ArticleClinical epidemiology and global health
A simple mathematical tool to forecast COVID-19 cumulative case numbers.
Article in Clinical epidemiology and global health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Quo Vadis Temporomandibular Disorders? By 2050, the Global Prevalence of TMD May Approach 44.Journal of clinical medicine · 2025Article
- The impact of the COVID-19 pandemic on neurosurgery in the elderly population in Sweden.BMC public health · 2024Observational
- Effects of the COVID-19 Pandemic on Everyday Neurosurgical Practice in Alsace, France: Lessons Learned, Current Perspectives, and Future Challenges-Preliminary Results of a Longitudinal Multicentric Study Registry.Medicina (Kaunas, Lithuania) · 2024Article
- Predicting the distribution of COVID-19 through CGAN-Taking Macau as an example.Frontiers in big data · 2023Article
- The crucial role of ethical hospital administration in neurosurgery education.Frontiers in health services · 2022Article
- Real-time mortality statistics during the COVID-19 pandemic: A proposal based on Spanish data, January-March, 2021.Frontiers in public health · 2022Article
- Unequal Impact of COVID-19 on Private and Academic Neurosurgical Workforce: Results of an International Survey.Frontiers in surgery · 2021Article
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveMathematical 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
methodsAs 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.
resultsThe 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.
conclusionsThe 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.
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