Table 2.
comparison of studies analyzing the Covid-19 and meteorological relationship
| Study | Country(s) | Methodology | Time period | Findings |
|---|---|---|---|---|
| Adhikari and Yin (2020) | New York, USA | Negative binomial regression model | March 1 to April 20, 2020 | Significant and positive association between temperature, O3 concentration, relative humidity, cloud percentages, and Covid-19 cases; however, none of these are related to death |
| Al-Rousan and Al-Najjar (2020) | 30 Chinese provinces | Pearson’s correlation | January 22 to March 1, 2020 | Temperature, shortwave radiation and pressure are positively correlated with Covid-19 cases. Other variables are provincially distinct, and snowfall has no correlation |
| Auler et al. (2020) | Brazil | Exploratory data analysis, Shapiro-Wilk test, Clausius-Clapeyron equation | March 13 to April 13, 2020 | High mean temperatures and intermediate relative humidity influence the Covid-19 transmission rate |
| Berman and Ebisu (2020) | USA | Summary statistics and comparisons between pollution concentrations during historical versus current periods done using two-sided t-tests | January 8 to April 21 from 2017 to 2020 | Statistically significant declines in NO2 and PM2.5 were observed during the Covid-19 period |
| Bontempi (2020) | Italy | Reported data analysis | February 10 to March 27, 2020 | It is not necessary that PM10 as a carrier causes Covid-19 transmission |
| Briz-Redón and Serrano-Aroca (2020) | Spain | Spatio-temporal analysis | February 25 to March 28, 2020 | Warmer mean, minimum and maximum temperatures does not lead to any reduction in the Covid-19 cases |
| Chien and Chen (2020) | USA | Generalized additive model (GAM) | March 22, 2020, to April 22, 2020 | Average temperature, minimum relative humidity, and precipitation minimize the Covid-19 risk after some peak value |
| Gupta et al. (2020) | USA, India | Distribution modeling- mean, standard deviation | January 1 to April 9, 2020 | Covid-19 spread in the USA is significant for states with 4 < AH < 6 g/m3, and temperature in a wider range of 4–11 °C with number of new cases N 10,000 |
| Iqbal et al. (2020) | China | Continuous wavelet transform (CWT), wavelet transform coherence (WTC), partial wavelet coherence (PWC), and multiple wavelet coherence (MWC) | January 21 to March 31, 2020 | No significant role of temperature in containing Covid-19 cases |
| Jain and Sharma (2020) | India | Trend analysis, paired t-test, GIS technique |
March to April 2019 and 2020 March 10 to 20, 2020 March 25 to April 6, 2020 |
Significant decline in all the pollutants except for O3 during the lock-down phase. Low relative humidity and very high wind speed and temperature lead to dispersion of air pollutants |
| Kumar (2020) | India | Pearson correlation | March to April, 2020 |
Positive association between temperature and Covid-19 cases. Negative association between humidity and Covid-19 cases |
| Zhu et al. 2020, b | 8 South American cities | Multiple regression analysis: Spearman’s correlation coefficient | February 23 to May 12, 2020 | The association between absolute humidity and incubative cases is negative. There were large differences between the effects of the coefficient of correlation in individual cases and Rt. Average wind speed and visibility were not closely related to daily incubation |
| Lin et al. (2020) | 20 Chinese provinces | Mechanism-based parameterisation scheme | January 22 to February 29, 2020 | Higher population density was linearly whereas a lower temperature was exponentially associated with an increased transmission rate of Covid-19 |
| Liu et al. fliu (2020) | China | Generalized linear models, meta-analysis | January 20 to March 2, 2020 | The low temperature climate, moderate diurnal temperatures and low humidity probably contribute to Covid-19 transmission |
| Ma et al. (2020) | China | Generalized additive model (GAM) | January 20 to February 29, 2020 | A positive association is found between daily deaths and DTR and SO2. Relative humidity and PM2.5 is negatively associated with daily deaths |
| Mandal and Panwar (2020) | China | Univariate analysis and statistical modeling | March 25 to April 18, 2020 | Strong negative correlations with statistical significance exist between MAET and several Covid-19 cases |
| Méndez-Arriaga (2020) | Mexico | Spearman’s non-parametric test | February 29 to March 31, 2020 | Negative association between temperature, atmospheric evaporation and Covid-19 cases while there is a positive association between precipitation and Covid-19 cases |
| Pani et al. (2020) | Singapore | Spearman and Kendall’s rank correlation tests | January 23 to May 31, 2020 | Temperature, dew point, relative humidity, absolute humidity, and water vapor show positive significant correlation with Covid-19 cases |
| Prata et al. (2020) | Brazil | Generalized Additive Model (GAM) | February 27 to April 1, 2020 | Negative linear relationship between temperature and Covid-19 cases |
| Rosario et al. (2020) | Brazil | Spearman’s rank correlation | March 6 to April 30, 2020 | Significant correlation between temperature maximum and average, radiation, wind speed and Covid-19 cases |
| Sarkodie and Owusu (2020) | Top 20 countries | CIPS and CADF panel unit root, Granger causality test, split-panel jack-knife method, kernel density estimation | January 22 to April 27, 2020 | Temperature and humidity have negative impact on COVID-19 whereas wind speed, dew/frost point, precipitation, and surface pressure have a positive impact |
| Sethwala et al. (2020) | USA, China, Canada, and Australia | Wilcoxon’s test | January 23 to April 11, 2020 | Definitive association between Covid-19 cases, death from Covid-19 cases, and ambient temperature exists |
| Sharma et al. (2020, b, c, d) | India |
Weather research forecasting (WRF) Air quality dispersion modeling system (AERMOD) |
March 16 to April 14 from 2017 to 2020 | Levels of PM2.5, PM10, CO, and NO2 decreased significantly while O3 level increased and SO2 showed negligible changes. Wind speed varies with direction whereas temperature has negligible variations in different regions. |
| Shi et al. (2020) | 31 Chinese provinces | Modified susceptible-exposed-infectious-recovered (M-SEIR) model | January 20 to February 29, 2020 |
Negative association between temperature and Covid-19 cases. No significant association between Covid-19 cases and absolute humidity |
| Tosepu et al. (2020) | Indonesia | Spearman-rank correlation test | January 1 to March 29, 2020 | Average temperature is significantly correlated with Covid-19 pandemic |
| Wang et al. (2020) | Globally 166 countries except China | Restricted cubic spline function and generalized linear mixture model | January 20 to February 4, 2020 | Temperature could significantly change Covid-19 cases to a certain extent |
| Wu et al. (2020, b) | Globally 166 countries except China | Log-linear generalized additive model, sensitivity analysis | As of March 27, 2020 | Both temperature and relative humidity were negatively associated with reported daily cases and deaths |
| Xie and Zhu (2020) | 122 Chinese cities | Generalized additive model (GAM) and piecewise linear regression | January 23 to February 29, 2020 | Results indicate that mean temperature has a positive linear relationship with the number of Covid-19 cases |
| Xu et al. (2020) | China | Observational analysis | 2017–2019 | Air quality near central China improved significantly |
| Zangari et al. (2020) | USA | Linear time lag models show | First 17 weeks from 2015 to 2020 | No difference in air quality between 2020 and 2015–2019 is found |
| Zhu et al. (2020) | 122 Chinese cities | Generalized additive model (GAM) | January 23 to February 29, 2020 | Results indicate a significant relationship between air pollution and Covid-19 infection |
| Zoran et al. (2020) | Italy | Spatial analysis | January 1–April 30, 2020 | Strong influence of daily averaged ground levels of concentrations, positively associated with average surface air temperature and inversely related to relative humidity and wind speed on Covid-19 cases |
Source: authors’ contribution