Attitude and perception towards vaccination against poliomyelitis in Peshawar, Pakistan.

OBJECTIVE: This research aimed to quantitatively assess the general public's awareness, attitude and perception of polio and its vaccination in Peshawar KPK, Pakistan. METHODS: We conducted a survey-based study to understand the surge in polio cases from 2015 to 2019 in the Peshawar city of the Khyber Pakhtunkhwa (KPK), Pakistan. A pre-tested questionnaire-based study was conducted in 2019 to assess the attitude and general perception of residents of Peshawar KPK towards polio vaccination. RESULTS: Out of 241 country-wide polio cases, 63 (26.1%) polio cases were reported in Peshawar city from 2015-2019. The questionnaire revealed that individuals between 18-30 years of age had sufficient knowledge (65.1%) about polio. Male and female participants had equal awareness (~ 43%). Participants with higher education (45.9%), those with better financial status (49.5%), individuals with children < 5 years of age (46.4%), and those who had experience of a polio patient (63.1%) had better knowledge. Participants inhabiting the central city were better aware (50.5%) of polio than individuals living in the outskirts. CONCLUSION: The data indicated that poor knowledge and negative attitudes of people towards polio vaccination are the main causes of the polio eradication program's failure. Moreover, religious beliefs, unchecked migration between the Pak-Afghan border, and lack of knowledge about polio vaccination are identified as critical barriers to polio eradication.

Characterizations of novel pesticide-degrading bacterial strains from industrial wastes found in the industrial cities of Pakistan and their biodegradation potential.

BACKGROUND: Lack of infrastructure for disposal of effluents in industries leads to severe pollution of natural resources in developing countries. These pollutants accompanied by solid waste are equally hazardous to biological growth. Natural attenuation of these pollutants was evidenced that involved degradation by native microbial communities. The current study encompasses the isolation of pesticide-degrading bacteria from the vicinity of pesticide manufacturing industries. METHODS: The isolation and identification of biodegrading microbes was done. An enrichment culture technique was used to isolate the selected pesticide-degrading bacteria from industrial waste. RESULTS: Around 20 different strains were isolated, among which six isolates showed significant pesticide biodegrading activity. After 16S rRNA analysis, two isolated bacteria were identified as Acinetobacter baumannii (5B) and Acidothiobacillus ferroxidans, and the remaining four were identified as different strains of Pseudomonas aeruginosa (1A, 2B, 3C, 4D). Phylogenetic analysis confirmed their evolution from a common ancestor. All strains showed distinctive degradation ability up to 36 hours. The Pseudomonas aeruginosa strains 1A and 4D showed highest degradation percentage of about 80% for DDT, and P. aeruginosa strain 3C showed highest degradation percentage, i.e., 78% for aldrin whilst in the case of malathion, A. baumannii and A. ferroxidans have shown considerable degradation percentages of 53% and 54%, respectively. Overall, the degradation trend showed that all the selected strains can utilize the given pesticides as sole carbon energy sources even at a concentration of 50 mg/mL. CONCLUSION: This study provided strong evidence for utilizing these strains to remove persistent residual pesticide; thus, it gives potential for soil treatment and restoration.

Prediction of Multidrug-Resistant Tuberculosis Using Machine Learning Algorithms in SWAT, Pakistan.

In this paper, we have focused on machine learning (ML) feature selection (FS) algorithms for identifying and diagnosing multidrug-resistant (MDR) tuberculosis (TB). MDR-TB is a universal public health problem, and its early detection has been one of the burning issues. The present study has been conducted in the Malakand Division of Khyber Pakhtunkhwa, Pakistan, to further add to the knowledge on the disease and to deal with the issues of identification and early detection of MDR-TB by ML algorithms. These models also identify the most important factors causing MDR-TB infection whose study gives additional insights into the matter. ML algorithms such as random forest, k-nearest neighbors, support vector machine, logistic regression, leaset absolute shrinkage and selection operator (LASSO), artificial neural networks (ANNs), and decision trees are applied to analyse the case-control dataset. This study reveals that close contacts of MDR-TB patients, smoking, depression, previous TB history, improper treatment, and interruption in first-line TB treatment have a great impact on the status of MDR. Accordingly, weight loss, chest pain, hemoptysis, and fatigue are important symptoms. Based on accuracy, sensitivity, and specificity, SVM and RF are the suggested models to be used for patients' classifications.

Attitude and perception towards vaccination against poliomyelitis in Peshawar, Pakistan

ABSTRACT OBJECTIVE: This research aimed to quantitatively assess the general public's awareness, attitude and perception of polio and its vaccination in Peshawar KPK, Pakistan. METHODS: We conducted a survey-based study to understand the surge in polio cases from 2015 to 2019 in the Peshawar city of the Khyber Pakhtunkhwa (KPK), Pakistan. A pre-tested questionnaire-based study was conducted in 2019 to assess the attitude and general perception of residents of Peshawar KPK towards polio vaccination. RESULTS: Out of 241 country-wide polio cases, 63 (26.1%) polio cases were reported in Peshawar city from 2015-2019. The questionnaire revealed that individuals between 18-30 years of age had sufficient knowledge (65.1%) about polio. Male and female participants had equal awareness (~ 43%). Participants with higher education (45.9%), those with better financial status (49.5%), individuals with children < 5 years of age (46.4%), and those who had experience of a polio patient (63.1%) had better knowledge. Participants inhabiting the central city were better aware (50.5%) of polio than individuals living in the outskirts. CONCLUSION: The data indicated that poor knowledge and negative attitudes of people towards polio vaccination are the main causes of the polio eradication program's failure. Moreover, religious beliefs, unchecked migration between the Pak-Afghan border, and lack of knowledge about polio vaccination are identified as critical barriers to polio eradication.

A Gull Alpha Power Weibull distribution with applications to real and simulated data.

In this paper, we produced a new family of distribution called Gull Alpha Power Family of distributions (GAPF). A Special case of GAPF is derived by considering the Weibull distribution as a baseline distribution called Gull Alpha Power Weibull distribution (GAPW). The suitability of the proposed distribution derives from its ability to model both the monotonic and non-monotonic hazard rate functions which are a common practice in survival analysis and reliability engineering. Various statistical properties were derived in addition to their special cases. The unknown parameters of the model are estimated using the maximum likelihood method. Moreover, the usefulness of the proposed distribution is supported by using two real lifetime data sets as well as simulated data.

A New Extended-X Family of Distributions: Properties and Applications.

During the past couple of years, statistical distributions have been widely used in applied areas such as reliability engineering, medical, and financial sciences. In this context, we come across a diverse range of statistical distributions for modeling heavy tailed data sets. Well-known distributions are log-normal, log-t, various versions of Pareto, log-logistic, Weibull, gamma, exponential, Rayleigh and its variants, and generalized beta of the second kind distributions, among others. In this paper, we try to supplement the distribution theory literature by incorporating a new model, called a new extended Weibull distribution. The proposed distribution is very flexible and exhibits desirable properties. Maximum likelihood estimators of the model parameters are obtained, and a Monte Carlo simulation study is conducted to assess the behavior of these estimators. Finally, we provide a comparative study of the newly proposed and some other existing methods via analyzing three real data sets from different disciplines such as reliability engineering, medical, and financial sciences. It has been observed that the proposed method outclasses well-known distributions on the basis of model selection criteria.

Sample size issues in multilevel logistic regression models.

Educational researchers, psychologists, social, epidemiological and medical scientists are often dealing with multilevel data. Sometimes, the response variable in multilevel data is categorical in nature and needs to be analyzed through Multilevel Logistic Regression Models. The main theme of this paper is to provide guidelines for the analysts to select an appropriate sample size while fitting multilevel logistic regression models for different threshold parameters and different estimation methods. Simulation studies have been performed to obtain optimum sample size for Penalized Quasi-likelihood (PQL) and Maximum Likelihood (ML) Methods of estimation. Our results suggest that Maximum Likelihood Method performs better than Penalized Quasi-likelihood Method and requires relatively small sample under chosen conditions. To achieve sufficient accuracy of fixed and random effects under ML method, we established ''50/50" and ''120/50" rule respectively. On the basis our findings, a ''50/60" and ''120/70" rules under PQL method of estimation have also been recommended.

Alpha-Power Pareto distribution: Its properties and applications.

In Statistical theory, inclusion of an additional parameter to standard distributions is a usual practice. In this study, a new distribution referred to as Alpha-Power Pareto distribution is introduced by including an extra parameter. Several properties of the proposed distribution, including moment generating function, mode, quantiles, entropies, mean residual life function, stochastic orders and order statistics are obtained. Parameters of the proposed distribution have been estimated using maximum likelihood estimation technique. Two real datasets have been considered to examine the usefulness of the proposed distribution. It has been observed that the proposed distribution outperforms different variants of Pareto distribution on the basis of model selection criteria.

Bayesian Analysis of Three-Parameter Frechet Distribution with Medical Applications.

The medical data are often filed for each patient in clinical studies in order to inform decision-making. Usually, medical data are generally skewed to the right, and skewed distributions can be the appropriate candidates in making inferences using Bayesian framework. Furthermore, the Bayesian estimators of skewed distribution can be used to tackle the problem of decision-making in medicine and health management under uncertainty. For medical diagnosis, physician can use the Bayesian estimators to quantify the effects of the evidence in increasing the probability that the patient has the particular disease considering the prior information. The present study focuses the development of Bayesian estimators for three-parameter Frechet distribution using noninformative prior and gamma prior under LINEX (linear exponential) and general entropy (GE) loss functions. Since the Bayesian estimators cannot be expressed in closed forms, approximate Bayesian estimates are discussed via Lindley's approximation. These results are compared with their maximum likelihood counterpart using Monte Carlo simulations. Our results indicate that Bayesian estimators under general entropy loss function with noninformative prior (BGENP) provide the smallest mean square error for all sample sizes and different values of parameters. Furthermore, a data set about the survival times of a group of patients suffering from head and neck cancer is analyzed for illustration purposes.

Rules for resolving Mendelian inconsistencies in nuclear pedigrees typed for two-allele markers.

Gene-mapping studies, regularly, rely on examination for Mendelian transmission of marker alleles in a pedigree as a way of screening for genotyping errors and mutations. For analysis of family data sets, it is, usually, necessary to resolve or remove the genotyping errors prior to consideration. At the Center of Inherited Disease Research (CIDR), to deal with their large-scale data flow, they formalized their data cleaning approach in a set of rules based on PedCheck output. We scrutinize via carefully designed simulations that how well CIDR's data cleaning rules work in practice. We found that genotype errors in siblings are detected more often than in parents for less polymorphic SNPs and vice versa for more polymorphic SNPs. Through computer simulations, we conclude that some of the CIDR's rules work poorly in some circumstances, and we suggest a set of modified data cleaning rules that may work better than CIDR's rules.