scholarly journals Forecasting COVID-19 Epidemic in Spain and Italy Using A Generalized Richards Model with Quantified Uncertainty

2021 ◽  
Vol 3 (2) ◽  
pp. 90-100
Author(s):  
Isnani Darti ◽  
Agus Suryanto ◽  
Hasan S. Panigoro ◽  
Hadi Susanto
Keyword(s):  
Confidence Interval ◽  
Time Series Data ◽  
Interval Estimation ◽  
Epidemiological Data ◽  
Series Data ◽  
Parameter Estimates ◽  
Cumulative Number ◽  
Deterministic Models ◽  
Richards Model ◽  
Parameter Estimations

The Richards model and its generalized version are deterministic models that are often implemented to fit and forecast the cumulative number of infective cases in an epidemic outbreak. In this paper we employ a generalized Richards model to predict the cumulative number of COVID-19 cases in Spain and Italy, based on available epidemiological data. To quantify uncertainty in the parameter estimation, we use a parametric bootstrapping approach to construct a 95% confidence interval estimation for the parameter model. Here we assume that the time series data follow a Poisson distribution. It is found that the 95% confidence interval of each parameter becomes narrow with the increasing number of data. All in all, the model predicts daily new cases of COVID-19 reasonably well during calibration periods. However, the model fails to produce good forecasts when the amount of data used for parameter estimations is not sufficient. Based on our parameter estimates, it is found that the early stages of COVID-19 epidemic, both in Spain and in Italy, followed an almost exponentially growth. The epidemic peak in Spain and Italy is respectively on 2 April 2020 and 28 March 2020. The final sizes of cumulative number of COVID-19 cases in Spain and Italy are forecasted to be at 293220 and 237010, respectively.

Forecast of COVID-19 Cases in Nigeria: An implementation of an ARIMA model (Preprint)

2020 ◽  
Author(s):  
Sanyaolu Ameye ◽  
Michael Awoleye ◽  
Emmanuel Agogo ◽  
Ette Etuk
Keyword(s):  
Predictive Model ◽  
Time Series Data ◽  
Moving Average ◽  
Arima Model ◽  
Epidemiological Data ◽  
Series Data ◽  
Global Pandemic ◽  
First Case ◽  
Epidemiological Trend ◽  
Data Points

BACKGROUND The Coronavirus disease 2019 (COVID-2019) is a global pandemic and Nigeria is not left out in being affected. Though, the disease is just over three months since first case was identified in the country, we present a predictive model to forecast the number of cases expected to be seen in the country in the next 100 days. OBJECTIVE To implement a predictive model in forecasting the near future number of positive cases expected in the country following the present trend METHODS We performed an Auto Regressive Integrated Moving Average (ARIMA) model prediction on the epidemiological data obtained from Nigerian Centre for Disease Control to predict the epidemiological trend of the prevalence and incidence of COVID-2019. RESULTS There were 93 time series data points which lacked stationarity. From our ARIMA model, it is expected that the number of new cases declared per day will keep rising and towards the early September, 2020, Nigeria is expected to have well above sixty thousand confirmed cases. CONCLUSIONS We however believe that as we have more data points our model will be better fine-tuned.

Confidence Interval of Pavement Layer Parameters Estimated from FWD Time Series Data

2013 ◽  
Author(s):  
Satoshi Horiuchi ◽  
Futoshi Kawana ◽  
Masaru Terada ◽  
Kazuyuki Kubo ◽  
Kunihito Matsui
Keyword(s):  
Time Series ◽  
Confidence Interval ◽  
Time Series Data ◽  
Series Data

Bias in the Estimation of Functional Relationships from Time Series Data

1985 ◽  
Vol 42 (1) ◽  
pp. 147-149 ◽  
Author(s):  
Carl J. Walters
Keyword(s):  
Time Series ◽  
Measurement Errors ◽  
Time Series Data ◽  
Series Data ◽  
Parameter Estimates ◽  
Natural Experiments ◽  
Independent Variables ◽  
Functional Relationships ◽  
Stock Recruitment ◽  
The Relationship

Functional relationships, such as stock–recruitment curves, are generally estimated from time series data where natural "random" factors have generated both deviations from the relationship and also informative variation in the independent variables. Even in the absence of measurement errors, such natural experiments can lead to severely biased parameter estimates. For stock–recruitment models, the bias is misleading for management: the stock will appear too productive when it is low, and too unproductive when it is large. The likely magnitude of such biases can and should be determined for any particular case by Monte Carlo simulations.

scholarly journals Prediction of the Infectious Outbreak COVID-19 and Prevalence of Anxiety: Global Evidence

2021 ◽  
Vol 13 (20) ◽  
pp. 11339
Author(s):  
Daniyal Alghazzawi ◽  
Atika Qazi ◽  
Javaria Qazi ◽  
Khulla Naseer ◽  
Muhammad Zeeshan ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Disease Outbreaks ◽  
Epidemiological Data ◽  
World Health ◽  
Series Data ◽  
Systems Science ◽  
Support Vector ◽  
Twitter Users ◽  
Health Organization

Forecasting disease outbreaks in real-time using time-series data can help for the planning of public health interventions. We used a support vector machine (SVM) model using epidemiological data provided by Johns Hopkins University Centre for Systems Science and Engineering (JHU CCSE), World Health Organization (WHO), and the Centers for Disease Control and Prevention (CDC) to predict upcoming records before the WHO made an official declaration. Our study, conducted on the time series data available from 22 January till 10 March 2020, revealed that COVID-19 was spreading at an alarming rate and progressing towards a pandemic. The initial insight that confirmed COVID-19 cases were increasing was because these received the highest number of effects for our selected dataset from 22 January to 10 March 2020, i.e., 126,344 (64%). The recovered cases were 68289 (34%), and the death rate was around 2%. Moreover, we classified the tweets from 22 January to 15 April 2020 into positive and negative sentiments to identify the emotions (stress or relaxed) posted by Twitter users related to the COVID-19 pandemic. Our analysis identified that tweets mostly conveyed a negative sentiment with a high frequency of words for #coronavirus and #lockdown amid COVID-19. However, these anxiety tweets are an alarm for healthcare authorities to devise plans accordingly.

scholarly journals Time Stand Still: Effects of Temporal Window Selection on Eye Tracking Analysis

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Jonathan E. Peelle ◽  
Kristin J. Van Engen
Keyword(s):  
Eye Tracking ◽  
Degrees Of Freedom ◽  
Time Series Data ◽  
Time Window ◽  
Time Windows ◽  
Basis Set ◽  
Series Data ◽  
Parameter Estimates ◽  
Start Time ◽  
Window Selection

The number of possible approaches to conducting and analyzing a research study—often referred to as researcher degrees of freedom—has been increasingly under scrutiny as a challenge to the reproducibility of experimental results. Here we focus on the specific instance of time window selection for time series data. As an example, we use data from a visual world eye tracking paradigm in which participants heard a word and were instructed to click on one of four pictures corresponding to the target (e.g., “Click on the hat”). We examined statistical models for a range of start times following the beginning of the carrier phrase, and for each start time a range of window lengths, resulting in 8281 unique time windows. For each time window we ran the same logistic linear mixed effects model, including effects of time, age, noise, and word frequency on an orthogonalized polynomial basis set. Comparing results across these time ranges shows substantial changes in both parameter estimates and p values, even within intuitively “reasonable” boundaries. In some cases varying the window selection in the range of 100–200 ms caused parameter estimates to change from positive to negative. Rather than rush to provide specific recommendations for time window selection (which differs across studies), we advocate for transparency regarding time window selection and awareness of the effects this choice may have on results. Preregistration and multiverse model exploration are two complementary strategies to help mitigate bias introduced by any particular time window choice.

Digital Twin Behavior Matching of Gas Plumes Using a Fixed Sensor Mesh and Dynamic Mode Decomposition

2021 ◽  
Author(s):  
Derek Hollenbeck ◽  
YangQuan Chen
Keyword(s):  
Source Localization ◽  
Time Series Data ◽  
Computational Cost ◽  
Low Frequency ◽  
Dynamic Mode Decomposition ◽  
Series Data ◽  
Smart Manufacturing ◽  
Dynamic Mode ◽  
Deterministic Models ◽  
Mode Decomposition

Abstract Digital twins (DT) have become a useful tool in smart manufacturing, engineering and controls. Behavior matching of DTs to their physical twin counterparts is essential for capturing the evolution of key system parameters. Given that environmental gas emissions are governed by partial differential equations, the behavior matching optimization can often be ill posed and computationally expensive. Stochastic models have shown good agreement to deterministic models while having a significant computational cost reduction. This work presents a method for solving the source localization problem using a DT implementation of a stochastic point source emission with a fixed-mesh of gas sensors. The DT source localization is determined through behavior matching process with low frequency modes after dynamic mode decomposition using spatial interpolation on measured time series data. That is, the minimization of the mismatch between the DT and the unknown physical model can given an estimate of the source location.

Monitoring abrupt changes in satellite time series by seasonal confidence interval of regression residuals

2016 ◽  
Vol 14 (04) ◽  
pp. 1650022
Author(s):  
Zeng-Guang Zhou ◽  
Chang-Miao Hu ◽  
Ping Tang ◽  
Zheng Zhang
Keyword(s):  
Time Series ◽  
Confidence Interval ◽  
Regression Model ◽  
Seasonal Variability ◽  
Abrupt Change ◽  
Time Series Data ◽  
Series Data ◽  
Test Technique ◽  
Regression Residuals ◽  
Abrupt Changes

Near real-time monitoring of abrupt changes in satellite time series is important for timely warning of land covers changes. Regression model-based method has been frequently used to detect abrupt change (outlier or anomaly) in time series data. Abrupt change is often determined by residuals test after regression. A simple and widely used residuals test technique is confidence interval (CI), which is often time-independent or constant in many studies. However, satellite time series data is characterized by seasonal variability and periodicity. Although the periodicity could be fitted well by a seasonal-trend regression model, the seasonal variability still remains in the residuals of the regression model. The seasonal variability would lead to less reliable results if abrupt changes are detected by a constant confidence interval (CCI). In order to improve the reliability of abrupt change monitoring in satellite time series, in this paper we develop a criterion namely seasonal confidence interval (SCI) of regression residuals. Experimental evaluations with some simulated and actual satellite time series data demonstrate better performance of the proposed SCI criterion than the CCI criterion for monitoring abrupt changes in satellite time series.

scholarly journals Modelling the Cumulative Number of COVID-19 Cases

2021 ◽  
Vol 02 (02) ◽  
pp. 1-1
Author(s):  
Mieczysław Szyszkowicz ◽  
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Distribution Functions ◽  
Cumulative Distribution ◽  
Series Data ◽  
Cumulative Number ◽  
Daily Time Series ◽  
Non Linear ◽  
The Usa ◽  
Daily Time

Each country has its own characteristics of COVID-19 infection trajectory and epidemic waves. Differences in government-implemented restrictions and social regulations result in variability of the virus transmissions and spread dynamics. This in turn results in various shapes of the growth function used to represent and describe the propagation of infection. Statistical methods are applied to fit non-linear functions to represent daily time-series data of the cumulative numbers of COVID-19 cases. The aim of this work is to fit various statistical models to the cumulative number of COVID-19 cases. Also to overview various types of the existed numerical methodologies. The data (since December 31, 2019) are available for almost each country in the world. As the examples, we used daily time-series data of the cumulative number of COVID-19 cases in Poland, Italy, Canada, and the USA. Non-linear approximations are applied to represent these time series data. The fitted functions allow us to investigate the dynamics of the pandemic. The constructed approximations are compositions of a few nonlinear functions, which describe the growth process of the COVID-19 infection trajectories. Two Gompertz functions and cumulative distribution functions (cdf) were estimated for the data of Poland and Italy (using the cdf for the normal distribution) and for the data of Canada and the USA (using the cdf for the gamma distribution). An analytical (parametric) functions representation of the number of COVID-19 cumulative cases is a useful tool to study the propagation of epidemics.

scholarly journals Estimating the SARS-CoV-2 infected population fraction and the infection to-fatality ratio: A data-driven case study based on Swedish time series data

2021 ◽  
Author(s):  
Andreas Wacker ◽  
Anna Jöud ◽  
Bo Bernhardsson ◽  
Philip Gerlee ◽  
Fredrik Gustafsson ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Least Squares Method ◽  
Finite Impulse Response ◽  
Herd Immunity ◽  
Late Summer ◽  
Epidemiological Data ◽  
Series Data ◽  
Filter Function ◽  
Study Results

Aim: To estimate the COVID-19 infection-to-fatality ratio (IFR), infection-to-case ratio (ICR), and infection-to-ICU admission ratio (IIAR) in Sweden; to suggest methods for time series reconstruction and prediction. Methods: We optimize a set of simple finite impulse response (FIR) models comprising of a scaling factor and time-delay between officially reported cases, ICU admissions and deaths time series using the least squares method. Combined with randomized PCR study results, we utilize this simple model to estimate the total number of infections in Sweden, and the corresponding IFR. Results: The model class provides a good fit between ICU admissions and deaths throughout 2020. Cases fit consistently from July 2020, by when PCR tests had become broadly available. We observe a diminished IFR in late summer as well as a strong decline during 2021, following the launch of a nation-wide vaccination program. The total number of infections during 2020 is estimated to $1.3$ million. Conclusions: A FIR model with a delta filter function describes the evolution of epidemiological data in Sweden well. The fact that we found IFR, ICR and IIAR constant over large parts of 2020 is in contrast with claims of healthcare adaptation or mutated virus variants importantly affecting these ratios. The model allows us to retrospectively estimate the COVID-19 epidemiological trajectory, and conclude that Sweden was far from herd immunity by the end of 2020.

Sign in / Sign up

Export Citation Format

Share Document