scholarly journals The statistical analysis of daily data associated with different parameters of the New Coronavirus COVID-19 pandemic in Georgia and their short-term interval prediction from September 2020 to February 2021

2021 ◽  
Author(s):  
Avtandil G. Amiranashvili ◽  
Ketevan R. Khazaradze ◽  
Nino D. Japaridze
Keyword(s):  
Time Series ◽  
Statistical Analysis ◽  
Confidence Interval ◽  
Infection Rate ◽  
Maximum Effect ◽  
Regression Equations ◽  
Daily Mortality ◽  
Daily Data ◽  
The World ◽  
Upper Level

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.

scholarly journals The statistical analysis of daily data associated with different parameters of the New Coronavirus COVID-19 pandemic in Georgia and their short-term interval prediction in spring 2021

2021 ◽  
Author(s):  
Avtandil G. Amiranashvili ◽  
Ketevan R. Khazaradze ◽  
Nino D. Japaridze
Keyword(s):  
Statistical Analysis ◽  
Infection Rate ◽  
Maximum Effect ◽  
Similar Data ◽  
Regression Equations ◽  
Daily Mortality ◽  
Short Term ◽  
Mean Values ◽  
Daily Data ◽  
The World

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 - first half of March 2021. However, in April-May 2021 there was a significant deterioration in the epidemiological situation. In this work results of the next 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 March 01, 2021 to May 31, 2021 are presented. It also presents the results of the analysis of 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 monthly mean values of infection and deaths cases in spring 2021 (per 1 million population) was determined. Among 156 countries with population ≥ 1 million inhabitants in May 2021 Georgia was in the 11 place on new infection cases and in the 14 place on Death. A comparison between the daily mortality from Covid-19 in Georgia in spring 2021 with the average daily mortality rate in 2015-2019 shows, that the largest share value of D from mean death in 2015-2019 was 25.3 % (22.05.2021), the smallest 1.42 % (15.03.2021). Data about infection rate of the population of Georgia with Covid-19 according to traffic light system shown, that Georgia in April and May 2021 was in the red zone. 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 = 2171 (05.05.2021), R = 2038 (17.05.2021), D = 33 (22.05.2021), I = 8.05 % (04.05.2020). Maximum mean decade values of investigation parameters are following: C = 1258 (3 Decade of April 2021), R = 1283 (2 Decade of May 2021), D = 24 (2 Decade of May 2021), I = 6.54 % (1 Decade of May 2021). It was found that as with September 2020 to February 2021 [8], in spring 2021 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), deaths - V(D) and infection rate V(I) coronavirus-related cases in different decades of months in the spring 2021 were determined. Maximum mean decade values of investigation parameters are following: V(C) = +37 cases/day (1 Decade of April 2021), V(R) = +36 cases/day (3 Decade of April 2021), V(D) = +0.6 cases/day (3 Decade of April 2021), V(I) = + 0.17 %/ day (2 and 3 decades of April 2021). Cross-correlations analysis between confirmed COVID-19 cases with recovered and deaths cases shows, that the maximum effect of recovery is observed 9 and 13 days after infection, and deaths - after 12-17 days. Comparison of real and calculated predictions data of C, D and I in Georgia are carried out. It was found that two-week daily and mean two-week real values of C, D and I practically fall into the 67% - 99.99% confidence interval of these predicted values for the specified time periods. The comparison of data about C and D in Georgia (GEO) with similar data in Armenia (ARM), Azerbaijan (AZE), Russia (RUS), Turkey (TUR) and in the World (WRL) is also carried out. Key words: New Coronavirus COVID-19, statistical analysis, short-term prediction.

scholarly journals The statistical analysis of daily data associated with different parameters of the New Coronavirus COVID-19 pandemic in Georgia and their two-week interval prediction in summer 2021

2021 ◽  
Author(s):  
Avtandil G. Amiranashvili ◽  
Ketevan R. Khazaradze ◽  
Nino D. Japaridze
Keyword(s):  
Statistical Analysis ◽  
Infection Rate ◽  
Maximum Effect ◽  
Similar Data ◽  
Regression Equations ◽  
Daily Mortality ◽  
Mean Values ◽  
Daily Data ◽  
The World ◽  
Epidemiological Situation

AbstractThe lockdown introduced in Georgia on November 28, 2020 brought positive results. There were clearly positive tendencies in the spread of COVID-19 to February - first half of March 2021. However, in April-May 2021 there was a significant deterioration in the epidemiological situation. From June to August 2021, the epidemiological situation with Covid-19 in Georgia became very difficult.In this work results of the next 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 June 01, 2021 to August 31, 2021 are presented. It also presents the results of the analysis of two-week forecasting of the values of C, D and I. As earlier, 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 monthly mean values of infection and deaths cases in summer 2021 (per 1 million population) was determined. Among 159 countries with population ≥ 1 million inhabitants in August 2021 Georgia was in the 1 place on new infection cases and on Death.A comparison between the daily mortality from Covid-19 in Georgia in summer 2021 with the average daily mortality rate in 2015-2019 shows, that the largest share value of D from mean death in 2015-2019 was 66.0 % (26.08.2021 and 31.08.2021), the smallest 6.0 % (09.07.2021).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 = 6208 (17.08.2021), R = 6177 (29.08.2021), D = 79 (26.08.2021 and 31.08.2021), I = 13.0 % (17.08.2021). Maximum mean decade values of investigation parameters are following: C = 5019 (2 Decade of August 2021), R = 4822 (3 Decade of August 2021), D = 69 (3 Decade of August 2021), I = 10.88 % (2 Decade of August 2021).It was found that as with September 2020 to February 2021 and in spring 2021 [7,8], from June to August 2021 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), deaths - V(D) and infection rate V(I) coronavirus-related cases in different decades of months in the summer 2021 were determined. Maximum mean decade values of investigation parameters are following: V(C) = +134 cases/day (1 Decade of August 2021), V(R) = +134 cases/day (2 Decade of August 2021), V(D) = +2.4 cases/day (3 Decade of August 2021), V(I) = + 0.25 %/ day (1 decades of August 2021).Cross-correlations analysis between confirmed COVID-19 cases with recovered and deaths cases shows, that the maximum effect of recovery is observed 19 days after infection (RC=0.95), and deaths - after 16 and 18 days (RC=0.94). In Georgia in the summer 2021, the duration of the impact of the delta variant of the coronavirus on people (recovery, mortality) could be up to two months.Comparison of real and calculated predictions data of C, D and I in Georgia are carried out. It was found that in summer 2021 two-week daily and mean two-week real values of C, D and I practically fall into the 67% - 99.99% confidence interval of these predicted values.With September 1, 2021, it is started monthly forecasting of C, D and I values.As earlier, the comparison of data about C and D in Georgia (GEO) with similar data in Armenia (ARM), Azerbaijan (AZE), Russia (RUS), Turkey (TUR) and in the World (WRL) is also carried out.

scholarly journals The statistical analysis of daily data associated with different parameters of the New Coronavirus COVID-19 pandemic in Georgia and their monthly interval prediction from September 1, 2021 to December 31, 2021

2022 ◽  
Author(s):  
Avtandil G. Amiranashvili ◽  
Ketevan R. Khazaradze ◽  
Nino D. Japaridze
Keyword(s):  
Statistical Analysis ◽  
Infection Rate ◽  
Monthly Interval ◽  
Maximum Effect ◽  
Similar Data ◽  
Daily Mortality ◽  
Mean Values ◽  
Daily Data ◽  
The World ◽  
Investigation Period

The lockdown introduced in Georgia on November 28, 2020 contributed to positive trends in the spread of COVID-19 until February - the first half of March 2021. Then, in April-May 2021, the epidemiological situation worsened significantly, and from June to the end of December COVID - situation in Georgia was very difficult. In this work results of the next 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, 2021 to December 31, 2021 are presented. It also presents the results of the analysis of monthly forecasting of the values of C, D and I. As earlier, 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 monthly mean values of infection and deaths cases in investigation period (per 1 million population) was determined. Among 157 countries with population ≥ 1 million inhabitants in October 2021 Georgia was in the 4 place on new infection cases, and in September - in the 1 place on death. Georgia took the best place in terms of confirmed cases of diseases (thirteenth) in December, and in mortality (fifth) - in October. A comparison between the daily mortality from Covid-19 in Georgia from September 01, 2021 to December 31, 2021with the average daily mortality rate in 2015-2019 shows, that the largest share value of D from mean death in 2015-2019 was 76.8 % (September 03, 2021), the smallest 18.7 % (November 10, 2021). As in previous work [9,10] 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 = 6024 (November 3, 2021), R = 6017 (November 15, 2021), D = 86 (September 3, 2021), I = 12.04 % (November 24, 2021). Maximum mean decade values of investigation parameters are following: C = 4757 (1 Decade of November 2021), R = 4427 (3 Decade of November 2021), D = 76 (2 Decade of November 2021), I = 10.55 % (1 Decade of November 2021). It was found that as in spring and summer 2021 [9,10], from September to December 2021 the regression equations for the time variability of the daily values of C, R, D and I have the form of a tenth order polynomial. Mean values of speed of change of confirmed -V(C), recovered - V(R), deaths - V(D) and infection rate V(I) coronavirus-related cases in different decades of months for the indicated period of time were determined. Maximum mean decade values of investigation parameters are following: V(C) = +139 cases/day (1 Decade of October 2021), V(R) = +124 cases/day (3 Decade of October 2021), V(D) = +1.7 cases/day (3 Decade of October 2021), V(I) = + 0.20 %/ day (1 decades of October 2021). Cross-correlations analysis between confirmed COVID-19 cases with recovered and deaths cases shows, that from September 1, 2021 to November 30, 2021 the maximum effect of recovery is observed on 12 and 14 days after infection (CR=0.77 and 0.78 respectively), and deaths - after 7, 9, 11, 13 and 14 days (0.70≤CR≤0.72); from October 1, 2021 to December 31, 2021 - the maximum effect of recovery is observed on 14 days after infection (RC=0.71), and deaths - after 9 days (CR=0.43). In Georgia from September 1, 2021 to November 30, 2021 the duration of the impact of the delta variant of the coronavirus on people (recovery, mortality) could be up to 28 and 35 days respectively; from October 1, 2021 to December 31, 2021 - up to 21 and 29 days respectively. Comparison of daily real and calculated monthly predictions data of C, D and I in Georgia are carried out. It was found that in investigation period of time daily and mean monthly real values of C, D and I practically fall into the 67% - 99.99% confidence interval of these predicted values. Traditionally, the comparison of data about C and D in Georgia (GEO) with similar data in Armenia (ARM), Azerbaijan (AZE), Russia (RUS), Turkey (TUR) and in the World (WRL) is also carried out.

scholarly journals Analysis of twenty-week time-series of confirmed cases of New Coronavirus COVID-19 and their simple short-term prediction for Georgia and Neighboring Countries (Armenia, Azerbaijan, Turkey, Russia) in amid of a global pandemic

2020 ◽  
Author(s):  
Avtandil G. Amiranashvili ◽  
Ketevan R. Khazaradze ◽  
Nino D. Japaridze
Keyword(s):  
Time Series ◽  
Statistical Analysis ◽  
Confidence Interval ◽  
Global Pandemic ◽  
Daily Data ◽  
The Mean ◽  
Upper Level ◽  
Predicted Values ◽  
Study Sites ◽  
Short Term Prediction

Results of a comparative statistical analysis of the daily data associated with New coronavirus COVID-19 infection of confirmed cases (Č) of the population in Georgia (GEO), Armenia (ARM), Azerbaijan (AZE), Turkey (TUR) and Russia (RUS) amid a global pandemic (WLD) in the period from March 14 to July 31, 2020 are presented. 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, a correlation and autocorrelation analysis of the observational data was carried out, the periodicity in the time- series of Č were revealed, the calculation of the interval prediction values of Č taking into account the periodicity in the time-series of observations from August 1 to 31, 2020 (ARM, AZE) and from August 1 to September 11, 2020 (WLD, GEO, TUR, RUS) were carried out. Comparison of real and calculated predictions data on Č in the study sites from August 1 to August 31, 2020 is carried out. It was found that daily, monthly and mean weekly real values of Č for all the studied locations practically fall into the 99% confidence interval of the predicted values of Č for the specified time period. A dangerous situation with the spread of coronavirus infection may arise when the mean weekly values of Č of the 99% upper level of the forecast confidence interval are exceeded within 1-2 weeks. Favorable - when the mean weekly values of Č decrease below 99% of the lower level of the forecast confidence interval.

scholarly journals Coronavirus (COVID-19) Pandemic in Indonesia: Cases Overview and Daily Data Time Series using Naïve Forecast Method

2021 ◽  
Vol 3 (1) ◽  
pp. 1-8
Author(s):  
Annisa Puspa Kirana ◽  
Adhitya Bhawiyuga
Keyword(s):  
Time Series ◽  
Percentage Error ◽  
Forecast Method ◽  
Monthly Basis ◽  
Average Case ◽  
Monthly Average ◽  
Daily Data ◽  
Time Series Dataset ◽  
The World ◽  
Forecasting Analysis

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%.

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
Keyword(s):  
Time Series ◽  
Confidence Interval ◽  
South Korea ◽  
Growth Curve ◽  
Delay Time ◽  
Time Parameter ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
The World ◽  
Second Wave

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.

scholarly journals An Effective Prediction on COVID-19 Prevalence for India and Japan using Fbprophet Model

2021 ◽  
pp. 16-28
Author(s):  
Md. Mehedi Rahman Rana ◽  
Farjana Rahman ◽  
Jabed Al Faysal ◽  
Md. Anisur Rahman
Keyword(s):  
Time Series ◽  
Infection Rate ◽  
Satisfactory Result ◽  
Death Rate ◽  
Large Scale ◽  
Time Series Forecasting ◽  
Economic Life ◽  
Substantial Influence ◽  
The World

Coronavirus has become a significant concern for the whole world. It has had a substantial influence on our social and economic life. The infection rate is rapidly increasing at every moment throughout the world. At present, predicting coronavirus has become one of the challenging issues for us. As the pace of COVID-19 detection increases, so does the death rate. This research predicts the number of coronavirus detection and deaths using Fbprophet, a tool designed to assist in performing time series forecasting at a large scale. Two major affected countries, India and Japan, have been taken into consideration in our approach.  Using the prophet model, a prediction is performed on the number of total cases, new cases, total deaths and new deaths. This model works considerably well, and it has given a satisfactory result that may help the authority in taking early and appropriate decisions depending on the predicted COVID situation.

scholarly journals Time series analysis of tuberculosis registration rates from 2007 to 2019 and prediction: could tuberculosis control milestones be achieved in 2020 and 2025 in Henan Province, China?

2020 ◽  
Author(s):  
Yanqiu Zhang ◽  
Weibin Li ◽  
Jianguo Jiang ◽  
Guolong Zhang ◽  
Yan Zhuang ◽  
...  
Keyword(s):  
Time Series ◽  
Confidence Interval ◽  
Early Response ◽  
Information Management System ◽  
Early Summer ◽  
Henan Province ◽  
World Health ◽  
The World ◽  
Response Measures ◽  
Health Organization

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

scholarly journals Real-Time Forecasting of COVID-19 prevalence in India using ARIMA Model

2020 ◽  
Vol 4 (10) ◽  
pp. 78-83
Keyword(s):  
Time Series ◽  
Virus Disease ◽  
Moving Average ◽  
Time Series Prediction ◽  
Arima Model ◽  
Autoregressive Integrated Moving Average ◽  
Daily Data ◽  
The World ◽  
Increasing Trend ◽  
Short Term Prediction

Corona virus disease (COVID -19) has changed the world completely due to unavailability of its exact treatment. It has affected 215 countries in the world in which India is no exception where COVID patients are increasing exponentially since 15th of Feb. The objective of paper is to develop a model which can predict daily new cases in India. The autoregressive integrated moving average (ARIMA) models have been used for time series prediction. The daily data of new COVID-19 cases act as an exogenous variable in this framework. The daily data cover the sample period of 15th February, 2020 to 24th May, 2020. The time variable under study is a non-stationary series as 𝒚𝒕 is regressed with 𝒚𝒕−𝟏 and the coefficient is 1. The time series have clearly increasing trend. Results obtained revealed that the ARIMA model has a strong potential for short-term prediction. In PACF graph. Lag 1 and Lag 13 is significant. Regressed values implies Lag 1 and Lag 13 is significant in predicting the current values. The model predicted maximum COVID-19 cases in India at around 8000 during 5thJune to 20th June period. As per the model, the number of new cases shall start decreasing after 20th June in India only. The results will help governments to make necessary arrangements as per the estimated cases. The limitation of this model is that it is unable to predict jerks on either lower or upper side of daily new cases. So, in case of jerks re-estimation will be required.

scholarly journals Estimating COVID-19 mortality in Italy early in the COVID-19 pandemic

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Chirag Modi ◽  
Vanessa Böhm ◽  
Simone Ferraro ◽  
George Stein ◽  
Uroš Seljak
Keyword(s):  
Time Series ◽  
Confidence Interval ◽  
Time Series Analysis ◽  
Infection Rate ◽  
Mortality Data ◽  
Cruise Ship ◽  
Series Analysis ◽  
Case Ascertainment ◽  
Population Mortality ◽  
Overall Mortality

AbstractEstimating rates of COVID-19 infection and associated mortality is challenging due to uncertainties in case ascertainment. We perform a counterfactual time series analysis on overall mortality data from towns in Italy, comparing the population mortality in 2020 with previous years, to estimate mortality from COVID-19. We find that the number of COVID-19 deaths in Italy in 2020 until September 9 was 59,000–62,000, compared to the official number of 36,000. The proportion of the population that died was 0.29% in the most affected region, Lombardia, and 0.57% in the most affected province, Bergamo. Combining reported test positive rates from Italy with estimates of infection fatality rates from the Diamond Princess cruise ship, we estimate the infection rate as 29% (95% confidence interval 15–52%) in Lombardy, and 72% (95% confidence interval 36–100%) in Bergamo.

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