Characterizing Mpox (Monkeypox) Spread in the Americas Using Retrospective Spreadsheet Approach for Nonexperts.

Authors

Arthur Yosef, David Ezra, Idan Roth

Published in

Inquiry : a journal of medical care organization, provision and financing. Volume 63. Pages 469580261506197. Epub Nov 11, 2026.

Abstract

IntroductionThis study aimed to evaluate the spread of Mpox (monkeypox) during the 2022 outbreak in six countries across the Americas-Brazil, Chile, Colombia, Mexico, Peru, and the USA, using a simplified and accessible retrospective approach based on Microsoft Excel and non-expert analytical tools.MethodsWeekly confirmed Mpox case data were obtained from the Our World in Data repository between May and September 2022. For each country, Microsoft Excel was used to apply three trendline models, linear, quadratic, and exponential, to visualize and statistically assess case progression. Model fit and performance were rigorously evaluated using a comprehensive suite of evaluation metrics, including the coefficient of determination (R²), Mean Squared Error (MSE), Chi-square goodness-of-fit, and p-values.ResultsAcross the selected observation windows, the quadratic trendline provided the best in-sample fit among the three candidate functional forms in most countries in terms of R² values. For instance, in Mexico, the quadratic model achieved an R² of 0.987 compared to 0.882 (linear) and 0.888 (exponential). Similar trends were observed in the USA (R² = 0.986 for quadratic vs. 0.852 linear, and 0.813), Brazil (0.978 vs. 0.816 vs. 0.752), and Colombia (0.947 vs. 0.795 vs. 0.725). The chi-squared and Mean Squared Error (MSE) values supported these findings, with the quadratic model yielding lower errors and greater statistical significance in most cases (p < 0.001).ConclusionsAcross the selected observation windows, the quadratic trendline provided the best in-sample fit among the three candidate functional forms in most countries. These retrospective comparisons of descriptive curve fit findings provide practical value of the approach, in its accessibility and transparency for rapid trend characterization, though, is not a substitution for formally evaluated epidemic forecasting frameworks.

PMID:
43012160
Bibliographic data and abstract were imported from PubMed on 11 Nov 2026.

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