The Dynamics of Real-Time Online Information and Disease Progression: Understanding Spatial Heterogeneity in the Relationship

The re-emergence of infectious diseases such as measles and polio is creating logistics challenges for the state authorities to curb their spread and contain them. (CL, 2015) Real-time surveillance of infectious diseases is important to detect possible epidemics in advance to prevent shortages of medications (FDA, 2018). The outbreak of an infectious disease creates panic in the community and is accompanied by a sudden increase in the online interest in knowing more about the disease and its symptoms. Prior studies have found a strong relationship between web-based information and disease outbreak but the influence of dynamics of web-based information in real-time is often not considered (Zhang, 2017). The dynamics or rate of change of the online interest in a disease can inform or misinform about perspective cases of the disease in a region. Oftentimes, especially in this connected world individuals overreact to the situation which may send spurious online signals regarding the disease progression. Hence, we study the relationship between the dynamics of online information and the infectious disease outbreak. We also investigate if this relationship could be influenced by regional demographic factors. We analyze weekly online interest dynamics for five infectious diseases over a period of three years across 50 states of the United States. We control for several factors (including weather, demographics, and travelers) and utilize hierarchical functional data models to incorporate real-time dynamics and clustering at the regional level. Preliminary findings suggest that online interest dynamics have a significant relationship with disease outbreak and the effect is segregated at the regional level. These findings are important to develop a system for real-time surveillance and account for the influence of heterogonous online interest during an endemic outbreak

ICT as a Corruption Deterrent: A Theoretical Perspective

Investigations of white collar crimes such as corruption are often hindered by the lack of information or physical evidence. However, vast amount of information recorded, stored, analyzed, and shared using information and communication technologies (ICT) by businesses, governments, and citizens may help in investigating and prosecuting these crimes, and in deterring future crimes. This paper investigates the relationship between ICT and corruption at the country level using the theoretical lens of general deterrence theory. Using time-lagged regression and multilevel analysis of country level data from 97 countries for the years 2010-2012, we demonstrate that countries with higher ICT penetration and higher rule of law tend to have lesser corruption after accounting for social, economic, and political controls

Implications of Vital Sign Monitor and Electronic Medical Record Integration on Identification of Patients in Deteriorating Condition

The manual transcription of patients’ vital signs often delays entry of critical information to Electronic Medical Record (EMR) systems. This documentation delay within inpatient settings results in a lack of recent information on patient condition, decreased ability for providers to make clinical decisions, and an increased risk of data error. To alleviate these concerns, hospitals are adopting device interface systems which digitally integrate medical devices and EMRs. Prior studies have found that this type of system integration can potentially reduce the time spent on manual entry of information in the EMR and support other value-added activities in the hospital. However, these studies suffered from intervention bias from direct monitoring of clinicians using time-motion methodologies, which are resource restrictive and can affect patient care. In this study, we utilize a natural experiment setting to understand how the implementation of a device interface system between vitals monitors used on medical/surgical units and the EMR has impacted hospital workflows and patient care in a regional hospital. Our investigation focuses on two areas. First, we examine if the new system influenced documentation delays, and whether the impact was similar for different employee roles. Since vitals on medical/surgical units are typically taken by Patient Care Assistants (PCA’s) or other ancillary staff, we hypothesize that a greater average decrease in documentation delay will be found in their role. Second, we study the effect of interface system implementation on downstream patient care activities, such as models designed to identify patients in deteriorating condition. We analyze data on documentation delays across more than 5,000 patients and 330,000 documentation events for one week before and after system implementation. Additionally, we intend to utilize hierarchical models to distinguish the impact of systems for various roles (including PCA’s and nurses) across the hospital. Preliminary findings suggest that the interface system results in a statistically significant decrease in time between when vital signs are taken and documented, as well as The findings from this research would inform hospitals of the benefits and the requirements for a successful integration of medical devices and EMR systems, as well as the impact on activities dependent on accurate and timely vital signs documentation

Extreme Value Analysis for Record Loss Prediction during Volatile Market

Year after year stock markets of the world kept on breaking records. They reached new heights and plunged to new depths. During financial crisis of 2008 many markets shed as many points as they never did in their history. It is extremely difficult to predict future index value due to their high randomness but is it possible to know if markets are going to achieve a record fall in near future or not. Daily changes in stock market index are not normally distributed, analysis showed they exhibit fatter tails that normal distribution, while extreme fall and rise generally follow generalized extreme value distribution explained by Extreme Value theory. The study models worst losses suffered in a day by National Stock Exchange index CNX-Nifty by fitting GEV distribution on yearly and quarterly maximum losses. GEV distribution function hence obtained is used for predicting probability of obtaining a record maximum loss next year / quarter of 2008. As Indian markets shed maximum point in a day during financial crisis of 2008, study verifies if model gives indication about such extreme event. Key words: GEV distribution; Extreme Value Theory; Record Loss; Frechet Density Function; Block Maxim

Analytics for Novel Consumer Insights (A Three Essay Dissertation)

Both literature and practice have investigated how the vast amount of ever increasing customer information can inform marketing strategy and decision making. However, the customer data is often susceptible to modeling bias and misleading findings due to various factors including sample selection and unobservable variables. The available analytics toolkit has continued to develop but in the age of nearly perfect information, the customer decision making has also evolved. The dissertation addresses some of the challenges in deriving valid and useful consumer insights from customer data in the digital age. The first study addresses the limitations of traditional customer purchase measures to account of dynamic temporal variations in the customer purchase history. The study proposes a new approach for representation and summarization of customer purchases to improve promotion forecasts. The method also accounts for sample selection bias that arises due to biased selection of customers for the promotion. The second study investigates the impact of increasing internet penetration on the consumer choices and their response to marketing actions. Using the case study of physician’s drug prescribing, the study identifies how marketers can misallocate resources at the regional level by not accounting for variations in internet penetration. The third paper develops a data driven metric for measuring temporal variations in the brand loyalty. Using a network representation of brand and customer the study also investigates the spillover effects of manufacturer related information shocks on the brand’s loyalty

A data driven framework for early prediction of customer response to promotions

The study proposes a framework based on functional analysis of transaction data to predict customer spending during promotional events. Retailers face the challenge of considering an extant number of variables and of accounting for methodological constraints while modeling customer response to promotions. Noise and uneven distribution of spending further introduces error into traditional models’ estimates. We represent each customer’s spending as a continuous curve that accounts for heterogeneity due to the purchase cycle as well as cross sectional differences. In order to obtain an optimal functional representation we utilize a data driven iterative procedure. Statistical information from the collection of spending curves is drawn using functional data analysis (FDA). Analysis of a real customer dataset from a North American retail chain shows that the dynamics of information captured by optimal functional representation of transaction data significantly improves predictions for out of sample observations

Quantifying the impacts of online fake news on the equity value of social media platforms – Evidence from Twitter

Online Fake News characterized by falsehood and ambiguity is significantly shaking up various aspects of social, economic, and political life across the globe. In addition, it can also be detrimental to the existence of social media platforms. In this research, by synthesizing the prior literature on negativity bias in online settings, reasons for platform failure and the characteristics of social media platforms, we explain the mechanisms through which online fake news impacts the equity value of these platforms. We also develop and implement a two-stage Bayesian Vector Autoregression technique to test these mechanisms using a dataset from Twitter. Our results show that falsehood results in an equity value loss of approximately 2.11 Million USD over a ten-day period while a mere 67.17 Million falsehood tweets can interact with ambiguity to produce a loss of 10 Million USD. We also find that ambiguity helps mitigate the negative impact of fake news which we attribute to the fact that users converse and disambiguate news that arrives on social media platforms. Our research has theoretical and practical implications for the impact of fake news on these platforms and their valuations