Open AccessA hybrid approach to forecast the COVID-19 epidemic trend
Author(s) -
Saqib Ali Nawaz,
Jingbing Li,
Uzair Aslam Bhatti,
Sibghat Ullah Bazai,
Anessa Zafar,
Mughair Aslam Bhatti,
Adeel Mehmood,
Qurat ul Ain,
Muhammad Usman Shoukat
Publication year - 2021
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0256971
Subject(s) - covid-19 , epidemic model , computer science , public health interventions , transmission (telecommunications) , gompertz function , quarantine , infectious disease (medical specialty) , pandemic , econometrics , realization (probability) , statistics , public health , medicine , virology , environmental health , machine learning , disease , mathematics , telecommunications , population , outbreak , nursing , pathology
Studying the progress and trend of the novel coronavirus pneumonia (COVID-19) transmission mode will help effectively curb its spread. Some commonly used infectious disease prediction models are introduced. The hybrid model is proposed, which overcomes the disadvantages of the logistic model’s inability to predict the number of confirmed diagnoses and the drawbacks of too many tuning parameters of the SEIR (Susceptible, Exposed, Infectious, Recovered) model. The realization and superiority of the prediction of the proposed model are proven through experiments. At the same time, the influence of different initial values of the parameters that need to be debugged on the hybrid model is further studied, and the mean error is used to quantify the prediction effect. By forecasting epidemic size and peak time and simulating the effects of public health interventions, this paper aims to clarify the transmission dynamics of COVID-19 and recommend operation suggestions to slow down the epidemic. It is suggested that the quick detection of cases, sufficient implementation of quarantine and public self-protection behaviours are critical to slow down the epidemic.