Macro-level drivers of SARS-CoV-2 transmission: A data-driven analysis of factors contributing to epidemic growth during the first wave of outbreaks in the United States.

BACKGROUND: Many questions remain unanswered about how SARS-CoV-2 transmission is influenced by aspects of the economy, environment, and health. A better understanding of how these factors interact can help us to design early health prevention and control strategies, and develop better predictive models for public health risk management of SARS-CoV-2. This study examines the associations between COVID-19 epidemic growth and macro-level determinants of transmission such as demographic, socio-economic, climate and health factors, during the first wave of outbreaks in the United States. METHODS: A spatial-temporal data-set was created from a variety of relevant data sources. A unique data-driven study design was implemented to assess the relationship between COVID-19 infection and death epidemic doubling times and explanatory variables using a Generalized Additive Model (GAM). RESULTS: The main factors associated with infection doubling times are higher population density, home overcrowding, manufacturing, and recreation industries. Poverty was also an important predictor of faster epidemic growth perhaps because of factors associated with in-work poverty-related conditions, although poverty is also a predictor of poor population health which is likely driving infection and death reporting. Air pollution and diabetes were other important drivers of infection reporting. Warmer temperatures are associated with slower epidemic growth, which is most likely explained by human behaviors associated with warmer locations i.e. ventilating homes and workplaces, and socializing outdoors. The main factors associated with death doubling times were population density, poverty, older age, diabetes, and air pollution. Temperature was also slightly significant slowing death doubling times. CONCLUSIONS: Such findings help underpin current understanding of the disease epidemiology and also supports current policy and advice recommending ventilation of homes, work-spaces, and schools, along with social distancing and mask-wearing. Given the strong associations between doubling times and the stringency index, it is likely that those states that responded to the virus more quickly by implementing a range of measures such as school closing, workplace closing, restrictions on gatherings, close public transport, restrictions on internal movement, international travel controls, and public information campaigns, did have some success slowing the spread of the virus.

The rise of West Nile Virus in Southern and Southeastern Europe: A spatial-temporal analysis investigating the combined effects of climate, land use and economic changes.

West Nile Virus (WNV) has recently emerged as a major public health concern in Europe; its recent expansion also coincided with some remarkable socio-economic and environmental changes, including an economic crisis and some of the warmest temperatures on record. Here we empirically investigate the drivers of this phenomenon at a European wide scale by constructing and analyzing a unique spatial-temporal data-set, that includes data on climate, land-use, the economy, and government spending on environmental related sectors. Drivers and risk factors of WNV were identified by building a conceptual framework, and relationships were tested using a Generalized Additive Model (GAM), which could capture complex non-linear relationships and also account for spatial and temporal auto-correlation. Some of the key risk factors identified in our conceptual framework, such as a higher percentage of wetlands and arable land, climate factors (higher summer rainfall and higher summer temperatures) were positive predictors of WNV infections. Interestingly, winter temperatures of between 2 °C and 6 °C were among some of the strongest predictors of annual WNV infections; one possible explanation for this result is that successful overwintering of infected adult mosquitoes (likely Culex pipiens) is key to the intensity of outbreaks for a given year. Furthermore, lower surface water extent over the summer is also associated with more intense outbreaks, suggesting that drought, which is known to induce positive changes in WNV prevalence in mosquitoes, is also contributing to the upward trend in WNV cases in affected regions. Our indicators representing the economic crisis were also strong predictors of WNV infections, suggesting there is an association between austerity and cuts to key sectors, which could have benefited vector species and the virus during this crucial period. These results, taken in the context of recent winter warming due to climate change, and more frequent droughts, may offer an explanation of why the virus has become so prevalent in Europe.

Influence of socio-economic, demographic and climate factors on the regional distribution of dengue in the United States and Mexico.

BACKGROUND: This study examines the impact of climate, socio-economic and demographic factors on the incidence of dengue in regions of the United States and Mexico. We select factors shown to predict dengue at a local level and test whether the association can be generalized to the regional or state level. In addition, we assess how different indicators perform compared to per capita gross domestic product (GDP), an indicator that is commonly used to predict the future distribution of dengue. METHODS: A unique spatial-temporal dataset was created by collating information from a variety of data sources to perform empirical analyses at the regional level. Relevant regions for the analysis were selected based on their receptivity and vulnerability to dengue. A conceptual framework was elaborated to guide variable selection. The relationship between the incidence of dengue and the climate, socio-economic and demographic factors was modelled via a Generalized Additive Model (GAM), which also accounted for the spatial and temporal auto-correlation. RESULTS: The socio-economic indicator (representing household income, education of the labour force, life expectancy at birth, and housing overcrowding), as well as more extensive access to broadband are associated with a drop in the incidence of dengue; by contrast, population growth and inter-regional migration are associated with higher incidence, after taking climate into account. An ageing population is also a predictor of higher incidence, but the relationship is concave and flattens at high rates. The rate of active physicians is associated with higher incidence, most likely because of more accurate reporting. If focusing on Mexico only, results remain broadly similar, however, workforce education was a better predictor of a drop in the incidence of dengue than household income. CONCLUSIONS: Two lessons can be drawn from this study: first, while higher GDP is generally associated with a drop in the incidence of dengue, a more granular analysis reveals that the crucial factors are a rise in education (with fewer jobs in the primary sector) and better access to information or technological infrastructure. Secondly, factors that were shown to have an impact of dengue at the local level are also good predictors at the regional level. These indices may help us better understand factors responsible for the global distribution of dengue and also, given a warming climate, may help us to better predict vulnerable populations on a larger scale.