tetano
Editor, Senior Moderator
Sci Rep
. 2024 Aug 4;14(1):18039.
doi: 10.1038/s41598-024-67161-z. Forecasting of coronavirus active cases by utilizing logistic growth model and fuzzy time series techniques
Chandrakanta Mahanty[SUP] 1 [/SUP], S Gopal Krishna Patro[SUP] 2 [/SUP], Sandeep Rathor[SUP] 3 [/SUP], Venubabu Rachapudi[SUP] 4 [/SUP], Khursheed Muzammil[SUP] 5 [/SUP], Saiful Islam[SUP] 6 [/SUP], Abdul Razak[SUP] 7 [/SUP], Wahaj Ahmad Khan[SUP] 8 [/SUP]
Affiliations
Coronavirus has long been considered a global epidemic. It caused the deaths of nearly 7.01 million individuals and caused an economic downturn. The number of verified coronavirus cases is increasing daily, putting the whole human race at danger and putting strain on medical experts to eradicate the disease as rapidly as possible. As a consequence, it is vital to predict the upcoming coronavirus positive patients in order to plan actions in the future. Furthermore, it has been discovered all across the globe that asymptomatic coronavirus patients play a significant part in the disease's transmission. This prompted us to incorporate similar examples in order to accurately forecast trends. A typical strategy for analysing the rate of pandemic infection is to use time-series forecasting technique. This would assist us in developing better decision support systems. To anticipate COVID-19 active cases for a few countries, we recommended a hybrid model utilizing a fuzzy time series (FTS) model mixed with a non-linear growth model. The coronavirus positive case outbreak has been evaluated for Italy, Brazil, India, Germany, Pakistan, and Myanmar through June 5, 2020 in phase-1, and January 15, 2022 in phase-2, and forecasts active cases for the next 26 and 14 days respectively. The proposed framework fitting effect outperforms individual logistic growth and the fuzzy time series techniques, with R-scores of 0.9992 in phase-1 and 0.9784 in phase-2. The proposed model provided in this article may be utilised to comprehend a country's epidemic pattern and assist the government in developing better effective interventions.
Keywords: COVID-19; Fuzzy time series; Hybrid; Logistic growth; Non-linear growth; Prediction.
. 2024 Aug 4;14(1):18039.
doi: 10.1038/s41598-024-67161-z. Forecasting of coronavirus active cases by utilizing logistic growth model and fuzzy time series techniques
Chandrakanta Mahanty[SUP] 1 [/SUP], S Gopal Krishna Patro[SUP] 2 [/SUP], Sandeep Rathor[SUP] 3 [/SUP], Venubabu Rachapudi[SUP] 4 [/SUP], Khursheed Muzammil[SUP] 5 [/SUP], Saiful Islam[SUP] 6 [/SUP], Abdul Razak[SUP] 7 [/SUP], Wahaj Ahmad Khan[SUP] 8 [/SUP]
Affiliations
- PMID: 39098877
- PMCID: PMC11298557
- DOI: 10.1038/s41598-024-67161-z
Coronavirus has long been considered a global epidemic. It caused the deaths of nearly 7.01 million individuals and caused an economic downturn. The number of verified coronavirus cases is increasing daily, putting the whole human race at danger and putting strain on medical experts to eradicate the disease as rapidly as possible. As a consequence, it is vital to predict the upcoming coronavirus positive patients in order to plan actions in the future. Furthermore, it has been discovered all across the globe that asymptomatic coronavirus patients play a significant part in the disease's transmission. This prompted us to incorporate similar examples in order to accurately forecast trends. A typical strategy for analysing the rate of pandemic infection is to use time-series forecasting technique. This would assist us in developing better decision support systems. To anticipate COVID-19 active cases for a few countries, we recommended a hybrid model utilizing a fuzzy time series (FTS) model mixed with a non-linear growth model. The coronavirus positive case outbreak has been evaluated for Italy, Brazil, India, Germany, Pakistan, and Myanmar through June 5, 2020 in phase-1, and January 15, 2022 in phase-2, and forecasts active cases for the next 26 and 14 days respectively. The proposed framework fitting effect outperforms individual logistic growth and the fuzzy time series techniques, with R-scores of 0.9992 in phase-1 and 0.9784 in phase-2. The proposed model provided in this article may be utilised to comprehend a country's epidemic pattern and assist the government in developing better effective interventions.
Keywords: COVID-19; Fuzzy time series; Hybrid; Logistic growth; Non-linear growth; Prediction.