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Proc Natl Acad Sci 117(41):25897–25903īritton T, Ball F, Trapman P (2020) A mathematical model reveals the influence of population heterogeneity on herd immunity to SARS-CoV-2. Sci Rep 12(1):2872īrett TS, Rohani P (2020) Transmission dynamics reveal the impracticality of COVID-19 herd immunity strategies. Vetted Real Time Pap 11:1–36īicher M, Rippinger C, Schneckenreither G, Weibrecht N, Urach C, Zechmeister M, Brunmeir D, Huf W, Popper N (2022) Model based estimation of the SARS-CoV-2 immunization level in Austria and consequences for herd immunity effects. Lancet 396(10263):1614–1616īethune Z, Korinek A (2020) COVID-19 infection externalities: pursuing herd immunity or containment? Covid econ. Jama 324(20):2095–2096Īnderson RM, Vegvari C, Truscott J et al (2020) Challenges in creating herd immunity to SARS-CoV-2 infection by mass vaccination. Omer SB, Yildirim I, Forman HP (2020) Herd immunity and implications for SARS-CoV-2 control. Li ML, Bouardi HT, Lami OS, Trikalinos TA, Trichakis N, Bertsimas D (2023) Forecasting COVID-19 and analyzing the effect of government interventions. Kodera S, Takada A, Rashed EA, Hirata A (2023) Projection of COVID-19 positive cases considering hybrid immunity: case study in Tokyo. Sher T, Rehman A, Kim D (2023) COVID-19 outbreak prediction by using machine learning algorithms. Ustebay S, Sarmis A, Kaya GK, Sujan M (2023) A comparison of machine learning algorithms in predicting COVID-19 prognostics. Naderipour A, Abdullah A, Marzbali MH et al (2022) An improved corona-virus herd immunity optimizer algorithm for network reconfiguration based on fuzzy multi-criteria approach. Georgette NT (2009) Predicting the herd immunity threshold during an outbreak: a recursive approach. J Theor Biol 530(1):110874Īlweshah M, Alkhalaileh S, Al-Betar MA et al (2022) Coronavirus herd immunity optimizer with greedy crossover for feature selection in medical diagnosis. Kemp F, Proverbio D, Aalto A et al (2021) Modelling COVID-19 dynamics and potential for herd immunity by vaccination in Austria, Luxembourg and Sweden. Park H, Kim SH (2020) A study on herd immunity of COVID-19 in South Korea: using a stochastic economic-epidemiological model.

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MacIntyre CR, Costantino V, Trent M (2022) Modelling of COVID-19 vaccination strategies and herd immunity, in scenarios of limited and full vaccine supply in NSW. Tetteh JN, Hernandez-Vargas EA (2021) Network models to evaluate vaccine strategies towards herd immunity in COVID-19. The results indicate that the proposed model performs better than the existing standard.Ĭihan P (2021) Forecasting fully vaccinated people against COVID-19 and examining future vaccination rate for herd immunity in the US, Asia, Europe, Africa, South America, and the world. Various deep learning models, including DNN, RNN, CNN, RBM, LSTM, and RNN + LSTM, are compared to evaluate the performance of the proposed model.

The selected features are then fed into the RNN and LSTM, and the epoch and neuron count in the RNN and LSTM is optimised using ACHIO to improve model performance. Once the features are extracted using statistical methods, the optimal feature selection is performed by ACHIO. Feature extraction and feature selection methods are used to select the most important features that enhance the model’s performance. To develop a prediction model, a hybrid RNN and LSTM are combined with the proposed ACHIO. The main goal of this research is to develop an enhanced herd immunity prediction model for the COVID-19 pandemic. The development of herd immunity against any deadly virus will stop the pandemic. The most effective method of disease prevention is immunisation and vaccination. The rapid spread of COVID-19 threatened the entire world because of its adverse effects and high mortality rate.

Aer precision transmission