SARS-COV-2 Vaccines: What Indicators are Associated with the Worldwide Distribution of the First Doses.

The present study aimed to identify the factors associated with the distribution of the first doses of the COVID-19 vaccine. In this study, we used 9 variables: human development index (HDI), gross domestic product (GDP per capita), Gini index, population density, extreme poverty, life expectancy, COVID cases, COVID deaths, and reproduction rate. The time period was until February 1, 2021. The variable of interest was the sum of the days after the vaccine arrived in the countries. Pearson's correlation coefficients were calculated, and t-test was performed between the groups that received and did not receive the immunizer, and finally, a stepwise linear regression model was used. 58 (30.4%) of the 191 countries received the SARS-CoV-2 vaccine. The countries that received the most doses were the United States, China, the United Kingdom, and Israel. Vaccine access in days showed a positive Pearson correlation HDI, GDP, life expectancy, COVID-19 cases, deaths, and reproduction rate. Human development level, COVID-19 deaths, GDP per capita, and population density are able to explain almost 50% of the speed of access to immunizers. Countries with higher HDI and per capita income obtained priority access.

Determinants of access to the SARS-CoV-2 vaccine: a preliminary approach.

BACKGROUND: The determinants of access to immunizers are still poorly understood, leading to questions about which criteria were considered in this distribution. Given the above, the present study aimed to analyze the determinants of access to the SARS-CoV-2 vaccine by different countries. METHODS: The study covered 189 countries using data from different public databases, and collected until February 19, 2021. We used eight explanatory variables: gross domestic product (GDP), extreme poverty, human development index (HDI), life expectancy, median age, coronavirus disease 2019 (COVID-19) cases, COVID-19 tests, and COVID-19 deaths. The endogenous variables were total vaccine doses, vaccine doses per thousand, and days of vaccination. The structural equation modeling (SEM) technique was applied to establish the causal relationship between the country's COVID-19 impact, socioeconomic variables, and vaccine access. To support SEM, we used confirmatory factor analysis, t-test, and Pearson's correlation. RESULTS: We collected the sample on February 19, and to date, 80 countries (42.1%) had already received a batch of immunizers against COVID-19. The countries with first access to the vaccine (e.g., number of days elapsed since they took the first dose) were the United Kingdom (68), China (68), Russia (66), and Israel (62). The countries receiving the highest doses were the United States, China, India, and Israel. The countries with extreme poverty had lower access to vaccines and the richer countries gained priority access. Countries most affected by COVID (deaths and cases) also received immunizers earlier and in greater volumes. Unfortunately, similar to other vaccines, indicators, such as income, poverty, and human development, influence vaccines' access. Thus affecting the population of vulnerable and less protected countries. Therefore, global initiatives for the equitable distribution of COVID need to be discussed and encouraged. CONCLUSIONS: Determinants of vaccine distribution consider the impact of the disease in the country and are also affected by favorable socioeconomic indicators. The COVID-19 vaccines need to be accessible to all affected countries, regardless of their social hands.

The impact of social distancing on COVID-19 infections and deaths.

BACKGROUND: To assess the impact of the social isolation index on the number of infections and deaths by COVID-19 in the state of São Paulo (Brazil). METHODS: Daily isolation data, obtained through geolocation information by mobile phone, were evaluated together with the number of daily infections and deaths by COVID-19 in the state of São Paulo. The study was conducted from February 26 to May 19, 2020. The data were modeled through the vector autoregression (VAR) model. RESULTS: The isolation index has an effect of approximately 5% in variation in the number of infections, and 7% in the number of deaths. The impulse response function (IRF) caused a drop of 0.15% in the number of new cases/day, and 0.17% in the number of deaths/day following a shock in the isolation index. For both cases, this effect occurred 1 day after the shock and stabilized after 10 periods. An increase of 1% in the isolation index led to a reduction of 6.91% in new cases and 6.90% in the number of deaths. The 30 cumulative day reduction reached 22.72% in terms of transmission and 35.39% for deaths. CONCLUSIONS: The social isolation index is related to deaths and infections from SARS-CoV-2. Although distancing measures are accompanied with impacts on the economy and the emergence of other morbidities, the benefits caused by the reduction in the speed of contagion are significant. The adoption of distancing measures has a substantial impact on the number of infected individuals and deaths by COVID-19.

Association between climate variables and global transmission oF SARS-CoV-2.

In this study, we aimed at analyzing the associations between transmission of and deaths caused by SARS-CoV-2 and meteorological variables, such as average temperature, minimum temperature, maximum temperature, and precipitation. Two outcome measures were considered, with the first aiming to study SARS-CoV-2 infections and the second aiming to study COVID-19 mortality. Daily data as well as data on SARS-CoV-2 infections and COVID-19 mortality obtained between December 1, 2019 and March 28, 2020 were collected from weather stations around the world. The country's population density and time of exposure to the disease were used as control variables. Finally, a month dummy variable was added. Daily data by country were analyzed using the panel data model. An increase in the average daily temperature by one degree Fahrenheit reduced the number of cases by approximately 6.4 cases/day. There was a negative correlation between the average temperature per country and the number of cases of SARS-CoV-2 infections. This association remained strong even with the incorporation of additional variables and controls (maximum temperature, average temperature, minimum temperature, and precipitation) and fixed country effects. There was a positive correlation between precipitation and SARS-CoV-2 transmission. Countries with higher rainfall measurements showed an increase in disease transmission. For each average inch/day, there was an increase of 56.01 cases/day. COVID-19 mortality showed no significant association with temperature.