Background: Mexico has experienced six waves were experiences between 2019 and 2023 of coronavirus disease 2019 (COVID-19). In the first wave, diagnosis relied on RT-qPCR;rapid tests for SARS-CoV-2 were introduced fro...
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Background: Mexico has experienced six waves were experiences between 2019 and 2023 of coronavirus disease 2019 (COVID-19). In the first wave, diagnosis relied on RT-qPCR;rapid tests for SARS-CoV-2 were introduced from the second wave onward. However, the patterns of results with this test in Mexico have not been documented in population-based studies. Objective: To describe the temporal patterns of the results from RT-qPCR and rapid immunochromatography tests for SARS-CoV-2 detection from the second to the sixth epidemic wave in Mexico. Materials and methods: This was a multicenter study at the national level. The operational definition of suspected cases of viral respiratory illness, confirmed cases of SARS-CoV-2 in Mexico, and data for asymptomatic individuals who required rapid testing between 2020 and 2023 were employed;testing was employed for the entirety of the four years. Positivity was estimated per epidemiological week, state, and condition for both tests. Spearman's correlation coefficient, trend chi-square analysis, and odds ratios with 95 % confidence intervals were used. Results: A total of 1749,765 cases had RT-qPCR data recorded from 2020 to 2023, and 8356,903 cases with rapid tests were conducted for virus identification. Compared with the rest of the country, the southeastern region exhibited different patterns. The positivity rate of rapid tests from the epidemiological surveillance system was lower than that of RT-qPCR during interepidemics periods, whereas the positivity rate of rapid tests for symptomatic individuals was higher than that of RT-qPCR tests over two years. Conclusion: Rapid tests for SARS-CoV-2 identification in Mexico were affordable and timely at the local level. These tests revealed differing epidemic wave patterns in the southeastern region of the country.
Introduction Dengue is a disease with a wide clinical spectrum. The early identification of dengue cases is crucial but challenging for health professionals;therefore, it is necessary to have effective diagnostic inst...
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Introduction Dengue is a disease with a wide clinical spectrum. The early identification of dengue cases is crucial but challenging for health professionals;therefore, it is necessary to have effective diagnostic instruments to initiate timely *** To evaluate the effectiveness of an algorithm based on an artificial neural network (ANN) to diagnose dengue in an endemic *** A single-center case-control study was conducted in a secondary-care hospital in Ciudad Obregon, Sonora. An algorithm was built with the official operational definitions, which was called the "direct algorithm," and for the ANN algorithm, the *** library was used. The data analysis was performed with the diagnostic tests of sensitivity, specificity, positive predictive value (ppv), and negative predictive value (npv), with 95% confidence intervals and Cohen's kappa *** A total of 233 cases and 233 controls from 2022 were included. The ANN presented a sensitivity of 0.90 (95% CI [0.85, 0.94]), specificity of 0.82 (95% CI [0.77, 0.87]), npv of 0.91 (95% CI [0.87, 0.94]) and ppv of 0.81 (95% CI [0.76, 0.85]) and a kappa of 0.72. The direct algorithm had a sensitivity of 0.97 (95% CI [0.94, 0.99]), specificity of 0.96 (95% CI [0.92, 0.98]), npv 0.97 (95% CI [0.94, 0.98]), ppv 0.96 (95% CI [0.93, 0.98]) and kappa *** The direct algorithm performed better than the ANN in the diagnosis of dengue.
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