Spatial modeling could not differentiate early SARS-CoV-2 cases from the distribution of humans on the basis of climate in the United States

A Spatio-Temporal Analysis of the Environmental Correlates of COVID-19 Incidence in Spain

The novel SARS-CoV2 has disrupted health systems and the economy, and public health interventions to slow its spread have been costly. How and when to ease restrictions to movement hinges in part on whether SARS-CoV2 will display seasonality due to variations in temperature, humidity, and hours of sunshine. Here, we address this question by means of a spatio-temporal analysis in Spain of the incidence of COVID-19, the disease caused by the virus. Use of spatial Seemingly Unrelated Regressions (SUR) allows us to model the incidence of reported cases of the disease per 100,000 population as an interregional contagion process, in addition to a function of temperature, humidity, and sunshine. In the analysis we also control for GDP per capita, percentage of older adults in the population, population density, and presence of mass transit systems. The results support the hypothesis that incidence of the disease is lower at higher temperatures and higher levels of humidity. Sunshine, in contrast, displays a positive association with incidence of the disease. Our control variables also yield interesting insights. Higher incidence is associated with higher GDP per capita and presence of mass transit systems in the province; in contrast, population density and percentage of older adults display negative associations with incidence of COVID-19.

A Spatio-Temporal Analysis of the Environmental Correlates of COVID-19 Incidence in Spain

The novel SARS-CoV2 has disrupted health systems and the economy, and public health interventions to slow its spread have been costly. How and when to ease restrictions to movement hinges in part on whether SARS-CoV2 will display seasonality due to variations in temperature, humidity, and hours of sunshine. Here, we address this question by means of a spatio-temporal analysis in Spain of the incidence of COVID-19, the disease caused by the virus. Use of spatial Seemingly Unrelated Regressions (SUR) allows us to model the incidence of reported cases of the disease per 100,000 population as an interregional contagion process, in addition to a function of temperature, humidity, and sunshine. In the analysis we also control for GDP per capita, percentage of older adults in the population, population density, and presence of mass transit systems. The results support the hypothesis that incidence of the disease is lower at higher temperatures and higher levels of humidity. Sunshine, in contrast, displays a positive association with incidence of the disease. Our control variables also yield interesting insights. Higher incidence is associated with higher GDP per capita and presence of mass transit systems in the province; in contrast, population density and percentage of older adults display negative associations with incidence of COVID-19.

Is coronavirus disease (COVID-19) seasonal? A critical analysis of empirical and epidemiological studies at global and local scales

Coronavirus disease (COVID-19) has infected more than 50 million people and killed more than one million, worldwide, during less than a year course. COVID-19, which has already become the worst pandemic in the last 100 years, is still spreading worldwide. Since the beginning of the outbreak, it has been of particular interest to understand whether COVID-19 is seasonal; the finding might help for better planning and preparation for the fight against the disease. Over the past 12 months, numerous empirical and epidemiological studies have been performed to define the distinct diffusion patterns of COVID-19. Thereby, a wealth of data has accumulated on the relationship between various seasonal meteorological factors and COVID-19 transmissibility at global and local scales. In this review, we aimed to discuss whether COVID-19 exhibits any seasonal features in a global and local perspective by collecting and providing summaries of the findings from empirical and epidemiological studies on the COVID-19 pandemic during its first seasonal cycle.

Prospects for SARS-CoV-2 diagnostics, therapeutics and vaccines in Africa

The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in a global pandemic, prompting unprecedented efforts to contain the virus. Many developed countries have implemented widespread testing and have rapidly mobilized research programmes to develop vaccines and therapeutics. However, these approaches may be impractical in Africa, where the infrastructure for testing is poorly developed and owing to the limited manufacturing capacity to produce pharmaceuticals. Furthermore, a large burden of HIV-1 and tuberculosis in Africa could exacerbate the severity of infection and may affect vaccine immunogenicity. This Review discusses global efforts to develop diagnostics, therapeutics and vaccines, with these considerations in mind. We also highlight vaccine and diagnostic production platforms that are being developed in Africa and that could be translated into clinical development through appropriate partnerships for manufacture.

The COVID-19 pandemic has prompted unparalleled progress in the development of vaccines and therapeutics in many countries, but it has also highlighted the vulnerability of resource-limited countries in Africa. Margolin and colleagues review global efforts to develop SARS-CoV-2 diagnostics, therapeutics and vaccines, with a focus on the opportunities and challenges in Africa.

Global expansion of COVID-19 pandemic is driven by population size and airport connections

The pandemic state of COVID-19 caused by the SARS CoV-2 put the world in quarantine, led to hundreds of thousands of deaths and is causing an unprecedented economic crisis. However, COVID-19 is spreading in different rates at different countries. Here, we tested the effect of three classes of predictors, i.e., socioeconomic, climatic and transport, on the rate of daily increase of COVID-19 on its exponential phase. We found that population size and global connections, represented by countries’ importance in the global air transportation network, are the main explanations for the early growth rate of COVID-19 in different countries. Climate and socioeconomics had no significant effect in this big picture analysis. Our results indicate that the current claims that the growth rate of COVID-19 may be lower in warmer and humid countries should be taken very carefully, risking to disturb well-established and effective policy of social isolation that may help to avoid higher mortality rates due to the collapse of national health systems.

A Preliminary Investigation on the Statistical Correlations between SARS-CoV-2 Spread and Local Meteorology

The statistical correlation between meteorological parameters and the spread of Coronavirus Disease-2019 (COVID-19) was investigated in five provinces of Italy selected according to the number of infected individuals and the different trends of infection in the early stages of the epidemic: Bergamo and Brescia showed some of the highest trends of infections while nearby Cremona and Mantova, showed lower trends. Pesaro-Urbino province was included for further investigation as it was comparably affected by the epidemic despite being the area far from the Po valley. Moving means of the variables were considered to take into account the variability of incubation periods and uncertainties in the epidemiological data. The same analyzes were performed normalizing the number of new daily cases based on the number of checks performed. For each province, the moving mean of adjusted and unadjusted new daily cases were independently plotted versus each meteorological parameter, and linear regressions were determined in the period from 29th of February 2020 to 29th of March 2020. Strong positive correlations were observed between new cases and temperatures within three provinces representing 86.5% of the contagions. Strong negative correlations were observed between the moving means of new cases and relative humidity values for four provinces and more than 90% of the contagions.