scholarly journals Application Of Statistical Modeling To Interpret A Health System Crisis In Sri Lanka Due To COVID- 19

2021 ◽  
Author(s):  
Indika Karunathilake ◽  
Mayuri Amarasiri ◽  
Anver Hamdani
Keyword(s):  
Sri Lanka ◽  
Regression Model ◽  
Health System ◽  
Positive Relationship ◽  
Correlation Coefficients ◽  
Multiple Linear Regression Model ◽  
System Capacity ◽  
Contact Tracing ◽  
Statistic Modeling ◽  
Strong Positive Relationship

This paper will discuss the application of statistic modeling to interpret a health system crisis in Sri Lanka due to COVID- 19.A strong focus on the preventive approach and the contact tracing with the utilization of available resources in a rational manner describes Sri Lanka’s response towards COVID- 19 prevention and mitigation. The early contact tracing, preemptive quarantining, isolation, and treatment were implemented as a concerted effort. This approach, proven efficient during the early phase of the pandemic, was sustainable when there was a rapid increase in the COVID- 19 patients since July 2021, exceeding the health system capacity.The country’s COVID- 19 situation during the period from 01st of August 2021 to 31st of October 2021 was taken into consideration. Variables used for analysis were; total number of cases, recovered cases, comorbid and O2 dependent patients, ICU patients, and deaths. The regression model was applied to analyze the data by using the EViews 12 (x64) software application.The correlation coefficients of all the independent variables under consideration implies that they have a strong positive relationship with the number of deaths occurred during the said period. According to the computed multiple linear regression model, the number of positive cases and O2 dependents have a positive relationship with the dependent variable. Further, the Durbin- Watson stat value of the model and multicollinearity test reflect that it is free from serial correlation thereby the model is fit. From the perspective of epidemiological control, these findings highlight the importance of keeping the number of cases within the limits of health system capacity.

scholarly journals A novel comprehensive metric to assess COVID-19 testing outcomes: Effects of geography, government, and policy response

2020 ◽  
Author(s):  
Anthony C. Kuster ◽  
Hans J. Overgaard
Keyword(s):  
Detection Rate ◽  
Case Fatality ◽  
Multiple Linear Regression Model ◽  
System Capacity ◽  
Contact Tracing ◽  
Extrinsic Factors ◽  
Detection Rates ◽  
Case Identification ◽  
Rate Test ◽  
Reported Data

AbstractTesting and case identification are key strategies in controlling the COVID-19 pandemic. Contact tracing and isolation are only possible if cases have been identified. The effectiveness of testing must be tracked, but a single comprehensive metric is not available to assess testing effectiveness, and no timely estimates of case detection rate are available globally, making inter-country comparisons difficult. The purpose of this paper was to propose a single, comprehensive metric, called the COVID-19 Testing Index (CovTI) scaled from 0 to 100, that incorporated several testing metrics. The index was based on case-fatality rate, test positivity rate, active cases, and an estimate of the detection rate. It used parsimonious modeling to estimate the true total number of COVID-19 cases based on deaths, testing, health system capacity, and government transparency. Publicly reported data from 188 countries and territories were included in the index. Estimates of detection rates aligned with previous estimates in literature (R2=0.97). As of June 3, 2020, the states with the highest CovTI included Iceland, Australia, New Zealand, Hong Kong, and Thailand, and some island nations. Globally, CovTI increased from April 20 to June 3 but declined in ca. 10% of countries. Bivariate analyses showed the average in countries with open public testing policies (59.7, 95% CI 55.6-63.8) were significantly higher than countries with no testing policy (30.2, 95% CI 18.1-42.3) (p<0.0001). A multiple linear regression model assessed the association of independent grouping variables with CovTI. Open public testing and extensive contact tracing were shown to significantly increase CovTI, after adjusting for extrinsic factors, including geographic isolation and centralized forms of government. This tool may be useful for policymakers to assess testing effectiveness, inform decisions, and identify model countries. It may also serve as a tool for researchers in analyses by combining it with other databases.

scholarly journals An overview of basics for developing a reopening roadmap amid COVID-19 pandemic, a suggestion for the world

2020 ◽  
Author(s):  
Mehrdad Askarian ◽  
Gary Groot ◽  
Ehsan Taherifard ◽  
Erfan Taherifard ◽  
Hossein Akbarialiabad ◽  
...  
Keyword(s):  
High Risk ◽  
Health System ◽  
Risk Groups ◽  
System Capacity ◽  
Contact Tracing ◽  
Social Distancing ◽  
Epidemiological Monitoring ◽  
Preventive Actions ◽  
The World ◽  
Previous Phase

Abstract The necessity of easing pandemic restrictions is apparent, and due to the harsh consequences of lockdowns, governments are willing to find a rational pathway to reopen their activities. To find out the basics of developing a reopening roadmap, we reviewed 16 roadmaps. The most notable findings are as following: Protecting the high-risk groups, increasing testing and contact tracing capacity, making decisions scientifically, and making the decisions to impose the lowest risks to the economy were the most principles mentioned in the roadmaps. Social distancing, using a face-covering mask, and washing hands were the necessary preventive actions that were recommended for individuals. Health key metrics pointed out in the roadmaps were categorized into four subsets; sufficient preventive capacities, appropriate diagnosis capacity, appropriate epidemiological monitoring capacity, and sufficient health system capacity to be resilient in facing the surges and next phases of the pandemic. All roadmaps describe their in-phases strategy in three major steps, with a minimum of two weeks considered for each phase. Based on the health key metrics, most of the roadmaps noted when progressing to the next phases, while some of them did not focus on the criteria of returning to the previous phase; which may alter the dynamicity of a roadmap.

scholarly journals The Containment Scouts: First Insights into an Initiative to Increase the Public Health Workforce for Contact Tracing during the COVID-19 Pandemic in Germany

2021 ◽  
Vol 18 (17) ◽  
pp. 9325
Author(s):  
Dunja Said ◽  
Simon Brinkwirth ◽  
Angelina Taylor ◽  
Robby Markwart ◽  
Tim Eckmanns
Keyword(s):  
Public Health ◽  
Health System ◽  
Health Workforce ◽  
System Capacity ◽  
World Health ◽  
Public Health Workforce ◽  
Contact Tracing ◽  
Short Term ◽  
The Public ◽  
Health Authorities

The COVID-19 pandemic in Germany has demanded a substantially larger public health workforce to perform contact tracing and contact management of COVID-19 cases, in line with recommendations of the World Health Organization (WHO). In response, the Robert Koch Institute (RKI) established the national “Containment Scout Initiative” (CSI) to support the local health authorities with a short-term workforce solution. It is part of a range of measures for strengthening the public health system in order to limit the spread of SARS-CoV-2 in Germany. The CSI is an example of how solutions to address critical health system capacity issues can be implemented quickly. It also demonstrates that medical or health-related backgrounds may not be necessary to support health authorities with pandemic-specific tasks and fulfil accurate contact tracing. However, it is a short-term solution and cannot compensate for the lack of existing qualified staff as well as other deficits that exist within the public health sector in Germany. This article describes the structure and process of the first phase of this initiative in order to support health policymakers, public health practitioners, and researchers considering innovative and flexible approaches for addressing urgent workforce capacity issues.

scholarly journals The Relationship between Personality Five Factor Theory and the Career Success of Executive Workers in Apparel Sector Organizations in Sabaragamuwa Province Sri Lanka

2018 ◽  
Vol 5 (1) ◽  
Author(s):  
SH Dias ◽  
P Jayasekara
Keyword(s):  
Sri Lanka ◽  
Career Success ◽  
Positive Relationship ◽  
Negative Relationship ◽  
Process Models ◽  
Future Research ◽  
Work Related ◽  
Strong Positive Relationship ◽  
The Impact ◽  
The Relationship

Area of the Study As a significant determinant of career success of employee, this study is discussing the relationship between Personality Five Factor theory and the Career Success of executive workers in the apparel sector organizations in Sabaragamuwa province Sri Lanka.Problem of the Study There is an empirical knowledge gap in the context of the impact of personality five factor theory on the career success of employees in Sri Lanka. therefore, the problem of the study is: Does Five Factor Theory of personality affect to the career success of executives in the apparel sector organization in Sabaragamuwa province.Method of the study The data were collected from a selected sample of 122 executives in the apparel industry in Sabaragamuwa province Sri Lanka by administrating a structured questionnaire, which consisted of 63 questions/ statements with 5 points scale. The data analyses included the univariate and bivariate analyses.Findings of the Study The authors found that some of the factors have strong positive relationship and some have negative relationship and some haven’t any relationship with career success of executives in apparel sector organizations in Sabaragamuwa province, Sri Lanka. Extraversion and Conscientiousness have strong positive relationship with career success of the executives and Agreeableness and Neuroticism have negative relationship with the executives of the apparel sector organizations. However, there is no any relationship in Openness to experience with career success of the executives.Conclusion of the Study Future research based on the current theoretical model can investigate the relationship of personality with other work related behaviors and outcomes. The empirical confirmation of this conceptual model is another area of future research. Future research should attempt to replicate these results and develop process models that may explain why conscientiousness and Extraversion have such apparently enduring associations with career success.Keywords: Personality Five Factors, Career Success, Executives, Apparel Sector Organizations

scholarly journals A novel comprehensive metric to assess effectiveness of COVID-19 testing: Inter-country comparison and association with geography, government, and policy response

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0248176
Author(s):  
Anthony C. Kuster ◽  
Hans J. Overgaard
Keyword(s):  
Detection Rate ◽  
Case Fatality ◽  
Multiple Linear Regression Model ◽  
System Capacity ◽  
Contact Tracing ◽  
Extrinsic Factors ◽  
Detection Rates ◽  
Case Identification ◽  
Rate Test ◽  
Reported Data

Testing and case identification are key strategies in controlling the COVID-19 pandemic. Contact tracing and isolation are only possible if cases have been identified. The effectiveness of testing should be assessed, but a single comprehensive metric is not available to assess testing effectiveness, and no timely estimates of case detection rate are available globally, making inter-country comparisons difficult. The purpose of this paper was to propose a single, comprehensive metric, called the COVID-19 Testing Index (CovTI) scaled from 0 to 100, derived from epidemiological indicators of testing, and to identify factors associated with this outcome. The index was based on case-fatality rate, test positivity rate, active cases, and an estimate of the detection rate. It used parsimonious modeling to estimate the true total number of COVID-19 cases based on deaths, testing, health system capacity, and government transparency. Publicly reported data from 165 countries and territories that had reported at least 100 confirmed cases by June 3, 2020 were included in the index. Estimates of detection rates aligned satisfactorily with previous estimates in literature (R2 = 0.44). As of June 3, 2020, the states with the highest CovTI included Hong Kong (93.7), Australia (93.5), Iceland (91.8), Cambodia (91.3), New Zealand (90.6), Vietnam (90.2), and Taiwan (89.9). Bivariate analyses showed the mean CovTI in countries with open public testing policies (66.9, 95% CI 61.0–72.8) was significantly higher than in countries with no testing policy (29.7, 95% CI 17.6–41.9) (p<0.0001). A multiple linear regression model assessed the association of independent grouping variables with CovTI. Open public testing and extensive contact tracing were shown to significantly increase CovTI, after adjusting for extrinsic factors, including geographic isolation and centralized forms of government. The correlation of testing and contact tracing policies with improved outcomes demonstrates the validity of this model to assess testing effectiveness and also suggests these policies were effective at improving health outcomes. This tool can be combined with other databases to identify other factors or may be useful as a standalone tool to help inform policymakers.

Effects of Financial Innovations on Profitability for Deposit Taking Microfinancing Institutions in Kenya

2018 ◽  
Vol 9 (6) ◽  
pp. 529-536
Author(s):  
Martin Khoya Odipo ◽  
Keyword(s):  
Financial Institutions ◽  
Positive Relationship ◽  
Financial Institution ◽  
Secondary Data ◽  
Financial Statements ◽  
Descriptive Statistics ◽  
Primary Data ◽  
Microfinance Institutions ◽  
Financial Innovations ◽  
Strong Positive Relationship

Recent studies have documented that innovations improve profitability of firms. This article documents that deposit taking micro financial institutions that have adopted financial innovations have increased their profitability. The study covered five years between 2009-2013. Both primary and secondary data were used in the study. Primary data was obtained through administration of drop and pick questionnaires to selected employees of the institutions. Secondary data was obtained from financial statements and management reports of these deposit taking microfinance institutions. Data was analyzed using descriptive statistics, return on asset and multi-liner regression model to determine the effect of each financial innovation applied on profitability on the micro-financial institution. The results showed that most deposit taking microfinance institutions adopted these financial innovations in their current operations. There was strong positive relationship between individual innovations and profitability. In line with profitability ROA also showed improvement each year after the adoption of these financial innovations.

INTERCONNECTION OF SUSTAINABLE DEVELOPMENT COMPONENTS FOR THE INDIGENOUS PEOPLES OF THE NORTH

2020 ◽  
Vol 2 (8) ◽  
pp. 101-110
Author(s):  
N. N. ILYSHEVA ◽  
◽  
E. V. KARANINA ◽  
G. P. LEDKOV ◽  
E. V. BALDESKU ◽  
...  
Keyword(s):  
Climate Change ◽  
Sustainable Development ◽  
Economic Development ◽  
Indigenous Peoples ◽  
Ecological Footprint ◽  
Positive Relationship ◽  
The North ◽  
Strong Positive Relationship ◽  
The Face ◽  
The Relationship

The article deals with the problem of achieving sustainable development. The purpose of this study is to reveal the relationship between the components of sustainable development, taking into account the involvement of indigenous peoples in nature conservation. Climate change makes achieving sustainable development more difficult. Indigenous peoples are the first to feel the effects of climate change and play an important role in the environmental monitoring of their places of residence. The natural environment is the basis of life for indigenous peoples, and biological resources are the main source of food security. In the future, the importance of bioresources will increase, which is why economic development cannot be considered independently. It is assumed that the components of resilience are interrelated and influence each other. To identify this relationship, a model for the correlation of sustainable development components was developed. The model is based on the methods of correlation analysis and allows to determine the tightness of the relationship between economic development and its ecological footprint in the face of climate change. The correlation model was tested on the statistical materials of state reports on the environmental situation in the Khanty-Mansiysk Autonomous Okrug – Yugra. The approbation revealed a strong positive relationship between two components of sustainable development of the region: economy and ecology.

An Enhanced Multiple Linear Regression Model for Seasonal Rainfall Prediction

2020 ◽  
Vol 10 (4) ◽  
pp. 473-483
Author(s):  
Pundra Chandra Shaker Reddy ◽  
Alladi Sureshbabu
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Weather Prediction ◽  
Big Data Analytics ◽  
Multiple Linear Regression Model ◽  
Seasonal Rainfall ◽  
Climate Data ◽  
Rainfall Forecasting

Aims & Background: India is a country which has exemplary climate circumstances comprising of different seasons and topographical conditions like high temperatures, cold atmosphere, and drought, heavy rainfall seasonal wise. These utmost varieties in climate make us exact weather prediction is a challenging task. Majority people of the country depend on agriculture. Farmers require climate information to decide the planting. Weather prediction turns into an orientation in farming sector to deciding the start of the planting season and furthermore quality and amount of their harvesting. One of the variables are influencing agriculture is rainfall. Objectives & Methods: The main goal of this project is early and proper rainfall forecasting, that helpful to people who live in regions which are inclined natural calamities such as floods and it helps agriculturists for decision making in their crop and water management using big data analytics which produces high in terms of profit and production for farmers. In this project, we proposed an advanced automated framework called Enhanced Multiple Linear Regression Model (EMLRM) with MapReduce algorithm and Hadoop file system. We used climate data from IMD (Indian Metrological Department, Hyderabad) in 1901 to 2002 period. Results: Our experimental outcomes demonstrate that the proposed model forecasting the rainfall with better accuracy compared with other existing models. Conclusion: The results of the analysis will help the farmers to adopt effective modeling approach by anticipating long-term seasonal rainfall.

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