| { | |
| "original_study": { | |
| "claim": { | |
| "hypothesis": "At thecountry level,the democracy index will be positively associated with the total number of confirmed infections per one million people.", | |
| "hypothesis_location": "p. 8, Determinants of Infectious Disease Spread section; it also mentioned in the abstract.", | |
| "statement": "The positive sign of democracy index indicates that more democratic countries are affected more by the disease. coefficient = 86.76467, p = 0.0001.", | |
| "statement_location": "Table1, Model1.", | |
| "study_type": "Observational" | |
| }, | |
| "data": { | |
| "source": "European Centre for Disease Prevention and Control (ECDC) for data on coronavirus and population; meteoblue.com for the average yearly temperature; indexmundi.com for the average precipitation; Databank of the World Bank for the openness data; Wikipedia for the scores of democracy index.", | |
| "wave_or_subset": "coronavirus total infection cases: from 31 December 2019 to 03 April 2020; population data: year 2018; Wikipedia data: year 2019.", | |
| "sample_size": "163", | |
| "unit_of_analysis": "countries", | |
| "access_details": "not stated; all sources seem to be open access.", | |
| "notes": "Some countries were excluded (it is not mentioned which ones) due to unavailability of all data. Total cases of infection were converted to cases per one million population to capture the population effect. The economic and social variables, viz. openness, democracy index and population density were measured on yearly basis. The explanatory variables are static in nature.The estimated models suffer from heteroscedasticity and violate the normality assumption. Also, the model of interest (Model 1) is not free from autocorrelation." | |
| }, | |
| "method": { | |
| "description": "The authors check linkages between the severity of COVID-19 infections and environmental, economic, and social factors across countries.", | |
| "steps": "1. The process is not described explicitly but it can be infered that: the authors collected the data from relevant sources. \n2. After cleaning they converted total infection cases to cases per one million population. \n3. Then the data was merged and the model estimated with least squares method.", | |
| "models": "least squares method", | |
| "outcome_variable": "Y = cases of infection per one million people on 03 April 2020 by countries", | |
| "independent_variables": "yearly average temperature, yearly average precipitation, openness (ratio of international trade to GDP), democracy index (proxy for social cohesion) , population density", | |
| "control_variables": "NA", | |
| "tools_software": "Excel and STATA for manipulation and transformation of data; EVIEWS for estimation." | |
| }, | |
| "results": { | |
| "summary": "The results show that higher democracy scores are associated with greater COVID-19 impact (coefficient = 86.76, p = 0.0001).", | |
| "numerical_results": [ | |
| { | |
| "outcome_name": "Y", | |
| "value": "86.76467", | |
| "unit": "infection cases", | |
| "effect_size": "not stated", | |
| "confidence_interval": { | |
| "lower": "not stated", | |
| "upper": "not stated", | |
| "level": "not stated" | |
| }, | |
| "p_value": "0.0001", | |
| "statistical_significance": "true", | |
| "direction": "positive" | |
| } | |
| ] | |
| }, | |
| "metadata": { | |
| "original_paper_id": "https://doi.org/10.1101/2020.04.08.20058164", | |
| "original_paper_title": "Is the spread of COVID-19 across countries influenced by environmental, economic and social factors?", | |
| "original_paper_code": "not stated", | |
| "original_paper_data": "not stated" | |
| } | |
| } | |
| } | |