An integrated data framework for policy guidance during the coronavirus pandemic: Towards real-time decision support for economic policymakers.

Usually, official and survey-based statistics guide policymakers in their choice of response instruments to economic crises. However, in an early phase, after a sudden and unforeseen shock has caused unexpected and fast-changing dynamics, data from traditional statistics are only available with non-negligible time delays. This leaves policymakers uncertain about how to most effectively manage their economic countermeasures to support businesses, especially when they need to respond quickly, as in the COVID-19 pandemic.

Changing climate - Changing livelihood: Smallholder's perceptions and adaption strategies.

Experts expect that climate change will soon have a severe impact on the lives of farmers in the region surrounding Kerala, India. This region, which is known for its monsoon climate (which involves a distinct temporal and spatial variation in rainfall), has experienced a decrease in annual rainfall over the last century. This study is aimed at investigating how smallholder farmers perceive climate change and at identifying the methods that these smallholders use to adapt to climate change.

Measuring the diffusion of innovations with paragraph vector topic models.

Measuring the diffusion of innovations from textual data sources besides patent data has not been studied extensively. However, early and accurate indicators of innovation and the recognition of trends in innovation are mandatory to successfully promote economic growth through technological progress via evidence-based policy making. In this study, we propose Paragraph Vector Topic Model (PVTM) and apply it to technology-related news articles to analyze innovation-related topics over time and gain insights regarding their diffusion process.

New Methods in Exploring Old Topics: Case Studying Brittle Diabetes in the Family Context.

Background: In questing for a more refined quantitative research approach, we revisited vector autoregressive (VAR) modeling for the analysis of time series data in the context of the so far poorly explored concept of family dynamics surrounding instable diabetes type 1 (or brittle diabetes).

Method: We adopted a new approach to VAR analysis from econometrics referred to as the optimized multivariate lag selection process and applied it to a set of raw data previously analyzed through standard approaches.

Results: We illustrated recurring psychosomatic circles of cause and effect relationships between emotional and somatic parameters surrounding glycemic control of the child's diabetes and the affective states of all family members.

Family system dynamics and type 1 diabetic glycemic variability: a vector-auto-regressive model.

Statistical approaches rooted in econometric methodology, so far foreign to the psychiatric and psychological realms have provided exciting and substantial new insights into complex mind-body interactions over time and individuals. Over 120 days, this structured diary study explored the mutual interactions of emotions within a classic 3-person family system with its Type 1 diabetic adolescent's daily blood glucose variability. Glycemic variability was measured through daily standard deviations of blood glucose determinations (at least 3 per day).