Bank in Vietnam. For their valuable inputs to our work, we thank Nguyen The Dung (the World Bank), and members of the project research consortium, especially Dao The Anh (Centre for Agrarian System Research and Development), Nguyen Ngoc Que and Do Anh Phong (Institute of Policy and Strategy for Agricultural and Rural Development), Vo Thi Thanh Loc, Le Canh Dung and their teams at the Mekong Development Institute. We thank Michael Jerie (Centre of Policy Studies) for helpful comments on the paper.
The Centre of Policy Studies (COPS) is a research centre at Monash University devoted to economy-wide modelling of economic policy issues.
During normal childhood development, functional brain networks evolve over time in parallel with changes in neuronal oscillations. Previous studies have demonstrated differences in network topology with age, particularly in neonates and in cohorts spanning from birth to early adulthood. Here, we evaluate the developmental changes in EEG functional connectivity with a specific focus on the first 2 years of life. Functional connectivity networks (FCNs) were calculated from the EEGs of 240 healthy infants aged 0–2 years during wakefulness and sleep using a cross-correlation-based measure and the weighted phase lag index. Topological features were assessed via network strength, global clustering coefficient, characteristic path length, and small world measures. We found that cross-correlation FCNs maintained a consistent small-world structure, and the connection strengths increased after the first 3 months of infancy. The strongest connections in these networks were consistently located in the frontal and occipital regions across age groups. In the delta and theta bands, weighted phase lag index networks decreased in strength after the first 3 months in both wakefulness and sleep, and a similar result was found in the alpha and beta bands during wakefulness. However, in the alpha band during sleep, FCNs exhibited a significant increase in strength with age, particularly in the 21–24 months age group. During this period, a majority of the strongest connections in the networks were located in frontocentral regions, and a qualitatively similar distribution was seen in the beta band during sleep for subjects older than 3 months. Graph theory analysis suggested a small world structure for weighted phase lag index networks, but to a lesser degree than those calculated using cross-correlation. In general, graph theory metrics showed little change over time, with no significant differences between age groups for the clustering coefficient (wakefulness and sleep), characteristics path length (sleep), and small world measure (sleep). These results suggest that infant FCNs evolve during the first 2 years with more significant changes to network strength than features of the network structure. This study quantifies normal brain networks during infant development and can serve as a baseline for future investigations in health and neurological disease.
BackgroundEffective response to emerging infectious disease (EID) threats relies on health care systems that can detect and contain localised outbreaks before they reach a national or international scale. The Asia-Pacific region contains low and middle income countries in which the risk of EID outbreaks is elevated and whose health care systems may require international support to effectively detect and respond to such events. The absence of comprehensive data on populations, health care systems and disease characteristics in this region makes risk assessment and decisions about the provision of such support challenging.Methodology/principal findingsWe describe a mathematical modelling framework that can inform this process by integrating available data sources, systematically explore the effects of uncertainty, and provide estimates of outbreak risk under a range of intervention scenarios. We illustrate the use of this framework in the context of a potential importation of Ebola Virus Disease into the Asia-Pacific region. Results suggest that, across a wide range of plausible scenarios, preemptive interventions supporting the timely detection of early cases provide substantially greater reductions in the probability of large outbreaks than interventions that support health care system capacity after an outbreak has commenced.Conclusions/significanceOur study demonstrates how, in the presence of substantial uncertainty about health care system infrastructure and other relevant aspects of disease control, mathematical models can be used to assess the constraints that limited resources place upon the ability of local health care systems to detect and respond to EID outbreaks in a timely and effective fashion. Our framework can help evaluate the relative impact of these constraints to identify resourcing priorities for health care system support, in order to inform principled and quantifiable decision making.
Modern levels of global travel have intensified the risk of new infectious diseases becoming pandemics. Particularly at risk are developing countries whose health systems may be less well equipped to detect quickly and respond effectively to the importation of new infectious diseases. This chapter examines what might have been the economic consequences if the 2014 West African Ebola epidemic had been imported to a small Asia-Pacific country. Hypothetical outbreaks in two countries were modelled. The post-importation estimations were carried out with two linked models: a stochastic disease transmission (SEIR) model and a quarterly version of the multi-country GTAP model, GTAP-Q. The SEIR model provided daily estimates of the number of persons in various disease states. For each intervention strategy the stochastic disease model was run 500 times to obtain the probability distribution of disease outcomes. Typical daily country outcomes for both controlled and uncontrolled outbreaks under five alternative intervention strategies were converted to quarterly disease-state results, which in turn were used in the estimation of GTAP-Q shocks to country-specific health costs and labour productivity during the outbreak, and permanent reductions in each country’s population and labour force due to mortality. Estimated behavioural consequences (severe reductions in international tourism and crowd avoidance) formed further shocks. The GTAP-Q simulations revealed very large economic costs for each country if they experienced an uncontrolled Ebola outbreak, and considerable economic costs for controlled outbreaks in Fiji due to the importance of the tourism sector to its economy. A major finding was that purely reactive strategies had virtually no effect on preventing uncontrolled outbreaks, but pre-emptive strategies substantially reduced the proportion of uncontrolled outbreaks, and in turn the economic and social costs.
Half a century of centrally planned policy in the Central and Eastern European countries resulted in outdated technologies, inefficient allocation of resources and low productivity. Following the end of communism there was a fifteen year process of transition which ended in 2004 with eight post-communist countries joining the European Union (EU) of which Poland was the largest. As part of the EU these countries now face the challenge of the common EU strategy Europe 2020, which has set the target of achieving R&D expenditure to GDP ratio (called the R&D intensity) of 3% by 2020 for the Union as a whole in an effort to increase the competitiveness of the region. Poland, like the other post-communist countries, faces a lower target of R&D intensity, set at 1.7%. Nevertheless, the challenge is immense, since the country is still at only half that level and has little experience in developing policies to help achieve it. In this paper we tested two possible policy options to achieve the target: (1) to increase government expenditures on R&D and; (2) to provide tax relief on R&D to businesses. The method applied to assess the options is a recursive dynamic computable general equilibrium (CGE) model for Poland with an explicit link between productivity and R&D stock. The results show that achieving the R&D intensity target via the use of tax relief is 2.5 times more costly to the government budget, but it has a greater impact on the economy in terms of a higher GDP growth. Tax relief proved efficient in the short run while in the long run the government expenditure policy provides better value for money.
Thousands of economists spread across almost every country use the GTAP model to analyse trade policies including trade wars and trade agreements. GTAP has an impressive regional coverage (140 countries), but the standard commodity coverage (57 commodities/industries) can cause frustration when tariffs on narrowly defined products are being negotiated. This article sets out a method for disaggregating commodities/industries in computable general equilibrium models such as GTAP and applies it to GTAP’s motor vehicle sector. The method makes use of readily available highly disaggregated trade data supplemented by detailed input–output data where available and data from a variety of other sources such as commercial market reports. JEL Codes: C68, F13, F14, F17
A country’s economic dependence on its trade with various other countries is often expressed in terms of trade values and shares. A country’s vulnerability to economic coercion by the countries with which it trades is similarly expressed in such terms. Using the recent issues relating to Australia’s coal trade with China as an example, we propose a better framework for assessing vulnerability to coercive trade instruments. We argue that the capacity for a given export trade to fund real consumption is a superior indicator of economic vulnerability than the simple value of the underlying trade flow. Our framework takes account of trade diversion, foreign capital ownership, the terms of trade, resource mobility, and capital and production tax rates. Using this framework, we demonstrate that the damage from trade sanction is far less than might be expected from a simple focus on the value of the affected trade flow alone.
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