East Pacific Region Research Articles

Refining Heterogeneities near the Core–Mantle Boundary Beneath East Pacific Regions: Enhanced Differential Travel-Time Analysis Using USArray

Recent advancements in seismic data analysis have enhanced our grasp of the seismic heterogeneities near the core–mantle boundary (CMB). Through seismic tomography, persistent lower-mantle structures like the large low shear velocity provinces (LLSVPs) beneath the Pacific and South Africa have been identified. However, variations in the finer-scale features across different models raise questions about their origins. This study utilizes differential travel-time measurements from the USArray, operational across the contiguous United States from 2007 to 2014, to examine the impact of upper-mantle heterogeneities on tomographic models. By averaging the P-wave travel times and calibrating them with diffracted P-waves at the same stations, we mitigate the effects of shallow heterogeneities. The findings confirm that this method accurately maps the seismic anomalies beneath the USArray, consistent with other North American studies. Calibrated Pdiff travel-time data indicate significant anomalies in the mid-Pacific and Bering Sea and lesser anomalies in the northern Pacific, aligning with the global tomographic images. Moreover, the study highlights sharp travel-time variations over short distances, such as those across the northern boundary of the mid-Pacific anomaly, suggesting a chemically heterogeneous Pacific LLSVP.

National early warning systems for emerging AMR in high-income countries: a systematic review

Abstract Background An urgent need of implementing national surveillance systems for timely detection and reporting of emerging antimicrobial resistance (AMR) was recently advocated by the World Health Organization (WHO). However, public information on existing national early warning systems (EWSs) is often incomplete. Furthermore, when findings are available, understanding these systems is challenging due to different approaches used for data collection, reporting and definitions, with a comprehensive overview on this topic currently lacking. The aim of this study was to map existing EWSs for emerging AMR, focusing on high-income countries, and describe their main characteristics. Methods A systematic review was performed on bibliographic databases, and a targeted search was conducted on national websites. Any article, report or webpage describing national EWSs in high-income countries was eligible for inclusion. EWSs were identified considering the emerging AMR reporting WHO framework. Results We identified seven national EWSs in 72 high-income countries: two (Australia, Japan) in the East Asia and Pacific Region, three (France, Sweden, United Kingdom) in Europe and Central Asia, and two (United States, Canada) in North America. The systems were established quite recently; in most cases they covered both community and hospital settings, but their main characteristics varied widely across countries in terms of organization and microorganisms under surveillance, with also different definitions of emerging AMR and alert functioning. A formal system assessment was available only in Australia. Conclusions A broader implementation and investment of national surveillance systems that allow early detection of emerging AMR is still needed to establish EWSs in countries and regions lacking such capabilities. A more standardized data collection and reporting is also advisable to improve cooperation on a global scale. Key messages • This study provides a synthesis of publicly available information on national EWSs for emerging AMR in high-income countries, highlighting the urgent need for a broader implementation of such systems. • Main characteristics of EWSs have been outlined, varying widely across countries. Findings could help stakeholders in strengthening current standard national AMR surveillance systems.

Numerical investigation of the effective receptive field and its relationship with convolutional kernels and layers in convolutional neural network

The receptive field (RF) plays a crucial role in convolutional neural networks (CNNs) because it determines the amount of input information that each neuron in a CNN can perceive, which directly affects the feature extraction ability. As the number of convolutional layers in CNNs increases, there is a decay of the RF according to the two-dimensional Gaussian distribution. Thus, an effective receptive field (ERF) can be used to characterize the available part of the RF. The ERF is calculated by the kernel size and layer number within the neural network architecture. Currently, ERF calculation methods are typically applied to single-channel input data that are both independent and identically distributed. However, such methods may result in a loss of effective information if they are applied to more general (i.e., multi-channel) datasets. Therefore, we proposed a multi-channel ERF calculation method. By conducting a series of numerical experiments, we determined the relationship between the ERF and the convolutional kernel size in conjunction with the layer number. To validate the new method, we used the recently published global wave surrogate model for climate simulation (GWSM4C) and its accompanying dataset. According to the newly established relationship, we refined the kernel size and layer number in each neural network of the GWSM4C to produce the same ERF but lower RF attenuation rates than those of the original version. By visualizing the gradient map at several points in West African and East Pacific areas, the high gradient value regions confirmed the known swell sources, which indicated effective feature extraction in these areas. Furthermore, the new version of the GWSM4C yielded better prediction accuracy for significant wave height in global swell pools. The root mean square errors in the West African and East Pacific regions reduced from approximately 0.3 m, in the original model to about 0.15 m, in the new model. Moreover, these improvements were attributed to the higher efficiency of the newly modified neural network structure that allows the inclusion of more historical winds while maintaining acceptable computational consumption.

Online media sentiment analysis of shrimp and salmon in the United States

There is a growing interest in the U.S. seafood and aquaculture market; however, studies utilizing online information to understand this market are limited. This study employs online media analytics to obtain insights into consumer perceptions and potential demand signals for shrimp and salmon in the U.S. Our results show that platform X is the preferred medium for shrimp and salmon discussions, with shrimp's physical quality and salmon's environmental impact being the two major perception drivers. Additionally, autoregressive modeling is conducted to explore the dynamics of net sentiments. Farmed shrimp and salmon consistently exhibit lower net sentiments compared to their wild counterparts, indicating that production methods (aquaculture-raised versus wild-harvested) significantly influence people's preferences. Both wild shrimp and salmon sentiments show a more positive trajectory compared to their farmed counterparts. Negative drops in wild shrimp sentiment are linked to boat incidents, while negative wild salmon sentiments are associated with climate change. Regional analysis demonstrates that public attitudes toward farmed shrimp in East South Central and Mountain regions are unstable, as are attitudes toward farmed salmon in the East North Central, Pacific, and West South Central regions. The fitted forecast model serves as a supplementary tool for industry stakeholders to quickly respond to negative sentiments, preparing for possible public perception changes and potentially enhancing demand. Regional statistics also assist the seafood industry in tailoring business strategies to address geographical differences.

Prediction of Tropical Pacific Rain Rates with Overparameterized Neural Networks

Abstract The prediction of tropical rain rates from atmospheric profiles poses significant challenges, mainly due to the heavy-tailed distribution exhibited by tropical rainfall. This study introduces overparameterized neural networks not only to forecast tropical rain rates but also to explain their heavy-tailed distribution. The investigation is separately conducted for three rain types (stratiform, deep convective, and shallow convective) observed by the Global Precipitation Measurement satellite radar over the west and east Pacific regions. Atmospheric profiles of humidity, temperature, and zonal and meridional winds from the MERRA-2 reanalysis are considered as features. Although overparameterized neural networks are well known for their “double descent phenomenon,” little has been explored about their applicability to climate data and capability of capturing the tail behavior of data. In our results, overparameterized neural networks accurately estimate the rain-rate distributions and outperform other machine learning methods. Spatial maps show that overparameterized neural networks also successfully describe the spatial patterns of each rain type across the tropical Pacific. In addition, we assess the feature importance for each overparameterized neural network to provide insight into the key factors driving the predictions, with low-level humidity and temperature variables being the overall most important. These findings highlight the capability of overparameterized neural networks in predicting the distribution of the rain rate and explaining extreme values. Significance Statement This study aims to introduce the capability of overparameterized neural networks, a type of neural network with more parameters than data points, in predicting the distribution of tropical rain rates from gridscale environmental variables and explaining their tail behavior. Rainfall prediction has been a topic of importance, yet it remains a challenging problem for its heavy-tailed nature. Overparameterized neural networks correctly captured rain-rate distributions and the spatial patterns and heterogeneity of the observed rain rates for multiple rain types, which could not be achieved by any other previous statistical or machine learning frameworks. We find that overparameterized neural networks can play a key role in general prediction tasks, with potential expanded applicability to other domains with heavy-tailed data distribution.

Comparative analysis of gender disparity in academic positions based on U.S. region and STEM discipline.

Despite a move toward gender parity in the United States (U.S.) workforce, a large gender gap persists in the fields of science, technology, engineering, and mathematics (STEM); this is particularly true for academic (i.e., instructor and tenure track) STEM positions. This gap increases as women advance through the traditional steps of academia, with the highest degree of gender disparity in tenured positions. As policies, politics, and culture, which all contribute to gender equity across the world, vary across regions in the United States, we expect that the gender gap in STEM might also vary across geographic regions. Here, we evaluated over 20,000 instructor and tenure track positions in university STEM departments across the U.S. to evaluate whether and how the geographic region of a university might determine its proportion of women in STEM academic positions. Similar to previous research, regardless of geographic region, more men were employed in both tenure track and instructor positions across STEM fields. However, variation existed regionally within the U.S., with the Mountain region employing the lowest proportion of women in tenure track positions and the East North Central and Pacific regions employing the greatest proportion. We expect this regional variation could be caused by differences in state and local policies, regional representation, and mentorship, resulting in inconsistent support for women, leading to differences in work environments, hiring, and job retention rates across the country. A better understanding of which geographic areas within the U.S. have more equal distributions of women in the STEM field will help us to identify the specific mechanisms that facilitate more equal and inclusive opportunities for women and other underrepresented groups across all levels of STEM academia.

Social exclusion concepts, measurement, and a global estimate.

Multiple estimates exist of global monetary and multidimensional poverty, but populations at risk of social exclusion still lack a worldwide estimate. This paper fills this gap by providing the first estimates of the share and number of populations at risk of social exclusion worldwide. The paper contributes to the literature in three important respects. First, it develops a conceptual framework of social exclusion that emphasizes the relative, multidimensional, and dynamic features of exclusion. Second, it proposes a macro-counting methodology that allows measuring populations at risk of exclusion based on identity, circumstances, and socioeconomic conditions, while advancing a protocol to avoid double counting of individuals at risk of social exclusion. Third, the empirical strategy provides to the best of our knowledge, the first estimates of populations at risk of social exclusion by dimensions of exclusion on a global and regional scale. Overall, we estimate that between 2.33 and 2.43 billion people-roughly 32 per cent of the global population-are at risk of social exclusion. The South Asia and East Asia and Pacific regions contain 1.3 billion such people, with India and China alone home to 840 million of them. Meanwhile, 52 per cent of sub-Saharan Africa's population is vulnerable to exclusion, the greatest share of any region. Our findings have important policy implications. While antipoverty policies can support household consumption and smooth its volatility among the poor, they are unlikely to address social exclusion stemming from ethnic, racial, or gender discrimination. Therefore, addressing exclusion necessitates a suite of multiple interventions tailored to distinct groups and sustained over time.

Government Effectiveness Moderation On The Effect Of Per Capita Income And Exchange Rate On Goods And Services Tax Revenue In East Asia And Pacific

Taxes have been known as the main source of revenue in every country. Tax on Goods and Services is a clear example of how tax revenue can contribute as a source of government revenue. This type of tax has been implemented in more than 143 countries in the world under the name Goods and Services Tax (GST) or Value Added Tax (VAT), including developed and developing countries which are members of the East Asia and Pacific region. Over the past few years, the issue of GST has become a hot topic of discussion as an approach in fiscal policy to reduce the budget deficit. New challenges and risks arose in line with the efforts of policy makers to maintain and accelerate economic growth in the East Asia and Pacific region. Deglobalization, population aging, and climate change overshadow the prospects for economic growth in this region, which has initially developed rapidly through trade. This research contributes to presenting the results of empirical literature regarding the determinants of tax revenue on goods and services in 12 countries that are members of the East Asia and Pacific region during the 2010-2019 period. Through a quantitative approach and panel data regression analysis method, the results show that the dependent variable GST can be explained by the independent variables consisting of Per Capita Income (PCI) and Exchange Rate (EXCH) of 56.42%. Per Capita Income, Exchange Rate has a positive and significant effect on Goods and Services Tax revenue. Meanwhile, moderation by the Government Effectiveness variable weakens the influence of the two independent variables so that the PCI and EXCH coefficient values become negative. This study also uses the variable Service Sector Contribution to GDP (SERV) as a control variable.

Global, regional, and national temporal trends in mortality and disability-adjusted life years for cardiovascular disease attributable to low temperature during 1990-2019: an age-period-cohort analysis of the global burden of disease 2019 study.

Few studies have focused on the region-specific relationship between cardiovascular disease (CVD) and low temperature worldwide. We aimed to provide an overview of trends in mortality and disability-adjusted life years (DALYs) for CVD and its subtypes attributable to low temperature over the past 30 years in 204 countries and regions, along with the associations of these trends with age, period, and birth cohorts. Data on the estimated burden of CVDs (including ischemic heart disease, hypertensive heart disease, and stroke) attributable to low temperature were obtained from the Global Burden of Disease Study 2019. We utilized an age-period-cohort model to estimate overall annual percentage changes in mortality (net drifts), annual percentage changes from 15 ~ 19 to 81 ~ 85 years (local drifts), and period and cohort relative risk (period/cohort effects) between 1990 and 2019. Among noncommunicable diseases, CVDs had the highest mortality rate and DALY loss attributable to low temperature worldwide and has increased from 65.7 to 67.3%, which is mainly attributed to the increase in East Asia and Pacific region. In terms of the level of economic and social development, an inverted U-shape was found in the age-standardized mortality rates (ASMR) due to low-temperature across different sociodemographic indices (SDI) regions. Both high CVD mortality (19.45, 95% CI [14.54, 24.17%]) and a decreasing mortality rate related to low temperature (from 1990 to 2019, net drift, -3.25% [-3.76, 2.73%] per year) was found in high SDI countries or territories, with opposite outcome found in low SDIs regions. The older adults (70+) and men share the highest rate of CVD ASMR and DALY attributed to low temperature across all regions, especially in North America and Europe and Central Asia. Mortality and DALY loss from CVD attributable to low temperature showed an overall decreasing trend globally except for East Asia and Pacific region. SDI, sex, age and geographic location contributed to the diversity of the CVD disease burden associated with low temperature worldwide. More attention should be given to the older adults, men, and low SDI regions.

Real Exchange Rates and Inward Foreign Direct Investment in Japan and East Asia and Pacific Region

Real Exchange Rates and Inward Foreign Direct Investment in Japan and East Asia and Pacific Region

Inferring the Ionospheric State With the Far Ultraviolet Imager on the Fengyun‐4C Geostationary Satellite: Retrieval Algorithm and Verification

AbstractThe Multiband Ultraviolet Spectrum Imager (MUSI) is an optical remote sensing instrument scheduled to launch on the Fengyun‐4C meteorological satellite in 2024. MUSI is designed to measure the airglow emissions between ∼120 and 160 nm above the East Asia and Pacific region from a geostationary orbit to infer ionospheric parameters. Here we develop a lookup‐table algorithm for retrieving the peak electron density (nmF2) and the Total Electron Content (TEC) from nighttime observations of the OI 135.6 nm emission. The algorithm takes into account the north‐south asymmetry of the equatorial ionization anomaly and the atmospheric variations with solar activity and local time. Analysis of synthetic observations shows that the assumptions made in the algorithm only lead to ∼2%–4% uncertainties in the retrievals under the modeled ionospheric conditions. The algorithm is verified by retrieving nmF2 and TEC from the Global‐Scale Observations of the Limb and Disk (GOLD) mission and comparing with coincided radio measurements. The comparison indicates almost negligible systematic differences (typically less than ∼3%) between the GOLD and the ionosonde nmF2, which are within the error limit of the GOLD retrievals. The accuracy of the GOLD TEC retrievals is shown to be comparable to (or in some sense even better than) that of the measurements from the Global Navigation Satellite System. Several simplified yet robust retrieval methods are also developed, which can be readily implemented for both existing and upcoming missions. Our results promote far ultraviolet (FUV) remote sensing to be a key technology for accurate determination of the ionospheric state.

Chemotherapy Refusal Patterns in Multiple Myeloma Patients: An in-Depth Analysis of the NCDB Data from 2004 to 2020

Background: Chemotherapy is a cornerstone in the treatment of multiple myeloma (MM), contributing to prolonged patient survival. However, despite its proven efficacy, some individuals opt to decline chemotherapy, leading to variations in treatment strategies and outcomes. Understanding the factors associated with the decision to decline chemotherapy in MM patients is crucial for optimizing patient-centered care and ensuring equitable access to appropriate treatments. Our investigation aims to illuminate potential areas for targeted interventions, with the goal of enhancing patient engagement and optimizing treatment strategies to improve overall outcomes in MM management. Methods: N=152,299 patients diagnosed with MM between 2004 and 2020 were identified using the NCDB, all of whom were recommended for chemotherapy. Overall survival (OS) of patients who received chemotherapy compared to those who did not receive it was evaluated and presented using Kaplan-Meier (KM) curves. Multivariate logistic regression analysis with backward elimination was used to identify factors associated with refusal of chemotherapy. SAS version 9.4 was used to analyze the data. Results: Among 152,299 patients who received a recommendation for chemotherapy from their physicians, 3,289 individuals (2.16%) opted to refuse the treatment. Descriptive statistics for patients who refused chemotherapy are presented in Table 1. The median OS for patients who received chemotherapy was 61.37 months, in contrast to 8.8 months for those who declined this treatment (Figure 1). Our multivariate logistic regression analysis revealed several significant factors associated with chemotherapy refusal in MM patients. Age exhibited a statistically significant association, with increasing odds of refusal observed in patients aged 60-69 years (OR=1.29, p=0.045), 70-79 years (OR=2.26, p<0.0001), and 80 years and older (OR=7.29, p<0.0001) compared to those younger than 50 years. Male patients exhibited lower odds for refusal (OR=0.85, p<0.0001) compared to female patients. Black patients had higher odds for refusal (OR=1.26, p<0.0001) compared to White patients. Patients with Medicare, Medicaid/other government insurance, and no insurance had higher likelihood of refusal (OR=1.71, 1.89 and 3.13 respectively, p<0.0001) than patients with private insurance. The Charlson-Deyo score demonstrated a significant association, with higher scores correlating with increased odds of refusal (p<0.0001). Patients treated at non-academic facilities had higher odds of refusal (OR=1.47, p<0.0001) compared to academic facilities. Education was significantly associated with refusal, with patients having lower education levels exhibiting increased odds of refusal. Patients living in areas where more than 21% of the area residents have no high school diploma (HSD) had higher odds of chemotherapy refusal compared to patients living in areas where less than 7% have no HSD (OR=1.28, p=0.0026). The year of diagnosis was also linked to refusal, with patients diagnosed between 2004-2007, 2008-2011, and 2012-2015 having higher odds of refusal compared to those diagnosed between 2016-2020 (OR=1.62, 1.25 and 1.27 respectively, p<0.0001). It is also important to note that geographical location played a role, with patients treated in the East North Central, Mountain, and Pacific regions exhibiting significantly higher odds for refusal compared to the South Atlantic region (OR=1.17, 1.35 and 1.16 respectively, p=0.01, 0.002 and 0.03 respectively). Conclusion: Our comprehensive analysis of MM patients shows that the chemotherapy refusal rate in the real-world setting is low at 2.16%. Despite the low rate of refusal, this study did identify several important factors significantly associated with chemotherapy refusal in this patient population. Older age, female sex, Black race, non-private insurance, higher comorbidities, lower education level, non-academic facility type, earlier year of diagnosis, and East North Central, Mountain, and Pacific facility locations were associated with higher likelihood of declining chemotherapy. Our findings emphasize the importance of considering socioeconomic (level of education, insurance status), as well as racial and regional factors, while developing targeted interventions to address disparities in order to enhance engagement in myeloma care.

Renewable energy consumption and business density as drivers of sustainable development

The East Asia and Pacific region have witnessed significant economic growth in recent decades, leading to improved living standards and increased energy consumption. However, this rapid development has also resulted in environmental degradation and heightened concerns about climate change. In response, countries in the region have been increasingly focusing on sustainable development, with renewable energy consumption and business density emerging as key drivers. Given this background, this research empirically examines the impact of entrepreneurship and clean technology on sustainable development in East Asia and the Pacific region. To control for endogeneity, the study applied system the Generalized Method of Moments (SGMM) for the data sourced from the World Development Indicators of the World Bank for the period 2015–2021. The finding indicates that both entrepreneurship and clean technology have an adverse significant impact on sustainable development in the East Asia-Pacific region. When the regions were analysed individually, the results revealed that both entrepreneurship and clean technology have a negative substantial effect on sustainable development in East Asia, but not in the Pacific region. The study concludes that entrepreneurship and renewable energy by themselves would not result in sustainable development, except some certain conditions are met. The positive impacts of entrepreneurship and renewable energy on sustainable development can only be attained when the nature, location, and practices of new businesses are low carbon in nature.JEL Classifications: Q01, Q42, Q56

Global meat demand projection: Quo Vadimus?

Meat demand is a crucial part of limiting the rise in global temperature below 1.5 °C or, at least, 2 °C and has an important role in maintaining public health. We combined machine learning with comprehensive meat consumption drivers to analyze and project meat demand both in history (1961–2019) and future scenarios (2020–2100). Our results revealed the disproportionate climate impacts of meat consumption in the West. The country group, Western Countries, was the biggest consumer in history, consuming 38% of global meat cumulatively but accounting for only 14% of the world population from 1961 to 2019. Our projections show that global total meat demand will decline in this century under most future scenarios. The East Asia & Pacific region is expected to contribute 56%–125% of global meat demand decline, but the region is used to being deemed the main driver of many current dramatically rising environmental problems. On the contrary, meat demand in Western Countries may be more likely to increase by 15%–71% between 2020 and 2100. However, both the general public and governments in Western Countries seem reluctant to promote lifestyle changing to mitigate climate change. Thus, it is essential to take measures to limit the negative environmental impacts of increasing meat demand. Especially western high-income countries need to take proportional responsibility for international cooperation to reduce meat consumption for climate change mitigation.

Systematic review of the effectiveness of menstrual health interventions in low- and middle-income countries in the East Asia and Pacific region

In the context of rapidly expanding policy and practice, this systematic review collates and appraises evidence for the effectiveness of menstrual health interventions in the East Asia and Pacific region. Structured searches were undertaken in 7 databases and Google Scholar. Grey literature was identified through searching and survey of stakeholders. Quantitative evaluations were eligible. We audited the interventions and outcomes assessed in current evidence, undertook risk of bias assessment, and narrative synthesis of findings. The review protocol was registered prior to searching (PROSPERO: 343613). Eighteen studies were eligible; categorised according to the requirements for menstrual health they addressed. Information and education intervention studies (n=11) found school-based programs improved menstrual knowledge test scores but did not evaluate impacts on broader outcomes. Evaluations of interventions providing materials, facilities, and services for menstruation (n=4) focused on product acceptability. Studies exhibited a serious risk of bias without adequate controls, limitations in intervention allocation, adherence, and participant retention. Six studies of interventions to improve care for menstrual discomforts found decreased self-reported pain but had serious bias without placebo controls. Two interventions targeted the supportive social environment for menstruation. There is insufficient evidence for the effectiveness of menstrual health interventions in the East Asia and Pacific region. Future research must improve reporting, provide clear intervention theory of change, and improve measurement of core concepts. Evaluations of interventions that align with policy and practice are needed, facilitated by partnerships between researchers, government, and practitioners. UNICEF. Reckitt Global Hygiene Institute. NHMRC.

High-quality genome of a pioneer mangrove Laguncularia racemosa explains its advantages for intertidal zone reforestation.

Ecological restoration of mangrove ecosystems that became susceptible to recent habitat perturbations is crucial for tropical coast conservation. The white mangrove Laguncularia racemosa, a pioneer species inhabiting intertidal environments of the Atlantic East Pacific (AEP) region, has been used for reforestation in China for decades. However, the molecular mechanisms underlying its fast growth and high adaptive potential remain unknown. Using PacBio single-molecule real-time sequencing, we completed a high-quality L. racemosa genome assembly covering 1105 Mb with scaffold N50 of 3.46 Mb. Genomic phylogeny shows that L. racemosa invaded intertidal zones during a period of global warming. Multi-level genomic convergence analyses between L. racemosa and three native dominant mangrove clades show that they experienced convergent changes in genes involved in nutrient absorption and high salinity tolerance. This may explain successful L. racemosa adaptation to stressful intertidal environments after introduction. Without recent whole-genome duplications or activated transposable elements, L. racemosa has retained many tandem gene duplications. Some of them are involved in auxin biosynthesis, intense light stress and cold stress response pathways, associated with L. racemosa's ability to grow fast under high light or cold conditions when used for reforestation. In summary, our study identifies shared mechanisms of intertidal environmental adaptation and unique genetic changes underlying fast growth in mangrove-unfavourable conditions and sheds light on the molecular mechanisms of the white mangrove utility in ecological restoration.

Are born global firms environmentally more responsible? Evidence from the East Asia and Pacific region

Are born global firms environmentally more responsible? Evidence from the East Asia and Pacific region

Government policy interventions during COVID-19 pandemic and economic performance

This paper examines the impacts of government policy interventions during COVID-19 pandemic on economic performance. The study pooled daily data from 108 countries across 5 regions based on World Bank classification from March 22, 2020 to July 31, 2020. These regions are East Asia and Pacific; Europe and Central Asia; Latin America and the Caribbean; Middle East and North Africa as well as sub-Saharan Africa. The study employs Autoregressive Distributed Lagged (ARDL) model. This study uses STATA 15.0 to estimate error correction-based Pooled Mean Group (PMG), Mean Group (MG) and Dynamic Fixed Effect (DFE) model for analyzing dynamic heterogeneous panel data. The empirical result from each region of the World is as follows: (1) Result from PMG on East Asia and Pacific region reveals that government policy of debt/contract relief towards cushioning the effects of COVID-19 pandemic has positive impact on economic performance in the short run while none of the government policy measure is statistically significant in the long run. (2) Result from MG on Europe and Central Asia shows that in both the long run and the short run, number of confirmed cases and school closure policy have negative impacts on economic performance and statistically significant at 1 percent level while in the long run, international travel control and public information campaign have negative and positive impacts on economic performance in the region respectively. Both International travel control and public information campaign are statistically significant at 1 percent and 10 percent levels respectively. (3) Result from PMG on Latin America and the Caribbean shows that the number of confirmed cases and debt/contract relief are statistically significant and have negative and positive impacts on economic performance respectively in the short run. The former is statistically significant at 5 percent level while the latter is statistically significant at 10 percent level. In the long run however, only international travel control imposed by government is significant at 5 percent level and has negative impact on economic performance in the region. (4) Result from PMG on Middle East and North Africa shows that only number of confirmed cases is significant and has positive impact on economic performance at 10 percent level in the short run. No robust impacts is found in the long run. (5) Result from PMG on sub-Saharan African region shows that number of confirmed cases has positive impact, which is statistically significant at 10 percent level on economic performance while in the long run, income support package has negative impact on economic performance at 5 percent level of significance. The study therefore, concludes that, the relative economic potency and effectiveness of government interventionist policy measures towards putting COVID-19 pandemic under control vary across regions of the world. Economically, Europe and Central Asia is the region that is worst hit by the pandemic when compared with other regions.

Forecasting Magnitude and Frequency of Seasonal Streamflow Extremes Using a Bayesian Hierarchical Framework

AbstractWe develop a space‐time Bayesian hierarchical modeling (BHM) framework for two flood risk attributes—seasonal daily maximum flow and the number of events that exceed a threshold during a season (NEETM)—at a suite of gauge locations on a river network. The model uses generalized extreme value (GEV) and Poisson distributions as marginals for these flood attributes with non‐stationary parameters. The rate parameters of the Poisson distribution and location, scale, and shape parameters of the GEV are modeled as linear functions of suitable covariates. Gaussian copulas are applied to capture the spatial dependence. The best covariates are selected using the Watanabe‐Akaike information criterion (WAIC). The modeling framework results in the posterior distribution of the flood attributes at all the gauges and various lead times. We demonstrate the utility of this modeling framework to forecast the flood risk attributes during the summer peak monsoon season (July‐August) at five gauges in the Narmada River basin (NRB) of West‐Central India for several lead times (0–3 months). As potential covariates, we consider climate indices such as El Niño–Southern Oscillation (ENSO), the Indian Ocean Dipole (IOD), and the Pacific Warm Pool Region (PWPR) from antecedent seasons, which have shown strong teleconnections with the Indian monsoon. We also include new indices related to the East Pacific and West Indian Ocean regions depending on the lead times. We show useful long lead skill from this modeling approach which has a strong potential to enable robust risk‐based flood mitigation and adaptation strategies 3 months before flood occurrences.

The effect of the Xpert HIV-1 Qual test on early infant diagnosis of HIV in Myanmar and Papua New Guinea: a pragmatic, cluster-randomised, stepped-wedge, open-label trial.

Despite proven benefits for child health, coverage of early infant diagnosis of HIV remains suboptimal in many settings. We aimed to assess the effect of a point-of-care early infant diagnosis test on time-to-results communication for infants vertically exposed to HIV. This pragmatic, cluster-randomised, stepped-wedge, open-label trial assessed the effect of the Xpert HIV-1 Qual early infant diagnosis test (Cepheid) on time-to-results communication, compared with standard care laboratory-based testing of dried blood spots using PCR. Hospitals were the unit of randomisation for one-way crossover from control to intervention phase. Each site had between 1 month and 10 months of control phase before transitioning to the intervention, with a total of 33 hospital-months in the control phase and 45 hospital-months in the intervention phase. We enrolled infants vertically exposed to HIV at six public hospitals: four in Myanmar and two in Papua New Guinea. Infants had to have mothers with confirmed HIV infection, be younger than 28 days, and required HIV testing to be eligible for enrolment. Health-care facilities providing prevention of vertical transmission services were eligible for participation. The primary outcome was communication of early infant diagnosis results to the infant's caregiver by 3 months of age, assessed by intention to treat. This completed trial was registered with the Australian and New Zealand Clinical Trials Registry, 12616000734460. In Myanmar, recruitment took place between Oct 1, 2016, and June 30, 2018; in Papua New Guinea, recruitment was between Dec 1, 2016, and Aug 31, 2018. A total of 393 caregiver-infant pairs were enrolled in the study across both countries. Independent of study time, the Xpert test reduced time to early infant diagnosis results communication by 60%, compared with the standard of care (adjusted time ratio 0·40, 95% CI 0·29-0·53, p<0·0001). In the control phase, two (2%) of 102 study participants received an early infant diagnosis test result by 3 months of age compared with 214 (74%) of 291 in the intervention phase. No safety and adverse events were reported related to the diagnostic testing intervention. This study reinforces the importance of scaling up point-of-care early infant diagnosis testing in resource-constrained and low HIV-prevalence settings, typical of the UNICEF East Asia and Pacific region. National Health and Medical Research Council of Australia.