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A simple mathematical tool to forecast COVID-19 cumulative case numbers

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dc.contributor.authorAKGÜL, AHMET
dc.contributor.authorsBalak, Naci; Inan, Deniz; Ganau, Mario; Zoia, Cesare; Sonmez, Sinan; Kurt, Batuhan; Akgul, Ahmet; Tez, Mujgan
dc.date.accessioned2022-03-14T09:57:41Z
dc.date.available2022-03-14T09:57:41Z
dc.date.issued2021-10
dc.description.abstractObjective: Mathematical models are known to help determine potential intervention strategies by providing an approximate idea of the transmission dynamics of infectious diseases. To develop proper responses, not only are more accurate disease spread models needed, but also those that are easy to use. Materials and methods: As of July 1, 2020, we selected the 20 countries with the highest numbers of COVID-19 cases in the world. Using the Verhulst-Pearl logistic function formula, we calculated estimates for the total number of cases for each country. We compared these estimates to the actual figures given by the WHO on the same dates. Finally, the formula was tested for longer-term reliability at t = 18 and t = 40 weeks. Results: The Verhulst-Pearl logistic function formula estimated the actual numbers precisely, with only a 0.5% discrepancy on average for the first month. For all countries in the study and the world at large, the estimates for the 40th week were usually overestimated, although the estimates for some countries were still relatively close to the actual numbers in the forecasting long term. The estimated number for the world in general was about 8 times that actually observed for the long term. Conclusions: The Verhulst-Pearl equation has the advantage of being very straightforward and applicable in clinical use for predicting the demand on hospitals in the short term of 4-6 weeks, which is usually enough time to reschedule elective procedures and free beds for new waves of the pandemic patients.
dc.identifier.doi10.1016/j.cegh.2021.100853
dc.identifier.eissn2213-3984
dc.identifier.issn2452-0918
dc.identifier.pubmed34395949
dc.identifier.urihttps://hdl.handle.net/11424/243773
dc.identifier.wosWOS:000704764700009
dc.language.isoeng
dc.publisherELSEVIER - DIVISION REED ELSEVIER INDIA PVT LTD
dc.relation.ispartofCLINICAL EPIDEMIOLOGY AND GLOBAL HEALTH
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectCOVID-19
dc.subjectEpidemic forecasting
dc.subjectMathematical model
dc.subjectPandemic
dc.subjectSARS-CoV-2
dc.subjectHERD-IMMUNITY
dc.subjectPANDEMIC INFLUENZA
dc.subjectGROWTH
dc.subjectEPIDEMIC
dc.subjectMODELS
dc.subjectRATES
dc.titleA simple mathematical tool to forecast COVID-19 cumulative case numbers
dc.typearticle
dspace.entity.typePublication
local.avesis.idebef789b-e41c-469c-beb0-c51364ecffa6
local.import.packageSS16
local.indexed.atWOS
local.indexed.atSCOPUS
local.indexed.atPUBMED
local.journal.articlenumber100853
local.journal.numberofpages11
oaire.citation.titleCLINICAL EPIDEMIOLOGY AND GLOBAL HEALTH
oaire.citation.volume12
relation.isAuthorOfPublication210d3190-cd43-4955-8da3-32d8fcdc12d6
relation.isAuthorOfPublication.latestForDiscovery210d3190-cd43-4955-8da3-32d8fcdc12d6

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