Statistical Modeling for the Prediction of Infectious Disease Dissemination With Special Reference to COVID-19 Spread

In this review, we have discussed the different statistical modeling and prediction techniques for various infectious diseases including the recent pandemic of COVID-19. The distribution fitting, time series modeling along with predictive monitoring approaches, and epidemiological modeling are illustrated. When the epidemiology data is sufficient to fit with the required sample size, the normal distribution in general or other theoretical distributions are fitted and the best-fitted distribution is chosen for the prediction of the spread of the disease. The infectious diseases develop over time and we have data on the single variable that is the number of infections that happened, therefore, time series models are fitted and the prediction is done based on the best-fitted model. Monitoring approaches may also be applied to time series models which could estimate the parameters more precisely. In epidemiological modeling, more biological parameters are incorporated in the models and the forecasting of the disease spread is carried out. We came up with, how to improve the existing modeling methods, the use of fuzzy variables, and detection of fraud in the available data. Ultimately, we have reviewed the results of recent statistical modeling efforts to predict the course of COVID-19 spread.

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Estimating infection-related human mobility networks based on time series data of COVID-19 infection in Japan

10.1101/2021.08.02.21261486 ◽

2021 ◽

Author(s):

Tetsuya Yamada ◽

Shoi Shi

Keyword(s):

Time Series ◽

Infectious Diseases ◽

Time Series Data ◽

Human Mobility ◽

Emerging Infectious Diseases ◽

Human Movement ◽

Data Driven ◽

Disease Spread ◽

Series Data ◽

Data Infrastructure

Comprehensive and evidence-based countermeasures against emerging infectious diseases have become increasingly important in recent years. COVID-19 and many other infectious diseases are spread by human movement and contact, but complex transportation networks in 21 century make it difficult to predict disease spread in rapidly changing situations. It is especially challenging to estimate the network of infection transmission in the countries that the traffic and human movement data infrastructure is not yet developed. In this study, we devised a method to estimate the network of transmission of COVID-19 from the time series data of its infection and applied it to determine its spread across areas in Japan. We incorporated the effects of soft lockdowns, such as the declaration of a state of emergency, and changes in the infection network due to government-sponsored travel promotion, and predicted the spread of infection using the Tokyo Olympics as a model. The models used in this study are available online, and our data-driven infection network models are scalable, whether it be at the level of a city, town, country, or continent, and applicable anywhere in the world, as long as the time-series data of infections per region is available. These estimations of effective distance and the depiction of infectious disease networks based on actual infection data are expected to be useful in devising data-driven countermeasures against emerging infectious diseases worldwide.

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Time-Series Models in Marketing: Some Recent Developments

Marketing ZFP ◽

10.15358/0344-1369-2010-jrm-1-24 ◽

2010 ◽

Vol 32 (JRM 1) ◽

pp. 24-29

Author(s):

Marnik G. Dekimpe ◽

Dominique M. Hanssens

Keyword(s):

Time Series ◽

Time Series Models ◽

Recent Developments

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A COMPARATIVE STUDY BETWEEN UNIVARIATE AND BIVARIATE TIME SERIES MODELS FOR CRUDE PALM OIL INDUSTRY IN PENINSULAR MALAYSIA

MALAYSIAN JOURNAL OF COMPUTING ◽

10.24191/mjoc.v5i1.6760 ◽

2020 ◽

Vol 5 (1) ◽

pp. 374

Author(s):

Pauline Jin Wee Mah ◽

Nur Nadhirah Nanyan

Keyword(s):

Time Series ◽

Palm Oil ◽

Moving Average ◽

Forecast Accuracy ◽

Peninsular Malaysia ◽

Time Series Models ◽

Crude Palm Oil ◽

Univariate Time Series ◽

Import And Export ◽

Bivariate Time Series

The main purpose of this study is to compare the performances of univariate and bivariate models on four time series variables of the crude palm oil industry in Peninsular Malaysia. The monthly data for the four variables, which are the crude palm oil production, price, import and export, were obtained from Malaysian Palm Oil Board (MPOB) and Malaysian Palm Oil Council (MPOC). In the first part of this study, univariate time series models, namely, the autoregressive integrated moving average (ARIMA), fractionally integrated autoregressive moving average (ARFIMA) and autoregressive autoregressive (ARAR) algorithm were used for modelling and forecasting purposes. Subsequently, the dependence between any two of the four variables were checked using the residuals’ sample cross correlation functions before modelling the bivariate time series. In order to model the bivariate time series and make prediction, the transfer function models were used. The forecast accuracy criteria used to evaluate the performances of the models were the mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE). The results of the univariate time series showed that the best model for predicting the production was ARIMA while the ARAR algorithm were the best forecast models for predicting both the import and export of crude palm oil. However, ARIMA appeared to be the best forecast model for price based on the MAE and MAPE values while ARFIMA emerged the best model based on the RMSE value. When considering bivariate time series models, the production was dependent on import while the export was dependent on either price or import. The results showed that the bivariate models had better performance compared to the univariate models for production and export of crude palm oil based on the forecast accuracy criteria used.

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