Analysis of google trends and search results for ophthalmic symptoms of monkeypox.

PURPOSE: In July 2022, the World Health Organization (WHO) declared monkeypox virus's global spread a "public health emergency of international concern." About a quarter of monkeypox cases feature ophthalmic symptoms. We assessed trends in worldwide search interest in monkeypox ophthalmic involvement and inclusion in online search engine queries. METHODS: The following keywords were searched on Google Trends from April 1, 2022, to August 12, 2022: monkeypox + eye, pink eye, eye infection, eyelid, vision, blurry vision, vision loss, blindness, eye symptoms, eye problems, eye pain, eye redness, conjunctivitis, conjunctiva, cornea, keratitis, corneal ulcer, and blepharitis. We analyzed trends, correlated search interest with case count data, and compared popularity of search terms via nonparametric Mann-Whitney-U analysis. Inclusion of ophthalmic symptoms in Google search results for "monkeypox symptoms" was assessed. RESULTS: "Monkeypox eye" had the highest average search interest worldwide and in the United States. Search interest peaked between mid-May and late July 2022. When compared to interest in "monkeypox rash," the most searched monkeypox symptom, the average interest in "monkeypox eye" was lower (p < 0.01). Of the first 50 results from the Google search of "monkeypox symptoms," 10/50 (20%) mentioned ophthalmic symptoms. 6/50 (12%) mentioned the eye as a route of virus transmission. CONCLUSION: Search interest in monkeypox ophthalmic symptoms corresponds with geographic and temporal trends, i.e., timing and location of the first reported non-endemic cases and WHO announcement. Although ophthalmic symptoms are not as widely searched currently, inclusion in public health messaging is key for diagnosis, appropriate management, and reduction of further transmission.

From Fitting the Average to Fitting the Individual: A Cautionary Tale for Mathematical Modelers.

An outstanding challenge in the clinical care of cancer is moving from a one-size-fits-all approach that relies on population-level statistics towards personalized therapeutic design. Mathematical modeling is a powerful tool in treatment personalization, as it allows for the incorporation of patient-specific data so that treatment can be tailor-designed to the individual. Herein, we work with a mathematical model of murine cancer immunotherapy that has been previously-validated against the average of an experimental dataset. We ask the question: what happens if we try to use this same model to perform personalized fits, and therefore make individualized treatment recommendations? Typically, this would be done by choosing a single fitting methodology, and a single cost function, identifying the individualized best-fit parameters, and extrapolating from there to make personalized treatment recommendations. Our analyses show the potentially problematic nature of this approach, as predicted personalized treatment response proved to be sensitive to the fitting methodology utilized. We also demonstrate how a small amount of the right additional experimental measurements could go a long way to improve consistency in personalized fits. Finally, we show how quantifying the robustness of the average response could also help improve confidence in personalized treatment recommendations.