scholarly journals Delay Time Parameter and Its Confidence Interval of Predictive Time Series of COVID-19 Outbreak Between the First and the Second Wave

2021 ◽  
Vol 53 (2) ◽  
pp. 305-322
Author(s):  
Rapin Sunthornwat ◽  
Sirikanlaya Sookkhee

The outbreak of coronavirus disease 2019 (COVID-19) has become a major problem facing humans all around the world. For governments, in order to deal with the outbreak and protect the population, it is important to predict the number of infectious cases in the future to monitor the COVID-19 situation. This research aimed to compare the effectiveness of the logistic and the delay logistic time series in predicting the total number of infectious cases by using actual data from four countries, i.e. Thailand, South Korea, Egypt, and Nigeria. The total number of COVID-19 cases was collected during the first and the second wave of the COVID-19 outbreak. The validation and accuracy of the predictive growth curve time series were determined based on statistical values, i.e. the coefficient of determination and the root mean squared percentage error. It was found that the logistic time series was more appropriate for predicting the first wave in the four countries. For the second wave, the delay logistic time series was preferable. Moreover, the confidence interval based on Chebyshev’s inequality of delay time between the first and the second wave of the COVID-19 outbreak is also proposed.

Author(s):  
Annisa Puspa Kirana ◽  
Adhitya Bhawiyuga

At the end of December 2019, the virus emerges from Wuhan, China, and resulted in a severe outbreak in many cities in China and expanding globally, including Indonesia. Indonesia is the fourth most populated country globally. As of February 2021, Indonesia in the first rank of positive cases of COVID-19 in Southeast Asia, number 4 in Asia, and number 19 in the world. Our paper aims to provide detailed reporting and analysis of the COVID-19 case overview and forecasting that have hit Indonesia. Our time-series dataset from March 2020 to January 2021. Summary of cases studied included the number of positive cases and deaths due to COVID-19 on a daily or monthly basis. We use time series and forecasting analysis using the Naïve Forecast method.  The prediction is daily case prediction for six months starting from February 1, 2021, to June 30, 2021, using active cases daily COVID-19 data in all provinces in Indonesia. The highest monthly average case prediction is in June, which is 35,662 cases. Our COVID-19 prediction study has a mean absolute percentage error (MAPE) score of 15.85%.


2020 ◽  
Author(s):  
Yanqiu Zhang ◽  
Weibin Li ◽  
Jianguo Jiang ◽  
Guolong Zhang ◽  
Yan Zhuang ◽  
...  

Abstract Background: The World Health Organization (WHO) End TB Strategy meant that compared with 2015 baseline, the reduction in pulmonary tuberculosis(PTB) incidence rate should be 20% and 50% in 2020 and 2025, respectively. The incidence number of PTB in China accounted for 9% of the global total in 2018, which ranked the second high in the world. From 2007 to 2019, 854,672active PTB cases were registered and treated in Henan Province, China. We need to assess whether the WHO milestones could be achieved in Henan Province. Methods: The active PTB numbers in Henan Province from 2007 to2019, registered in Chinese Tuberculosis Information Management System (CTIMS) were analyzed to predict the active PTB registration rates in 2020 and 2025, which is conductive to early response measures to ensure the achievement of the WHO milestones. The time series model was created by monthly active PTB registration rates from 2007 to 2016, and the optimal model was verified by data from 2017 to 2019. Monthly and annual active PTB registration rates and 95% confidence interval (CI) from 2020 to 2025 were predicted. Results: High active PTB registration rates in March, April, May and June showed the seasonal variations. The exponential smoothing winter’s multiplication model was selected as the best-fitting model. The predicted values were approximately consistent with the observed ones from 2017 to 2019. The annual active PTB registration rates were predicted as 49.2 (95% CI: 36.0-62.5) and 34.3 (95% CI: 17.7-50.8) per 100 ,000 population in 2020 and 2025 , respectively. Compared with the active PTB registration rate in 2015, the reduction will reach 23.7% (95% CI: 3.1%-44.2%) and 46.9% (95% CI: 21.3%-72.5%) in 2020 and 2025, respectively. Conclusions: The high active PTB registration rates in spring and early summer indicates that high risk of tuberculosis infection in late autumn and winter in Henan Province. Without regard to the confidence interval, the first milestone of WHO End TB Strategy in 2020 will be achieved. However, the second milestone in 2025 will not be easily achieved unless there are early response measures in Henan Province, China. Trial registration: Not applicable


Author(s):  
A. U. Noman ◽  
S. Majumder ◽  
M. F. Imam ◽  
M. J. Hossain ◽  
F. Elahi ◽  
...  

Export plays an important role in promoting economic growth and development. The study is conducted to make an efficient forecasting of tea export from Bangladesh for mitigating the risk of export in the world market. Forecasting has been done by fitting Box-Jenkins type autoregressive integrated moving average (ARIMA) model. The best ARIMA model is selected by comparing the criteria- coefficient of determination (R2), root mean square error (RMSE), mean absolute percentage error (MAPE), mean absolute error (MAE) and Bayesian information criteria (BIC). Among the Box-Jenkins ARIMA type models for tea export the ARIMA (1,1,3) model is the most appropriate one for forecasting and the forecast values in thousand kilogram for the year 2017-18, 2018-19, 2019-20, 2020-21 and 2021-22, are 1096.48, 812.83, 1122.02, 776.25 and 794.33 with upper limit 1819.70, 1348.96, 1862.09, 1288.25, 1318.26 and lower limit 660.69, 489.78, 676.08, 467.74, 478.63, respectively. So, the result of this model may be helpful for the policymaker to make an export development plan for the country.


2021 ◽  
Vol 16 (1) ◽  
Author(s):  
David San-Martín-Roldán ◽  
Francisca Rojo-Lazo ◽  
Aracelis Calzadilla-Núñez ◽  
Pablo San-Martín-Roldán ◽  
Patricia Díaz-Calzadilla ◽  
...  

After months of blockades and restriction, the decision of the best time to end the lockdown after the first wave of the COVID-19 pandemic is the big question for health rectors. This study aimed to evaluate the characteristics and conditions for ending the blockade after the first wave of COVID-19. Data on the variables of interest were subjected to linear and non-linear regression studies to determine the curve that best explains the data. The coefficient of determination, the standard deviation of y in x, and the observed curve of the confidence interval were estimated. Regression which was estimated subsequently revealed the trend curve. The study found that all dependent variables tend to decrease over time in a quadratic fashion, except for the variable for new cases. In general, the R2 and mean absolute percentage error (MAPE) estimates were satisfactory: gradual and cautious steps should be taken before ending the lockdown. The results suggested that a surveillance of crucial indicators (e.g., incidence, prevalence, and PCR test positivity) should be maintained before lockdown is terminated. Moreover, the findings indicated that long-term preparations should be made to contain future waves of new cases.


BMJ ◽  
2021 ◽  
pp. e066768
Author(s):  
Nazrul Islam ◽  
Dmitri A Jdanov ◽  
Vladimir M Shkolnikov ◽  
Kamlesh Khunti ◽  
Ichiro Kawachi ◽  
...  

Abstract Objective To estimate the changes in life expectancy and years of life lost in 2020 associated with the covid-19 pandemic. Design Time series analysis. Setting 37 upper-middle and high income countries or regions with reliable and complete mortality data. Participants Annual all cause mortality data from the Human Mortality Database for 2005-20, harmonised and disaggregated by age and sex. Main outcome measures Reduction in life expectancy was estimated as the difference between observed and expected life expectancy in 2020 using the Lee-Carter model. Excess years of life lost were estimated as the difference between the observed and expected years of life lost in 2020 using the World Health Organization standard life table. Results Reduction in life expectancy in men and women was observed in all the countries studied except New Zealand, Taiwan, and Norway, where there was a gain in life expectancy in 2020. No evidence was found of a change in life expectancy in Denmark, Iceland, and South Korea. The highest reduction in life expectancy was observed in Russia (men: −2.33, 95% confidence interval −2.50 to −2.17; women: −2.14, −2.25 to −2.03), the United States (men: −2.27, −2.39 to −2.15; women: −1.61, −1.70 to −1.51), Bulgaria (men: −1.96, −2.11 to −1.81; women: −1.37, −1.74 to −1.01), Lithuania (men: −1.83, −2.07 to −1.59; women: −1.21, −1.36 to −1.05), Chile (men: −1.64, −1.97 to −1.32; women: −0.88, −1.28 to −0.50), and Spain (men: −1.35, −1.53 to −1.18; women: −1.13, −1.37 to −0.90). Years of life lost in 2020 were higher than expected in all countries except Taiwan, New Zealand, Norway, Iceland, Denmark, and South Korea. In the remaining 31 countries, more than 222 million years of life were lost in 2020, which is 28.1 million (95% confidence interval 26.8m to 29.5m) years of life lost more than expected (17.3 million (16.8m to 17.8m) in men and 10.8 million (10.4m to 11.3m) in women). The highest excess years of life lost per 100 000 population were observed in Bulgaria (men: 7260, 95% confidence interval 6820 to 7710; women: 3730, 2740 to 4730), Russia (men: 7020, 6550 to 7480; women: 4760, 4530 to 4990), Lithuania (men: 5430, 4750 to 6070; women: 2640, 2310 to 2980), the US (men: 4350, 4170 to 4530; women: 2430, 2320 to 2550), Poland (men: 3830, 3540 to 4120; women: 1830, 1630 to 2040), and Hungary (men: 2770, 2490 to 3040; women: 1920, 1590 to 2240). The excess years of life lost were relatively low in people younger than 65 years, except in Russia, Bulgaria, Lithuania, and the US where the excess years of life lost was >2000 per 100 000. Conclusion More than 28 million excess years of life were lost in 2020 in 31 countries, with a higher rate in men than women. Excess years of life lost associated with the covid-19 pandemic in 2020 were more than five times higher than those associated with the seasonal influenza epidemic in 2015.


Author(s):  
Yupaporn AREEPONG ◽  
Rapin SUNTHORNWAT

Since December 2019, the world has been facing an emerging infectious disease named coronavirus disease 2019. Thailand has also been affected by the spread of the coronavirus. The Thai government have announced policies to protect people, based on the emergency decree and curfew law for flattening the curve of the number of the coronavirus disease 2019 cases without vaccination in Thailand. This research estimated of the number of total infectious cases of coronavirus disease 2019 in Thailand. Two growth curves, including an exponential growth curve under a non-flattened curve policy (herd immunity policy without vaccination), and a logistic growth curve under a flattened curve policy without vaccination, were selected to estimate the parameters of the curves by the least square method to represent the number of the total infectious cases in Thailand. Moreover, the maximum infectious cases of coronavirus disease 2019 and the speed of spreading for coronavirus disease 2019 in Thailand were also explored. Based on the number of the total infectious cases of coronavirus disease 2019 in Thailand, the findings demonstrated that the coefficient of determination of the logistic growth curve was greater than the exponential growth curve and the root means squared percentage error of the logistic growth curve was less than the exponential growth curve. These results suggest that the logistic growth curve is suitable for describing the number of total infectious cases of coronavirus disease 2019 in Thailand under the fattened curve policy. GRAPHICAL ABSTRACT


Author(s):  
Nor Farah Atiqah Binti Ahmad ◽  
Sobri Harun ◽  
Haza Nuzly Abdull Hamed ◽  
Muhamad Askari ◽  
Zulkiflee Ibrahim ◽  
...  

The search for an accurate evapotranspiration (ET) continues when the world has responsibility to cope with the water scarcity issue, population outgrown and uncertain change of weather. Measuring actual evapotranspiration (ETa) can be tedious and requires a lot of time and cost. Therefore, numbers of empirical ET models have been developed to overcome this problem. The Valiantzas’ models are quite familiar to the hydrologist community as it has been developed based on Penman evaporation equation. This paper presents the evaluation on the selected six Valiantzas’ models by comparing to Food and Agricultural Organization Penman-Montieth (FAO-PM) empirical model in estimating ET in the Peninsular Malaysia. Seventeen meteorological stations around Peninsular Malaysia with data gathered from 1987 till 2003 were tested. The performance for each model was evaluated by root mean square error (RMSE), coefficient of determination (R2), percentage error (PE) and mean bias error (MBE). All the six models showed good agreement to FAO-PM with R2> 0.90. The PETval2 model which gave R2 of 0.97 was the best performer with the lowest RMSE, PE and MBE of 0.26, 5.5% and 0.14, respectively. The good and sensible performance on the ET estimation displayed by Valiantzas’ model may promise an accurate method for calculation on the water management for irrigation and catchment studies.


2020 ◽  
Vol 2 (2) ◽  
pp. 1-14
Author(s):  
Agus Salihin

Abstract The purpose of this study was to determine the effect of the Dow Jones Islamic Market Index (DJIM) and World Gold Prices on the Jakarta Islamic Index (JII) for the 2014-2018 Period. This type of research uses descriptive quantitative research. The data used is secondary data in the form of a time series with a period of 5 (five) years namely 2014 to 2018. The analysis technique uses multiple linear regression with the help of the SPSS persi program 16.0. Based on the results of the analysis it can be concluded that partially and simultaneously the Dow Jones Islamic Market Index (DJIM) and World Gold Prices have a significant positive effect on the Jakarta Islamic Index (JII) for the 2014-2018 Period. From the results of the analysis of the coefficient of determination (Adjusted R Square) the Dow Jones Islamic Market Index (DJIM) variable, and the World Gold Price can affect the Jakarta Islamic Index for the 2014-2018 period by 34.8% while the remaining 65.2% is influenced by other variables Keywords: Dow Jones Islamic Market Index, World Gold Prices, and Jakarta Islamic Index Abstrak Tujuan penelitian ini untuk mengetahui Pengaruh Dow Jones Islamic Market Indeks (DJIM) dan Harga Emas Dunia Terhadap Jakarta Islamic Indeks (JII) Priode 2014-2018. Jenis penelitian ini menggunakan penelitian kuantitatif deskriptif. Data yang digunakan merupakan data sekunder berupa rangkai waktu (time series)  dengan priode 5 (lima) tahun yakni tahun 2014 sampai dengan tahun 2018. Teknik analisis menggunakan regresi linier berganda dengan bantuan program SPSS persi 16.0. Berdasarkan hasil analisis dapat disimpulkan bahwa secara parsial dan simultan Dow Jones Islamic Market Indeks (DJIM) dan Harga Emas Dunia berpengaruh positif signifikan terhadap Jakarta Islamic Indeks (JII) Priode 2014-2018. Dari hasil analisis koefisien determinasi (Adjusted R Square) variabel Dow Jones Islamic Market Indeks (DJIM), dan Harga Emas Dunia dapat mempengaruhi Jakarta Islamic Indeks priode 2014-2018 sebesar 34,8%  sedangkan sisanya 65.2% dipengaruhi oleh variabel lain Kata Kunci : Dow Jones Islamic Market Indeks, Harga Emas Dunia, dan Jakarta Islamic Indeks


2018 ◽  
Vol 9 (1) ◽  
Author(s):  
Muhammad Reza ◽  
Sobri Harun ◽  
Muhammad Askari

This paper presents the application of linear and non-linear time series modeling approaches for simulating and forecasting streamflow at three stations located in three different rivers namely Kurau River, Ara River and Krian River of Bukit Merah watershed of Malaysia. The performance of linear autoregressive integrated moving average (ARIMA) model and non-linear artificial neural networks (ANN) model in forecasting the monthly streamflow of Malaysian river basins has been evaluated based on mean absolute percentage error (MAPE), root mean squared error (RMSE) and coefficient of determination (R2). The results show that both ARIMA and ANN methods are suitable for streamflow forecasting. However, ANN is better than ARIMA in dealing with short-memory streamflow data. In addition, ANN method is more flexible to use against the inconsistent data.Keywords: time series, streamflow forecasting, ARIMA, ANN, Bukit Merah


2021 ◽  
Author(s):  
Avtandil G. Amiranashvili ◽  
Ketevan R. Khazaradze ◽  
Nino D. Japaridze

AbstractIn the autumn - winter period of 2020, very difficult situation arose in Georgia with the course of the pandemic of the New Coronavirus COVID-19. In particular, in November-December period of 2020, Georgia eight days was rank a first in the world in terms of COVID-19 infection rate per 1 million populations.In this work results of a statistical analysis of the daily data associated with New Coronavirus COVID-19 infection of confirmed (C), recovered (R), deaths (D) and infection rate (I) cases of the population of Georgia in the period from September 01, 2020 to February 28, 2021 (for I - from December 05, 2020 to February 28, 2021) are presented. It also presents the results of the analysis of ten-day (decade) and two-week forecasting of the values of C, D and I, the information was regularly sent to the National Center for Disease Control & Public Health of Georgia and posted on the Facebook page https://www.facebook.com/Avtandil1948/.The analysis of data is carried out with the use of the standard statistical analysis methods of random events and methods of mathematical statistics for the non-accidental time-series of observations. In particular, the following results were obtained.Georgia’s ranking in the world for Covid-19 infection and deaths from September 1, 2020 to February 28, 2021 (per 1 million population) was determined. Georgia was in the first place: Infection - November 21, 22, 27, 28 and December 04, 05, 06, 09, 2020; Death - November 22, 2020.A comparison between the daily mortality from Covid-19 in Georgia from September 1, 2020 to February 28, 2021 with the average daily mortality rate in 2015-2019 was made. The largest share value of D from mean death in 2015-2019 was 36.9% (19.12.2020), the smallest - 0.9% (21.09.2020, 24.09.2020 - 26.09.2020).The statistical analysis of the daily and decade data associated with coronavirus COVID-19 pandemic of confirmed, recovered, deaths cases and infection rate of the population of Georgia are carried out. Maximum daily values of investigation parameters are following: C = 5450 (05.12.2020), R = 4599 (21.12.2020), D = 53 (19.12.2020), I = 30.1 % (05.12.2020). Maximum mean decade values of investigation parameters are following: C = 4337 (1 Decade of December 2020), R = 3605 (3 Decade of November 2020), D = 44 (2 Decade of December 2020), I = 26.8 % (1 Decade of December 2020).It was found that the regression equations for the time variability of the daily values of C, R and D have the form of a tenth order polynomial.Mean values of speed of change of confirmed -V(C), recovered - V(R) and deaths - V(D) coronavirus-related cases in different decades of months from September 2020 to February 2021 were determined. Maximum mean decade values of investigation parameters are following: V(C) = +104 cases/day (1 Decade of November 2020), V(R) = +94 cases/day (3 Decade of October and 1 Decade of November 2020), V(D) = +0.9 cases/day (1 Decade of November 2020).Cross-correlations analysis between confirmed COVID-19 cases with recovered and deaths cases from 05.12.2020 to 28.02.2021 is carried out. So, the maximum effect of recovery is observed 13-14 days after infection, and deaths - after 13-14 and 17-18 days.The scale of comparing real data with the predicted ones and assessing the stability of the time series of observations in the forecast period in relation to the pre-predicted one was offered.Comparison of real and calculated predictions data of C (23.09.2020-28.02.2021), D (01.01.2021-28.02.2021) and I (01.02.2021-28.02.2021) in Georgia are carried out. It was found that daily, mean decade and two-week real values of C, D and I practically falls into the 67% - 99.99% confidence interval of these predicted values for the specified time periods (except the forecast of C for 13.10.2020-22.10.2020, when a nonlinear process of growth of C values was observed and its real values have exceeded 99.99% of the upper level of the confidence interval of forecast).Alarming deterioration with the spread of coronavirus parameters may arise when their daily values are higher 99.99% of upper level of the forecast confidence interval. Excellent improvement - when these daily values are below 99.99% of the lower level of the forecast confidence interval.The lockdown introduced in Georgia on November 28, 2020 brought positive results. There are clearly positive tendencies in the spread of COVID-19 to February 2021.


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