| { | |
| "original_study": { | |
| "claim": { | |
| "hypothesis": "Higher yearly average temperature is associated with fewer confirmed cases of COVID-19 infection per million population across countries.", | |
| "hypothesis_location": "Data sources and characteristics (p. 9) / Modeling Approach, p. 11)", | |
| "statement": "The study finds that yearly average temperature has a statistically significant negative association with cases of COVID-19 infection per million people, such that countries with higher average temperatures tend to have fewer confirmed cases per million.", | |
| "statement_location": "Abstract (p. 1) and Estimation Results and Discussion / Table 1 (p. 14, Temperature row).", | |
| "study_type": "Observational" | |
| }, | |
| "data": { | |
| "source": "Country-level data on confirmed COVID-19 cases and population from the European Centre for Disease Prevention and Control (ECDC); yearly average temperature from meteoblue.com; average precipitation from indexmundi.com; economic openness (trade as a percentage of GDP) from the World Bank Databank; and democracy index scores from the Economist Intelligence Unit’s Democracy Index 2019, as compiled on Wikipedia.", | |
| "wave_or_subset": "Cross-sectional snapshot of total confirmed COVID-19 cases by country on 03 April 2020, converted to cases per one million population, for 163 countries.", | |
| "sample_size": "163 countries.", | |
| "unit_of_analysis": "Country.", | |
| "access_details": "not stated (the paper lists data sources but does not describe specific access procedures or restrictions for these datasets).", | |
| "notes": "Total confirmed cases of COVID-19 by country are taken from 31 December 2019 to 03 April 2020, but only cases per one million people on 03 April 2020 are used as the dependent variable. Environmental, economic, and social explanatory variables (temperature, precipitation, openness, democracy index, population density) are measured on a yearly basis. Some countries are excluded due to missing data on one or more of these variables." | |
| }, | |
| "method": { | |
| "description": "The study uses cross-sectional ordinary least squares regressions to examine whether country-level environmental, economic, and social characteristics—including yearly average temperature—are associated with confirmed COVID-19 cases per million population across countries.", | |
| "steps": [ | |
| "Collect total confirmed COVID-19 cases and 2018 population by country from the ECDC and compute cases per one million population for 03 April 2020 as the dependent variable Y.", | |
| "Merge country-level yearly average temperature, average precipitation, economic openness (international trade as a percentage of GDP), democracy index scores, and population density into the dataset for the set of countries with complete data.", | |
| "Specify an initial linear regression model regressing cases of infection per million (Y) on temperature, precipitation, openness, democracy index, and population density.", | |
| "Estimate this model with least squares and identify that precipitation and population density are not statistically significant predictors.", | |
| "Re-estimate the model excluding precipitation and population density, retaining yearly average temperature, openness, and democracy index as predictors (Model 1, no lag).", | |
| "Conduct further models including a transformed lagged dependent variable via residuals to account for past infection levels (Models 2 and 3), while keeping temperature, openness, and democracy as predictors.", | |
| "Use Excel and STATA for data manipulation and EVIEWS software to estimate the regression models." | |
| ], | |
| "models": "Ordinary least squares linear regression of cases of infection per million population on yearly average temperature, economic openness, and democracy index (with additional specifications including a residual-based proxy for lagged infections).", | |
| "outcome_variable": "Total confirmed cases of COVID-19 infection per one million people in each country on 03 April 2020.", | |
| "independent_variables": "Yearly average temperature of countries (X1, measured as country-level average temperature).", | |
| "control_variables": "Economic openness (international trade as a percentage of GDP); democracy index score; and, in extended lag models, the residual term v_i derived from regressing lagged cases on the explanatory variables.", | |
| "tools_software": "Excel and STATA for data manipulation and transformation; EVIEWS for estimation of the regression models." | |
| }, | |
| "results": { | |
| "summary": "Across the reported regression models, yearly average temperature is negatively and significantly associated with COVID-19 cases per million.", | |
| "numerical_results": [ | |
| { | |
| "outcome_name": "Cases of COVID-19 infection per one million people (effect of yearly average temperature)", | |
| "value": -16.55022, | |
| "unit": "unstandardized OLS regression coefficient (change in cases per million per one-unit increase in yearly average temperature)", | |
| "effect_size": "unstandardized OLS regression coefficient", | |
| "confidence_interval": { | |
| "lower": "not stated", | |
| "upper": "not stated", | |
| "level": "not stated" | |
| }, | |
| "p_value": "0.0064 (Model 1, no lag)", | |
| "statistical_significance": 1, | |
| "direction": "negative" | |
| } | |
| ] | |
| }, | |
| "metadata": { | |
| "original_paper_id": "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" | |
| } | |
| } | |
| } |