scholarly journals Influence of important environmental parameters on the spread and severity of COVID-19: Part 1

2020 ◽  
pp. 1-12
Author(s):  
Vikrant Tiwari ◽  
Nimisha Sharma
Keyword(s):  
Large Scale ◽  
Environmental Parameters ◽  
Large Data ◽  
Epidemiological Data ◽  
Good Health ◽  
World Health ◽  
Data Set ◽  
Contagious Diseases ◽  
Health Organization ◽  
High Degree

In the absence of the detailed COVID-19 epidemiological data or large benchmark studies, an effort has been made to explore and correlate the relation of parameters like environment, economic indicators, and the large scale exposure of different prevalent diseases, with COVID-19 spread and severity amongst the different countries affected by COVID-19. Data for environmental, socio-economic and others important infectious diseases were collected from reliable and open source resources like World Health Organization, World Bank, etc. Further, this large data set is utilized to understand the COVID-19 worldwide spread using simple statistical tools. Important observations that are made in this study are the high degree of resemblance in the pattern of temperature and humidity distribution among the cities severely affected by COVID-19. Further, It is surprising to see that in spite of the presence of many environmental parameters that are considered favorable (like clean air, clean water, EPI, etc.), many countries are suffering with the severe consequences of this disease. Lastly a noticeable segregation among the locations affected by different prevalent diseases (like Malaria, HIV, Tuberculosis, and Cholera) was also observed. Among the considered environmental factors, temperature, humidity and EPI should be an important parameter in understanding and modelling COVID-19 spreads. Further, contrary to intuition, countries with strong economies, good health infrastructure and cleaner environment suffered disproportionately higher with the severity of this disease. Therefore, policymaker should sincerely review their country preparedness toward the potential future contagious diseases, weather natural or manmade.

Safe Reopening Following Generalized Lockdowns: A Strategy Based on Evidence from 24 Worldwide Countries

2021 ◽  
Author(s):  
Abu S. Shonchoy ◽  
Khandker S. Ishtiaq ◽  
Sajedul Talukder ◽  
Nasar U. Ahmed ◽  
Rajiv Chowdhury
Keyword(s):  
Low Income ◽  
Large Scale ◽  
Simulation Analysis ◽  
Epidemiological Data ◽  
World Health ◽  
Optimal Timing ◽  
Middle Income ◽  
Capacity Limits ◽  
Positive Rate ◽  
Health Organization

Abstract While the effectiveness of lockdowns to reduce Coronavirus Disease-2019 (COVID-19) transmission is well established, uncertainties remain on the lifting principles of these restrictive interventions. World Health Organization recommends case positive rate of 5% or lower as a threshold for safe reopening. However, inadequate testing capacity limits the applicability of this recommendation, especially in the low-income and middle-income countries (LMICs). To develop a practical reopening strategy for LMICs, in this study, we first identify the optimal timing of safe reopening by exploring accessible epidemiological data of 24 countries during the initial COVID-19 surge. We find that safely reopening requires a two-week waiting period, after the crossover of daily infection and recovery rates – coupled with a post-crossover continuous negative trend in daily new cases. Epidemiologic SIRM model-based simulation analysis validates our findings. Finally, we develop an easily interpretable large-scale reopening (LSR) index, which is an evidence-based toolkit – to guide/inform the reopening decisions for LMICs.

scholarly journals Depression analysis of voice samples using machine learning

2021 ◽  
Vol 23 (11) ◽  
pp. 429-438
Author(s):  
Yashi Sharma ◽  
◽  
Dr. Brajesh Kumar Singh ◽  
Keyword(s):  
Machine Learning ◽  
Large Data ◽  
World Health ◽  
Medical Attention ◽  
Data Set ◽  
Symptoms Of Depression ◽  
Medical Response ◽  
Lifestyle Pattern ◽  
Health Organization ◽  
The Masses

Depression is seen as an emerging mental challenge in the lives of various people. Nowadays it is also becoming one of the major reasons for mental disability across the world. Depression has manifested itself as a silent killer and according to statistics it has affected more than 300 million people in United States of America majorly affecting individuals in the age group of 15 to 44 yrs. According to a study by World Health Organization, the effects of depression have been dangerous in life, it is seen causing threatening diseases like cancer, diabetic issues or even heart disease. However, the problem that mainly is associated with the disease of depression is that it is not treated as a disease. Where the common understanding of the word “Disease” is any medical ailment that require doctor’s attention or quick medical response, depression on the other hand, even after qualifying as a disease is hidden in societal barriers to appear for a proper treatment. People whose lifestyle pattern has been intruded by depression either do not avail proper medical attention or are too shy to appear in the masses for proper attention on their physical as well as condition. Our motivation here is to investigate through the phenomenon of depression and predict whether an individual is having symptoms of depression by accessing his/her voice sample. In order to establish a link between depression and voice features, we obtain a large data set and then train a model accordingly by applying machine learning methods on it. This model when given a voice sample can now predict, whether a particular subject is depressed or not, to a nearby accurate measure.

scholarly journals A novel index-based decision support toolkit for safe reopening following a generalized lockdown in low and middle-income countries

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Abu S. Shonchoy ◽  
Khandker S. Ishtiaq ◽  
Sajedul Talukder ◽  
Nasar U. Ahmed ◽  
Rajiv Chowdhury
Keyword(s):  
Low Income ◽  
Large Scale ◽  
Epidemiological Data ◽  
World Health ◽  
Optimal Timing ◽  
Middle Income ◽  
Capacity Limits ◽  
Middle Income Countries ◽  
Positive Rate ◽  
Health Organization

AbstractWhile the effectiveness of lockdowns to reduce Coronavirus Disease-2019 (COVID-19) transmission is well established, uncertainties remain on the lifting principles of these restrictive interventions. World Health Organization recommends case positive rate of 5% or lower as a threshold for safe reopening. However, inadequate testing capacity limits the applicability of this recommendation, especially in the low-income and middle-income countries (LMICs). To develop a practical reopening strategy for LMICs, in this study, we first identify the optimal timing of safe reopening by exploring accessible epidemiological data of 24 countries during the initial COVID-19 surge. We find that a safe opening can occur two weeks after the crossover of daily infection and recovery rates while maintaining a negative trend in daily new cases. Epidemiologic SIRM model-based example simulation supports our findings. Finally, we develop an easily interpretable large-scale reopening (LSR) index, which is an evidence-based toolkit—to guide/inform reopening decision for LMICs.

Risk Factor Identification, Classification and Prediction Summary of Chronic Kidney Disease

2020 ◽  
Vol 13 ◽  
Author(s):  
Pramila Arulanthu ◽  
Eswaran Perumal
Keyword(s):  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Disease Diagnosis ◽  
Medical Data ◽  
Machine Learning Algorithms ◽  
World Health ◽  
Medical Field ◽  
Data Set ◽  
Medical Issues ◽  
Health Organization

: The medical data has an enormous quantity of information. This data set requires effective classification for accurate prediction. Predicting medical issues is an extremely difficult task in which Chronic Kidney Disease (CKD) is one of the major unpredictable diseases in medical field. Perhaps certain medical experts do not have identical awareness and skill to solve the issues of their patients. Most of the medical experts may have underprivileged results on disease diagnosis of their patients. Sometimes patients may lose their life in nature. As per the Global Burden of Disease (GBD-2015) study, death by CKD was ranked 17th place and GBD-2010 report 27th among the causes of death globally. Death by CKD is constituted 2·9% of all death between the year 2010 and 2013 among people from 15 to 69 age. As per World Health Organization (WHO-2005) report, 58 million people expired by CKD. Hence, this article presents the state of art review on Chronic Kidney Disease (CKD) classification and prediction. Normally, advanced data mining techniques, fuzzy and machine learning algorithms are used to classify medical data and disease diagnosis. This study reviews and summarizes many classification techniques and disease diagnosis methods presented earlier. The main intention of this review is to point out and address some of the issues and complications of the existing methods. It is also attempts to discuss the limitations and accuracy level of the existing CKD classification and disease diagnosis methods.

scholarly journals Galaxy spin direction distribution in HST and SDSS show similar large-scale asymmetry

2020 ◽  
Vol 37 ◽  
Author(s):  
Lior Shamir
Keyword(s):  
Large Scale ◽  
Spiral Galaxies ◽  
Hubble Space Telescope ◽  
Gravitational Interaction ◽  
Large Data ◽  
Sloan Digital Sky Survey ◽  
Data Sets ◽  
Dipole Axis ◽  
Data Set ◽  
The Asymmetry

Abstract Several recent observations using large data sets of galaxies showed non-random distribution of the spin directions of spiral galaxies, even when the galaxies are too far from each other to have gravitational interaction. Here, a data set of $\sim8.7\cdot10^3$ spiral galaxies imaged by Hubble Space Telescope (HST) is used to test and profile a possible asymmetry between galaxy spin directions. The asymmetry between galaxies with opposite spin directions is compared to the asymmetry of galaxies from the Sloan Digital Sky Survey. The two data sets contain different galaxies at different redshift ranges, and each data set was annotated using a different annotation method. The results show that both data sets show a similar asymmetry in the COSMOS field, which is covered by both telescopes. Fitting the asymmetry of the galaxies to cosine dependence shows a dipole axis with probabilities of $\sim2.8\sigma$ and $\sim7.38\sigma$ in HST and SDSS, respectively. The most likely dipole axis identified in the HST galaxies is at $(\alpha=78^{\rm o},\delta=47^{\rm o})$ and is well within the $1\sigma$ error range compared to the location of the most likely dipole axis in the SDSS galaxies with $z>0.15$ , identified at $(\alpha=71^{\rm o},\delta=61^{\rm o})$ .

Triage Management, Survival, and the Law in the Age of Ebola

2014 ◽  
Vol 9 (1) ◽  
pp. 38-43 ◽  
Author(s):  
Frederick M Burkle ◽  
Christopher M Burkle
Keyword(s):  
Public Health ◽  
International Health ◽  
Epidemiological Data ◽  
Population Based ◽  
World Health ◽  
Economic Stability ◽  
Budget Cuts ◽  
The Public ◽  
Level 3 ◽  
Health Organization

AbstractLiberia, Sierra Leone, and Guinea lack the public health infrastructure, economic stability, and overall governance to stem the spread of Ebola. Even with robust outside assistance, the epidemiological data have not improved. Vital resource management is haphazard and left to the discretion of individual Ebola treatment units. Only recently has the International Health Regulations (IHR) and World Health Organization (WHO) declared Ebola a Public Health Emergency of International Concern, making this crisis their fifth ongoing level 3 emergency. In particular, the WHO has been severely compromised by post-2003 severe acute respiratory syndrome (SARS) staffing, budget cuts, a weakened IHR treaty, and no unambiguous legal mandate. Population-based triage management under a central authority is indicated to control the transmission and ensure fair and decisive resource allocation across all triage categories. The shared responsibilities critical to global health solutions must be realized and the rightful attention, sustained resources, and properly placed legal authority be assured within the WHO, the IHR, and the vulnerable nations. (Disaster Med Public Health Preparedness. 2014;0:1-6)

Comparativa de modelos multivariados basados en aprendizaje automático para la predicción del riesgo de diabetes en etapas tempranas

2021 ◽  
Author(s):  
◽  
Zayra Ramírez Gaytán
Keyword(s):  
World Health ◽  
Degenerative Diseases ◽  
Decision Tree Algorithm ◽  
Data Set ◽  
The World ◽  
Long Time ◽  
Life Threatening ◽  
Health Organization ◽  
Logistic Regression Algorithm ◽  
Asymptomatic Phase

Diabetes is one of the fastest-growing, life-threatening, chronic degenerative diseases. According to the World Health Organization (WHO), it has affected 422 million people worldwide in 2018. Approximately 50% of all people who suffer diabetes are not diagnosed due to the asymptomatic phase which usually lasts a long time. In this work, a data set of 520 instances has been used. The data set has been analyzed with the next three algorithms: logistic regression algorithm, decision trees and random forest. The results show that the decision tree algorithm had better performance with an AUC of 98%. Also, it was found the most common symptoms that a person with a risk of diabetes presents are polyuria, polydipsia and sudden weight loss.

scholarly journals A comparative analysis of world health systems and Covid-19

2021 ◽  
Vol 38 (2) ◽  
pp. 115-120
Author(s):  
Ayşe İKİNCİ KELEŞ ◽  
Gökhan KELEŞ
Keyword(s):  
Health Systems ◽  
Social Life ◽  
Meta Analysis ◽  
Good Health ◽  
The United States ◽  
World Health ◽  
The Face ◽  
The Usa ◽  
Health Organization ◽  
Second Wave

Coronavirus disease 2019 (COVID-19), which causes severe airway problems, first emerged in the Chinese city of Wuhan. The virus led to a pandemic that affected the entire world. COVID-19 affects not only health, but also economic and social life. The emergence of this pandemic has led to health systems across the world being questioned. The aim of this study was to assess the adequacy of world health systems in the face of this pandemic. Twelve countries were selected and analyzed in the study. The choice of these countries was determined by the number of COVID-19 cases and deaths. Information concerning health systems and COVID-19 was obtained from Organization for Economic Co-operation and Development 2018, World Health Organization 2020 and Deep Knowledge Group data and was subjected to statistical analysis. According to the analysis, the country with the highest investment in health expenditures is the United States (10586 US dollars/capita), and Germany stands out as the best in health services. Another finding is the first and second wave of COVID-19 was identified as the USA with the highest case and death rate (First wave cases 1.942.363 and deaths 110.514; second wave cases at 7.419.230 and deaths 2.09.450). As a result of the meta-analysis, it is revealed that only socio-economic power is not enough, countries with good health systems are more successful in the pandemic. In addition, the analysis once again reveal how important health systems are in the face of such a pandemic.

scholarly journals Nutritional status among children aged one to six years in tribal area

2020 ◽  
Vol 7 (7) ◽  
pp. 2715
Author(s):  
Palle Lokhnath Reddy ◽  
Aluka Anand Chand
Keyword(s):  
Nutritional Status ◽  
Normal Growth ◽  
Good Health ◽  
World Health ◽  
Microsoft Excel ◽  
Cross Sectional ◽  
Tribal Area ◽  
South Indian ◽  
Health Organization ◽  
Severe Degree

Background: Nutrition in children is considered as a major concern for good health and also for normal growth and development. The present study aimed to estimate the prevalence of malnutrition in 1 to 6 years children.Methods: This was a community based cross sectional carried out in a south Indian tribal area for a period of 5 years among 1020 children. The anthropometric measurements categorization among children was done using world health organization (WHO) guidelines. Data was analyzed using microsoft excel 2010.Results: Out of 1020 children, nutritional status based on underweight, stunting and wasting was 30.80%, 26.8% and 15.68% respectively. Severe degree of underweight, stunting and wasting was observed in 76.4%, 64.7% and 5.49% respectively.Conclusions: Under nutrition was significantly high in infants and it decreased with increasing in age and significantly higher number of female children were stunted and underweight compared to male children.

scholarly journals Estimates of mortality rates in people with diabetes and cardiovascular disease using administrative pharmaceutical data

2019 ◽  
Vol 4 (3) ◽  
Author(s):  
Shaun Purkiss ◽  
Tessa Keegel ◽  
Hassan Vally ◽  
Dennis Wollersheim
Keyword(s):  
Cardiovascular Disease ◽  
Mortality Risk ◽  
Age Groups ◽  
Mortality Rates ◽  
World Health ◽  
Data Set ◽  
Medication Discontinuation ◽  
Disease Mortality ◽  
Health Organization

BackgroundQuantifying the mortality risk for people with diabetes is challenging because of associated comorbidities. The recording of cause specific mortality from accompanying cardiovascular disease in death certificate notifications has been considered to underestimate the overall mortality risk in persons with diabetes. Main AimDevelop a technique to quantify mortality risk from pharmaceutical administrative data and apply it to persons diagnosed with diabetes, and associated cardiovascular disease and dyslipidaemia before death. MethodsPersons with diabetes, cardiovascular disease and dyslipidaemia were identified in a publicly available Australian Pharmaceutical data set using World Health Organization anatomic therapeutic codes assigned to medications received. Diabetes associated multi-morbidity cohorts were constructed and a proxy mortality (PM) event determined from medication and service discontinuation. Estimates of mortality rates were calculated from 2004 for 10 years and compared persons with diabetes alone and associated cardiovascular disease and dyslipidemia. ResultsThis study identified 346,201 individuals within the 2004 calendar year as having received treatments for diabetes (n=51,422), dyslipidaemia (n=169,323) and cardiovascular disease including hypertension (n=280,105). Follow up was 3.3 x 106 person-years. Overall crude PM was 26.1 per 1000 person-years. PM rates were highest in persons with cardiovascular disease and diabetes in combination (47.5 per 100 person years). Statin treatments significantly improved the mortality rates in all persons with diabetes and cardiovascular disease alone and in combination over age groups >44 years (p<.001). Age specific diabetes PM rates using pharmaceutical data correlated well with Australian data from the National Diabetes Service Scheme (r=0.82) ConclusionProxy mortality events calculated from medication discontinuation in persons with chronic conditions can provide an alternative method to estimate disease mortality rates. The technique also allows the assessment of mortality risk in persons with chronic disease multi-morbidity.

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