Neeraj Tiwari

Investigation of geo-spatial hotspots for the occurrence of tuberculosis in Almora district, India, using GIS and spatial scan statistic

BackgroundThe World Health Organization has declared tuberculosis a global emergency in 1993. It has been estimated that one third of the world population is infected with Mycobacterium tuberculosis, the causative agent of tuberculosis. The emergence of TB/HIV co-infection poses an additional challenge for the control of tuberculosis throughout the world. The World Health Organization is supporting many developing countries to eradicate tuberculosis. It is an agony that one fifth of the tuberculosis patients worldwide are in India. The eradication of tuberculosis is the greatest public health challenge for this developing country. The aim of the present population based study on Mycobacterium tuberculosis is to test a large set of tuberculosis cases for the presence of statistically significant geographical clusters. A spatial scan statistic is used to identify purely spatial and space-time clusters of tuberculosis.ResultsSignificant (p < 0.05 for primary clusters and p < 0.1 for secondary clusters) high rate spatial and space-time clusters were identified in three areas of the district.ConclusionThere is sufficient evidence about the existence of statistically significant tuberculosis clusters in Almora district of Uttaranchal, India. The spatial scan statistics methodology used in this study has a potential use in surveillance of tuberculosis for detecting the true clusters of the disease.

Investigation of tuberculosis clusters in Dehradun city of India

Objective: To investigate the presence of statistically significant geographical clusters of tuberculosis (TB) using Geographical Information System and spatial scan statistics in Dehradun, India. Methods: The spatial scan statistic implemented with a software program, SaTScan v6.1, was used to test the presence of statistically significant spatial clusters of TB and to identify their approximate locations (P＜0.05 for primary clusters and P＜0.1 for secondary clusters). Geographical Information System was used for geographical analysis. Results: Significant high rate spatial clusters were identified in seven wards of the Dehradun Municipal area. Conclusions: There is sufficient evidence about the existence of statistically significant TB clusters in seven wards of Dehradun, India. The purely spatial scan statistics methodology used in this study has a potential use in surveillance of TB for detecting the true clusters of the disease.

A spatial scan statistic for survival data based on Weibull distribution

The spatial scan statistic has been developed as a geographical cluster detection analysis tool for different types of data sets such as Bernoulli, Poisson, ordinal, normal and exponential. We propose a scan statistic for survival data based on Weibull distribution. It may also be used for other survival distributions, such as exponential, gamma, and log normal. The proposed method is applied on the survival data of tuberculosis patients for the years 2004-2005 in Nainital district of Uttarakhand, India. Simulation studies reveal that the proposed method performs well for different survival distribution functions.

Geographical Distribution and Surveillance of Tuberculosis (TB) Using Spatial Statistics

Socio-demographic and health indices vary across the administrative units in a country. Thus, reported morbidity and mortality figures vary and inter/intra state comparison becomes a challenge. To handle such issues and administer a centralized health management system, identifying disease clusters and providing services to high risk population become important. Exploring a small part of the immense potential of geographic information systems (GIS) in centralized health management, this study presents a method of generating effective information for proper health management at local level. Such information is important for infectious diseases like tuberculosis (TB). The present paper discusses quarterly GIS mapping and assessment of TB in 1,965 villages of Almora district, Uttarakhand, India from 2003 to 2008. The values for Morbidity Rate (MBR) are depicted in risk maps for each quarter. Moran’s I indices were used to estimate the global spatial autocorrelation between the morbidity rates. Local Moran’s I (LISA) was used to detect spatial clusters and outliers, and for the prediction of hotspots of the disease. The result of this study has the potential to reflect a realistic assessment of the disease situation at the local level. Future work on this study can be utilized for planning and policy framework related to TB and other diseases. DOI: 10.4018/jagr.2013040103 40 International Journal of Applied Geospatial Research, 4(2), 39-53, April-June 2013 Copyright

On two-dimensional optimal controlled selection

Abstract This article deals with a method for two-dimensional controlled selection. In addition to achieve the goal of ‘controls beyond stratification’, the proposed method also minimizes the selection probabilities of non-preferred combinations of units. An improved version of Jessens ‘split-sample’ estimator for estimating the variance in controlled selection is also proposed. The utility of these methods is demonstrated through examples.

On Minimum Variance Optimal Controlled Sampling: A Simplified Approach

Controlled sampling, a technique of avoiding the nonpreferred samples in favor of preferred samples while maintaining the initial probabilities of each unit, is widely used due to its practical importance. With the introduction of linear programming methods, the time and labor involved in solving a controlled sampling problem have been reduced to a large extent. In this problem, we introduce a simple technique to solve the optimal controlled sampling problem with minimum variance, using the multiple objective linear programming approach. Some examples are considered to demonstrate the utility of the proposed procedure in comparison to the existing controlled selection procedures.

Evaluation of predictive models based on random forest, decision tree and support vector machine classifiers and virtual screening of anti-mycobacterial compounds

Three machine learning classifiers: random forest, decision tree and support vector machine were used to build predictive models of an anti-mycobacterial ChEMBL database and evaluated for their predictive capability. Before the development of predictive models, data pre-processing was carried out to fix the class imbalance problem by applying cost-sensitive classifier, and filtration of data instance by supervised synthetic minority oversampling technique (SMOTE), spread subsample and resample method. The statistical evaluation indicated that random forest model was the best model as it showed the best accuracy 93.83%, specificity 90.5%, receiver operating characteristic (ROC) 0.984, MCC 0.772 and kappa statistics 0.768 in comparison to other models whereas LibSVM showed the highest sensitivity 94.4% compared with others. Additionally, toxicity predictive models based on SingleCellcall DSSTox carcinogenicity database (AID1189) was developed which resulted in random forest model as the best model. The deployment of both RF predictive models on two unknown datasets resulted in 1317 compounds out of 1554 approved drugs and 2234 compounds out of 18,746 ChEMBL anti-malarial dataset as non-toxic and anti-mycobacterial compounds. Thus machine learning models present highly efficient methods to find out novel hit anti-mycobacterial compounds. We suggest that such machine learning techniques could be very useful to screen drug candidates not only for tuberculosis but also for other diseases.

High Throughput Virtual Screening to Identify Novel natural product Inhibitors for MethionyltRNA-Synthetase of Brucella melitensis

The Brucella melitensis methionyl-tRNA-synthetase (MetRSBm) is a promising target for brucellosis drug development. The virtual screening of large libraries of a drug like molecules against a protein target is a common strategy used to identify novel inhibitors. A High throughput virtual screening was performed to identify hits to the potential antibrucellosis drug target, MetRSBm. The best inhibitor identified from the literature survey was 1312, 1415, and 1430. In the virtual screening 56,400 compounds of ChEMBL antimycobacterial library, 1596 approved drugs, 419 Natural product IV library, and 2396 methionine analogous were docked and rescoring, identified top 10 ranked compounds as anti-mycobacterial leads showing G-scores -10.27 to -8.42 (in kcal/mol), approved drugs G-scores -9.08 to -6.60 (in kcal/mol), Natural product IV library G-scores -10.55 to -6.02 (in kcal/mol), methionine analogous Gscores -11.20 to -8.51 (in kcal/mol), and compared with all three known inhibitors (as control) G-scores -3.88 to -3.17 (in kcal/mol). This result indicates these novel compounds have the best binding affinity for MetRSBm. In this study, we extrapolate that the analogous of methionine for find novel drug likeness has been identified [4-(L-histidyl)-2-phenylbenzoyl] methionine hydrochloride, might show the inhibitor of Brucella melitensis effect by interacting with MetRS enzyme. We suggests that Prumycin as a natural product is the novel drugs for brucellosis.

A spatial scan statistic for survival data based on generalized life distribution

ABSTRACT For many years, detection of clusters has been of great public health interest and widely studied. Several methods have been developed to detect clusters and their performance has been evaluated in various contexts. Spatial scan statistics are widely used for geographical cluster detection and inference. Different types of discrete or continuous data can be analyzed using spatial scan statistics for Bernoulli, Poisson, ordinal, exponential, and normal models. In this paper, we propose a scan statistic for survival data which is based on generalized life distribution model that provides three important life distributions, viz. Weibull, exponential, and Rayleigh. The proposed method is applied to the survival data of tuberculosis patients in Nainital district of Uttarakhand, India, for the year 2004–05. The Monte Carlo simulation studies reveal that the proposed method performs well for different survival distributions.

An optimal controlled selection procedure for sample coordination problem using linear programming and distance function

A number of procedures have been developed for maximizing and minimizing the overlap of sampling units in dierent/repeated surveys. The concepts of controlled selection, transportation theory and controlled rounding have been used to solve the sample co-ordination problem. In this article, we proposed a procedure for sample co-ordination problem using linear programming with the concept of distance function that facilitates variance estimation using the Horvitz-Thompson estimator. The proposed procedure can be applied to any two-sample surveys having identical universe and stratification. Some examples have been considered to demonstrate the utility of the proposed procedure in comparison to the existing procedures. 2000 AMS Classification: 62D05