Impact of geographical, meteorological, demographic, and economic indicators on the trend of COVID-19: A global evidence from 202 affected countries

Research problem

Public health and the economy face immense problems because of pathogens in history globally. The outbreak of novel SARS-CoV-2 emerged in the form of coronavirus (COVID-19), which affected global health and the economy in almost all countries of the world.

Study design

The objective of this research is to examine the trend of COVID-19, deaths, and transmission rates in 202 affected countries. The virus-affected countries were grouped according to their continent, meteorological indicators, demography, and income. This is quantitative research in which we have applied the Poisson regression method to assess how temperature, precipitation, population density, and income level impact COVID-19 cases and fatalities. This has been done by using a semi-parametric and additive polynomial model.

Findings

The trend analysis depicts that COVID-19 cases per million were comparatively higher for two groups of countries i.e., (a) average temperature below 7.5 °C and (b) average temperature between 7.5 °C and 15 °C, up to the 729th day of the outbreak. However, COVID-19 cases per million were comparatively low in the countries having an average temperature between 22.5 °C and 30 °C. The day-wise trend was comparatively higher for the countries having average precipitation between (a) 1 mm and 750 mm and (b) 750 mm and 1500 mm up to the 729th day of the outbreak. The day-wise trend was comparatively higher for the countries having more than 1000 people per sq. km. Discussing the COVID-19 cases per million, the day-wise trend was higher for the HICs, followed by UMICs, LMICs, and LIC.

Conclusion

The study highlights the need for targeted interventions and responses based on the specific circumstances and factors affecting each country, including their geographical location, temperature, precipitation levels, population density, and per capita income.

Association between meteorological indicators and COVID-19 pandemic in Pakistan

This study was designed to investigate the impact of meteorological indicators (temperature, rainfall, and humidity) on total COVID-19 cases in Pakistan, its provinces, and administrative units from March 10, 2020, to August 25, 2020. The correlation analysis showed that COVID-19 cases and temperature showed a positive correlation. It implies that the increase in COVID-19 cases was reported due to an increase in the temperature in Pakistan, its provinces, and administrative units. The generalized Poisson regression showed that the rise in the expected log count of COVID-19 cases was 0.024 times for a 1 °C rise in the average temperature in Pakistan. Second, the correlation between rainfall and COVID-19 cases was negative in Pakistan. However, the regression coefficient between the expected log count of COVID-19 cases and rainfall was insignificant in Pakistan. Third, the correlation between humidity and the total COVID-19 cases was negative, which implies that the increase in humidity is beneficial to stop the transmission of COVID-19 in Pakistan, its provinces, and administrative units. The reduction in the expected log count of COVID-19 cases was 0.008 times for a 1% increase in the humidity per day in Pakistan. However, humidity and COVID-19 cases were positively correlated in Sindh province. It is required to create awareness among the general population, and the government should include the causes, symptoms, and precautions in the educational syllabus. Moreover, people should adopt the habit of hand wash, social distancing, personal hygiene, mask-wearing, and the use of hand sanitizers to control the COVID-19.

Impact of wind speed and air pollution on COVID-19 transmission in Pakistan

This study investigated the effect of wind speed and air pollution on COVID-19 from March 10, 2020, to October 04, 2020, in Pakistan. Wind speed and COVID-19 had positive correlation in Pakistan and its provinces. The inverted U-shaped dose-response curve was found for wind speed and COVID-19 in Punjab. Initially, the dose-response curve showed a positive link between wind speed and COVID-19 in Pakistan, Sindh, Khyber Pakhtunkhwa, and Islamabad Capital Territory. Later, it becomes downward sloped in Sindh, Khyber Pakhtunkhwa, and Islamabad Capital Territory. The expected log count of COVID-19 was increased by 0.113 times (Pakistan), 0.074 times (Punjab), 0.042 times (Sindh), and 0.082 times (Khyber Pakhtunkhwa) for a 1 km/h increase in the wind speed. The correlation between particulate matter and COVID-19 was positive (Pakistan, Punjab, and Islamabad Capital Territory) and negative (Sindh). The dose-response curve for particulate matter and COVID-19 had inverted U-shaped (Pakistan, Punjab, and Khyber Pakhtunkhwa) positively sloped (Islamabad Capital Territory), and negatively sloped (Sindh). The inverted U-shaped association shows that the COVID-19 initially increased due to a rise in the particulate matter but reduced when the particulate matter was above the threshold level. The particulate matter was also responsible to wear face masks and restricted mobility. The expected log count of COVID-19 cases was reduced by 0.005 times in Sindh for 1 unit increase in particulate matter. It is recommended to reduce particulate matter to control respiratory problems. The government should use media (print, electronic, social) and educational syllabus to create awareness about precautionary measures.

[Study on the relationship between frequencies of prenatal care and neonatal low birth weight in women of childbearing age from rural areas of Shaanxi province]

Objective: To investigate the relationship between frequencies of prenatal care and neonatal low birth weight (LBW) among women of childbearing age from the rural areas of Shaanxi province. Methods: A questionnaire survey was conducted among the childbearing-aged women from the rural areas. Samples were selected through multi stage stratified random sampling method. The childbearing aged women were in pregnancy or having had definite outcomes of pregnancy. Measurement of data was described by median±standard deviation, and chi square test was used to compare the rates. Neonatal low birth weight and frequencies of prenatal care were dependent variables and independent variables grouped into the generalized Poisson regression model. Confounding factors were under control. Results: The overall incidence rate of LBW was 3.75% among 18 911 rural women of childbearing age during 2010-2013. Frequencies on pregnancy care were up to 15 times (0.70%), with a minimum of 0 (0.70%), an average of 5.65±2.74 times (including ≥10 times accounted for 12.37%; ≥7 times accounted for 28.52%; ≥5 times accounted for 62.80% and <4 times accounted for 21.49%). After controlling confounding factors, results from the generalized Poisson regression analysis revealed that the difference was statistically significant when compared to the reference group. The incidence of neonatal LBW in the <4 group was 1.61 times of the one in the >7 group (OR=1.61, 95%CI:1.31-2.00) while in the <4 group it was 1.23 times of the 4-7 group (OR=1.23, 95% CI:1.04-1.45). Conclusion: In the rural areas of Shaanxi province, the incidence of neonatal LBW was gradually reduced through the increasing number of frequencies on prenatal care, among women of childbearing age.