@article {Bhardwaj2020.04.13.20063271,
	author = {Bhardwaj, Rajneesh},
	title = {A Predictive Model for the Evolution of COVID-19},
	elocation-id = {2020.04.13.20063271},
	year = {2020},
	doi = {10.1101/2020.04.13.20063271},
	publisher = {Cold Spring Harbor Laboratory Press},
	abstract = {We predict the evolution of the COVID-19 pandemic in several countries by using a regression-based predictive model. In particular, the growth rate of the infection has been fitted as an exponential decay, as compared to a linear decay, reported previously. The model has been validated with the data of China and South Korea, where the pandemic is nearing to its end. The data of Italy, Germany, Spain, and Sweden show that the peak of the infection has reached i.e. a time when the new infections will start to decrease as compared to the previous day. The model predicts the approximate number of total infections at the end of the outbreak. The model prediction of the USA, and Brazil show that the peak will reach in the next two-three weeks. The total number of infections in the USA is estimated to be around 4 million by the model. The reported data of India show the start of disease evolution, with a large initial scatter in the growth rate. The possible peak date and the total number of infections are predicted using the data available.Competing Interest StatementThe authors have declared no competing interest.Clinical TrialThe present study is about a predictive mathematical/computational model of a pandemic and no trial were conducted.Funding StatementNo external funding was received for this work.Author DeclarationsAll relevant ethical guidelines have been followed; any necessary IRB and/or ethics committee approvals have been obtained and details of the IRB/oversight body are included in the manuscript.YesAll necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.YesThe data used in the study is given in public domain on https://coronavirus.jhu.edu.},
	URL = {https://www.medrxiv.org/content/early/2020/04/17/2020.04.13.20063271},
	eprint = {https://www.medrxiv.org/content/early/2020/04/17/2020.04.13.20063271.full.pdf},
	journal = {medRxiv}
}

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author = {Aloufi, Khalid},
year = {2006},
month = {09},
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title = {Active Queue Management based on the use of Self Similarity and Long Range Dependence found in Internet Traffic}
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