No abstract

This paper uncovers the socioeconomic and health/lifestyle factors that can explain the differential impact of the coronavirus pandemic on different parts of the United States. Using a dynamic panel model with daily reported number of cases for US counties over a 20-day period, the paper develops a Vulnerability Index for each county from an epidemiological model of disease spread. County-level economic, demographic, and health factors are used to explain the differences in the values of this index and thereby the transmission and concentration of the disease across the country. These factors are also used in a zero-inflated negative binomial pooled model to examine the number of reported deaths. The paper finds that counties with high per capita personal income have a high incidence of both reported cases and deaths. The unemployment rate is negative for deaths implying that places with low unemployment rates or higher economic activity have higher reported deaths. Counties with higher income inequality as measured by the Gini coefficient experienced more deaths and reported more cases. There is a remarkable similarity in the distribution of cases across the country and the distribution of distance-weighted international passengers served by the top international airports. Counties with high concentrations of non-Hispanic Blacks, Native Americans, and immigrant populations have a higher incidence of both cases and deaths. The distributions of health risk factors such as obesity, diabetes, smoking are found to be particularly significant factors in explaining the differences in mortality across counties. Counties with higher numbers of primary care physicians have lower deaths and so do places with lower hospital stays for preventable causes. The stay-at-home orders are found to be associated with places of higher cases and deaths implying that they were perhaps imposed far too late to have contained the virus in the places with high-risk populations. It is hoped that research such as these will help policymakers to develop risk factors for each region of the country to better contain the spread of infectious diseases in the future.

High growth and progressive regions possess a culture that promotes innovation. Innovation depends on a region's ability to use its own existing knowledge and knowledge generated elsewhere. This paper demonstrates the importance of the ability to absorb external knowledge in explaining innovation productivity for 106 U.S. metropolitan areas. Using a spatial interaction model of patent citation flows with origin and destination dependence, the destination fixed‐effects coefficients provides a measure of a region's absorptive capacity. We identify local conditions that shape a region's absorptive capacity and demonstrate it has a positive and significant impact on innovation productivity.

No abstract

Alzheimer’s Disease (AD) is the most expensive and currently incurable disease that affects a large number of the elderly globally. One in five Medicare dollars is spent on AD-related tests and treatments. Accurate AD diagnosis is critical but often involves invasive and expensive tests that include brain scans and spinal taps. Recommending these tests for only patients who are likely to develop the disease will save families of cognitively normal individuals and hospitals from unnecessary expenditures. Moreover, many of the subjects chosen for clinical trials for AD therapies never develop any cognitive impairment and prove not to be ideal candidates for those trials. It is thereby critical to find inexpensive ways to first identify individuals who are likely to develop cognitive impairment and focus attention on them for in-depth testing, diagnosing, and clinical trial participation. Research shows that AD is a slowly progressing disease. This slow progression allows for early detection and treatment, but more importantly, gives the opportunity to predict the likelihood of disease development from early indications of memory lapses. Neuropsychological tests have been shown to be effective in identifying cognitive impairment. Relying exclusively on a set of longitudinal neuropsychological test data available from the ADNI database, this paper has developed Recurrent Neural Networks (RNN) to diagnose the current and predict the future cognitive states of individuals. The RNNs use sequence prediction techniques to predict test scores for two to four years in the future. The predicted scores and predictions of cognitive states based on them showed a high level of accuracy for a group of test subjects, when compared with their known future cognitive assessments conducted by ADNI. This shows that a battery of neuropsychological tests can be used to track the cognitive states of people above a certain age and identify those who are likely to develop cognitive impairment in the future. This ability to triage individuals into those who are likely to remain normal and those who will develop cognitive impairment in the future, advances the quest to find appropriate candidates for invasive tests like spinal taps for disease identification, and the ability to identify suitable candidates for clinical trials.