Applying symptom dynamics to accurately predict influenza virus infection: An international multicenter influenza‐like illness surveillance study

Abstract

Background Public health organizations have recommended various definitions of influenza‐like illnesses under the assumption that the symptoms do not change during influenza virus infection. To explore the relationship between symptoms and influenza over time, we analyzed a dataset from an international multicenter prospective emergency department (ED)‐based influenza‐like illness cohort study. Methods We recruited patients in the US and Taiwan between 2015 and 2020 with: (1) flu‐like symptoms (fever and cough, headache, or sore throat), (2) absence of any of the respiratory infection symptoms, or (3) positive laboratory test results for influenza from the current ED visit. We evaluated the association between the symptoms and influenza virus infection on different days of illness. The association was evaluated among different subgroups, including different study countries, influenza subtypes, and only patients with influenza. Results Among the 2471 recruited patients, 45.7% tested positive for influenza virus. Cough was the most predictive symptom throughout the week (odds ratios [OR]: 7.08–11.15). In general, all symptoms were more predictive during the first 2 days (OR: 1.55–10.28). Upper respiratory symptoms, such as sore throat and productive cough, and general symptoms, such as body ache and fatigue, were more predictive in the first half of the week (OR: 1.51–3.25). Lower respiratory symptoms, such as shortness of breath and wheezing, were more predictive in the second half of the week (OR: 1.52–2.52). Similar trends were observed for most symptoms in the different subgroups. Conclusions The time course is an important factor to be considered when evaluating the symptoms of influenza virus infection.

Multiplexed on-site sample-in-result-out test through microfluidic real-time PCR (MONITOR) for the detection of multiple pathogens causing influenza-like illness

“…This enables precise outbreak risk assessment and the implementation of suitable public health interventions, ultimately curtailing pathogen spread, reducing disease burden, and minimizing severe complications and mortality. Consequently, many countries employ ILI as an indicator to track respiratory infections and provide preemptive alerts regarding emerging infectious diseases ( 1 , 3 – 5 ). The global COVID-19 pandemic, responsible for widespread devastation, was initially isolated from throat swabs of patients with respiratory infections ( 6 ).…”

lab: an R package for generating analysis-ready data from laboratory records

“…Leveraging secondary EHR data, clinicians and researchers can gain deeper insights into disease progression, identify appropriate treatments strategies ( Safran et al, 2007 ; Murdoch & Detsky, 2013 ), and make data-driven healthcare decisions in healthcare ( Hersh, 2007 ; Rudin et al, 2020 ). Within EHR, laboratory test results play a crucial role ( Murdoch & Detsky, 2013 ; Shickel et al, 2017 ), providing valuable information for developing risk models and predicting disease progression ( Perotte et al, 2015 ; Tseng et al, 2019 ; Norgeot et al, 2019 ; Li et al, 2023 ; Chien et al, 2023 ). These predictive models can be further enhanced by incorporating machine learning and deep learning technologies, enabling more accurate assessments and predictions.…”