Forecasting the COVID-19 Outbreak: An Application of Arima and Fuzzy Time Series Models

Abstract Purpose: The whole world is surfaced with an inordinate challenge of mankind due to COVID-19, caused by 2019 novel coronavirus (SARS-CoV-2). After taking hundreds of thousands of lives, millions of people are still in the substantial grasp of this virus. This virus is highly contagious with reproduction number R0, as high as 6.5 worldwide and between 1.5 to 2.6 in India. So, the number of total infections and the number of deaths will get a day-to-day hike until the curve flattens. Under the current circumstances, it becomes inevitable to develop a model, which can anticipate future morbidities, recoveries, and deaths. Methods: We have developed some models based on ARIMA and FUZZY time series methodology for the forecasting of COVID-19 infections, mortalities and recoveries in India and Maharashtra explicitly, which is the most affected state in India, following the COVID-19 statistics till “Lockdown 3.0” (17th May 2020). Results: Both models suggest that there will be an exponential uplift in COVID-19 cases in the near future. We have forecasted the COVID-19 data set for next seven days. The forecasted values are in good agreement with real ones for all six COVID-19 scenarios for Maharashtra and India as a whole as well.Conclusion: The forecasts for the ARIMA and FUZZY time series models will be useful for the policymakers of the health care systems so that the system and the medical personnel can be prepared to combat the pandemic.

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Prediction of Malaysian–Indonesian Oil Production and Consumption Using Fuzzy Time Series Model

Advances in Data Science and Adaptive Analysis ◽

10.1142/s2424922x17500012 ◽

2017 ◽

Vol 09 (01) ◽

pp. 1750001 ◽

Cited By ~ 1

Author(s):

Riswan Efendi ◽

Mustafa Mat Deris

Keyword(s):

Time Series ◽

Probabilistic Models ◽

Oil Production ◽

Time Series Model ◽

Fuzzy Time Series ◽

Interval Length ◽

Time Series Models ◽

Oil Consumption ◽

Data Set ◽

Forecasting Accuracy

Fuzzy time series has been implemented for data prediction in the various sectors, such as education, finance-economic, energy, traffic accident, others. Moreover, many proposed models have been presented to improve the forecasting accuracy. However, the interval-length adjustment and the out-sample forecast procedure are still issues in fuzzy time series forecasting, where both issues are yet clearly investigated in the previous studies. In this paper, a new adjustment of the interval-length and the partition number of the data set is proposed. Additionally, the determining of the out-sample forecast is also discussed. The yearly oil production (OP) and oil consumption (OC) of Malaysia and Indonesia from 1965 to 2012 are examined to evaluate the performance of fuzzy time series and the probabilistic time series models. The result indicates that the fuzzy time series model is better than the probabilistic models, such as regression time series, exponential smoothing in terms of the forecasting accuracy. This paper thus highlights the effect of the proposed interval length in reducing the forecasting error significantly, as well as the main differences between the fuzzy and probabilistic time series models.

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An Efficient Time Series Forecasting Method Exploiting Fuzziness and Turbulences in Data

International Journal of Fuzzy System Applications ◽

10.4018/ijfsa.2017100106 ◽

2017 ◽

Vol 6 (4) ◽

pp. 83-98 ◽

Cited By ~ 5

Author(s):

Prateek Pandey ◽

Shishir Kumar ◽

Sandeep Shrivastava

Keyword(s):

Time Series ◽

Performance Comparison ◽

Time Series Forecasting ◽

Fuzzy Time Series ◽

Time Series Models ◽

Data Set ◽

Common Trend ◽

Proposed Model ◽

Forecasting Method ◽

High Degree

In recent years, there has been a growing interest in Time Series forecasting. A number of time series forecasting methods have been proposed by various researchers. However, a common trend found in these methods is that they all underperform on a data set that exhibit uneven ups and downs (turbulences). In this paper, a new method based on fuzzy time-series (henceforth FTS) to forecast on the fundament of turbulences in the data set is proposed. The results show that the turbulence based fuzzy time series forecasting is effective, especially, when the available data indicate a high degree of instability. A few benchmark FTS methods are identified from the literature, their limitations and gaps are discussed and it is observed that the proposed method successfully overcome their deficiencies to produce better results. In order to validate the proposed model, a performance comparison with various conventional time series models is also presented.

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A Comprehensive Study of Artificial Intelligence and Machine Learning Approaches in Confronting the Coronavirus (COVID-19) Pandemic

International Journal of Health Services ◽

10.1177/00207314211017469 ◽

2021 ◽

pp. 002073142110174

Author(s):

Md Mijanur Rahman ◽

Fatema Khatun ◽

Ashik Uzzaman ◽

Sadia Islam Sami ◽

Md Al-Amin Bhuiyan ◽

...

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Health Care ◽

Language Processing ◽

Probabilistic Models ◽

Health Care Systems ◽

Learning Approaches ◽

Care Systems ◽

Novel Coronavirus ◽

Comprehensive Study

The novel coronavirus disease (COVID-19) has spread over 219 countries of the globe as a pandemic, creating alarming impacts on health care, socioeconomic environments, and international relationships. The principal objective of the study is to provide the current technological aspects of artificial intelligence (AI) and other relevant technologies and their implications for confronting COVID-19 and preventing the pandemic’s dreadful effects. This article presents AI approaches that have significant contributions in the fields of health care, then highlights and categorizes their applications in confronting COVID-19, such as detection and diagnosis, data analysis and treatment procedures, research and drug development, social control and services, and the prediction of outbreaks. The study addresses the link between the technologies and the epidemics as well as the potential impacts of technology in health care with the introduction of machine learning and natural language processing tools. It is expected that this comprehensive study will support researchers in modeling health care systems and drive further studies in advanced technologies. Finally, we propose future directions in research and conclude that persuasive AI strategies, probabilistic models, and supervised learning are required to tackle future pandemic challenges.

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Contributory role of SARS-CoV-2 genomic variations and life expectancy in COVID-19 transmission and low fatality rate in Africa

Egyptian Journal of Medical Human Genetics ◽

10.1186/s43042-020-00116-x ◽

2020 ◽

Vol 21 (1) ◽

Author(s):

Olabode E. Omotoso

Keyword(s):

Mutational Analysis ◽

Health Care Systems ◽

Selective Advantage ◽

African Population ◽

Young Generation ◽

Regulatory Policies ◽

Recurrent Mutations ◽

Mutational Hotspots ◽

Care Systems ◽

Novel Coronavirus

Abstract Background The novel coronavirus disease (COVID-19) has claimed lots of lives, posing a dire threat to global health. It was predicted that the coronavirus outbreak in the African population would be very lethal and result to economic devastation owing to the prevalence of immune-compromised population, poverty, low lifespan, fragile health care systems, poor economy, and lifestyle factors. Accumulation of mutations gives virus selective advantage for host invasion and adaptation, higher transmissibility of more virulent strains, and drug resistance. The present study determined the severe acute respiratory syndrome-2 (SARS-CoV-2) genomic variability and the contributory factors to the low COVID-19 fatality in Africa. To assess the SARS-CoV-2 mutational landscape, 924 viral sequences from the Africa region with their sociobiological characteristics mined from the Global Initiative on Sharing All Influenza Data (GISAID) database were analyzed. Results Mutational analysis of the SARS-CoV-2 sequences revealed highly recurrent mutations in the SARS-CoV-2 spike glycoprotein D614G (97.2%), concurrent R203K, and G204R (65.2%) in the nucleocapsid phosphoprotein, and P4715L (97.2%) in the RNA-dependent RNA polymerase flagging these regions as SARS-CoV-2 mutational hotspots in the African population. COVID-19 is more severe in older people (> 65 years); Africa has a low percentage of people within this age group (4.36%). The average age of the infected patients observed in this study is 46 years with only 47 infected patients (5.1%) above 65 years in Africa in comparison to 13.12% in countries in other continents with the highest prevalence of COVID-19. Conclusions Africa’s young generation, the late incidence of the disease, and adherence to public health guidelines are important indicators that may have contributed to the observed low COVID-19 deaths in Africa. However, with the easing of lockdown and regulatory policies, daily increasing incidence in most countries, and low testing and sequencing rate, the epidemiology and the true impact of the pandemic in Africa remain to be unraveled.

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A Novel Intuitionistic Fuzzy Time Series Method Based on Bootstrapped Combined Pi-Sigma Artificial Neural Network

Journal of Fuzzy Logic and Modeling in Engineering ◽

10.2174/2666294901666211011104410 ◽

2021 ◽

Vol 01 ◽

Author(s):

Eren Bas ◽

Erol Egrioglu ◽

Emine Kölemen

Keyword(s):

Neural Network ◽

Time Series ◽

Artificial Neural Network ◽

Stock Exchange ◽

Fuzzy Time Series ◽

Time Series Models ◽

Intuitionistic Fuzzy ◽

Forecasting Methods ◽

Time Series Method ◽

Artificial Neural

Background: Intuitionistic fuzzy time series forecasting methods have been started to solve the forecasting problems in the literature. Intuitionistic fuzzy time series methods use both membership and non-membership values as auxiliary variables in their models. Because intuitionistic fuzzy sets take into consideration the hesitation margin and so the intuitionistic fuzzy time series models use more information than fuzzy time series models. The background of this study is about intuitionistic fuzzy time series forecasting methods. Objective: The study aims to propose a novel intuitionistic fuzzy time series method. It is expected that the proposed method will produce better forecasts than some selected benchmarks. Method: The proposed method uses bootstrapped combined Pi-Sigma artificial neural network and intuitionistic fuzzy c-means. The combined Pi-Sigma artificial neural network is proposed to model the intuitionistic fuzzy relations. Results and Conclusion: The proposed method is applied to different sets of SP&500 stock exchange time series. The proposed method can provide more accurate forecasts than established benchmarks for the SP&500 stock exchange time series. The most important contribution of the proposed method is that it creates statistical inference: probabilistic forecasting, confidence intervals and the empirical distribution of the forecasts. Moreover, the proposed method is better than the selected benchmarks for the SP&500 data set.

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Potential impact of seasonal forcing on a SARS-CoV-2 pandemic

10.1101/2020.02.13.20022806 ◽

2020 ◽

Cited By ~ 10

Author(s):

Richard A. Neher ◽

Robert Dyrdak ◽

Valentin Druelle ◽

Emma B. Hodcroft ◽

Jan Albert

Keyword(s):

Seasonal Variation ◽

Infection Control ◽

Transmission Rate ◽

Health Care Systems ◽

Control Measures ◽

Rate Variation ◽

Small Peak ◽

Infection Control Measures ◽

Care Systems ◽

Novel Coronavirus

A novel coronavirus (SARS-CoV-2) first detected in Wuhan, China, has spread rapidly since December 2019, causing more than 80,000 confirmed infections and 2,700 fatalities (as of Feb 27, 2020). Imported cases and transmission clusters of various sizes have been reported globally suggesting a pandemic is likely.Here, we explore how seasonal variation in transmissibility could modulate a SARS-CoV-2 pandemic. Data from routine diagnostics show a strong and consistent seasonal variation of the four endemic coronaviruses (229E, HKU1, NL63, OC43) and we parameterize our model for SARS-CoV-2 using these data. The model allows for many subpopulations of different size with variable parameters. Simulations of different scenarios show that plausible parameters result in a small peak in early 2020 in temperate regions of the Northern Hemisphere and a larger peak in winter 2020/2021. Variation in transmission and migration rates can result in substantial variation in prevalence between regions.While the uncertainty in parameters is large, the scenarios we explore show that transient reductions in the incidence rate might be due to a combination of seasonal variation and infection control efforts but do not necessarily mean the epidemic is contained. Seasonal forcing on SARS-CoV-2 should thus be taken into account in the further monitoring of the global transmission. The likely aggregated effect of seasonal variation, infection control measures, and transmission rate variation is a prolonged pandemic wave with lower prevalence at any given time, thereby providing a window of opportunity for better preparation of health care systems.

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Algorithm for preprocessing and unification of time series based on machine learning for data structuring

Программные системы и вычислительные методы ◽

10.7256/2454-0714.2020.3.33958 ◽

2020 ◽

pp. 40-50

Author(s):

Andrey Sergeevich Kopyrin ◽

Irina Leonidovna Makarova

Keyword(s):

Time Series ◽

Domain Knowledge ◽

Fuzzy Time Series ◽

Data Set ◽

Structure Information ◽

Combined Use ◽

Primary Documents ◽

Preliminary Preparation ◽

Single Data ◽

Different Sources

The subject of the research is the process of collecting and preliminary preparation of data from heterogeneous sources. Economic information is heterogeneous and semi-structured or unstructured in nature. Due to the heterogeneity of the primary documents, as well as the human factor, the initial statistical data may contain a large amount of noise, as well as records, the automatic processing of which may be very difficult. This makes preprocessing dynamic input data an important precondition for discovering meaningful patterns and domain knowledge, and making the research topic relevant.Data preprocessing is a series of unique tasks that have led to the emergence of various algorithms and heuristic methods for solving preprocessing tasks such as merge and cleanup, identification of variablesIn this work, a preprocessing algorithm is formulated that allows you to bring together into a single database and structure information on time series from different sources. The key modification of the preprocessing method proposed by the authors is the technology of automated data integration.The technology proposed by the authors involves the combined use of methods for constructing a fuzzy time series and machine lexical comparison on the thesaurus network, as well as the use of a universal database built using the MIVAR concept.The preprocessing algorithm forms a single data model with the ability to transform the periodicity and semantics of the data set and integrate data that can come from various sources into a single information bank.

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Nurse Leaders’ Reflections and Difficulties Fighting the NCP Epidemic： A Qualitative Study

10.21203/rs.3.rs-197693/v1 ◽

2021 ◽

Author(s):

Jing Wang ◽

Lihua Tang ◽

Huanyuan Da ◽

Huan Lu ◽

Xinping Shi ◽

...

Keyword(s):

Interpersonal Relationships ◽

Health Care Systems ◽

Nurse Leaders ◽

Fear Learning ◽

Survival Strategies ◽

Internal Conflicts ◽

The Future ◽

Care Systems ◽

Novel Coronavirus ◽

Outstanding Leadership

Abstract Objective. To understand the difficulties and survival strategies in nursing during NCP outbreak, and to reflect and summarize the experience. Background. Since December 2019, the highly infectious novel coronavirus pneumonia overwhelmed health care systems and medical workers who had to provide care in situations involving high personal risk and stress, some becoming infected and dying. Nurse leaders had to develop new strategies for nursing care. Methods. Using the phenomenological research method in qualitative research, 8 head nurses who participated in NCP treatment were interviewed in-depth, and then Colaizzi 7-step analysis method was used to summarize.Results. Working under great pressure, nursing leaders led the team through a period of crisis: shock and fear, learning in chaos, supporting nurses, and rewarding nurses. Conclusion. As important intervention performers in the crisis, nurse leaders need to have their own outstanding leadership to effectively manage internal conflicts and interpersonal relationships, strengthen teamwork training and establish supportive system so as to better deal with the management of similar public health events in the future. Relevance to clinical practice. Findings will assist nurse leaders to prepare themselves in the outbreak. It is hoped that the results of this study will contribute to disaster management in similar infectious outbreaks in the future.

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