scholarly journals Improved real-time influenza surveillance using Internet search data in eight Latin American countries

2018 ◽  
Author(s):  
Leonardo Clemente ◽  
Fred Lu ◽  
Mauricio Santillana
Keyword(s):  
Real Time ◽  
Latin American ◽  
Influenza Surveillance ◽  
Machine Learning Method ◽  
Learning Method ◽  
Search Activity ◽  
Middle Income ◽  
Middle Income Countries ◽  
Search Data ◽  
Latin American Countries

AbstractA real-time methodology for monitoring flu activity in middle income countries that is simultaneously accurate and generalizable has not yet been presented. We demonstrate here that a self-correcting machine learning method leveraging Internet-based search activity produces reliable and timely flu estimates in multiple Latin American countries.

Inward and Outward Foreign Direct Investment and Inequality: Evidence from a Group of Middle-Income Countries

2016 ◽  
Vol 16 (3) ◽  
pp. 511-538 ◽  
Author(s):  
Chao-Hsi Huang ◽  
Kai-Fang Teng ◽  
Pan-Long Tsai
Keyword(s):  
Income Distribution ◽  
Transition Economies ◽  
Latin American ◽  
Direct Investment ◽  
Reference Group ◽  
Outward Foreign Direct Investment ◽  
Outward Fdi ◽  
Middle Income ◽  
Middle Income Countries ◽  
Latin American Countries

Using panel data of a group of 39 middle-income countries over 1981–2006, this paper examines how globalization in general and inward and outward FDI in particular affects inequality. Depending on geographical region and economic system, each component of globalization affects inequality in three groups of countries in different ways: open to inward FDI tends to affect income distribution adversely in transition economies and Latin American countries, but marginally improves income distribution in countries of the reference group. In contrast, open to outward FDI is positively associated with inequality in the reference group whereas negatively associated with that of the other two groups of countries. Crucially, improvement in human capital appears to be the single most reliable way to reduce inequality.

Do Remittances Attract Foreign Direct Investment? An Empirical Investigation

2014 ◽  
Vol 14 (1) ◽  
pp. 1-9 ◽  
Author(s):  
Hem C. Basnet ◽  
Kamal P. Upadhyaya
Keyword(s):  
Foreign Direct Investment ◽  
Latin American ◽  
Direct Investment ◽  
Asia Pacific ◽  
African Countries ◽  
Middle Income ◽  
Middle Income Countries ◽  
Negative Effect ◽  
Using Data ◽  
Latin American Countries

Remittances are a major source of household income in many Asian, African, and Latin American countries. Households spend a significant portion of remittances on health and education. Given that human capital is one of the primary determinants of foreign direct investment (FDI) inflow, this study develops a model in which remittances are one of several determinants of the observed variation in FDI. The model is estimated using data from a group of 35 middle-income countries from Latin America, Asia–Pacific, and Africa. The estimated results ascribe no significance to remittances in explaining cross-country variation in FDI. However, geographically-disaggregated estimated results do establish a positive effect for African countries, no significant effect for Latin American countries, and a negative effect for the Asia–Pacific region.

Insiders, Outsiders, Skills, and Preferences for Social Protection: Evidence From a Survey Experiment in Argentina

2021 ◽  
pp. 001041402110243
Author(s):  
Irene Menéndez González
Keyword(s):  
Labor Market ◽  
Social Policy ◽  
Latin American ◽  
Social Protection ◽  
Survey Experiment ◽  
Middle Income ◽  
Informal Labor ◽  
Middle Income Countries ◽  
Nationally Representative ◽  
Latin American Countries

Standard theories in comparative political economy predict that labor market insiders oppose redistribution to poorer, often informal, labor market outsiders. In contrast, I argue that not all insiders oppose redistribution to outsiders. Extending recent work emphasizing the importance of economic insecurity for insiders, I argue that exposure to risk leads to greater polarization regarding preferences for non-contributory social policy between low- and high-skilled insiders. I test implications of this logic using a survey experiment from a nationally representative sample in Argentina and complement this with analysis of observational data for 16 Latin American countries. I find strong evidence of polarization regarding preferences over social protection among low- and high-skilled insiders. The experiment reveals that low (high)-skilled insiders primed about the risk of becoming outsiders become more supportive of transfers to outsiders (insiders). The article provides new micro-foundations for insider–outsider coalitions in support of social policy expansion in middle-income countries.

scholarly journals Estimating the COVID-19 spread through real-time population mobility patterns: surveillance in Low- and Middle- income countries (Preprint)

2020 ◽  
Author(s):  
Stefanos Tyrovolas ◽  
Iago Giné-Vázquez ◽  
Daniel Fernández ◽  
Mariathi Morena ◽  
Ai Koyanagi ◽  
...  
Keyword(s):  
Real Time ◽  
Latin American ◽  
Space Time ◽  
Population Mobility ◽  
Mobility Patterns ◽  
African Countries ◽  
Control Policies ◽  
Middle Income ◽  
Middle Income Countries ◽  
Time Scan

BACKGROUND On January 30, 2020, World Health Organization (WHO) declared the current novel coronavirus disease 2019 (COVID-19) as a public health emergency of international concern and later characterized it as a pandemic. Since then the virus has also rapidly spread among Latin American, Caribbean and African countries. OBJECTIVE The first aim of this study was to identify new emerging COVID-19 clusters over time and in space in Latin American, Caribbean, and African regions [mostly low and middle-income countries (LMICs)], using a prospective space-time scan measurement approach. The second aim was to assess the impact of real-time population mobility patterns between January 21st to May 18th, under the implemented government interventions, measurements and policy restrictions, on COVID-19 spread, among those regions and globally. METHODS We created a global COVID-19 database merging WHO daily case reports (of 218 countries, regions and territories) with other measures such as population density, country income levels for January 21st to May 15th, 2020. A score of government policy interventions was created ranging from “light”, “intermediate”, and “high”, to “very high” interventions. Prospective space-time scan statistic methods were applied in five time periods between January to May 2020 and a stepped-wedged regression mixed model analysis was used. RESULTS We found that COVID-19 emerging clusters within these five periods of time grew from 7 emerging clusters to 28 by mid-May. We also detected various increasing and decreasing relative risk estimates of COVID-19 spread among Latin American, Caribbean and African countries within the period of analysis. Globally, as well as regionally (Latin American, Caribbean and Africa), population mobility to parks and similar leisure areas during all the implemented control policies were related with accelerated COVID-19 spread. For countries in Africa, population mobility for work reasons during high and very high levels of implemented control policies were related with increased virus spread. CONCLUSIONS Prospective space-time scan is a measurement approach that LMICs countries could easily use to detect emerging clusters in a timely manner and implement specific control policies and interventions to slow down COVID-19 transmission. In addition, real time population mobility obtained from crowdsourced digital data could be useful for current and future targeted public health and mitigation policies among Latin American, Caribbean and African countries as well as globally.

scholarly journals From classical developmentalism and post-Keynesian macroeconomics to new developmentalism

2019 ◽  
Vol 39 (2) ◽  
pp. 187-210 ◽  
Author(s):  
LUIZ CARLOS BRESSER-PEREIRA
Keyword(s):  
Developing Countries ◽  
Industrial Policy ◽  
Latin American ◽  
East Asian ◽  
Asian Countries ◽  
Middle Income ◽  
Middle Income Countries ◽  
Keynesian Economics ◽  
Catch Up ◽  
Latin American Countries

ABSTRACT New developmentalism was a response to the inability of classical developmentalism and post-Keynesian macroeconomics in leading middle-income countries to resume growth. New developmentalism was born in the 2000s to explain why Latin American countries stopped growing in the 1980s, while East Asian countries continued to catch up. This paper compares new developmentalism with classical developmentalism, which didn’t have a macroeconomics, and with post-Keynesian economics, whose macroeconomics is not devoted to developing countries. And shows that to follow the East Asian example is not enough industrial policy, it is also necessary a macroeconomic policy that sets the five macroeconomic prices right, rejects the growth with foreign savings policy, and keeps the macroeconomic accounts balanced.

scholarly journals Improved Real-Time Influenza Surveillance: Using Internet Search Data in Eight Latin American Countries (Preprint)

2018 ◽  
Author(s):  
Leonardo Clemente ◽  
Fred Lu ◽  
Mauricio Santillana
Keyword(s):  
Machine Learning ◽  
Latin America ◽  
Latin American ◽  
Influenza Surveillance ◽  
Surveillance Systems ◽  
Internet Search ◽  
Search Activity ◽  
Time Period ◽  
Out Of Sample ◽  
Latin American Countries

BACKGROUND Novel influenza surveillance systems that leverage Internet-based real-time data sources including Internet search frequencies, social-network information, and crowd-sourced flu surveillance tools have shown improved accuracy over the past few years in data-rich countries like the United States. These systems not only track flu activity accurately, but they also report flu estimates a week or more ahead of the publication of reports produced by healthcare-based systems, such as those implemented and managed by the Centers for Disease Control and Prevention. Previous work has shown that the predictive capabilities of novel flu surveillance systems, like Google Flu Trends (GFT), in developing countries in Latin America have not yet delivered acceptable flu estimates. OBJECTIVE The aim of this study was to show that recent methodological improvements on the use of Internet search engine information to track diseases can lead to improved retrospective flu estimates in multiple countries in Latin America. METHODS A machine learning-based methodology that uses flu-related Internet search activity and historical information to monitor flu activity, named ARGO (AutoRegression with Google search), was extended to generate flu predictions for 8 Latin American countries (Argentina, Bolivia, Brazil, Chile, Mexico, Paraguay, Peru, and Uruguay) for the time period: January 2012 to December of 2016. These retrospective (out-of-sample) Influenza activity predictions were compared with historically observed flu suspected cases in each country, as reported by Flunet, an influenza surveillance database maintained by the World Health Organization. For a baseline comparison, retrospective (out-of-sample) flu estimates were produced for the same time period using autoregressive models that only leverage historical flu activity information. RESULTS Our results show that ARGO-like models’ predictive power outperform autoregressive models in 6 out of 8 countries in the 2012-2016 time period. Moreover, ARGO significantly improves on historical flu estimates produced by the now discontinued GFT for the time period of 2012-2015, where GFT information is publicly available. CONCLUSIONS We demonstrate here that a self-correcting machine learning method, leveraging Internet-based disease-related search activity and historical flu trends, has the potential to produce reliable and timely flu estimates in multiple Latin American countries. This methodology may prove helpful to local public health officials who design and implement interventions aimed at mitigating the effects of influenza outbreaks. Our methodology generally outperforms both the now-discontinued tool GFT, and autoregressive methodologies that exploit only historical flu activity to produce future disease estimates.

Speech Organ Contour Extraction Using Real-Time MRI and Machine Learning Method

2019 ◽  
Author(s):  
Hironori Takemoto ◽  
Tsubasa Goto ◽  
Yuya Hagihara ◽  
Sayaka Hamanaka ◽  
Tatsuya Kitamura ◽  
...  
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Machine Learning Method ◽  
Learning Method ◽  
Contour Extraction

scholarly journals Spatial Externalities of Income Inequality on Security in Latin America

Mathematics ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 245
Author(s):  
Pablo Ponce ◽  
José Álvarez-García ◽  
Mary Cumbicus ◽  
María de la Cruz del Río-Rama
Keyword(s):  
Income Inequality ◽  
Latin American ◽  
Spatial Dependence ◽  
Generalized Least Squares ◽  
Homicide Rate ◽  
World Health ◽  
Control Variables ◽  
Spatial Error ◽  
Middle Income ◽  
Middle Income Countries

The aim of this research is to analyse the effect of income inequality on the homicide rate. The study is carried out in 18 Latin American countries for the period 2005–2018. The methodology used is the Generalized Least Squares (GLS) model and the data were obtained from World Development Indicators, the World Health Organization and the Inter-American Development Bank. Thus, the dependent variable is the homicide rate and the independent variable is income inequality. In addition, some control variables are included, such as: poverty, urban population rate, unemployment, schooling rate, spending on security and GDP per capita, which improve the consistency of the model. The results obtained through GLS model determine that inequality has a negative and significant effect on the homicide rate for high-income countries (HIC) and lower-middle-income countries (LMIC), whereas it is positive and significant for upper-middle-income countries (UMIC). On the other hand, the control variables show different results by group of countries. In the case of unemployment, it is not significant in any group of countries. Negative spatial dependence was found regarding spatial models such as: the spatial lag (SAR) and spatial error (SEM) method. In the spatial Durbin model (SDM), positive spatial dependence between the variables was corroborated. However, spatial auto-regressive moving average (SARMA) identified no spatial dependence. Under these results it is proposed: to improve productivity, education and improve the efficiency of security-oriented resources.

scholarly journals Novel multiplex real-time PCR assays reveal a high prevalence of diarrhoeagenic Escherichia coli pathotypes in healthy and diarrhoeal children in the South of Vietnam

2020 ◽  
Author(s):  
Vu Thuy Duong ◽  
Le Thi Phuong Tu ◽  
Ha Thanh Tuyen ◽  
Le Thi Quynh Nhi ◽  
James I Campbell ◽  
...  
Keyword(s):  
Escherichia Coli ◽  
Real Time ◽  
Real Time Pcr ◽  
Clinical Samples ◽  
Middle Income ◽  
Middle Income Countries ◽  
Virulence Markers ◽  
Low Middle Income Countries ◽  
High Prevalence ◽  
Pcr Assays

Abstract BackgroundDiarrhoeagenic Escherichia coli (DEC) infections are common in children in low-middle income countries (LMICs). However, detecting the various DEC pathotypes is complex as they cannot be differentiated by classical microbiology. We developed four multiplex real-time PCR assays were to detect virulence markers of six DEC pathotypes; specificity was tested using DEC controls and other enteric pathogens. PCR amplicons from the six E. coli pathotypes were purified and amplified to be used to optimize PCR reactions and to calculate reproducibility. After validation, these assays were applied to clinical samples from healthy and diarrhoeal Vietnamese children and associated with clinical data. ResultsThe multiplex real-time PCRs were found to be reproducible, and specific. At least one DEC variant was detected in 34.7% (978/2,815) of the faecal samples from diarrhoeal children; EAEC, EIEC and atypical EPEC were most frequent Notably, 41.2% (205/498) of samples from non-diarrhoeal children was positive with a DEC pathotype. In this population, only EIEC, which was detected in 34.3% (99/289) of diarrhoeal samples vs. 0.8% (4/498) non-diarrhoeal samples (p<0.001), was significantly associated with diarrhoea. Multiplex real-time PCR when applied to clinical samples is an efficient and high-throughput approach to DEC pathotypes. ConclusionsThis approach revealed high carriage rates of DEC pathotypes among Vietnamese children. We describe a novel diagnostic approach for DEC, which provides baseline data for future surveillance studies assessing DEC burden in LMICs.

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