Correlates of Hunger: Evidence from the Community Based Monitoring System (CBMS) Data of Pasay City

Hunger is one of the major problems in several countries, and the objective to reduce it has become a global concern. A major initiative of the United Nations Millenium Development Goals (MDG) is the eradication of extreme poverty and hunger. However, the attempt to reduce the abovementioned issues, calls for proper identification as to who the impoverished and hungry are. There are several ways to identify the poor, but pinpointing who the hungry are, is another task

Regression Analysis on the Chemical Descriptors of a Selected Class of DPP4 Inhibitors

The activity of a selected class of DPP4 inhibitors was assessed using quantum-chemical and physical descriptors. Using multiple linear regression model, it was found that ΔE, LUMO energy, dipole, area, volume, molecular weight and ΔH are the significant descriptors that can adequately assess the activity of the compounds. The model suggests that bulky and electrophilic inhibitors are desired. Furthermore a pair interaction between ΔE and dipole as well as for LUMO energy and dipole were determined as well. It is expected that the information derived herein will be beneficial for future design and development of DPP4 inhibitors. Key words: Multiple Linear Regression; Molecular Descriptors; 2D-QSAR; DPP4 Inhinitor

Some zero inflated Poisson-Based combined exponentially weighted moving average control charts for disease surveillance

© 2008 Philippine Statistical Association, Inc. One of the main areas of public health surveillance is infectious disease surveillance. With infectious disease backgrounds usually being more complex, appropriate surveillance schemes must be in order. One such procedure is through the use of control charts. However, with most background processes following a zero-inflated Poisson (ZIP) distribution as brought about by the extra variability due to excess zeros, the control charting procedures must be properly developed to address this issue. Hence in this paper, drawing inspiration from the development of combined control charting procedures for simultaneously monitoring each ZIP parameter individually in the context of statistical process control (SPC), several combined exponentially weighted moving average (EWMA) control charting procedures were proposed (Bernoulli-ZIP and CRL-ZTP EWMA charts). Through an extensive simulation study involving multiple parameter settings and outbreak model considerations (i.e., different shapes, magnitude, and duration), some key results were observed. These include the applicability of performing combined control charting procedures for disease surveillance with a ZIP background using EWMA techniques. For demonstration purposes, application with an actual data, using confirmed measles cases in the National Capital Region (NCR) from January 1, 2010 to January 14 2015, revealed the comparability of the Bernoulli-ZIP EWMA scheme to historical limits method currently in use

Regression analyses of the Philippine birth weight distribution

Low birth weight has both short-term and long-term effects. It can lead to complications among infants causing neonatal deaths. Several literatures also suggested relationships between low birth weight and delayed mental and physical development. These negative effects are further magnified in developing countries, one of which is the Philippines. In this paper, birth weight is analysed through logistic, ordinary least squares, and quantile regression techniques using a sample from the 2008 National Demographic and Health Survey. Quantile regression results offer a more dynamic picture of how these correlates affect the conditional distribution of birth weight. The estimates of the marginal effects of several demographical and maternal health correlates of birth weight suggest that socially and economically impoverished mothers are more likely to have low birth weight babies. These results would recommend a focus on improving maternal health care through proper education

Correlates of poverty: Evidence from the community-based monitoring system (CBMS) data

This study identified correlates of poverty for Pasay City and Mogpog, Marinduque representing an urban and a rural area in the Philippines, respectively, by utilizing the 2005 census data from its Community-Based Monitoring System. Regression models with the arcsine of the square root of barangay level poverty incidence as dependent variable were investigated which allowed the identification of the correlates of poverty at the barangay level. Results showed that the significant correlates of barangay poverty incidence were average household size, proportion of households whose housing units/lot are not owned and proportion of households who own telephone/cellphone. Furthermore, lower poverty incidences were observed in barangays located in an urban area. © 2011 De La Salle University, Philippines

Analysis of an SEIRS compartmental model for tuberculosis in Quezon City from 2007 to 2011

Tire Philippines is presently a country carrying a high burden of tuberculosis, being one of the four Western Pacific countries with the highest number of tuberculosis cases. As such, it has become increasingly important to study the dynamics of the disease in a population to identify ways in which its spread can be prevented. One such way of carrying out this analysis is to fit a Susceptible-Exposed-Infectious-Removed-Susceptible (SEIRS) compartmental model to actual data. In this paper, an SEIRS stochastic discrete-time model was fitted to data from Quezon City to determine the dynamics of tuberculosis transmission. Parameter distributions given the data were also identifled. An incidence rate of about 32 per 1000 infectious individuals was obtained from these distributions. These findings lead to a better understanding of the spread of tuberculosis within the population

A machine learning approach in predicting mosquito repellency of plant-derived compounds

© 2018 Jose Isagani B. Janairo et al., published by Sciendo. The increasing prevalence of mosquito - borne diseases has prompted intensified efforts in the prevention of being bitten by the vector. Among the various strategies of vector control, the application of repellents provides instant and effective protection from mosquitoes. However, emerging concerns regarding the safety of the widely used repellent, DEET, has led to initiatives to explore natural alternatives. In order to fully realize the potential of natural repellents, focusing on the discovery of natural compounds eliciting repellency is of paramount importance. In this paper, machine learning was utilized to establish association between the mosquito repellent activity of 33 natural compounds using 20 chemical descriptors. Individually, the descriptors had insignificant monotonic relationship with the response variable. But when optimized, the formulated model through boosted trees regression exhibited reliable predictive ability (r2train = 0.93, r2test = 0.66, r2overall = 0.87). The findings presented have also introduced new descriptors that exhibited association with repellency through ensemble learning such as heat capacity, Log P, entropy, enthalpy, Gibb\u27s free energy, energy, and zero-point energy

A machine learning approach in predicting mosquito repellency of plant – derived compounds

The increasing prevalence of mosquito – borne diseases has prompted intensified efforts in the prevention of being bitten by the vector. Among the various strategies of vector control, the application of repellents provides instant and effective protection from mosquitoes. However, emerging concerns regarding the safety of the widely used repellent, DEET, has led to initiatives to explore natural alternatives. In order to fully realize the potential of natural repellents, focusing on the discovery of natural compounds eliciting repellency is of paramount importance. In this paper, machine learning was utilized to establish association between the mosquito repellent activity of 33 natural compounds using 20 chemical descriptors. Individually, the descriptors had insignificant monotonic relationship with the response variable. But when optimized, the formulated model through boosted trees regression exhibited reliable predictive ability (r2 train = 0.93, r2 test = 0.66, r2 overall = 0.87). The findings presented have also introduced new descriptors that exhibited association with repellency through ensemble learning such as heat capacity, Log P, entropy, enthalpy, Gibb’s free energy, energy, and zero-point energy

Biosurveillance of measles using control charts: A case study using National Capital Region laboratory confirmed measles counts from January 2009 to January 2014

This paper aims to explore early outbreak detection methods for measles. Two methods adapted from statistical process control were modified and used to fit biosurveillance, namely Shewhart and Exponentially Weighted Moving Average (EWMA) charts. Seven variations of such control charts are proposed: two under Shewhart chart (normal-based and zero-inflated Poisson (ZIP)-based) and five under EWMA charts (λs of 0.05, 0.10, 0.15, 0.20, and 0.25). To study the proposed charts, daily counts of laboratory confirmed cases of measles in the National Capital Region from 2009 until 2014 were utilized to characterize both the disease background and outbreak equations. During this time span, three measles outbreaks have transpired. The proposed charts, set at average time between false signals (ATFSs) of both one and two months, were evaluated and compared using performance metrics such as conditional expected delay (CED), proportion of true signals (PTS), proportions of detections in an outbreak (PDO), and probability of successful detection (PSD), computed from 500 sets of simulated data. It was found that ZIP-based Shewhart and EWMA with a λ of 0.05 work best for ATFSs of one and two months, respectively. Health-governing bodies may seek to explore the possible utilization of these charts to improve measles surveillance

Spatial analysis of the distribution of reported dengue incidence in the National Capital Region, Philippines

Background and Objective. With an aim of developing an effective disease monitoring and surveillance of dengue fever, this study intends to analyze the spatial distribution of dengue incidences in the National Capital Region (NCR), across four years of reported dengue cases. Materials and Methods. Data used was provided by the Department of Health (DOH) consisting of all reported dengue cases in NCR from 2010-2013. For mapping and visualization, a shapefile of NCR was made readily available by www.philgis.org. Both Moran\u27s I and Kulldorff\u27s spatial scan statistics (SaTScan) were used to identify clusters across the same time period. Results and Conclusion. The analyses identified significant clustering of dengue incidence and revealed that the northern cities of NCR, such as Caloocan, Malabon, Navotas and Valenzuela, exhibited high spatial autocorrelation using local Moran\u27s I and Kulldorff\u27s SaTScan. A temporal analysis of the results also suggested movement in increased dengue incidence through time, from the northwest cities to the northeast cities. Presence of spatial autocorrelation in dengue incidence suggests possible enhancements of early detection schemes for dengue surveillance. Moreover, the results of these analyses will be of interest to both policymakers and health experts in providing a basis for which they can properly allocate resources for the prevention and treatment of dengue fever