KMK based hybrid approach for the performance estimation in case of diabetes data

In this paper k-means clustering algorithm has been used with k-points (KMK) selection. It has been applied on the PIMA Indian diabetes dataset. It has been used for distance estimation, centroid selection, effect of data size variations and for the analysis of the complete record. The cluster section has been found to be improved based on k-point selection. It has been used for the assignment of initial centroid. The results indicate that the KMK algorithm is capable in the improvement of centroid selection and distance measures in the assignments of data points. It is due to the better centroid selection mechanism by k-points selection based on the weight measures from the selected dataset. So, the obtained clusters are better in comparison to k-means.

Understanding COVID-19 Vaccine Hesitancy Through an Organizational Behaviour Lens.

Vaccine hesitancy during the COVID-19 pandemic has been a worldwide public health challenge. Organizational behaviour, the study of people's behaviours in organizational settings, can be used to identify the behavioural drivers contributing to vaccine hesitancy and to develop targeted strategies to combat those drivers and improve vaccine uptake. Some common behaviours driving vaccine hesitancy arise from individualism, motivation, attitude, perception, groupthink, heuristics and cognitive bias. Organizational behaviour strategies to combat vaccine hesitancy include fostering a collectivist attitude, overcoming personal barriers to communication such as individual beliefs and values, utilizing motivation theories to target the individualistic mindset, and overcoming attitudes and perceptions by addressing heuristics and cognitive biases.

Comparative Trends in Ischemic Heart Disease Admissions, Presentation and Outcomes Due to the COVID-19 Pandemic: First Insights From a Tertiary Medical Center in Pakistan.

Introduction COVID-19 has manifested a striking disarray in healthcare access and provision, particularly amongst patients presenting with life-threatening ischemic heart disease (IHD). The paucity of data from low-middle income countries has limited our understanding of the consequential burden in the developing world. We aim to compare volumes, presentations, management strategies, and outcomes of IHD amongst patients presenting in the same calendar months before and during the COVID-19 pandemic. Methods We conducted a retrospective cross-sectional analysis at the Aga Khan University Hospital, one of the premier tertiary care centres in Pakistan. Data were collected on all adult patients (>18 years) who were admitted with IHD (acute coronary syndrome (ACS) and stable angina) from March 1 to June 30, 2019 (pre-COVID) and March 1 to June 30, 2020 (during-COVID), respectively. Group differences for continuous variables were evaluated using student t-test or Mann-Whitney U test. The chi-squared test or Fisher test was used for categorical variables. Values of p less than 0.05 were considered statistically significant. P-value trend calculation and graphical visualization were done using STATA (StataCorp, College Station, TX). Results Data were assimilated on 1019 patients, with 706 (69.3%) and 313 (30.7%) patients presenting in each respective group (pre-COVID and during-COVID). Current smoking status (p=0.019), admission source (p<0.001), month of admission (p<0.001), proportions ACS (p<0.001), non-ST-elevation-myocardial-infarction (NSTEMI; p<0.001), unstable angina (p=0.025) and medical management (p=0.002) showed significant differences between the two groups, with a sharp decline in the during-COVID group. Monthly trend analysis of ACS patients showed the most significant differences in admissions (p=0.001), geographic region (intra-district vs intracity vs outside city) (p<0.001), time of admission (p=0.038), NSTEMI (p=0.002) and medical management (p=0.001). Conclusion These data showcase stark declines in ACS admissions, diagnostic procedures (angiography) and revascularization interventions (angioplasty and coronary artery bypass graft surgery, CABG) in a developing country where resources and research are already inadequate. This study paves the way for further investigations downstream on the short- and long-term consequences of untreated IHD and reluctance in health-seeking behaviour.

Dynamic analysis of the mathematical model of COVID-19 with demographic effects.

The coronavirus is currently extremely contagious for humankind, which is a zoonotic tropical disease. The pandemic is the largest in history, affecting almost the whole world. What makes the condition the worst of all is no specific effective treatment available. In this article, we present an extended and modified form of SIR and SEIR model, respectively. We begin by investigating a simple mathematical model that describes the pandemic. Then we apply different safety measures to control the pandemic situation. The mathematical model with and without control is solved by using homotopy perturbation method. Obtained solutions have been presented graphically. Finally, we develop another mathematical model, including quarantine and hospitalization.

HIV and shifting epicenters for COVID-19, an alert for some countries.

Were southern hemisphere countries right to undertake national lockdown during their summer time? Were they right to blindly follow the self-isolation wave that hit European countries in full winter? As a southern hemisphere country like South Africa stands now as the most COVID-19 and HIV affected country in Africa, we use in this paper, recent COVID-19 data to provide a statistical and comparative analysis that may alert southern hemisphere countries entering the winter season. After that, we use a generalized simple mathematical model of HIV-COVID-19 together with graphs, curves and tables to compare the pandemic situation in countries that were once the epicenter of the disease, such as China, Italy, Spain, United Kingdom (UK) and United States of America (USA). We perform stability and bifurcation analysis and show that the model contains a forward and a backward bifurcation under certain conditions. We also study different scenarios of stability/unstability equilibria for the model. The fractional (generalized) COVID-19 model is solved numerically and a predicted prevalence for the COVID-19 is provided. Recall that Brazil and South Africa share number of similar social features like Favellas (Brazil) and Townships (South Africa) with issues like promiscuity, poverty, and where social distanciation is almost impossible to observe. We can now ask the following question: Knowing its HIV situation, is South Africa the next epicenter in weeks to come when winter conditions, proven to be favorable to the spread of the new coronavirus are comfily installed?