124 research outputs found

    Causal Inference in Disease Spread across a Heterogeneous Social System

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    Diffusion processes are governed by external triggers and internal dynamics in complex systems. Timely and cost-effective control of infectious disease spread critically relies on uncovering the underlying diffusion mechanisms, which is challenging due to invisible causality between events and their time-evolving intensity. We infer causal relationships between infections and quantify the reflexivity of a meta-population, the level of feedback on event occurrences by its internal dynamics (likelihood of a regional outbreak triggered by previous cases). These are enabled by our new proposed model, the Latent Influence Point Process (LIPP) which models disease spread by incorporating macro-level internal dynamics of meta-populations based on human mobility. We analyse 15-year dengue cases in Queensland, Australia. From our causal inference, outbreaks are more likely driven by statewide global diffusion over time, leading to complex behavior of disease spread. In terms of reflexivity, precursory growth and symmetric decline in populous regions is attributed to slow but persistent feedback on preceding outbreaks via inter-group dynamics, while abrupt growth but sharp decline in peripheral areas is led by rapid but inconstant feedback via intra-group dynamics. Our proposed model reveals probabilistic causal relationships between discrete events based on intra- and inter-group dynamics and also covers direct and indirect diffusion processes (contact-based and vector-borne disease transmissions).Comment: arXiv admin note: substantial text overlap with arXiv:1711.0635

    RELATIVE AGRICULTURAL PRICE CHANGES IN DIFFERENT TIME HORIZONS

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    Using a monthly data covering from 1974:1 to 2002:12, this paper explores the linkage between changes in macroeconomic variables (real exchange rate and inflation rate) and changes in relative agricultural prices in different time horizons (1, 12, 24, 36, 48, and 60 months). Controlling for factors likely to determine the long run trend of relative agricultural prices, the results show that long-term changes in real exchange rate has had a significant negative correlation with the long-term changes in relative agricultural prices. Conversely, changes of the general price have a role in explaining short-term changes in relative agricultural price at best.Demand and Price Analysis,

    HOW DIFFERENTLY AGRICULTURAL AND INDUSTRIAL SECTORS RESPOND TO EXCHANGE RATE FLUCTUATION?

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    This study divides the U.S. economy into the agricultural and industrial sectors and compares the degree of the involvement of exchange rates in each sector without specifying the rigid assumption of either exogeneity or endogeneity of exchange rates. Both short- and long-run impacts of shocks in the exchange rate are found to be significant. However, the effect of an exchange rate shock on the agricultural sector is larger than the industrial sector. This study fulfills a fundamental question about the role of exchange rate between the two sectors. The exchange rate is exogenous in the agricultural sector, while being endogenous in the industrial sector.Agricultural Finance,

    DETERMINING BILATERAL TRADE PATTERNS USING A DYNAMIC GRAVITY EQUATION

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    Using a dynamic gravity equation, we show that the national product differentiation model explains food and agricultural trade more properly, while the product differentiation model is more appropriate to explain large-scale manufacturing trade. In this context, our result is not consistent with the one found by Head and Ries (2001) in the short-run. The intuitive explanation for this result is that inward foreign direct investment can occur through either merger or acquisition in the short-run. Second, the pattern of bilateral trade could quickly adjust to changes in relative income between countries. Furthermore, we illustrate the positive impacts of world income growth on bilateral trade, which is in sharp contrast with the conventional analysis. This reveals yet another way to test the pattern of bilateral trade.dynamic gravity equation, national product differentiation, product differentiation, world income growth, International Relations/Trade,

    THE CAUSES OF INTRA-INDUSTRY TRADE BETWEEN THE U.S. AND CANADA:TIME-SERIES APPROACH WITH A GRAVITY MODEL

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    This study proposes alternative reasons to explain an asymmetric intra-industry trade for agricultural products between Canada and the United States after the free trade agreement became effective. Using time-series data, a gravity model is developed which enables us to examine the significance of exchange rates and different trade patterns on bilateral trade.International Relations/Trade,

    Local Polynomial Kernel Forecasts and Management of Price Risks using Futures Markets

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    This study contributes to understanding price risk management through hedging strategies in a forecasting context. A relatively new forecasting method, nonparametric local polynomial kernel (LPK), is used and applied to the hog sector. The selective multiproduct hedge based on the LPK price and hedge ratio forecasts is, in general, found to be better than continuous hedge and alternative forecasting procedures in terms of reduction of variance of unhedged return. The findings indicate that combining hedging with forecasts, especially when using the LPK technique, can potentially improve price risk management.Marketing,

    Dynamics of Information Diffusion

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    Real diffusion networks are complex and dynamic, since underlying social structures are not only far-reaching beyond a single homogeneous system but also frequently changing with the context of diffusion. Thus, studying topic-related diffusion across multiple social systems is important for a better understanding of such realistic situations. Accordingly, this thesis focuses on uncovering topic-related diffusion dynamics across heterogeneous social networks in both model-driven and model-free ways. We first conduct empirical studies for analyzing diffusion phenomena in real world systems, such as new diffusion in social media and knowledge transfer in academic publications. We observe that large diffusion is more likely attributed to interactions between heterogeneous social networks as if they were in the same networks. Thus, external influences from out-of-the-network sources, as observed in previous work, need to be explained with the context of interactions between heterogeneous social networks. This observation motivates our new conceptual framework for cross-population diffusion, which extends the traditional diffusion mechanism to a more flexible and general one. Second, we propose both model-driven and model-free approaches to estimate global trends of information diffusion. Based on our conceptual framework, we propose a model-driven approach which allows internal influence to reach heterogeneous populations in a probabilistic way. This approach extends a simple and robust mass action diffusion model by incorporating the structural connectivity and heterogeneity of real-world networks. We then propose a model-free approach using informationtheoretic measures with the consideration of both time-delay and memory effects on diffusion. In contrast to the model-driven approach, this model-free approach does not require any assumptions on dynamic social interactions in the real world, providing the benefits of quantifying nonlinear dynamics of complex systems. Finally, we compare our model-driven and model-free approaches in accordance with different context of diffusion. This helps us to obtain a more comprehensive understanding of topic-related diffusion patterns. Both approaches provide a coherent macroscopic view of global diffusion in terms of the strength and directionality of influences among heterogeneous social networks. We find that the two approaches provide similar results but with different perspectives, which in conjunction can help better explain diffusion than either approach alone. They also suggest alternative options as either or both of the approaches can be used appropriate to the real situations of different application domains. We expect that our proposed approaches provide ways to quantify and understand cross-population diffusion trends at a macro level. Also, they can be applied to a wide range of research areas such as social science, marketing, and even neuroscience, for estimating dynamic influences among target regions or systems
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