Effectiveness of a diagnostic algorithm for dengue based on an artificial neural network

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 care. Objective To evaluate the effectiveness of an algorithm based on an artificial neural network (ANN) to diagnose dengue in an endemic area. Methods A single-center case–control study was conducted in a secondary-care hospital in Ciudad Obregón, Sonora. An algorithm was built with the official operational definitions, which was called the “direct algorithm,” and for the ANN algorithm, the brain.js 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 index. Results 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 0.93. Conclusions The direct algorithm performed better than the ANN in the diagnosis of dengue

Predict the incidence of Guillain Barré Syndrome and arbovirus infection in Mexico, 2014-2019.

The Dengue (DENV), Zika (ZIKV), and Chikungunya (CHIKV) virus infections have been linked to Guillain-Barré syndrome (GBS). GBS has an estimated lethality of 4% to 8%, even with effective treatment. Mexico is considered a hyperendemic country for DENV due to the circulation of four serotypes, and the ZIKV and CHIKV viruses have also been circulating in the country. The objective of this study was to predict the number of GBS cases in relation to the cumulative incidence of ZIKV / DENV / CHIKV in Mexico from 2014 to 2019. A six-year time series ecological study was carried out from GBS cases registered in the Acute Flaccid Paralysis (AFP) Epidemiological Surveillance System (ESS), and DENV, ZIKV and CHIKV estimated cases from cases registered in the epidemiological vector-borne diseases surveillance system. The results shows that the incidence of GBS in Mexico is positively correlated with DENV and ZIKV. For every 1,000 estimated DENV cases, 1.45 GBS cases occurred on average, and for every 1,000 estimated ZIKV cases, 1.93 GBS cases occurred on average. A negative correlation between GBS and CHIKV estimated cases was found. The increase in the incidence of GBS cases in Mexico can be predicted by observing DENV and ZIKV cases through the epidemiological surveillance systems. These results can be useful in public health by providing the opportunity to improve capacities for the prevention of arbovirus diseases and for the timely procurement of supplies for the treatment of GBS