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Article: COVID-19: Forecasting confirmed cases and deaths with a simple time series model.

Petropoulos, Fotios / Makridakis, Spyros / Stylianou, Neophytos

International journal of forecasting

2020 Volume 38, Issue 2, Page(s) 439–452

Abstract: ... of the two variables we predict (global confirmed cases and deaths) as well as their uncertainty. We present ... the timeline of producing and evaluating 10-day-ahead forecasts over a period of four months. Our simple model ... instead, we propose a statistical, time series approach to modelling and predicting the short-term ...

Abstract	Forecasting the outcome of outbreaks as early and as accurately as possible is crucial for decision-making and policy implementations. A significant challenge faced by forecasters is that not all outbreaks and epidemics turn into pandemics, making the prediction of their severity difficult. At the same time, the decisions made to enforce lockdowns and other mitigating interventions versus their socioeconomic consequences are not only hard to make, but also highly uncertain. The majority of modeling approaches to outbreaks, epidemics, and pandemics take an epidemiological approach that considers biological and disease processes. In this paper, we accept the limitations of forecasting to predict the long-term trajectory of an outbreak, and instead, we propose a statistical, time series approach to modelling and predicting the short-term behavior of COVID-19. Our model assumes a multiplicative trend, aiming to capture the continuation of the two variables we predict (global confirmed cases and deaths) as well as their uncertainty. We present the timeline of producing and evaluating 10-day-ahead forecasts over a period of four months. Our simple model offers competitive forecast accuracy and estimates of uncertainty that are useful and practically relevant.
Language	English
Publishing date	2020-12-04
Publishing country	Netherlands
Document type	Journal Article
ISSN	0169-2070
ISSN	0169-2070
DOI	10.1016/j.ijforecast.2020.11.010
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Insight into vaccination and meteorological factors on daily COVID-19 cases and mortality in Bangladesh.

Hasan, Mohammad Nayeem / Islam, Md Aminul / Sangkham, Sarawut / Werkneh, Adhena Ayaliew / Hossen, Foysal / Haque, Md Atiqul / Alam, Mohammad Morshad / Rahman, Md Arifur / Mukharjee, Sanjoy Kumar / Chowdhury, Tahmid Anam / Sosa-Hernández, Juan Eduardo / Jakariya, Md / Ahmed, Firoz / Bhattacharya, Prosun / Sarkodie, Samuel Asumadu

Groundwater for sustainable development

2023 Volume 21, Page(s) 100932

Abstract: ... COVID-19 cases and deaths changed over time while assessing meteorological characteristics ... Moving Average with explanatory variables (ARIMAX), and Automatic forecasting time-series model (Prophet ... in both cases and deaths model is significantly negative (for cases: 1.19, 95%CI: 2.35 to -0.38 and for deaths ...

Abstract	The ongoing COVID-19 contagious disease caused by SARS-CoV-2 has disrupted global public health, businesses, and economies due to widespread infection, with 676.41 million confirmed cases and 6.77 million deaths in 231 countries as of February 07, 2023. To control the rapid spread of SARS-CoV-2, it is crucial to determine the potential determinants such as meteorological factors and their roles. This study examines how COVID-19 cases and deaths changed over time while assessing meteorological characteristics that could impact these disparities from the onset of the pandemic. We used data spanning two years across all eight administrative divisions, this is the first of its kind--showing a connection between meteorological conditions, vaccination, and COVID-19 incidences in Bangladesh. We further employed several techniques including Simple Exponential Smoothing (SES), Auto-Regressive Integrated Moving Average (ARIMA), Auto-Regressive Integrated Moving Average with explanatory variables (ARIMAX), and Automatic forecasting time-series model (Prophet). We further analyzed the effects of COVID-19 vaccination on daily cases and deaths. Data on COVID-19 cases collected include eight administrative divisions of Bangladesh spanning March 8, 2020, to January 31, 2023, from available online servers. The meteorological data include rainfall (mm), relative humidity (%), average temperature (°C), surface pressure (kPa), dew point (°C), and maximum wind speed (m/s). The observed wind speed and surface pressure show a significant negative impact on COVID-19 cases (-0.89, 95% confidence interval (CI): 1.62 to -0.21) and (-1.31, 95%CI: 2.32 to -0.29), respectively. Similarly, the observed wind speed and surface pressure show a significant negative impact on COVID-19 deaths (-0.87, 95% CI: 1.54 to -0.21) and (-3.11, 95%CI: 4.44 to -1.25), respectively. The impact of meteorological factors is almost similar when vaccination information is included in the model. However, the impact of vaccination in both cases and deaths model is significantly negative (for cases: 1.19, 95%CI: 2.35 to -0.38 and for deaths: 1.55, 95%CI: 2.88 to -0.43). Accordingly, vaccination effectively reduces the number of new COVID-19 cases and fatalities in Bangladesh. Thus, these results could assist future researchers and policymakers in the assessment of pandemics, by making thorough efforts that account for COVID-19 vaccinations and meteorological conditions.
Language	English
Publishing date	2023-03-02
Publishing country	Netherlands
Document type	Journal Article
ISSN	2352-801X
ISSN (online)	2352-801X
DOI	10.1016/j.gsd.2023.100932
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Time series analysis of COVID-19 infection curve: A change-point perspective.

Jiang, Feiyu / Zhao, Zifeng / Shao, Xiaofeng

Journal of econometrics

2020 Volume 232, Issue 1, Page(s) 1–17

Abstract: In this paper, we model the trajectory of the cumulative confirmed cases and deaths of COVID-19 ... COVID-19 cases and deaths for 30 major countries and discover interesting patterns with potentially ... two-stage forecasting scheme for COVID-19 and demonstrate its promising performance in predicting ...

Abstract	In this paper, we model the trajectory of the cumulative confirmed cases and deaths of COVID-19 (in log scale) via a piecewise linear trend model. The model naturally captures the phase transitions of the epidemic growth rate via change-points and further enjoys great interpretability due to its semiparametric nature. On the methodological front, we advance the nascent self-normalization (SN) technique (Shao, 2010) to testing and estimation of a single change-point in the linear trend of a nonstationary time series. We further combine the SN-based change-point test with the NOT algorithm (Baranowski et al., 2019) to achieve multiple change-point estimation. Using the proposed method, we analyze the trajectory of the cumulative COVID-19 cases and deaths for 30 major countries and discover interesting patterns with potentially relevant implications for effectiveness of the pandemic responses by different countries. Furthermore, based on the change-point detection algorithm and a flexible extrapolation function, we design a simple two-stage forecasting scheme for COVID-19 and demonstrate its promising performance in predicting cumulative deaths in the U.S.
Keywords	covid19
Language	English
Publishing date	2020-07-30
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	1460617-3
ISSN	0304-4076
ISSN	0304-4076
DOI	10.1016/j.jeconom.2020.07.039
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Article ; Online: Insight into vaccination and meteorological factors on daily COVID-19 cases and mortality in Bangladesh

Hasan, Mohammad Nayeem / Islam, Md. Aminul / Sangkham, Sarawut / Werkneh, Adhena Ayaliew / Hossen, Foysal / Haque, Md Atiqul / Alam, Mohammad Morshad / Rahman, Md. Arifur / Mukharjee, Sanjoy Kumar / Chowdhury, Tahmid Anam / Sosa-Hernández, Juan Eduardo / Jakariya, Md / Ahmed, Firoz / Bhattacharya, Prosun / Sarkodie, Samuel Asumadu

Groundwater for Sustainable Development. 2023 May, v. 21 p.100932-

2023

Abstract	The ongoing COVID-19 contagious disease caused by SARS-CoV-2 has disrupted global public health, businesses, and economies due to widespread infection, with 676.41 million confirmed cases and 6.77 million deaths in 231 countries as of February 07, 2023. To control the rapid spread of SARS-CoV-2, it is crucial to determine the potential determinants such as meteorological factors and their roles. This study examines how COVID-19 cases and deaths changed over time while assessing meteorological characteristics that could impact these disparities from the onset of the pandemic. We used data spanning two years across all eight administrative divisions, this is the first of its kind––showing a connection between meteorological conditions, vaccination, and COVID-19 incidences in Bangladesh. We further employed several techniques including Simple Exponential Smoothing (SES), Auto-Regressive Integrated Moving Average (ARIMA), Auto-Regressive Integrated Moving Average with explanatory variables (ARIMAX), and Automatic forecasting time-series model (Prophet). We further analyzed the effects of COVID-19 vaccination on daily cases and deaths. Data on COVID-19 cases collected include eight administrative divisions of Bangladesh spanning March 8, 2020, to January 31, 2023, from available online servers. The meteorological data include rainfall (mm), relative humidity (%), average temperature (°C), surface pressure (kPa), dew point (°C), and maximum wind speed (m/s). The observed wind speed and surface pressure show a significant negative impact on COVID-19 cases (−0.89, 95% confidence interval (CI): 1.62 to −0.21) and (−1.31, 95%CI: 2.32 to −0.29), respectively. Similarly, the observed wind speed and surface pressure show a significant negative impact on COVID-19 deaths (−0.87, 95% CI: 1.54 to −0.21) and (−3.11, 95%CI: 4.44 to −1.25), respectively. The impact of meteorological factors is almost similar when vaccination information is included in the model. However, the impact of vaccination in both cases and deaths model is significantly negative (for cases: 1.19, 95%CI: 2.35 to −0.38 and for deaths: 1.55, 95%CI: 2.88 to −0.43). Accordingly, vaccination effectively reduces the number of new COVID-19 cases and fatalities in Bangladesh. Thus, these results could assist future researchers and policymakers in the assessment of pandemics, by making thorough efforts that account for COVID-19 vaccinations and meteorological conditions.
Keywords	COVID-19 infection ; Severe acute respiratory syndrome coronavirus 2 ; confidence interval ; dewpoint ; groundwater ; meteorological data ; models ; mortality ; pandemic ; public health ; rain ; relative humidity ; sustainable development ; temperature ; time series analysis ; vaccination ; wind speed ; Bangladesh ; Meteorological factors ; COVID-19 ; Mathematical models ; Temperature and rainfall ; SARS-CoV-2
Language	English
Dates of publication	2023-05
Publishing place	Elsevier B.V.
Document type	Article ; Online
ISSN	2352-801X
DOI	10.1016/j.gsd.2023.100932
Database	NAL-Catalogue (AGRICOLA)

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Article: Evolutionary modelling of the COVID-19 pandemic in fifteen most affected countries.

Salgotra, Rohit / Gandomi, Mostafa / Gandomi, Amir H

Chaos, solitons, and fractals

2020 Volume 140, Page(s) 110118

Abstract: ... functions and provide highly reliable results for time series prediction of COVID-19 in these countries. ... COVID-19 or SARS-Cov-2, affecting 6 million people and more than 300,000 deaths, the global ... namely confirmed cases (CC) and death cases (DC) were taken into consideration to estimate, how ...

Abstract	COVID-19 or SARS-Cov-2, affecting 6 million people and more than 300,000 deaths, the global pandemic has engulfed more than 90% countries of the world. The virus started from a single organism and is escalating at a rate of 3% to 5% daily and seems to be a never ending process. Understanding the basic dynamics and presenting new predictions models for evaluating the potential effect of the virus is highly crucial. In present work, an evolutionary data analytics method called as Genetic programming (GP) is used to mathematically model the potential effect of coronavirus in 15 most affected countries of the world. Two datasets namely confirmed cases (CC) and death cases (DC) were taken into consideration to estimate, how transmission varied in these countries between January 2020 and May 2020. Further, a percentage rise in the number of daily cases is also shown till 8 June 2020 and it is expected that Brazil will have the maximum rise in CC and USA have the most DC. Also, prediction of number of new CC and DC cases for every one million people in each of these countries is presented. The proposed model predicted that the transmission of COVID-19 in China is declining since late March 2020; in Singapore, France, Italy, Germany and Spain the curve has stagnated; in case of Canada, South Africa, Iran and Turkey the number of cases are rising slowly; whereas for USA, UK, Brazil, Russia and Mexico the rate of increase is very high and control measures need to be taken to stop the chains of transmission. Apart from that, the proposed prediction models are simple mathematical equations and future predictions can be drawn from these general equations. From the experimental results and statistical validation, it can be said that the proposed models use simple linkage functions and provide highly reliable results for time series prediction of COVID-19 in these countries.
Keywords	covid19
Language	English
Publishing date	2020-07-17
Publishing country	England
Document type	Journal Article
ZDB-ID	2003919-0
ISSN	1873-2887 ; 0960-0779
ISSN (online)	1873-2887
ISSN	0960-0779
DOI	10.1016/j.chaos.2020.110118
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Book ; Online: Time Series Analysis of COVID-19 Infection Curve

Jiang, Feiyu / Zhao, Zifeng / Shao, Xiaofeng

A Change-Point Perspective

2020

Abstract	In this paper, we model the trajectory of the cumulative confirmed cases and deaths of COVID-19 (in log scale) via a piecewise linear trend model. The model naturally captures the phase transitions of the epidemic growth rate via change-points and further enjoys great interpretability due to its semiparametric nature. On the methodological front, we advance the nascent self-normalization (SN) technique (Shao, 2010) to testing and estimation of a single change-point in the linear trend of a nonstationary time series. We further combine the SN-based change-point test with the NOT algorithm (Baranowski et al., 2019) to achieve multiple change-point estimation. Using the proposed method, we analyze the trajectory of the cumulative COVID-19 cases and deaths for 30 major countries and discover interesting patterns with potentially relevant implications for effectiveness of the pandemic responses by different countries. Furthermore, based on the change-point detection algorithm and a flexible extrapolation function, we design a simple two-stage forecasting scheme for COVID-19 and demonstrate its promising performance in predicting cumulative deaths in the U.S.
Keywords	Economics - Econometrics ; Physics - Physics and Society ; Statistics - Applications ; covid19
Subject code	310
Publishing date	2020-07-09
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Empirical Evaluation of Alternative Time-Series Models for COVID-19 Forecasting in Saudi Arabia.

Al-Turaiki, Isra / Almutlaq, Fahad / Alrasheed, Hend / Alballa, Norah

International journal of environmental research and public health

2021 Volume 18, Issue 16

Abstract: ... presents an empirical evaluation of several time-series models for forecasting COVID-19 cases, recoveries ... model had a smaller prediction error in forecasting confirmed cases, which is consistent with results ... in order to determine how to combat and track its spread. COVID-19 data is an example of time-series data ...

Abstract	COVID-19 is a disease-causing coronavirus strain that emerged in December 2019 that led to an ongoing global pandemic. The ability to anticipate the pandemic's path is critical. This is important in order to determine how to combat and track its spread. COVID-19 data is an example of time-series data where several methods can be applied for forecasting. Although various time-series forecasting models are available, it is difficult to draw broad theoretical conclusions regarding their relative merits. This paper presents an empirical evaluation of several time-series models for forecasting COVID-19 cases, recoveries, and deaths in Saudi Arabia. In particular, seven forecasting models were trained using autoregressive integrated moving average, TBATS, exponential smoothing, cubic spline, simple exponential smoothing Holt, and HoltWinters. The models were built using publicly available daily data of COVID-19 during the period of 24 March 2020 to 5 April 2021 reported in Saudi Arabia. The experimental results indicate that the ARIMA model had a smaller prediction error in forecasting confirmed cases, which is consistent with results reported in the literature, while cubic spline showed better predictions for recoveries and deaths. As more data become available, a fluctuation in the forecasting-accuracy metrics was observed, possibly due to abrupt changes in the data.
MeSH term(s)	COVID-19 ; Forecasting ; Humans ; Models, Statistical ; Pandemics ; SARS-CoV-2 ; Saudi Arabia/epidemiology
Language	English
Publishing date	2021-08-16
Publishing country	Switzerland
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2175195-X
ISSN	1660-4601 ; 1661-7827
ISSN (online)	1660-4601
ISSN	1661-7827
DOI	10.3390/ijerph18168660
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Article: Time series analysis of COVID-19 infection curve: A change-point perspective

Jiang, Feiyu / Zhao, Zifeng / Shao, Xiaofeng

Journal of econometrics

Abstract	In this paper, we model the trajectory of the cumulative confirmed cases and deaths of COVID-19 (in log scale) via a piecewise linear trend model. The model naturally captures the phase transitions of the epidemic growth rate via change-points and further enjoys great interpretability due to its semiparametric nature. On the methodological front, we advance the nascent self-normalization (SN) technique (Shao, 2010) to testing and estimation of a single change-point in the linear trend of a nonstationary time series. We further combine the SN-based change-point test with the NOT algorithm (Baranowski et al., 2019) to achieve multiple change-point estimation. Using the proposed method, we analyze the trajectory of the cumulative COVID-19 cases and deaths for 30 major countries and discover interesting patterns with potentially relevant implications for effectiveness of the pandemic responses by different countries. Furthermore, based on the change-point detection algorithm and a flexible extrapolation function, we design a simple two-stage forecasting scheme for COVID-19 and demonstrate its promising performance in predicting cumulative deaths in the U.S.
Keywords	covid19
Publisher	WHO
Document type	Article
Note	WHO #Covidence: #688879
Database	COVID19

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Article: Time series analysis of COVID-19 infection curve: A change-point perspective

Jiang, Feiyu / Zhao, Zifeng / Shao, Xiaofeng

Journal of econometrics. 2020 July 06,

2020

Abstract	In this paper, we model the trajectory of the cumulative confirmed cases and deaths of COVID-19 (in log scale) via a piecewise linear trend model. The model naturally captures the phase transitions of the epidemic growth rate via change-points and further enjoys great interpretability due to its semiparametric nature. On the methodological front, we advance the nascent self-normalization (SN) technique (Shao, 2010) to testing and estimation of a single change-point in the linear trend of a nonstationary time series. We further combine the SN-based change-point test with the NOT algorithm (Baranowski et al., 2019) to achieve multiple change-point estimation. Using the proposed method, we analyze the trajectory of the cumulative COVID-19 cases and deaths for 30 major countries and discover interesting patterns with potentially relevant implications for effectiveness of the pandemic responses by different countries. Furthermore, based on the change-point detection algorithm and a flexible extrapolation function, we design a simple two-stage forecasting scheme for COVID-19 and demonstrate its promising performance in predicting cumulative deaths in the U.S.
Keywords	COVID-19 infection ; algorithms ; econometric models ; economic analysis ; economic theory ; pandemic ; phase transition ; prediction ; time series analysis ; United States
Language	English
Dates of publication	2020-0706
Publishing place	Elsevier B.V.
Document type	Article
Note	Pre-press version
ZDB-ID	1460617-3
ISSN	0304-4076
ISSN	0304-4076
DOI	10.1016/j.jeconom.2020.07.039
Database	NAL-Catalogue (AGRICOLA)

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Article: Evolutionary modelling of the COVID-19 pandemic in fifteen most affected countries

Salgotra, Rohit / Gandomi, Mostafa / Gandomi, Amir H.

Chaos Solitons Fractals

Abstract	COVID-19 or SARS-Cov-2, affecting 6 million people and more than 300,000 deaths, the global pandemic has engulfed more than 90% countries of the world. The virus started from a single organism and is escalating at a rate of 3% to 5% daily and seems to be a never ending process. Understanding the basic dynamics and presenting new predictions models for evaluating the potential effect of the virus is highly crucial. In present work, an evolutionary data analytics method called as Genetic programming (GP) is used to mathematically model the potential effect of coronavirus in 15 most affected countries of the world. Two datasets namely confirmed cases (CC) and death cases (DC) were taken into consideration to estimate, how transmission varied in these countries between January 2020 and May 2020. Further, a percentage rise in the number of daily cases is also shown till 8 June 2020 and it is expected that Brazil will have the maximum rise in CC and USA have the most DC. Also, prediction of number of new CC and DC cases for every one million people in each of these countries is presented. The proposed model predicted that the transmission of COVID-19 in China is declining since late March 2020; in Singapore, France, Italy, Germany and Spain the curve has stagnated; in case of Canada, South Africa, Iran and Turkey the number of cases are rising slowly; whereas for USA, UK, Brazil, Russia and Mexico the rate of increase is very high and control measures need to be taken to stop the chains of transmission. Apart from that, the proposed prediction models are simple mathematical equations and future predictions can be drawn from these general equations. From the experimental results and statistical validation, it can be said that the proposed models use simple linkage functions and provide highly reliable results for time series prediction of COVID-19 in these countries.
Keywords	covid19
Publisher	WHO
Document type	Article
Note	WHO #Covidence: #652086
Database	COVID19

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