A time series-based statistical approach for outbreak spread forecasting: Application of COVID-19 in Greece

Background World Health Organization has reported fifty countries have now detected the new coronavirus (B.1.1.7 variant) since a couple of months ago. In Indonesia, the B.1.1.7 cases have been found in several provinces since January 2021, although they are still in a lower number than the old variant of COVID-19. Therefore, this study aims to create a forecast analysis regarding the occasions of COVID-19 and B.1.1.7 cases based on data from the 1st January to 18th March 2021, and also analyze the association between meteorological factors with B.1.1.7 incidences in three different provinces of Indonesia such as the west java, south sumatra and east kalimantan. Methods We used the Autoregressive Moving Average Models (ARIMA) to forecast the number of cases in the upcoming 14 days and the Spearman correlation analysis to analyze the relationship between B.1.1.7 cases and meteorological variables such as temperature, humidity, rainfall, sunshine, and wind speed. Results The results of the study showed the fitted ARIMA models forecasted there was an increase in the daily cases in three provinces. The total cases in three provinces would increase by 36% (west java), 13.5% (south sumatra), and 30% (east kalimantan) as compared with actual cases until the end of 14 days later. The temperature, rainfall and sunshine factors were the main contributors for B.1.1.7 cases with each correlation coefficients; r = -0.230; p < 0.05, r = 0.211; p < 0.05 and r = −0.418; p < 0.01, respectively. Conclusions We recapitulated that this investigation was the first preliminary study to analyze a short-term forecast regarding COVID-19 and B.1.1.7 cases as well as to determine the associated meteorological factors that become primary contributors to the virus spread.

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“…Elsheikh et al [ 24 ], Katoch and Sidhu [ 25 ] and Katris [ 26 ].…”

Section: Uncited Referencesmentioning

confidence: 99%

New COVID-19 variant (B.1.1.7): Forecasting the occasion of virus and the related meteorological factors

Rendana

Idris

2021

Journal of Infection and Public Health

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“…The literature also constitutes many studies that present statistical approaches, such as logistic regression, generalized additive, and time-series models to predict the growth of the disease over time and implement resource allocation strategies accordingly during the pandemic (see, e.g., Murray (2020), Manca et al (2020), and Katris (2020)). Graph-theoretical methods have also been quite useful in studying the COVID-19 transmission pattern and finding out the practices to reduce the transmission rate (see, e.g., Hu et al (2020), Loeffler-Wirth et al (2020), and Badr et al (2020)).…”

Section: Literature Reviewmentioning

confidence: 99%

COVID-19: Optimal Allocation of Ventilator Supply under Uncertainty and Risk

2021

Preprint

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This study presents a new risk-averse multi-stage stochastic epidemics-ventilator-logistics compartmental model to address the resource allocation challenges of mitigating COVID-19. This epidemiological logistics model involves the uncertainty of untested asymptomatic infections and incorporates short-term human migration. Disease transmission is also forecasted through a new formulation of transmission rates that evolve over space and time with respect to various non-pharmaceutical interventions, such as wearing masks, social distancing, and lockdown. The proposed multi-stage stochastic model overviews different scenarios on the number of asymptomatic individuals while optimizing the distribution of resources, such as ventilators, to minimize the total expected number of newly infected and deceased people. The Conditional Value at Risk (CVaR) is also incorporated into the multi-stage mean-risk model to allow for a trade-off between the weighted expected loss due to the outbreak and the expected risks associated with experiencing disastrous pandemic scenarios. We apply our multi-stage mean-risk epidemics-ventilator-logistics model to the case of controlling the COVID-19 in highly-impacted counties of New York and New Jersey. We calibrate, validate, and test our model using actual infection, population, and migration data. The results indicate that short-term migration influences the transmission of the disease significantly. The optimal number of ventilators allocated to each region depends on various factors, including the number of initial infections, disease transmission rates, initial ICU capacity, the population of a geographical location, and the availability of ventilator supply. Our data-driven modeling framework can be adapted to study the disease transmission dynamics and logistics of other similar epidemics and pandemics.

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“…In Brazil, until April 29, 2021, there had been confirmed 14,441,563 COVID-19 cases and 395,022 deaths caused by the virus ( WHO, 2021 ). For this reason, scientists around the world from the most diverse areas have focused their studies on understanding COVID-19 transmission dynamics ( Fang, Nie, & Penny, 2020 ), prevention ( Ali and Ghonimy, 2020 , Li et al, 2020 , Voysey et al, 2021 ), detection ( Ismael and Sengür, 2021 , Vidal et al, 2021 ), control measures ( Meo et al, 2020 ), and prediction analysis ( Hernandez-Matamoros et al, 2020 , Katris, 2021 , Petropoulos et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Meteorological and human mobility data on predicting COVID-19 cases by a novel hybrid decomposition method with anomaly detection analysis: A case study in the capitals of Brazil

Silva

Francisquini

Nascimento

2021

Expert Systems with Applications

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In 2020, Brazil was the leading country in COVID-19 cases in Latin America, and capital cities were the most severely affected by the outbreak. Climates vary in Brazil due to the territorial extension of the country, its relief, geography, and other factors. Since the most common COVID-19 symptoms are related to the respiratory system, many researchers have studied the correlation between the number of COVID-19 cases with meteorological variables like temperature, humidity, rainfall, etc. Also, due to its high transmission rate, some researchers have analyzed the impact of human mobility on the dynamics of COVID-19 transmission. There is a dearth of literature that considers these two variables when predicting the spread of COVID-19 cases. In this paper, we analyzed the correlation between the number of COVID-19 cases and human mobility, and meteorological data in Brazilian capitals. We found that the correlation between such variables depends on the regions where the cities are located. We employed the variables with a significant correlation with COVID-19 cases to predict the number of COVID-19 infections in all Brazilian capitals and proposed a prediction method combining the Ensemble Empirical Mode Decomposition (EEMD) method with the Autoregressive Integrated Moving Average Exogenous inputs (ARIMAX) method, which we called EEMD-ARIMAX. After analyzing the results poor predictions were further investigated using a signal processing-based anomaly detection method. Computational tests showed that EEMD-ARIMAX achieved a forecast 26.73% better than ARIMAX. Moreover, an improvement of 30.69% in the average root mean squared error (RMSE) was noticed when applying the EEMD-ARIMAX method to the data normalized after the anomaly detection.

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A time series-based statistical approach for outbreak spread forecasting: Application of COVID-19 in Greece

Cited by 90 publications

References 27 publications

New COVID-19 variant (B.1.1.7): Forecasting the occasion of virus and the related meteorological factors

New COVID-19 variant (B.1.1.7): Forecasting the occasion of virus and the related meteorological factors

COVID-19: Optimal Allocation of Ventilator Supply under Uncertainty and Risk

Meteorological and human mobility data on predicting COVID-19 cases by a novel hybrid decomposition method with anomaly detection analysis: A case study in the capitals of Brazil

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