum in the future. It would be a basis of policy decision making of local governments according to identify the life cycle stage of current regional eco-efficiency. As a beneficial supplement of the eco-efficiency measurement, this paper set up a DEA Neural Network recognition model which can effectively analyze and identify the current relative eco-efficiency of a regional. The empirical research shows that the phenomenon of forecasted value “floating upon” has been restrained effectively. It is an obvious advantage that this method is showed as a method of fast convergence, accurate identification, extensive application and promotion value in practice.

Download Full-text

Lessons and Challenges to be learned from different countries policy implication on COVID 19 recovery cases- A cross-sectional descriptive study

10.21203/rs.3.rs-21447/v1 ◽

2020 ◽

Author(s):

NAIYA PATEL

Keyword(s):

Decision Making ◽

Policy Decision ◽

Poisson Model ◽

Descriptive Study ◽

Cross Sectional ◽

Future Studies ◽

Social Distancing ◽

Policy Decision Making ◽

Using Data ◽

The Relationship

Abstract Background- The need to quantify the non-pharmaceutical measures in policy decision making is essential in current uncertain times of pandemic. The purpose of the current study is to quantify the relationship between Social Distancing measures and the Total number of tests performed with the Total number of recovered cases across 23 countries around the world, currently struck by COVID-19 pandemic.Methods- The cross-sectional descriptive study utilized STATA 16. for Poisson Model analysis using data collected across 23 countries. The statistical databases Statista, WHO situation reports, CDC website, respective country health ministry websites, and World Bank data was utilized to collected the lacking data details regarding COVID-19. The WHO regions/23 countries included in analysis are Republic of Korea, Japan, Australia, Malaysia, Philippines, Thailand, India, United States of America, Canada, Italy,Germany,United Kingdom,France,Austria,Croatia,Israel,Russian Federation,Spain,Belgium,Finland,Sweden,Switzerland,Iran (Islamic Republic of). The variables included in analysis are The factorial analysis of categorical data is included to quantify the levels of social distancing measures and its effect on the total number of recovered cases until April 2nd, 2020. Results- There exists a positive relationship between the improved number of recovered infected cases, and Social distancing measures of lockdown, the total number of tests performed depending on the stage at which it is completed. The availability of total medical doctors in each country affects the number of recovered cases in that particular country. Conclusion- Future studies might use it as a foundation for evaluation modeling in public health for policy decision making.

Download Full-text