OBJECTIVE: To describe the demographic and clinical characteristics of patients hospitalized with pandemic A(H1N1) 2009 infection in Queensland, Australia between 25 May and 3 October 2009 and to examine the relationship between timing of antiviral treatment and severity of illness.

METHOD: Using data from the Queensland Health EpiLog information system, descriptive analysis and logistic regression modelling were used to describe and model factors which influence patient outcomes (death, admission to intensive care unit and/or special care unit). Data on patients admitted to hospital in Queensland with confirmed pandemic A(H1N1) 2009 infection were included in this analysis.

RESULTS: 1236 patients with pandemic A(H1N1) 2009 infection were admitted to hospitals in Queensland during the study period. Of the total group: 15% were admitted to an intensive care unit or special care unit; 3% died; 34% were under the age of 18 years and 8% were 65 years of age or older; and 55% had at least one underlying medical condition. Among the 842 patients for whom data were available regarding the use of antiviral drugs, antiviral treatment was initiated in 737 (87.5%) patients, treatment commenced at a median of one day (range 1–33 days) after onset of illness. Admission to an intensive care unit or special care unit (ICU/SCU) or death was significantly associated with increased age, lack of timeliness of antiviral treatment, chronic renal disease and morbid obesity.

DISCUSSON: Early antiviral treatment was significantly associated with lower likelihood of ICU/SCU admission or death. Early antiviral treatment for influenza cases may therefore have important public health implications.

Objectives: Online misinformation has reached unprecedented levels during the coronavirus disease 2019 (COVID-19) pandemic. This study analyzed the magnitude and sentiment dynamics of misinformation and unverified information about public health interventions during a COVID-19 outbreak in Da Nang, Vietnam, between July and September 2020. Methods: We analyzed user-generated online information about five public health interventions during the Da Nang outbreak. We compared the volume, source, sentiment polarity, and engagements of online posts before, during, and after the outbreak using negative binomial and logistic regression, and assessed the content validity of the 500 most influential posts. Results: Most of the 54,528 online posts included were generated during the outbreak (n = 46,035; 84.42%) and by online newspapers (n = 32,034; 58.75%). Among the 500 most influential posts, 316 (63.20%) contained genuine information, 10 (2.00%) contained misinformation, 152 (30.40%) were non-factual opinions, and 22 (4.40%) contained unverifiable information. All misinformation posts were made during the outbreak, mostly on social media, and were predominantly negative. Higher levels of engagement were observed for information that was unverifiable (incidence relative risk [IRR] = 2.83; 95% confidence interval [CI], 1.33–0.62), posted during the outbreak (before: IRR = 0.15; 95% CI, 0.07–0.35; after: IRR = 0.46; 95% CI, 0.34-0.63), and with negative sentiment (IRR = 1.84; 95% CI, 1.23–2.75). Negatively toned posts were more likely to be misinformation (odds ratio [OR] = 9.59; 95% CI, 1.20–76.70) or unverified (OR = 5.03; 95% CI, 1.66–15.24). Conclusions Misinformation and unverified information during the outbreak showed clustering, with social media being particularly affected. This indepth assessment demonstrates the value of analyzing online “infodemics” to inform public health responses.