Machine learning for emerging infectious disease field responses.

Emerging infectious diseases (EIDs), including the latest COVID-19 pandemic, have emerged and raised global public health crises in recent decades. Without existing protective immunity, an EID may spread rapidly and cause mass casualties in a very short time. Therefore, it is imperative to identify cases with risk of disease progression for the optimized allocation of medical resources in case medical facilities are overwhelmed with a flood of patients.

National retrospective cohort study to identify age-specific fatality risks of comorbidities among hospitalised patients with influenza-like illness in Taiwan.

Objectives: This study aimed to examine comprehensively the prognostic impact of underlying comorbidities among hospitalised patients with influenza-like illness (ILI) in different age groups and provide recommendations targeting the vulnerable patients.

Setting And Participants: A retrospective cohort of 83 227 hospitalised cases with ILI were identified from Taiwan's National Health Insurance Research Database from January 2005 to December 2010. Cases were stratified into three different age groups: paediatric (0-17 years), adult (18-64 years) and elderly (≧65 years), and their age, sex, comorbidity and past healthcare utilisation were analysed for ILI-associated fatality.