Numerical treatment for a novel crossover mathematical model of the COVID-19 epidemic

This paper extends a novel piecewise mathematical model of the COVID-19 epidemic using fractional and variable-order differential equations and fractional stochastic derivatives in three intervals of time. The deterministic models are augmented with hybrid fractional order and variable order operators, while the stochastic differential equations incorporate fractional Brownian motion. To probe the behavior of the proposed models, we introduce two numerical techniques: the nonstandard modified Euler Maruyama method for the fractional stochastic model, and the Caputo proportional constant-Grünwald-Letnikov nonstandard finite difference method for the fractional and variable-order deterministic models. Several numerical experiments corroborate the theoretical assertions and demonstrate the efficacy of the proposed approaches

High frequency of Plasmodium falciparum chloroquine resistance marker (pfcrt T76 mutation) in Yemen: An urgent need to re-examine malaria drug policy

<p>Abstract</p> <p>Background</p> <p>Malaria remains a significant health problem in Yemen with <it>Plasmodium falciparum </it>being the predominant species which is responsible for 90% of the malaria cases. Despite serious concerns regarding increasing drug resistance, chloroquine is still used for the prevention and treatment of malaria in Yemen. This study was carried out to determine the prevalence of choloroquine resistance (CQR) of <it>P. falciparum </it>isolated from Yemen based on the <it>pfcrt </it>T76 mutation.</p> <p>Methods</p> <p>A cross-sectional study was carried out among 511 participants from four governorates in Yemen. Blood samples were screened using microscopic and species-specific nested PCR based on the 18S rRNA gene to detect and identify <it>Plasmodium </it>species. Blood samples positive for <it>P. falciparum </it>were used for detecting the <it>pfcrt </it>T76 mutation using nested-PCR.</p> <p>Results</p> <p>The prevalence of <it>pfcrt </it>T76 mutation was 81.5% (66 of 81 isolates). Coastal areas/foothills had higher prevalence of <it>pfcrt </it>T76 mutation compared to highland areas (90.5% <it>vs </it>71.8%) (p = 0.031). The <it>pfcrt </it>T76 mutation had a significant association with parasitaemia (p = 0.045). Univariate analysis shows a significant association of <it>pfcrt </it>T76 mutation with people aged > 10 years (OR = 9, 95% CI = 2.3 - 36.2, p = 0.001), low household income (OR = 5, 95% CI = 1.3 - 19.5, p = 0.027), no insecticide spray (OR = 3.7, 95% CI = 1.16 - 11.86, p = 0.025) and not sleeping under insecticide treated nets (ITNs) (OR = 4.8, 95% CI = 1.38 - 16.78, p = 0.01). Logistic regression model confirmed age > 10 years and low household income as predictors of <it>pfcrt </it>T76 mutation in Yemen <it>P. falciparum </it>isolates.</p> <p>Conclusions</p> <p>The high prevalence of <it>pfcrt </it>T76 mutation in Yemen could be a predictive marker for the prevalence of <it>P. falciparum </it>CQR. This finding shows the necessity for an in-vivo therapeutic efficacy test for CQ.<it> P. falciparum </it>CQR should be addressed in the national strategy to control malaria.</p

Opportunities and obstacles to the elimination of malaria from Peninsular Malaysia: knowledge, attitudes and practices on malaria among aboriginal and rural communities

<p>Abstract</p> <p>Background</p> <p>Despite continuous efforts by the government and private sectors, malaria is still a public health problem in rural Peninsular Malaysia. This study investigated household knowledge, attitude and practices (KAP) regarding malaria in two malaria endemic communities, forest-aboriginal and rural communities, in the Lipis district of Pahang state, Malaysia.</p> <p>Methods</p> <p>A descriptive cross-sectional study with a semi-structured questionnaire was carried out among 100 and 123 households from forest-aboriginal and rural areas, respectively.</p> <p>Results</p> <p>Knowledge about malaria and its transmission is significantly higher among the rural participants than the aborigines (86.2% vs 76%, p < 0.01). However, use of medicinal plants and beliefs in witchcraft and sorcery in treating febrile diseases were significantly higher among the aboriginal population (p < 0.01). There were no significant differences between the two communities in terms of the knowledge about malaria symptoms, attitudes towards its severity and practices in preventive measures against malaria by using mosquito bed nets. However, the knowledge and practice of different preventive measures to combat malaria, such as insecticide and the elimination of breeding areas, was significantly higher among the rural population than the aborigines (p < 0.001).</p> <p>Conclusions</p> <p>Both communities were aware of malaria as a disease, but knowledge, attitudes and practices were inadequate. Providing efficient health education to people residing in malaria endemic areas would improve their understanding about malaria prevention in order to bring about the elimination of malaria from the country.</p

Specific, sensitive and rapid detection of human plasmodium knowlesi infection by loop-mediated isothermal amplification (LAMP) in blood samples

<p>Abstract</p> <p>Background</p> <p>The emergence of <it>Plasmodium knowlesi </it>in humans, which is in many cases misdiagnosed by microscopy as <it>Plasmodium malariae </it>due to the morphological similarity has contributed to the needs of detection and differentiation of malaria parasites. At present, nested PCR targeted on <it>Plasmodium </it>ssrRNA genes has been described as the most sensitive and specific method for Plasmodium detection. However, this method is costly and requires trained personnel for its implementation. Loop-mediated isothermal amplification (LAMP), a novel nucleic acid amplification method was developed for the clinical detection of <it>P. knowlesi</it>. The sensitivity and specificity of LAMP was evaluated in comparison to the results obtained via microscopic examination and nested PCR.</p> <p>Methods</p> <p>LAMP assay was developed based on <it>P. knowlesi </it>genetic material targeting the apical membrane antigen-1 (AMA-1) gene. The method uses six primers that recognize eight regions of the target DNA and it amplifies DNA within an hour under isothermal conditions (65°C) in a water-bath.</p> <p>Results</p> <p>LAMP is highly sensitive with the detection limit as low as ten copies for AMA-1. LAMP detected malaria parasites in all confirm cases (n = 13) of <it>P. knowlesi </it>infection (sensitivity, 100%) and none of the negative samples (specificity, 100%) within an hour. LAMP demonstrated higher sensitivity compared to nested PCR by successfully detecting a sample with very low parasitaemia (< 0.01%).</p> <p>Conclusion</p> <p>With continuous efforts in the optimization of this assay, LAMP may provide a simple and reliable test for detecting <it>P. knowlesi </it>malaria parasites in areas where malaria is prevalent.</p

Prevalence and sociodemographic correlates of stunting, underweight, and overweight among Palestinian school adolescents (13-15 years) in two major governorates in the West Bank

<p>Abstract</p> <p>Background</p> <p>There is little information about height and weight status of Palestinian adolescents. The objective of this paper was to assess the prevalence of stunting, underweight, and overweight/obesity among Palestinian school adolescents (13-15 years) and associated sociodemographic factors in 2 major governorates in the West Bank.</p> <p>Methods</p> <p>A Cross-sectional survey was conducted in 2005 comprising 1942 students in 65 schools in Ramallah and Hebron governorates. Data was collected through self-administered questionnaires from students and parents. Weights and heights were measured. Overweight and obesity were assessed using the 2000 Centers for Disease Control and Prevention (CDC) reference and the International Obesity Task Force (IOTF) criteria. Stunting and underweight were assessed using the 2000 CDC reference.</p> <p>Results</p> <p>Overweight/obesity was more prevalent in Ramallah than in Hebron and affected more girls than boys. Using the 2000 CDC reference, the prevalence of overweight and obesity in Ramallah among boys was 9.6% and 8.2%, respectively versus 15.6% and 6.0% among girls (P < 0.01). In Hebron, the corresponding figures were 8.5% and 4.9% for boys and 13.5% and 3.4% for girls (P < 0.01). Using the IOTF criteria, the prevalence of overweight and obesity among boys in Ramallah was 13.3% and 5.2%, respectively versus 18.9% and 3.3% for girls. The prevalence of overweight and obesity among boys in Hebron was 10.9% and 2.2%, respectively versus 14.9% and 2.0% for girls. Overweight/obesity was associated with high standard of living (STL) among boys and with the onset of puberty among girls. More boys were underweight than girls, and the prevalence was higher in Hebron (12.9% and 6.0% in boys and girls, respectively (P < 0.01)) than in Ramallah (9.7% and 3.1% in boys and girls, respectively (p < 0.01)). The prevalence of stunting was similar in both governorates, and was higher among boys (9.2% and 9.4% in Ramallah and Hebron, respectively) than among girls (5.9% and 4.2% in Ramallah and Hebron, respectively). Stunting was negatively associated with father's education among boys and with urban residence, medium STL and onset of puberty among girls.</p> <p>Conclusion</p> <p>Under- and overnutrition co-exist among Palestinian adolescents, with differences between sexes. Region, residence, STL, and onset of puberty were associated factors.</p

Multi-Method Diagnosis of CT Images for Rapid Detection of Intracranial Hemorrhages Based on Deep and Hybrid Learning

Intracranial hemorrhaging is considered a type of disease that affects the brain and is very dangerous, with high-mortality cases if there is no rapid diagnosis and prompt treatment. CT images are one of the most important methods of diagnosing intracranial hemorrhages. CT images contain huge amounts of information, requiring a lot of experience and taking a long time for proper analysis and diagnosis. Thus, artificial intelligence techniques provide an automatic mechanism for evaluating CT images to make a diagnosis with high accuracy and help radiologists make their diagnostic decisions. In this study, CT images for rapid detection of intracranial hemorrhages are diagnosed by three proposed systems with various methodologies and materials, where each system contains more than one network. The first system is proposed by three pretrained deep learning models, which are GoogLeNet, ResNet-50 and AlexNet. The second proposed system using a hybrid technology consists of two parts: the first part is the GoogLeNet, ResNet-50 and AlexNet models for extracting feature maps, while the second part is the SVM algorithm for classifying feature maps. The third proposed system uses artificial neural networks (ANNs) based on the features of the GoogLeNet, ResNet-50 and AlexNet models, whose dimensions are reduced by a principal component analysis (PCA) algorithm, and then the low-dimensional features are combined with the features of the GLCM and LBP algorithms. All the proposed systems achieved promising results in the diagnosis of CT images for the rapid detection of intracranial hemorrhages. The ANN network based on fusion of the deep feature of AlexNet with the features of GLCM and LBP reached an accuracy of 99.3%, precision of 99.36%, sensitivity of 99.5%, specificity of 99.57% and AUC of 99.84

Global, regional, and national progress towards Sustainable Development Goal 3.2 for neonatal and child health : all-cause and cause-specific mortality findings from the Global Burden of Disease Study 2019

Background Sustainable Development Goal 3.2 has targeted elimination of preventable child mortality, reduction of neonatal death to less than 12 per 1000 livebirths, and reduction of death of children younger than 5 years to less than 25 per 1000 livebirths, for each country by 2030. To understand current rates, recent trends, and potential trajectories of child mortality for the next decade, we present the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 findings for all-cause mortality and cause-specific mortality in children younger than 5 years of age, with multiple scenarios for child mortality in 2030 that include the consideration of potential effects of COVID-19, and a novel framework for quantifying optimal child survival. Methods We completed all-cause mortality and cause-specific mortality analyses from 204 countries and territories for detailed age groups separately, with aggregated mortality probabilities per 1000 livebirths computed for neonatal mortality rate (NMR) and under-5 mortality rate (USMR). Scenarios for 2030 represent different potential trajectories, notably including potential effects of the COVID-19 pandemic and the potential impact of improvements preferentially targeting neonatal survival. Optimal child survival metrics were developed by age, sex, and cause of death across all GBD location-years. The first metric is a global optimum and is based on the lowest observed mortality, and the second is a survival potential frontier that is based on stochastic frontier analysis of observed mortality and Healthcare Access and Quality Index. Findings Global U5MR decreased from 71.2 deaths per 1000 livebirths (95% uncertainty interval WI] 68.3-74-0) in 2000 to 37.1 (33.2-41.7) in 2019 while global NMR correspondingly declined more slowly from 28.0 deaths per 1000 live births (26.8-29-5) in 2000 to 17.9 (16.3-19-8) in 2019. In 2019,136 (67%) of 204 countries had a USMR at or below the SDG 3.2 threshold and 133 (65%) had an NMR at or below the SDG 3.2 threshold, and the reference scenario suggests that by 2030,154 (75%) of all countries could meet the U5MR targets, and 139 (68%) could meet the NMR targets. Deaths of children younger than 5 years totalled 9.65 million (95% UI 9.05-10.30) in 2000 and 5.05 million (4.27-6.02) in 2019, with the neonatal fraction of these deaths increasing from 39% (3.76 million [95% UI 3.53-4.021) in 2000 to 48% (2.42 million; 2.06-2.86) in 2019. NMR and U5MR were generally higher in males than in females, although there was no statistically significant difference at the global level. Neonatal disorders remained the leading cause of death in children younger than 5 years in 2019, followed by lower respiratory infections, diarrhoeal diseases, congenital birth defects, and malaria. The global optimum analysis suggests NMR could be reduced to as low as 0.80 (95% UI 0.71-0.86) deaths per 1000 livebirths and U5MR to 1.44 (95% UI 1-27-1.58) deaths per 1000 livebirths, and in 2019, there were as many as 1.87 million (95% UI 1-35-2.58; 37% [95% UI 32-43]) of 5.05 million more deaths of children younger than 5 years than the survival potential frontier. Interpretation Global child mortality declined by almost half between 2000 and 2019, but progress remains slower in neonates and 65 (32%) of 204 countries, mostly in sub-Saharan Africa and south Asia, are not on track to meet either SDG 3.2 target by 2030. Focused improvements in perinatal and newborn care, continued and expanded delivery of essential interventions such as vaccination and infection prevention, an enhanced focus on equity, continued focus on poverty reduction and education, and investment in strengthening health systems across the development spectrum have the potential to substantially improve USMR. Given the widespread effects of COVID-19, considerable effort will be required to maintain and accelerate progress