Dengue is a disease of high interest for public health in the affected localities. Dengue virus is transmitted by Aedes species and presents hyperendemic behaviors in tropical and subtropical regions. Colombia is one of the countries most affected by the dengue virus in the Americas. Its central-west region is a hot spot in dengue transmission, especially the Department of Antioquia, which has suffered from multiple dengue outbreaks in recent years (2015–2016 and 2019–2020). In this article, we perform a retrospective analysis of the confirmed dengue cases in Antioquia, discriminating by both subregions and dengue severity from 2015 to 2020. First, we conduct an exploratory analysis of the epidemic data, and then a statistical survival analysis is carried out using a Cox regression model. Our findings allow the identification of the hazard and socio-demographic patterns of dengue infections in the Colombian subtropical region of Antioquia from 2015 to 2020.

Many infectious diseases are deadly to humans. The Aedes aegypi mosquito is the principal vector of infectious diseases that include chikungunya, dengue, yellow fever, and zika. Some factors such as survival time and aging are vital in its development and capacity to transmit the pathogens, which in turn are affected by environmental factors such as temperature. In this paper, we consider aging as the biological wear and tear presented in some mosquito populations over time, whereas survival is considered as the maximum time that a mosquito lives. We propose statistical methods that are commonly used in engineering for reliability analysis to compare transmission riskiness among different mosquitoes. We conducted a case study in three Colombian cities: Bello, Riohacha, and Villavicencio. In this study, we detected that the Aedes aegypi female mosquitoes in Bello live longer than in Riohacha and Villavicencio, and the females in Riohacha live longer than those in Villavicencio. Regarding aging, the females from Riohacha age slower than in Villavicencio and the latter age slower than in Bello. Mosquito populations that age slower are considered young and the other ones are old. In addition, we detected that the females from Bello in the temperature range of 27 ${}^{\circ }$C–28 ${}^{\circ }$C age slower than those in Bello at higher temperatures. In general, a young female has a higher risk of transmitting a disease to humans than an old female, regardless of its survival time. These findings have not been previously reported in studies of this type of infectious diseases and contributed to new knowledge in biomedicine.

Arboviral diseases such as dengue pose major public health challenges in endemic regions, notably in Norte de Santander (Colombia), where they place substantial pressure on healthcare services. We analyzed 8,814 confirmed dengue cases reported to the Colombian National Public Health Surveillance System (SIVIGILA) from January 2015 to June 2019 to investigate temporal dynamics and determinants of hospitalization. We applied a dual methodology based on: (i) machine-learning classifiers—logistic regression, random forest, and support vector machines—to predict hospitalization risk from symptom profiles and (ii) Cox models with time-varying coefficients to assess the timing of hospitalization as a function of socio-environmental and clinical predictors, accommodating non-proportional hazards. Our main findings are as follows. On average, patients sought medical attention about four days after symptom onset. Severe and non-severe cases had similar onset-to-hospitalization times, but severe cases were often admitted shortly after the appearance of key warning signs. Abdominal pain and low platelet count markedly increased the risk of hospitalization in classification models and were associated with higher hazards of earlier hospitalization in the time-to-event analysis, with vomiting likewise linked to earlier hospitalization. Among classifiers, random forest achieved the highest predictive accuracy, whereas logistic regression and Cox models yielded interpretable estimates of risk (odds ratios) and timing (time-varying hazard ratios). These findings highlight the value of early recognition of specific symptoms and the integration of machine learning with survival analysis to support proactive, resource-aware dengue management. All analyses were conducted in the R software.

A finales del año 2019, en la ciudad china de Wuhan, emergió una nueva enfermedad respiratoria conocida como COVID-19 que es producida por el virus SARS-CoV-2, similar al virus causante del síndrome respiratorio agudo grave (SARS-CoV). Actualmente, esta enfermedad se esparció rápidamente a nivel mundial y ha presentado una gran cantidad de afectados en diferentes regiones del mundo; por lo tanto, la Organización Mundial de la Salud elevó su categoría de epidemia a pandemia debido a los niveles alarmantes de propagación, gravedad e inacción. Dada esta situación, diferentes áreas de la ciencia han abordado su estudio, entre ellas esta la epidemiología matemática a través del modelado del fenómeno; por lo tanto en el presente documento se realizó una revisión sistematizada de literatura relacionada a modelos de transmisión del COVID-19, específicamente modelos discretos debido a la naturaleza de reporte diaria de casos de la enfermedad en diferentes localidades del mundo. Se lograron identificar diferentes características importantes de la enfermedad que son implementadas en los modelos matemáticos: división por grupos etarios, metapoblaciones, migración, cuarentena, inclusión de periodos de latencia e incubación, entre otros. Aun así, se encontró una gran cantidad de artículos que utilizaban directamente modelos SIR y SEIR sin ningún tipo de modificación, haciendo una simplificación desmedida del fenómeno de transmisión del COVID-19. Asimismo, se identificaron algunas problemáticas a la hora de implementar los modelos matemáticos: la presencia y calidad de los datos de casos diarios en algunos países afectados. Finalmente, se sugiere que los modelos matemáticos estén en constante actualización junto a la publicación de las investigaciones relacionadas con virología y epidemiología de la enfermedad.