Machine-learned patterns suggest that diversification drives economic development

We combine a sequence of machine-learning techniques, together called Principal Smooth-Dynamics Analysis (PriSDA), to identify patterns in the dynamics of complex systems. Here, we deploy this method on the task of automating the development of new theory of economic growth. Traditionally, economic growth is modelled with a few aggregate quantities derived from simplified theoretical models. PriSDA, by contrast, identifies important quantities. Applied to 55 years of data on countries' exports, PriSDA finds that what most distinguishes countries' export baskets is their diversity, with extra weight assigned to more sophisticated products. The weights are consistent with previous measures of product complexity. The second dimension of variation is proficiency in machinery relative to agriculture. PriSDA then infers the dynamics of these two quantities and of per capita income. The inferred model predicts that diversification drives growth in income, that diversified middle-income countries will grow the fastest, and that countries will converge onto intermediate levels of income and specialization. PriSDA is generalizable and may illuminate dynamics of elusive quantities such as diversity and complexity in other natural and social systems.

Per capita incidence of sexually transmitted infections increases systematically with urban population size: a cross-sectional study

OBJECTIVES

Rampant urbanisation rates across the globe demand that we improve our understanding of how infectious diseases spread in modern urban landscapes, where larger and more connected host populations enhance the thriving capacity of certain pathogens.

METHODS

A data-driven approach is employed to study the ability of sexually transmitted diseases (STDs) to thrive in urban areas. The conduciveness of population size of urban areas and their socioeconomic characteristics are used as predictors of disease incidence, using confirmed-case data on STDs in the USA as a case study.

RESULTS

A superlinear relation between STD incidence and urban population size is found, even after controlling for various socioeconomic aspects, suggesting that doubling the population size of a city results in an expected increase in STD incidence larger than twofold, provided that all other socioeconomic aspects remain fixed. Additionally, the percentage of African-Americans, income inequalities, education and per capita income are found to have a significant impact on the incidence of each of the three STDs studied.

CONCLUSIONS

STDs disproportionately concentrate in larger cities. Hence, larger urban areas merit extra prevention and treatment efforts, especially in low-income and middle-income countries where urbanisation rates are higher.