Machine Intelligence-Based Trend Analysis of COVID-19 for Total Daily Confirmed Cases in Asia and Africa

2021 ◽  
pp. 164-185
Author(s):  
Yibeltal Meslie ◽  
Wegayehu Enbeyle ◽  
Binay Kumar Pandey ◽  
Sabyasachi Pramanik ◽  
Digvijay Pandey ◽  
...  
Keyword(s):  
Time Series ◽  
Trend Analysis ◽  
Moving Average ◽  
Time Frame ◽  
Machine Intelligence ◽  
Public Health Services ◽  
Average Order ◽  
Significance Level ◽  
The First Variation ◽  
Health Organization

COVID-19 is likely to pose a significant threat to healthcare, especially for disadvantaged populations due to the inadequate condition of public health services with people's lack of financial ways to obtain healthcare. The primary intention of such research was to investigate trend analysis for total daily confirmed cases with new corona virus (i.e., COVID-19) in the countries of Africa and Asia. The study utilized the daily recorded time series observed for two weeks (52 observations) in which the data is obtained from the world health organization (WHO) and world meter website. Univariate ARIMA models were employed. STATA 14.2 and Minitab 14 statistical software were used for the analysis at 5% significance level for testing hypothesis. Throughout time frame studied, because all four series are non-stationary at level, they became static after the first variation. The result revealed the appropriate time series model (ARIMA) for Ethiopia, Pakistan, India, and Nigeria were Moving Average order 2, ARIMA(1, 1, 1), ARIMA(2, 1, 1), and ARIMA (1, 1, 2), respectively.

Trend analysis and forecasting of the Gökırmak River streamflow (Turkey)

2020 ◽  
Vol 49 (3) ◽  
pp. 230-246 ◽  
Author(s):  
Gökhan Arslan ◽  
Semih Kale ◽  
Adem Yavuz Sönmez
Keyword(s):  
Time Series ◽  
Trend Analysis ◽  
Change Point ◽  
Moving Average ◽  
Arima Model ◽  
Change Point Analysis ◽  
Point Analysis ◽  
Monthly Streamflow ◽  
Model Time Series ◽  
Streamflow Trend

AbstractThe objective of this paper is to determine the trend and to estimate the streamflow of the Gökırmak River. The possible trend of the streamflow was forecasted using an autoregressive integrated moving average (ARIMA) model. Time series and trend analyses were performed using monthly streamflow data for the period between 1999 and 2014. Pettitt’s change point analysis was employed to detect the time of change for historical streamflow time series. Kendall’s tau and Spearman’s rho tests were also conducted. The results of the change point analysis determined the change point as 2008. The time series analysis showed that the streamflow of the river had a decreasing trend from the past to the present. Results of the trend analysis forecasted a decreasing trend for the streamflow in the future. The decreasing trend in the streamflow may be related to climate change. This paper provides preliminary knowledge of the streamflow trend for the Gökırmak River.

scholarly journals Trend analysis of rainfall using Mann-Kendall, Sen’s slope, moving average and least square techniques for Ahmednagar district of Maharashtra

2021 ◽  
Vol 17 (1) ◽  
pp. 19-25
Author(s):  
Virendra N. Barai ◽  
Rohini M. Kalunge
Keyword(s):  
Time Series ◽  
Trend Analysis ◽  
Moving Average ◽  
Rainfall Variability ◽  
Least Square ◽  
Annual Rainfall ◽  
Series Data ◽  
Sen’S Slope ◽  
Increasing Trend ◽  
The Impact

The long-term behaviour of rainfall is necessary to study over space with different time series viz., annual, monthly and weekly as it is one of the most significant climatic variables. Rainfall trend is an important tool which assesses the impact of climate change and provides direction to cope up with its adverse effects on the agriculture. Several studies have been performed to establish the pattern of rainfall over various time periods for different areas that can be used for better agricultural planning, water supply management, etc. Consequently, the present report, entitled “Trend analysis of rainfall in Ahmednagar district of Maharashtra,” was carried out. 13 tahsils of the district of Ahmednagar were selected to carry out trend analysis. The daily rainfall data of 33 years (1980- 2012) of all stations has been processed out study the rainfall variability. The Mann Kendall (MK) Test, Sen’s slope method, moving average method and least square method were used for analysis. The statistical analysis of whole reference time series data highlighted that July and August month contributes highest amount of rainfall at all tahsils. Regarding trend in annual rainfall, these four methods showed increasing trend at most of the tahsils whereas a decreasing trend only at Shrigonda tahsil. For monthly trend analysis, Kopargaon, Newasa, Shevgaon and Shrirampur tahsils showed an increasing trend during July. During August and September month, most of the tahsils i.e. Kopargaon, Nagar, Parner and Sangamner showed increasing trends, whereas in June, only Shrigonda tahsil showed decreasing trend.

scholarly journals Tuberculosis forecasting and temporal trends by sex and age in a high endemic city in northeastern Brazil: where were we before the Covid-19 pandemic?

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Hamilton Leandro Pinto de Andrade ◽  
Dulce Gomes ◽  
Antônio Carlos Vieira Ramos ◽  
Luiz Henrique Arroyo ◽  
Marcelino Santos-Neto ◽  
...  
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Arima Model ◽  
Temporal Trends ◽  
Northeastern Brazil ◽  
World Health ◽  
Age Group ◽  
Health Organization ◽  
The City ◽  
Series Study

Abstract Background The aim of this study was to describe the temporal trend of tuberculosis cases according to sex and age group and evidence the level of disease before the Covid-19 pandemic in a TB high endemic city. Methods This was a time series study carried out in a city in northeast Brazil. The population was composed of cases of tuberculosis, excluding those with HIV-positive status, reported between the years 2002 and 2018. An exploratory analysis of the monthly rates of tuberculosis detection, smoothed according to sex and age group, was performed. Subsequently, the progression of the trend and prediction of the disease were also characterized according to these aspects. For the trends forecast, the seasonal autoregressive linear integrated moving average (ARIMA) model and the usual Box-Jenkins method were used to choose the most appropriate models. Results A total of 1620 cases of tuberculosis were reported, with an incidence of 49.7 cases per 100,000 inhabitants in men and 34.0 per 100,000 in women. Regarding the incidence for both sexes, there was a decreasing trend, which was similar for age. Evidence resulting from the application of the time series shows a decreasing trend in the years 2002–2018, with a trend of stability. Conclusions The study evidenced a decreasing trend in tuberculosis, even before the Covid-19 pandemic, for both sex and age; however, in a step really slow from that recommended by the World Health Organization. According to the results, the disease would have achieved a level of stability in the city next years, however it might have been aggravated by the pandemic. These findings are relevant to evidence the serious behavior and trends of TB in a high endemic scenario considering a context prior to the Covid-19 pandemic.

ARIMA Algebra

2019 ◽  
pp. 11-47
Author(s):  
David McDowall ◽  
Richard McCleary ◽  
Bradley J. Bartos
Keyword(s):  
Time Series ◽  
White Noise ◽  
Moving Average ◽  
Nonstationary Process ◽  
Time Frame ◽  
Stationary Time Series ◽  
Underlying Process ◽  
The Past ◽  
Seasonal Model ◽  
The One

Chapter 2 introduces ARIMA algebra. With a few exceptions, this material mirrors the authors’ earlier work. The chapter begins with stationary time series processes – white noise, moving average (MA), and autoregressive (AR) processes – and moves predictably to non-stationary and multiplicative (seasonal) models. Stationarity implies that the time series process operated identically in the past as it does in the present and that it will continue to operate identically in the future. Without stationarity, the properties of the time series would vary with the time frame and no inferences about the underlying process would be possible. A seasonally nonstationary process drifts or trends in annual steps. The “best” seasonal model structure is the one that transforms the series to white noise with the fewest number of parameters.

scholarly journals Can innovative trend analysis identify trend change points?

2020 ◽  
Vol 1 (3) ◽  
pp. 6-15
Author(s):  
Sadık Alashan
Keyword(s):  
Time Series ◽  
Trend Analysis ◽  
Statistical Significance ◽  
Bootstrap Method ◽  
Change Points ◽  
Trend Detection ◽  
Significance Level ◽  
Related Data ◽  
Innovative Trend Analysis ◽  
June July

Trends in temperature series are the main cause of climate change. Because solar energy directs hydro-meteorological events and increasing variations in this resource change the balance between events such as evaporation, wind, and rainfall. There are many methods for calculating trends in a time series such as Mann-Kendall, Sen's slope estimator, Spearman's rho, linear regression and the new Sen innovative trend analysis (ITA). In addition, Mann-Kendall's variant, the sequential Mann Kendall, has been developed to identify trend change points; however, it is sensitive to related data as specified by some researchers. Şen_ITA is a new trend detection method and does not require independent and normally distributed time series, but has never been used to detect trend change points. In the literature, multiple, half-time and multi-durations ITA methods are used to calculate partial trends in a time series without identifying trend change points. In this study, trend change points are detected using the Şen_ITA method and named ITA_TCP. This approach may allow researchers to identify trend change points in a time series. Diyarbakır (Turkey) is selected as a study area, and ITA_TCP has detected trends and trends change points in monthly average temperatures. Although ITA detects only a significant upward trend in August, given the 95% statistical significance level, ITA_TCP shows three upward trends in June, July and August, and a decreasing trend in September. Critical trend slope values are obtained using the bootstrap method, which does not require the normal distribution assumption.

scholarly journals Can innovative trend analysis identify trend change points?

2020 ◽  
Vol 1 (3) ◽  
pp. 6-15
Author(s):  
Sadık Alashan
Keyword(s):  
Time Series ◽  
Trend Analysis ◽  
Statistical Significance ◽  
Bootstrap Method ◽  
Change Points ◽  
Trend Detection ◽  
Significance Level ◽  
Related Data ◽  
Innovative Trend Analysis ◽  
June July

Trends in temperature series are the main cause of climate change. Because solar energy directs hydro-meteorological events and increasing variations in this resource change the balance between events such as evaporation, wind, and rainfall. There are many methods for calculating trends in a time series such as Mann-Kendall, Sen's slope estimator, Spearman's rho, linear regression and the new Sen innovative trend analysis (ITA). In addition, Mann-Kendall's variant, the sequential Mann Kendall, has been developed to identify trend change points; however, it is sensitive to related data as specified by some researchers. Şen_ITA is a new trend detection method and does not require independent and normally distributed time series, but has never been used to detect trend change points. In the literature, multiple, half-time and multi-durations ITA methods are used to calculate partial trends in a time series without identifying trend change points. In this study, trend change points are detected using the Şen_ITA method and named ITA_TCP. This approach may allow researchers to identify trend change points in a time series. Diyarbakır (Turkey) is selected as a study area, and ITA_TCP has detected trends and trends change points in monthly average temperatures. Although ITA detects only a significant upward trend in August, given the 95% statistical significance level, ITA_TCP shows three upward trends in June, July and August, and a decreasing trend in September. Critical trend slope values are obtained using the bootstrap method, which does not require the normal distribution assumption.

scholarly journals ARIMA modelling of predicting COVID-19 infections

2020 ◽  
Author(s):  
W.Regis Anne ◽  
S.Carolin Jeeva
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Arima Model ◽  
Information Criterion ◽  
World Health ◽  
Director General ◽  
Global Pandemic ◽  
Novel Coronavirus ◽  
Health Organization ◽  
Short Term Prediction

AbstractThe World Health Organization (WHO) Director-General, Dr. Tedros Adhanom Ghebreyesus on March 11, 2020 declared the novel coronavirus (COVID-19) outbreak a global pandemic [4] the reason being the number of cases outside China increased 13-fold and the number of countries with cases increased threefold. In this paper a time series model to predict short-term prediction of the transmission of the exponentially growing COVID-19 time series is modelled and studied. Auto Regressive Integrated Moving Average (ARIMA) model prediction is performed on the number of cumulative cases over a time period and is validated over Akaike information criterion (AIC) statistics.

scholarly journals COVID 19: Real-time Forecasts of confirmed cases, active cases, and health infrastructure requirements for India and its states using the ARIMA model

2020 ◽  
Author(s):  
Rishabh Tyagi ◽  
Mahadev Bramhankar ◽  
Mohit Pandey ◽  
M Kishore
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
Arima Model ◽  
Public Health Care ◽  
World Health ◽  
Series Data ◽  
Health Infrastructure ◽  
Future Demands ◽  
Health Organization

AbstractBackgroundCOVID-19 is an emerging infectious disease which has been declared a Pandemic by the World Health Organization (WHO) on 11th March 2020. The Indian public health care system is already overstretched, and this pandemic is making things even worse. That is why forecasting cases for India is necessary to meet the future demands of the health infrastructure caused due to COVID-19.ObjectiveOur study forecasts the confirmed and active cases for COVID-19 until July mid, using time series Autoregressive Integrated Moving Average (ARIMA) model. Additionally, we estimated the number of isolation beds, Intensive Care Unit (ICU) beds and ventilators required for the growing number of COVID-19 patients.MethodsWe used ARIMA model for forecasting confirmed and active cases till the 15th July. We used time-series data of COVID-19 cases in India from 14th March to 22nd May. We estimated the requirements for ICU beds as 10%, ventilators as 5% and isolation beds as 85% of the active cases forecasted using the ARIMA model.ResultsOur forecasts indicate that India will have an estimated 7,47,772 confirmed cases (95% CI: 493943, 1001601) and 296,472 active cases (95% CI:196820, 396125) by 15th July. While Maharashtra will be the most affected state, having the highest number of active and confirmed cases, Punjab is expected to have an estimated 115 active cases by 15th July. India needs to prepare 2,52,001 isolation beds (95% CI: 167297, 336706), 29,647 ICU beds (95% CI: 19682, 39612), and 14,824 ventilator beds (95% CI: 9841, 19806).ConclusionOur forecasts show an alarming situation for India, and Maharashtra in particular. The actual numbers can go higher than our estimated numbers as India has a limited testing facility and coverage.

scholarly journals Tuberculosis Forecasting and Temporal Trends by Sex and Age in a High Endemic City in Northeast Brazil: Where Were we Before the Covid-19 Pandemic?

2021 ◽  
Author(s):  
Hamilton Leandro Pinto de Andrade ◽  
Dulce Gomes ◽  
Antônio Carlos Vieira Ramos ◽  
Luiz Henrique Arroyo ◽  
Marcelino Santos Neto ◽  
...  
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Arima Model ◽  
Temporal Trends ◽  
World Health ◽  
Hiv Positive ◽  
Northeast Brazil ◽  
Age Group ◽  
Health Organization ◽  
Series Study

Abstract Background: The aim of this study was to describe the temporal trend of tuberculosis cases according to sex and age group and evidence the level of disease before the Covid-19 pandemic in a city in northeast Brazil. Methods: This was a time series study carried out in a city in northeast Brazil. The population was composed of cases of tuberculosis, excluding those with HIV-positive status, reported between the years 2002 and 2018. An exploratory analysis of the monthly rates of tuberculosis detection, smoothed according to sex and age group, was performed. Subsequently, the progression of the trend and prediction of the disease were also characterized according to these aspects. For the trends forecast, the seasonal autoregressive linear integrated moving average (ARIMA) model and the usual Box-Jenkins method were used to choose the most appropriate models. Results: A total of 1,620 cases of tuberculosis were reported, with an incidence of 49.7 cases per 100,000 inhabitants in men and 34.0 per 100,000 in women. Regarding the incidence for both sexes, there was a decreasing trend, which was similar for age. Evidence resulting from the application of the time series shows a decreasing trend in the years 2002–2018, with a trend of stability. Conclusion: The study demonstrated a decreasing trend in tuberculosis, even before the Covid-19 pandemic, for both sex and age; however, in a step really slow that recommended by the World Health Organization. According to the results, the disease would have achieved a level of stability had it not been for the Covid-19 pandemic. The results are relevant to evidence the problem of TB that transcends its aspects prior to the Covid-19 pandemic.

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