scholarly journals Stationary Distribution Markov Chain for COVID-19 Pandemic

2020 ◽  
Vol 1 (2) ◽  
pp. 71-74
Author(s):  
Audi Achmad ◽  
Mahrudinda Mahrudinda ◽  
Budi Ruchjana ◽  
Sudradjat Supian
Keyword(s):  
Markov Chain ◽  
Mathematical Modelling ◽  
Stationary Distribution ◽  
Observation Interval ◽  
Swine Influenza ◽  
New Disease ◽  
Infected People ◽  
First Case ◽  
Long Term Prediction

Coronavirus disease (COVID-19) is a new disease found in the late 2019. The first case was reported on December 31, 2019 in Wuhan, China and spreading all over the countries. The disease was quickly spread to all over the countries. There are 206.900 cases confirmed by March 18, 2020 causing 8.272 death. It was predicted that the number of confirmed cases will continue to increase. On January 30, 2020, WHO declared this as pandemic for the 6th time ever since the swine influenza. There are a lot of researchers which discuss pandemic spreading caused by virus with mathematical modelling. In this paper, we discuss a long-term prediction over the COVID-19 spreading using stationary distribution markov chain. The goal is to analyze the prediction of infected people in long-term by analyzing the COVID-19 daily cases in an observation interval. By analyzing the daily cases of COVID-19 in Indonesia from March 2nd, 2020 until November 1st, 2020, result shown that 53.91% of probability that the COVID-19 daily case will incline in long-term, 44.86% of chance will decline, and 1.23% of chance will stagnant.

scholarly journals Rules and guidelines for distancing in daily life to control coronavirus disease 2019 in Korea: 3rd version, announced on July 3, 2020

2020 ◽  
Vol 17 ◽  
pp. 20 ◽  
Author(s):  
Keyword(s):  
Daily Life ◽  
Well Being ◽  
Control Measures ◽  
Way Of Life ◽  
Infected People ◽  
First Case ◽  
Different Types ◽  
Health And Well Being ◽  
Intensive Control

In Korea, the first case of coronavirus disease 2019 (COVID-19) was reported on January 21, 2020, after which the number of infected people began to increase. Intensive control measures stabilized the spread of COVID-19 in Korea. Therefore, the Korean government introduced the policy of “distancing in daily life” to support the maintenance of normal life starting on March 22, 2020. This policy provides rules and guidelines on distancing in daily life to facilitate the control of COVID-19 in Korea. “Distancing in daily life” refers to a new, sustainable way of life and social interactions that prepares society to face the possibility of long-term prevalence of COVID-19. These guidelines aim to achieve the goal of infection prevention and containment, while sustaining people’s everyday life, economic, and social activities. All members of society and communities are called upon to fulfill their respective responsibilities to combat the COVID-19 pandemic and to safeguard everyone’s health and well-being. Five key rules govern personal distancing in daily life: stay home for 3–4 days if you feel unwell; keep a distance of 2 arms’ length from others; wash your hands for 30 seconds and cough or sneeze into your sleeve; ventilate spaces at least twice a day and disinfect regularly; and stay connected while physically distancing. Collective distancing in daily life for communities and organizations is supported by these 5 key rules, and detailed guidelines are set out for different types of facilities. All individuals and communities are obliged to abide by these rules and guidelines for distancing as part of daily life.

scholarly journals COVID-19 Response in Thailand and Its Implications on Future Preparedness

2021 ◽  
Vol 18 (3) ◽  
pp. 1089
Author(s):  
Wijitbusaba Marome ◽  
Rajib Shaw
Keyword(s):  
Early Stage ◽  
Health Resources ◽  
Holistic Approach ◽  
Disaster Risk ◽  
Community Level ◽  
Multilateral Cooperation ◽  
First Case ◽  
Multiple Dimensions ◽  
Economic And Social Impacts

Thailand has been affected by COVID-19, like other countries in the Asian region at an early stage, and the first case was reported as early as mid-January 2020. Thailand’s response to the COVID-19 pandemic has been guided by the “Integrated Plan for Multilateral Cooperation for Safety and Mitigation of COVID-19”. This paper analyses the health resources in the country and focuses on the response through community-level public health system and legislative measures. The paper draws some lessons on future preparedness, especially with respect to the four priorities of Sendai Framework for Disaster Risk Reduction. At the end, the paper puts some key learning for future preparedness. While Thailand’s response to COVID-19 has been effective in limiting the spread of the disease, it falls short at being able to address the multiple dimensions of the crisis such as the economic and social impacts. The socioeconomic sectors have been hardest hit, with significant impact on tourism sectors. Sociopolitical system also plays an important role in governance and decision-making for pandemic responses. The analysis suggests that one opportunity for enhancing resilience in Thailand is to strive for more multilevel governance that engages with various stakeholders and to support grassroots and community-level networks. The COVID-19 pandemic recovery is a chance to recover better while leaving no one behind. An inclusive long-term recovery plan for the various impacted countries needs to take a holistic approach to address existing gaps and work towards a sustainable society. Furthering the Health Emergency Disaster Risk Management (HEDRM) Framework may support a coordinated response across various linked sectors rather than straining one particular sector.

scholarly journals Short-Term Prediction of COVID-19 Cases Using Machine Learning Models

2021 ◽  
Vol 11 (9) ◽  
pp. 4266
Author(s):  
Md. Shahriare Satu ◽  
Koushik Chandra Howlader ◽  
Mufti Mahmud ◽  
M. Shamim Kaiser ◽  
Sheikh Mohammad Shariful Islam ◽  
...  
Keyword(s):  
Machine Learning ◽  
Web Application ◽  
Learning Models ◽  
Short Term ◽  
Infected People ◽  
Infected Case ◽  
First Case ◽  
Novel Coronavirus ◽  
Short Term Prediction ◽  
Machine Learning Models

The first case in Bangladesh of the novel coronavirus disease (COVID-19) was reported on 8 March 2020, with the number of confirmed cases rapidly rising to over 175,000 by July 2020. In the absence of effective treatment, an essential tool of health policy is the modeling and forecasting of the progress of the pandemic. We, therefore, developed a cloud-based machine learning short-term forecasting model for Bangladesh, in which several regression-based machine learning models were applied to infected case data to estimate the number of COVID-19-infected people over the following seven days. This approach can accurately forecast the number of infected cases daily by training the prior 25 days sample data recorded on our web application. The outcomes of these efforts could aid the development and assessment of prevention strategies and identify factors that most affect the spread of COVID-19 infection in Bangladesh.

scholarly journals Novel robust time series analysis for long-term and short-term prediction

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hiroshi Okamura ◽  
Yutaka Osada ◽  
Shota Nishijima ◽  
Shinto Eguchi
Keyword(s):  
Robust Regression ◽  
Simulated Data ◽  
New Method ◽  
Short Term ◽  
Least Absolute Deviations ◽  
Term Prediction ◽  
Regression Approach ◽  
Short Term Prediction ◽  
Long Term Prediction

AbstractNonlinear phenomena are universal in ecology. However, their inference and prediction are generally difficult because of autocorrelation and outliers. A traditional least squares method for parameter estimation is capable of improving short-term prediction by estimating autocorrelation, whereas it has weakness to outliers and consequently worse long-term prediction. In contrast, a traditional robust regression approach, such as the least absolute deviations method, alleviates the influence of outliers and has potentially better long-term prediction, whereas it makes accurately estimating autocorrelation difficult and possibly leads to worse short-term prediction. We propose a new robust regression approach that estimates autocorrelation accurately and reduces the influence of outliers. We then compare the new method with the conventional least squares and least absolute deviations methods by using simulated data and real ecological data. Simulations and analysis of real data demonstrate that the new method generally has better long-term and short-term prediction ability for nonlinear estimation problems using spawner–recruitment data. The new method provides nearly unbiased autocorrelation even for highly contaminated simulated data with extreme outliers, whereas other methods fail to estimate autocorrelation accurately.

scholarly journals Hypocalcemic cardiomyopathy after parathyroidectomy in a patient with uremia: A case report and literature review

2020 ◽  
Vol 48 (7) ◽  
pp. 030006052094211
Author(s):  
Wei Zhang ◽  
Feng Xue ◽  
Quandong Bu ◽  
Xuemei Liu
Keyword(s):  
Heart Failure ◽  
Case Report ◽  
Secondary Hyperparathyroidism ◽  
Calcium Supplementation ◽  
Severe Heart Failure ◽  
Cardiac Complications ◽  
Refractory Heart Failure ◽  
First Case ◽  
Insight Into

Hypocalcemia is a rare, but reversible, cause of dilated cardiomyopathy. Although cardiomyopathy may cause severe heart failure, calcium supplementation can reverse heart failure. We report here a patient with uremia and secondary hyperparathyroidism, who was complicated by persistent hypocalcemia and refractory heart failure. The cardiac failure was refractory to treatment with digitalis and diuretics, but dramatically responded to calcium therapy and restoration of normocalcemia. As a result, the patient was eventually diagnosed with hypocalcemic cardiomyopathy. To the best of our knowledge, this is the first case of this disease to be reported in a patient with uremia. Findings from our case may help clinicians to better understand hypocalcemic cardiomyopathy. Our case might also provide new insight into long-term cardiac complications and prognoses of patients undergoing parathyroidectomy due to secondary hyperparathyroidism.

scholarly journals Calibration of Transition Intensities for a Multistate Model: Application to Long-Term Care

Risks ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 37
Author(s):  
Manuel L. Esquível ◽  
Gracinda R. Guerreiro ◽  
Matilde C. Oliveira ◽  
Pedro Corte Real
Keyword(s):  
Markov Chain ◽  
Continuous Time ◽  
Long Term Care ◽  
Transition Probabilities ◽  
Markov Chain Model ◽  
Chain Model ◽  
Chain Process ◽  
Term Care ◽  
National Network

We consider a non-homogeneous continuous time Markov chain model for Long-Term Care with five states: the autonomous state, three dependent states of light, moderate and severe dependence levels and the death state. For a general approach, we allow for non null intensities for all the returns from higher dependence levels to all lesser dependencies in the multi-state model. Using data from the 2015 Portuguese National Network of Continuous Care database, as the main research contribution of this paper, we propose a method to calibrate transition intensities with the one step transition probabilities estimated from data. This allows us to use non-homogeneous continuous time Markov chains for modeling Long-Term Care. We solve numerically the Kolmogorov forward differential equations in order to obtain continuous time transition probabilities. We assess the quality of the calibration using the Portuguese life expectancies. Based on reasonable monthly costs for each dependence state we compute, by Monte Carlo simulation, trajectories of the Markov chain process and derive relevant information for model validation and premium calculation.

Approximating the stationary distribution of an infinite stochastic matrix

1991 ◽  
Vol 28 (1) ◽  
pp. 96-103 ◽  
Author(s):  
Daniel P. Heyman
Keyword(s):  
Markov Chain ◽  
Steady State ◽  
Stationary Distribution ◽  
Numerical Approximation ◽  
Stochastic Matrix ◽  
Sufficient Conditions ◽  
Finite System ◽  
Balance Equations ◽  
The Given ◽  
Steady State Balance

We are given a Markov chain with states 0, 1, 2, ···. We want to get a numerical approximation of the steady-state balance equations. To do this, we truncate the chain, keeping the first n states, make the resulting matrix stochastic in some convenient way, and solve the finite system. The purpose of this paper is to provide some sufficient conditions that imply that as n tends to infinity, the stationary distributions of the truncated chains converge to the stationary distribution of the given chain. Our approach is completely probabilistic, and our conditions are given in probabilistic terms. We illustrate how to verify these conditions with five examples.

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