Association between population density and infection rate suggests the importance of social distancing and travel restriction in reducing the COVID-19 pandemic.

Currently, 2019-nCoV has spread to most countries of the world. Understanding the environmental factors that affect the spread of the disease COVID-19 infection is critical to stop the spread of the disease. The purpose of this study is to investigate whether population density is associated with the infection rate of the COVID-19. We collected data from official webpages of cities in China and in the USA. The data were organized on Excel spreadsheets for statistical analyses. We calculated the morbidity and population density of cities and regions in these two countries. We then examined the relationship between morbidity and other factors. Our analysis indicated that the population density in cities in Hubei province where the COVID-19 was severe was associated with a higher percentage of morbidity, with an r value of 0.62. Similarly, in the USA, the density of 51 states and territories is also associated with morbidity from COVID-19 with an r value of 0.55. In contrast, as a control group, there is no association between the morbidity and population density in 33 other regions of China, where the COVID-19 epidemic is well under control. Interestingly, our study also indicated that these associations were not influenced by the first case of COVID-19. The rate of morbidity and the number of days from the first case in the USA have no association, with an r value of - 0.1288. Population density is positively associated with the percentage of patients with COVID-19 infection in the population. Our data support the importance of such as social distancing and travel restriction in the prevention of COVID-19 spread.

Toward a Country-Based Prediction Model of COVID-19 Infections and Deaths Between Disease Apex and End: Evidence From Countries With Contained Numbers of COVID-19.

The complexity of COVID-19 and variations in control measures and containment efforts in different countries have caused difficulties in the prediction and modeling of the COVID-19 pandemic. We attempted to predict the scale of the latter half of the pandemic based on real data using the ratio between the early and latter halves from countries where the pandemic is largely over. We collected daily pandemic data from China, South Korea, and Switzerland and subtracted the ratio of pandemic days before and after the disease apex day of COVID-19. We obtained the ratio of pandemic data and created multiple regression models for the relationship between before and after the apex day. We then tested our models using data from the first wave of the disease from 14 countries in Europe and the US. We then tested the models using data from these countries from the entire pandemic up to March 30, 2021. Results indicate that the actual number of cases from these countries during the first wave mostly fall in the predicted ranges of liniar regression, excepting Spain and Russia. Similarly, the actual deaths in these countries mostly fall into the range of predicted data. Using the accumulated data up to the day of apex and total accumulated data up to March 30, 2021, the data of case numbers in these countries are falling into the range of predicted data, except for data from Brazil. The actual number of deaths in all the countries are at or below the predicted data. In conclusion, a linear regression model built with real data from countries or regions from early pandemics can predict pandemic scales of the countries where the pandemics occur late. Such a prediction with a high degree of accuracy provides valuable information for governments and the public.

Capacity of transportation and spread of COVID-19-an ironical fact for developed countries.

The widespread epidemic of the COVID-19 in developed countries such as Europe and the USA has sparked many speculations. What factors caused the rapid early pandemic of the COVID-19 in developed countries is the main goal of this study. We collected the main disease indicators and various environmental and economic factors in 61 countries around the world. Our results show that the number of cases is positively correlated with the country's GDP. We further analyzed the factors related to the spread of the disease. They indicate a strong positive correlation between the total patient numbers and the number of airline passengers, with an r value of 0.80. There is also a positive correlation between the number of car ownership and the total patient, with an r value of 0.35. Both the flight passengers and car ownership contribute 66% to the number of total patients. The total death numbers and the number of airline passengers are positively correlated, with an r value of 0.71. A positive correlation between the number of car ownership and the total deaths is with an r value of 0.42. The total contribution of both the flight passengers and car ownership to the number of total deaths is 57%. Our conclusion is that the main cause of the coronavirus pandemic in developed countries is related to the transportation. In other words, the number of travelers determined the early coronavirus pandemic. Therefore, it is necessary to strengthen restrictions and screening of passengers at airports, especially international airports.

COVID-19 vaccine: Call for employees in international transportation industries and international travelers as the first priority in global distribution.

While countries are in a hurry to obtain SARS-CoV-2 vaccine, we are concerned with the availability of vaccine and whether a vaccine will be available to all in need. We predicted three possible scenarios for vaccine distributions and urge an international united action on the worldwide equitable access. In case the international community does not reach a consensus on how to distribute the vaccine to achieve worldwide equitable access, we call for a distribution plan that includes the employees in international transportation industries and international travelers to halt the disease transmission and promote the recovery of the global economy.