Deaths are frequently under-estimated during emergencies, times when accurate mortality estimates are crucial for emergency response. This study estimates excess all-cause, pneumonia and influenza mortality during the coronavirus disease 2019 (COVID-19) pandemic using the 11 September 2020 release of weekly mortality data from the United States (U.S.) Mortality Surveillance System (MSS) from 27 September 2015 to 9 May 2020, using semiparametric and conventional time-series models in 13 states with high reported COVID-19 deaths and apparently complete mortality data: California, Colorado, Connecticut, Florida, Illinois, Indiana, Louisiana, Massachusetts, Michigan, New Jersey, New York, Pennsylvania and Washington. We estimated greater excess mortality than official COVID-19 mortality in the U.S. (excess mortality 95% confidence interval (CI) 100 013–127 501 vs. 78 834 COVID-19 deaths) and 9 states: California (excess mortality 95% CI 3338–6344) vs. 2849 COVID-19 deaths); Connecticut (excess mortality 95% CI 3095–3952) vs. 2932 COVID-19 deaths); Illinois (95% CI 4646–6111) vs. 3525 COVID-19 deaths); Louisiana (excess mortality 95% CI 2341–3183 vs. 2267 COVID-19 deaths); Massachusetts (95% CI 5562–7201 vs. 5050 COVID-19 deaths); New Jersey (95% CI 13 170–16 058 vs. 10 465 COVID-19 deaths); New York (95% CI 32 538–39 960 vs. 26 584 COVID-19 deaths); and Pennsylvania (95% CI 5125–6560 vs. 3793 COVID-19 deaths). Conventional model results were consistent with semiparametric results but less precise. Significant excess pneumonia deaths were also found for all locations and we estimated hundreds of excess influenza deaths in New York. We find that official COVID-19 mortality substantially understates actual mortality, excess deaths cannot be explained entirely by official COVID-19 death counts. Mortality reporting lags appeared to worsen during the pandemic, when timeliness in surveillance systems was most crucial for improving pandemic response.

Influenza is a major human respiratory pathogen. Due to the high levels of influenza-like illness (ILI) in Zhejiang, China, the control and prevention of influenza was challenging during the 2017–2018 season. To identify the clinical spectrum of illness related to influenza and characterise the circulating influenza virus strains during this period, the characteristics of ILI were studied. Viral sequencing and phylogenetic analyses were conducted to investigate the virus types, substitutions at the amino acid level and phylogenetic relationships between sequences. This study has shown that the 2017/18 influenza season was characterised by the co-circulation of influenza A (H1N1) pdm09, A (H3N2) and B viruses (both Yamagata and Victoria lineage). From week 36 of 2017 to week 12 of 2018, ILI cases accounted for 5.58% of the total number of outpatient and emergency patient visits at the surveillance sites. Several amino acid substitutions were detected. Vaccination mismatch may be a potential reason for the high percentage of ILI. Furthermore, it is likely that multiple viral introductions played a role in the endemic co-circulation of influenza in Zhejiang, China. More detailed information regarding the molecular epidemiology of influenza should be included in long-term influenza surveillance.

In recent years, there have been a significant influenza activity and emerging influenza strains in China, resulting in an increasing number of influenza virus infections and leading to public health concerns. The aims of this study were to identify the epidemiological and aetiological characteristics of influenza and establish seasonal autoregressive integrated moving average (SARIMA) models for forecasting the percentage of visits for influenza-like illness (ILI%) in urban and rural areas of Shenyang. Influenza surveillance data were obtained for ILI cases and influenza virus positivity from 18 sentinel hospitals. The SARIMA models were constructed to predict ILI% for January–December 2019. During 2010–2018, the influenza activity was higher in urban than in rural areas. The age distribution of ILI cases showed the highest rate in young children aged 0–4 years. Seasonal A/H3N2, influenza B virus and pandemic A/H1N1 continuously co-circulated in winter and spring seasons. In addition, the SARIMA (0, 1, 0) (0, 1, 2)12 model for the urban area and the SARIMA (1, 1, 1) (1, 1, 0)12 model for the rural area were appropriate for predicting influenza incidence. Our findings suggested that there were regional and seasonal distinctions of ILI activity in Shenyang. A co-epidemic pattern of influenza strains was evident in terms of seasonal influenza activity. Young children were more susceptible to influenza virus infection than adults. These results provide a reference for future influenza prevention and control strategies in the study area.

Knowing the burden of influenza is helpful for policy decisions. Here we estimated the contribution of influenza-like illness (ILI) visits associated with laboratory-confirmed influenza among all clinic visits in a Senegal sentinel network. ILI data from ten sentinel sites were collected from January 2013 to December 2015. ILI was defined as an axillary measured fever of more than 37.5 °C with a cough or a sore throat. Collected nasopharyngeal swabs were tested for influenza viruses by rRT-PCR. Influenza-associated ILI was defined as ILI with laboratory-confirmed influenza. For the influenza disease burden estimation, we used all-case outpatient visits during the study period who sought care at selected sites. Of 4030 ILI outpatients tested, 1022 were influenza positive. The estimated proportional contribution of influenza-associated ILI was, per 100 outpatients, 1.2 (95% CI 1.1–1.3), 0.32 (95% CI 0.28–0.35), 1.11 (95% CI 1.05–1.16) during 2013, 2014, 2015, respectively. The age-specific outpatient visits proportions of influenza-associated ILI were higher among children under 5 years (0.68%, 95% CI: 0.62–0.70). The predominant virus during years 2013 and 2015 was influenza B while A/H3N2 subtype was predominant during 2014. Influenza viruses cause a substantial burden of outpatient visits particularly among children under 5 of age in Senegal and highlight the need of vaccination in risk groups.

The Houston Health Department (HHD) in Texas tracks influenza-like illness (ILI) in the community through its Influenza Sentinel Surveillance Program, which began in 2008. After the influenza A(H1N1) pandemic (pH1N1) in 2009, investigators sought to assess the feasibility of this program as a non-traditional data source for tracking and monitoring care-seeking activities. Through the process of characterizing and describing patients who had ‘return visits’, or who were considered the heaviest ILI-related care-utilizers, the investigators sought to understand the strengths and limitations of this data source. Data used for this study were obtained from a multispecialty clinic in Houston, Texas between August 2008 and January 2011 across three phases: pre-pH1N1, pH1N1, and post-pH1N1. The data, which comprised of 4047 patient visits, yielded 150 return visits. We found an increase in the number of visits for ILI and proportion of return visits during the pandemic phase (pH1N1), as well as differences in the likelihood of a return visit between genders and age groups. More broadly, the findings of this study provide important considerations for future research and expose important gaps in using surveillance data to assess sick-role behaviors.

In 2013–2014, the Public Health Agency of Sweden developed a web-based participatory surveillance system, Hӓlsorapport, based on a random sample of individuals reporting symptoms weekly online, to estimate the community incidence of self-reported acute gastrointestinal (AGI), acute respiratory (ARI) and influenza-like (ILI) illnesses and their severity. We evaluated Hӓlsorapport's acceptability, completeness, representativeness and its data correlation with other surveillance data. We calculated response proportions and Spearman correlation coefficients (r) between (i) incidence of illnesses in Hӓlsorapport and (ii) proportions of specific search terms to medical-advice website and reasons for calling a medical advice hotline. Of 34 748 invitees, 3245 (9·3%) joined the cohort. Participants answered 81% (139 013) of the weekly questionnaires and 90% (16 351) of follow-up questionnaires. AGI incidence correlated with searches on winter-vomiting disease [r = 0·81, 95% confidence interval (CI) 0·69–0·89], and ARI incidence correlated with searches on cough (r = 0·77, 95% CI 0·62–0·86). ILI incidence correlated with the web query-based estimated incidence of ILI patients consulting physicians (r = 0·63, 95% CI 0·42–0·77). The high response to different questionnaires and the correlation with other syndromic surveillance systems suggest that Hӓlsorapport offers a reasonable representation of AGI, ARI and ILI patterns in the community and can complement traditional and syndromic surveillance systems to estimate their burden in the community.

We compared the rates of fever in adult subjects with laboratory-confirmed influenza and other respiratory viruses and examined the factors that predict fever in adults. Symptom data on 158 healthcare workers (HCWs) with a laboratory-confirmed respiratory virus infection were collected using standardized data collection forms from three separate studies. Overall, the rate of fever in confirmed viral respiratory infections in adult HCWs was 23·4% (37/158). Rates varied by virus: human rhinovirus (25·3%, 19/75), influenza A virus (30%, 3/10), coronavirus (28·6%, 2/7), human metapneumovirus (28·6%, 2/7), respiratory syncytial virus (14·3%, 4/28) and parainfluenza virus (8·3%, 1/12). Smoking [relative risk (RR) 4·65, 95% confidence interval (CI) 1·33–16·25] and co-infection with two or more viruses (RR 4·19, 95% CI 1·21–14·52) were significant predictors of fever. Fever is less common in adults with confirmed viral respiratory infections, including influenza, than described in children. More than 75% of adults with a viral respiratory infection do not have fever, which is an important finding for clinical triage of adult patients with respiratory infections. The accepted definition of ‘influenza-like illness’ includes fever and may be insensitive for surveillance when high case-finding is required. A more sensitive case definition could be used to identify adult cases, particularly in event of an emerging viral infection.

Data were pooled from three Australian sentinel general practice influenza surveillance networks to estimate Australia-wide influenza vaccine coverage and effectiveness against community presentations for laboratory-confirmed influenza for the 2012, 2013 and 2014 seasons. Patients presenting with influenza-like illness at participating GP practices were swabbed and tested for influenza. The vaccination odds of patients testing positive were compared with patients testing negative to estimate influenza vaccine effectiveness (VE) by logistic regression, adjusting for age group, week of presentation and network. Pooling of data across Australia increased the sample size for estimation from a minimum of 684 to 3,683 in 2012, from 314 to 2,042 in 2013 and from 497 to 3,074 in 2014. Overall VE was 38% [95% confidence interval (CI) 24–49] in 2012, 60% (95% CI 45–70) in 2013 and 44% (95% CI 31–55) in 2014. For A(H1N1)pdm09 VE was 54% (95% CI–28 to 83) in 2012, 59% (95% CI 33–74) in 2013 and 55% (95% CI 39–67) in 2014. For A(H3N2), VE was 30% (95% CI 14–44) in 2012, 67% (95% CI 39–82) in 2013 and 26% (95% CI 1–45) in 2014. For influenza B, VE was stable across years at 56% (95% CI 37–70) in 2012, 57% (95% CI 30–73) in 2013 and 54% (95% CI 21–73) in 2014. Overall VE against influenza was low in 2012 and 2014 when A(H3N2) was the dominant strain and the vaccine was poorly matched. In contrast, overall VE was higher in 2013 when A(H1N1)pdm09 dominated and the vaccine was a better match. Pooling data can increase the sample available and enable more precise subtype- and age group-specific estimates, but limitations remain.

Seasonal respiratory infections place an increased burden on health services annually. We used a sentinel emergency department syndromic surveillance system to understand the factors driving respiratory attendances at emergency departments (EDs) in England. Trends in different respiratory indicators were observed to peak at different points during winter, with further variation observed in the distribution of attendances by age. Multiple linear regression analysis revealed acute respiratory infection and bronchitis/bronchiolitis ED attendances in patients aged 1–4 years were particularly sensitive indicators for increasing respiratory syncytial virus activity. Using near real-time surveillance of respiratory ED attendances may provide early warning of increased winter pressures in EDs, particularly driven by seasonal pathogens. This surveillance may provide additional intelligence about different categories of attendance, highlighting pressures in particular age groups, thereby aiding planning and preparation to respond to acute changes in EDs, and thus the health service in general.

We analysed long-term epidemiological trends in influenza-like illness (ILI) in The Netherlands and used an ecological analysis to estimate its relationship with age, influenza vaccination, and virological aspects. This study used data from weekly ILI consultation reports from sentinel general practitioners (1986/1987 to 2006/2007), virological data from sentinel ILI patients (1992/1993 to 2006/2007), and data for influenza vaccine uptake (1991–2005). The incidence of ILI consultations, although varying during the study period, was estimated to decrease in the total population by 12·2/10 000 persons each season (95% CI 8·6–15·9). Uptake of influenza vaccination in people aged ⩾65 years (elderly) increased from 28% in 1991 to >70% since 1997. ILI incidence in the elderly declined by 1·7/10 000 persons (P=0·05) per percentage vaccine uptake per season. The decline in ILI incidence over the last 20 years could be related to the increased vaccine uptake. However, insufficient data were present to assess the impact of other potential contributing factors, such as diminished fitness of influenza viruses and changes in consulting behaviour.