scholarly journals Predictive Analysis of Real-Time Strategy using Face book’s Prophet Model on Covid-19 Dataset of India

2021 ◽  
pp. 305-312
Author(s):  
Pankaj Kumar ◽  
Renuka Sharma ◽  
S. K. Singh
Keyword(s):  
Large Scale ◽  
Time Series Data ◽  
Predictive Analytics ◽  
Series Data ◽  
The Novel ◽  
Healthcare Facilities ◽  
Global Epidemic ◽  
The Government ◽  
Novel Coronavirus ◽  
And Control

The global epidemic of the novel coronavirus (COVID-19) called SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2) has infected millions and killed millions. The prevalence of the virus is of paramount importance in identifying future infections and preparing healthcare facilities to avoid death. Accurately predicting the spread of COVID-19 is a challenging analytical and practical task for the research community. We can learn to use predictive analytics to predict the positive outcomes of these risks. These predictive analytics can look at the risks of past successes and failures. In this paper, the Facebook prophet model discusses the number of large-scale cases and deaths in India based on daily time-series data from 30 January 2020 to 30 April 2021, for forecasting and visualization. The covid-19 pandemic could end prematurely if social distancing and safety measures are required to stabilize and control is required to achieve treatment in India. This paper suggests that the Prophet Model is more effective in predicting COVID-19 cases. The forecast results will help the government plan strategies to prevent the spread of the coronavirus.

scholarly journals How to Dispose of Medical Waste Caused by COVID-19? A Case Study of China

2021 ◽  
Vol 18 (22) ◽  
pp. 12127
Author(s):  
Min Su ◽  
Qiang Wang ◽  
Rongrong Li
Keyword(s):  
Waste Disposal ◽  
Medical Waste ◽  
Chinese Government ◽  
The Novel ◽  
Global Epidemic ◽  
Medical Institutions ◽  
Process System ◽  
The Government ◽  
Novel Coronavirus ◽  
And Control

The rapid increase in novel coronavirus (COVID-19) patients also means a rapid increase in medical waste that could carry the novel coronavirus (SARS-CoV-2). How to safely dispose of medical waste caused by COVID-19 is a huge challenge that needs to be solved urgently. The outbreak of the COVID-19 has led to a significant increase in the daily generation of medical waste in China and has placed a severe test on the Chinese medical waste disposal system. Unlike ordinary wastes and garbage, medical waste that is untreated or incompletely treated will not only cause environmental pollution, but also directly or indirectly cause infections and endanger people’s health. Faced with difficulties, the Chinese government formulated a policy for medical waste management and a response plan for the epidemic, which provides policy guarantee for the standardized disposal of epidemic medical waste. In addition, the government and medical institutions at all levels formed a comprehensive, refined, and standardized medical treatment process system during research and practice. China has increased the capacity of medical waste disposal in various places by constructing new centralized disposal centers and adding mobile disposal facilities. China has achieved good results in the fight against COVID-19, and the pressure on medical waste disposal has been relieved to a certain extent. However, the global epidemic situation is severe. How to ensure the proper and safe disposal of medical waste is related to the prevention and control of the epidemic situation. This study summarizes China’s experience in the disposal of medical waste in the special case of COVID-19 and hopes to provide some reference for other countries in the disposal of medical waste.

scholarly journals Multi-Objective Optimization of Integrated Civilian-Military Scheduling of Medical Supplies for Epidemic Prevention and Control

Healthcare ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 126
Author(s):  
Hai-Feng Ling ◽  
Zheng-Lian Su ◽  
Xun-Lin Jiang ◽  
Yu-Jun Zheng
Keyword(s):  
Prevention And Control ◽  
Large Scale ◽  
Medical Services ◽  
The Novel ◽  
Multi Objective ◽  
Medical Supplies ◽  
Problem Instances ◽  
Novel Coronavirus ◽  
And Control ◽  
Minimum Ratio

In a large-scale epidemic, such as the novel coronavirus pneumonia (COVID-19), there is huge demand for a variety of medical supplies, such as medical masks, ventilators, and sickbeds. Resources from civilian medical services are often not sufficient for fully satisfying all of these demands. Resources from military medical services, which are normally reserved for military use, can be an effective supplement to these demands. In this paper, we formulate a problem of integrated civilian-military scheduling of medical supplies for epidemic prevention and control, the aim of which is to simultaneously maximize the overall satisfaction rate of the medical supplies and minimize the total scheduling cost, while keeping a minimum ratio of medical supplies reservation for military use. We propose a multi-objective water wave optimization (WWO) algorithm in order to efficiently solve this problem. Computational results on a set of problem instances constructed based on real COVID-19 data demonstrate the effectiveness of the proposed method.

scholarly journals Evidence Graphs: Supporting Transparent and FAIR Computation, with Defeasible Reasoning on Data, Methods and Results

2021 ◽  
Author(s):  
Sadnan Al Manir ◽  
Justin Niestroy ◽  
Maxwell Adam Levinson ◽  
Timothy Clark
Keyword(s):  
Time Series ◽  
Large Scale ◽  
Time Series Data ◽  
Predictive Analytics ◽  
Defeasible Reasoning ◽  
Series Data ◽  
Inference Rules ◽  
Deep Networks ◽  
Evidence Graph ◽  
Over Time

Introduction: Transparency of computation is a requirement for assessing the validity of computed results and research claims based upon them; and it is essential for access to, assessment, and reuse of computational components. These components may be subject to methodological or other challenges over time. While reference to archived software and/or data is increasingly common in publications, a single machine-interpretable, integrative representation of how results were derived, that supports defeasible reasoning, has been absent. Methods: We developed the Evidence Graph Ontology, EVI, in OWL 2, with a set of inference rules, to provide deep representations of supporting and challenging evidence for computations, services, software, data, and results, across arbitrarily deep networks of computations, in connected or fully distinct processes. EVI integrates FAIR practices on data and software, with important concepts from provenance models, and argumentation theory. It extends PROV for additional expressiveness, with support for defeasible reasoning. EVI treats any com- putational result or component of evidence as a defeasible assertion, supported by a DAG of the computations, software, data, and agents that produced it. Results: We have successfully deployed EVI for very-large-scale predictive analytics on clinical time-series data. Every result may reference its own evidence graph as metadata, which can be extended when subsequent computations are executed. Discussion: Evidence graphs support transparency and defeasible reasoning on results. They are first-class computational objects, and reference the datasets and software from which they are derived. They support fully transparent computation, with challenge and support propagation. The EVI approach may be extended to include instruments, animal models, and critical experimental reagents.

scholarly journals Differential Equation Analysis on COVID-19

2020 ◽  
pp. 1-15
Keyword(s):  
Hot Spot ◽  
Sufficient Information ◽  
The Novel ◽  
Effective Response ◽  
The People ◽  
Equation Analysis ◽  
The World ◽  
The Government ◽  
Novel Coronavirus ◽  
And Control

An infectious disease caused by a novel coronavirus called COVID-19 has raged across the world since December 2019. The novel coronavirus first appeared in Wuhan, China, and quickly spread to Asia and now many countries around the world are affected by the epidemic. The deaths of many patients, including medical staff, caused social panic, media attention, and high attention from governments and world organizations. Today, with the joint efforts of the government, the doctors and all walks of life, the epidemic in Hubei Province has been brought under control, preventing its spread from affecting the lives of the people. Because of its rapid spread and serious consequences, this sudden novel coronary pneumonia epidemic has become an important social hot spot event. Through the analysis of the novel coronary pneumonia epidemic situation, we can also have a better understanding of sudden infectious diseases in the future, so that we can take more effective response measures, establish a truly predictable and provide reliable and sufficient information for prevention and control model.

scholarly journals Tracking and Classifying Global COVID-19 Cases by using 1D Deep Convolution Neural Networks

2020 ◽  
Author(s):  
Mark Amo-Boateng
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Time Series Data ◽  
Sequence Data ◽  
Series Data ◽  
The Novel ◽  
Response Curves ◽  
Convolution Neural Networks ◽  
Link Type ◽  
Novel Coronavirus

ABSTRACTThe novel coronavirus disease (COVID-19) and pandemic has taken the world by surprise and simultaneously challenged the health infrastructure of every country. Governments have resorted to draconian measures to contain the spread of the disease despite its devastating effect on their economies and education. Tracking the novel coronavirus 2019 disease remains vital as it influences the executive decisions needed to tighten or ease restrictions meant to curb the pandemic. One-Dimensional (1D) Convolution Neural Networks (CNN) have been used classify and predict several time-series and sequence data. Here 1D-CNN is applied to the time-series data of confirmed COVID-19 cases for all reporting countries and territories. The model performance was 90.5% accurate. The model was used to develop an automated AI tracker web app (AI Country Monitor) and is hosted on https://aicountrymonitor.org. This article also presents a novel concept of pandemic response curves based on cumulative confirmed cases that can be use to classify the stage of a country or reporting territory. It is our firm believe that this Artificial Intelligence COVID-19 tracker can be extended to other domains such as the monitoring/tracking of Sustainable Development Goals (SDGs) in addition to monitoring and tracking pandemics.

scholarly journals Reflection on Emergency Management in China Based on Novel Coronavirus Pneumonia Prevention and Control

2021 ◽  
Vol 5 (1) ◽  
pp. 222
Author(s):  
Huan Wang
Keyword(s):  
Emergency Management ◽  
Social Order ◽  
The Novel ◽  
Working Mechanism ◽  
Management Capability ◽  
Local Officials ◽  
The Government ◽  
Novel Coronavirus ◽  
And Control ◽  
Rescue Mechanism

In 2019, China had novel coronavirus pneumonia outbreak in Wuhan, China, which seriously threatened people’s health and affected social order and economic development. The novel coronavirus epidemic has been actively dealt with by the government. However, there are still some problems, such as weak awareness of crisis, ineffective emergency response, stagnant legal construction, unsound rumors, ineffective public opinion, insufficient material support, inefficient allocation of resources, the local officials with low ability, inadequate humanistic care and incomplete rescue mechanism. By analyzing how the government deal with the novel coronavirus pneumonia crisis, we can optimize the working mechanism, improving the working methods, and improving the government’s emergency management capability.

Transport infrastructure of the Rostov Oblast: Problems and prospects for development in the context of national projects implementation

2020 ◽  
Vol 19 (6) ◽  
pp. 1015-1034
Author(s):  
O.Yu. Patrakeeva
Keyword(s):  
Time Series Data ◽  
Mutual Influence ◽  
Rostov Oblast ◽  
Transport Infrastructure ◽  
Series Data ◽  
Cointegration Tests ◽  
Level Of Economic Development ◽  
Short Run ◽  
The Government ◽  
The Impact

Subject. The paper considers national projects in the field of transport infrastructure, i.e. Safe and High-quality Roads and Comprehensive Plan for Modernization and Expansion of Trunk Infrastructure, and the specifics of their implementation in the Rostov Oblast. Objectives. The aim is to conduct a statistical assessment of the impact of transport infrastructure on the region’s economic performance and define prospects for and risks of the implementation of national infrastructure projects in conditions of a shrinking economy. Methods. I use available statistics and apply methods and approaches with time-series data, namely stationarity and cointegration tests, vector autoregression models. Results. The level of economic development has an impact on transport infrastructure in the short run. However, the mutual influence has not been statistically confirmed. The paper revealed that investments in the sphere of transport reduce risk of accidents on the roads of the Rostov Oblast. Improving the quality of roads with high traffic flow by reducing investments in the maintenance of subsidiary roads enables to decrease accident rate on the whole. Conclusions. In conditions of economy shrinking caused by the complex epidemiological situation and measures aimed at minimizing the spread of coronavirus, it is crucial to create a solid foundation for further economic recovery. At the government level, it is decided to continue implementing national projects as significant tools for recovery growth.

Coronavirus: Towards controlling of the pandemic - Indian scenario

2020 ◽  
Vol 11 (SPL1) ◽  
pp. 462-468
Author(s):  
Latika kothari ◽  
Sanskruti Wadatkar ◽  
Roshni Taori ◽  
Pavan Bajaj ◽  
Diksha Agrawal
Keyword(s):  
Control Measures ◽  
The Novel ◽  
Medical Treatments ◽  
Health Crisis ◽  
The Public ◽  
Infection Control Measures ◽  
The Social ◽  
Asking Questions ◽  
The Government ◽  
Novel Coronavirus

Coronavirus disease 2019 (COVID-19) is a communicable infection caused by the novel coronavirus resulting in severe acute respiratory syndrome coronavirus 2 (SARS-CoV). It was recognized to be a health crisis for the general population of international concern on 30th January 2020 and conceded as a pandemic on 11th March 2020. India is taking various measures to fight this invisible enemy by adopting different strategies and policies. To stop the COVID-19 from spreading, the Home Affairs Ministry and the health ministry, of India, has issued the nCoV 19 guidelines on travel. Screening for COVID-19 by asking questions about any symptoms, recent travel history, and exposure. India has been trying to get testing kits available. The government of India has enforced various laws like the social distancing, Janata curfew, strict lockdowns, screening door to door to control the spread of novel coronavirus. In this pandemic, innovative medical treatments are being explored, and a proper vaccine is being hunted to deal with the situation. Infection control measures are necessary to prevent the virus from further spreading and to help control the current situation. Thus, this review illustrates and explains the criteria provided by the government of India to the awareness of the public to prevent the spread of COVID-19.

Novel Coronavirus 2019 Outbreak: A Global Epidemic

2020 ◽  
Vol 17 (12) ◽  
pp. 1458-1464
Author(s):  
Sweta Kamboj ◽  
Rohit Kamboj ◽  
Shikha Kamboj ◽  
Kumar Guarve ◽  
Rohit Dutt
Keyword(s):  
World Health ◽  
Upper Respiratory Tract ◽  
Global Epidemic ◽  
The Public ◽  
The Government ◽  
Tract Disease ◽  
The 1960S ◽  
Novel Coronavirus ◽  
Health Organization ◽  
The Impact

Background: In the 1960s, the human coronavirus was designated, which is responsible for the upper respiratory tract disease in children. Back in 2003, mainly 5 new coronaviruses were recognized. This study directly pursues to govern knowledge, attitude and practice of viral and droplet infection isolation safeguard among the researchers during the outbreak of the COVID-19. Introduction: Coronavirus is a proteinaceous and infectious pathogen. It is an etiological agent of severe acute respiratory syndrome (SARS) and the Middle East respiratory syndrome (MERS). Coronavirus, appeared in China from the seafood and poultry market last year, which has spread in various countries, and has caused several deaths. Methods: The literature data has been taken from different search platforms like PubMed, Science Direct, Embase, Web of Science, who.int portal and complied. Results: Corona virology study will be more advanced and outstanding in recent years. COVID-19 epidemic is a threatening reminder not solely for one country but all over the universe. Conclusion: In this review article, we encapsulated the pathogenesis, geographical spread of coronavirus worldwide, also discussed the perspective of diagnosis, effective treatment, and primary recommendations by the World Health Organization, and guidelines of the government to slow down the impact of the virus are also optimistic, efficacious and obliging for the public health. However, it will take a prolonged time in the future to overcome this epidemic.

The Classic Maya Collapse: Testing Class Conflict Hypotheses

1980 ◽  
Vol 45 (2) ◽  
pp. 246-267 ◽  
Author(s):  
Robert L. Hamblin ◽  
Brian L. Pitcher
Keyword(s):  
Large Scale ◽  
Time Series Data ◽  
Classic Maya ◽  
Class Conflict ◽  
Series Data ◽  
Geographical Patterns ◽  
Historical Societies ◽  
Testing Class ◽  
Lowland Area ◽  
Maya Collapse

Several lines of archaeological evidence are presented in this paper to suggest the existence of class warfare among the Classic Maya and of issues that historically have been associated with class conflict. This evidence indicates that class warfare may have halted the rule of the monument-producing, or Classic, elites and precipitated the depopulation of the lowland area. The theory is evaluated quantitatively by testing for time-related mathematical patterns that have been found to characterize large-scale conflicts in historical societies. The information used in the evaluation involves the time series data on the duration of rule by Classic elites as inferred from the production of monuments with Long Count dates at a sample of 82 ceremonial centers. The analyses confirm that the Maya data do exhibit the temporal and geographical patterns predicted from the class conflict explanation of the Classic Maya collapse. Alternative predictions from the other theories are considered but generally not found to be supported by these data.

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