Incidence Of Influenza-like Illness Research Articles

TDEFSI

Influenza-like illness (ILI) places a heavy social and economic burden on our society. Traditionally, ILI surveillance data are updated weekly and provided at a spatially coarse resolution. Producing timely and reliable high-resolution spatiotemporal forecasts for ILI is crucial for local preparedness and optimal interventions. We present T heory-guided D eep Learning-based E pidemic F orecasting with S ynthetic I nformation (TDEFSI), 1 an epidemic forecasting framework that integrates the strengths of deep neural networks and high-resolution simulations of epidemic processes over networks. TDEFSI yields accurate high-resolution spatiotemporal forecasts using low-resolution time-series data. During the training phase, TDEFSI uses high-resolution simulations of epidemics that explicitly model spatial and social heterogeneity inherent in urban regions as one component of training data. We train a two-branch recurrent neural network model to take both within-season and between-season low-resolution observations as features and output high-resolution detailed forecasts. The resulting forecasts are not just driven by observed data but also capture the intricate social, demographic, and geographic attributes of specific urban regions and mathematical theories of disease propagation over networks. We focus on forecasting the incidence of ILI and evaluate TDEFSI’s performance using synthetic and real-world testing datasets at the state and county levels in the USA. The results show that, at the state level, our method achieves comparable/better performance than several state-of-the-art methods. At the county level, TDEFSI outperforms the other methods. The proposed method can be applied to other infectious diseases as well.

Burden and seasonality of medically attended influenza like illness (ILI) in Ethiopia, 2012 to 2017

BackgroundThe influenza virus spreads rapidly around the world in seasonal epidemics, resulting in significant morbidity and mortality. Influenza-related incidence data are limited in many countries in Africa despite established sentinel surveillance. This study aimed to address the information gap by estimating the burden and seasonality of medically attended influenza like illness in Ethiopia.MethodInfluenza sentinel surveillance data collected from 3 influenza like illness (ILI) and 5 Severe Acute Respiratory Illness (SARI) sites from 2012 to 2017 was used for analysis. Descriptive statistics were applied for simple analysis. The proportion of medically attended influenza positive cases and incidence rate of ILI was determined using total admitted patients and catchment area population. Seasonality was estimated based on weekly trend of ILI and predicted threshold was done by applying the “Moving Epidemic Method (MEM)”.ResultA total of 5715 medically attended influenza suspected patients who fulfills ILI and SARI case definition (77% ILI and 23% SARI) was enrolled. Laboratory confirmed influenza virus (influenza positive case) among ILI and SARI suspected case was 25% (1130/4426) and 3% (36/1289). Of which, 65% were influenza type A. The predominantly circulating influenza subtype were seasonal influenza A(H3N2) (n = 455, 60%) and Influenza A(H1N1)pdm09 (n = 293, 38.81%). The estimated mean annual influenza positive case proportion and ILI incidence rate was 160.04 and 52.48 per 100,000 population. The Incidence rate of ILI was higher in the age group of 15–44 years of age [‘Incidence rate (R) = 254.6 per 100,000 population’, 95% CI; 173.65, 335.55] and 5–14 years of age [R = 49.5, CI 95%; 31.47, 130.43]. The seasonality of influenza has two peak seasons; in a period from October–December and from April–June.ConclusionSignificant morbidity of influenza like illness was observed with two peak seasons of the year and seasonal influenza A (H3N2) remains the predominantly circulating influenza subtype. Further study need to be considered to identify potential risks and improving the surveillance system to continue early detection and monitoring of circulating influenza virus in the country has paramount importance.

Symptomatic COVID-19 in advanced-cancer patients treated with immune-checkpoint inhibitors: prospective analysis from a multicentre observational trial by FICOG.

Background:This prospective, multicentre, observational INVIDIa-2 study is investigating the clinical efficacy of influenza vaccination in advanced-cancer patients receiving immune-checkpoint inhibitors (ICIs), enrolled in 82 Italian centres, from October 2019 to January 2020. The primary endpoint was the incidence of influenza-like illness (ILI) until 30 April 2020. All the ILI episodes, laboratory tests, complications, hospitalizations and pneumonitis were recorded. Therefore, the study prospectively recorded all the COVID-19 ILI events.Patients and methods:Patients were included in this non-prespecified COVID-19 analysis, if alive on 31 January 2020, when the Italian government declared the national emergency. The prevalence of confirmed COVID-19 cases was detected as ILI episode with laboratory confirmation of SARS-CoV-2. Cases with clinical-radiological diagnosis of COVID-19 (COVID-like ILIs), were also reported.Results:Out of 1257 enrolled patients, 955 matched the inclusion criteria for this unplanned analysis. From 31 January to 30 April 2020, 66 patients had ILI: 9 of 955 cases were confirmed COVID-19 ILIs, with prevalence of 0.9% [95% confidence interval (CI): 0.3–2.4], a hospitalization rate of 100% and a mortality rate of 77.8%. Including 5 COVID-like ILIs, the overall COVID-19 prevalence was 1.5% (95% CI: 0.5–3.1), with 100% hospitalization and 64% mortality. The presence of elderly, males and comorbidities was significantly higher among patients vaccinated against influenza versus unvaccinated (p = 0.009, p < 0.0001, p < 0.0001). Overall COVID-19 prevalence was 1.2% for vaccinated (six of 482 cases, all confirmed) and 1.7% for unvaccinated (8 of 473, 3 confirmed COVID-19 and 5 COVID-like), p = 0.52. The difference remained non-significant, considering confirmed COVID-19 only (p = 0.33).Conclusion:COVID-19 has a meaningful clinical impact on the cancer-patient population receiving ICIs, with high prevalence, hospitalization and an alarming mortality rate among symptomatic cases. Influenza vaccination does not protect from SARS-CoV-2 infection.

Use of Ambulance Dispatch Calls for Surveillance of Severe Acute Respiratory Infections

Ambulance dispatches for respiratory syndromes reflect incidence of influenza-like illness in primary care. Associations are highest in children (15%–34% of respiratory calls attributable to influenza), out-of-office hours (9%), and highest urgency-level calls (9%–11%). Ambulance dispatches might be an additional source of data for severe influenza surveillance.

Effectiveness of a Behavior Change Intervention with Hand Sanitizer Use and Respiratory Hygiene in Reducing Laboratory-Confirmed Influenza among Schoolchildren in Bangladesh: A Cluster Randomized Controlled Trial.

Schoolchildren are commonly linked to influenza transmission. Handwashing with soap has been shown to decrease infections; however, improving handwashing practices using soap and water is difficult in low-resource settings. In these settings, alternative hygiene options, such as hand sanitizer, could improve handwashing promotion to reduce influenza virus infections. We conducted a cluster randomized control trial in 24 primary schools in Dhaka to assess the effectiveness of hand sanitizer and a respiratory hygiene education intervention in reducing influenza-like illness (ILI) and laboratory-confirmed influenza during June-September 2015. Twelve schools were randomly selected to receive hand sanitizer and respiratory hygiene education, and 12 schools received no intervention. Field staff actively followed children daily to monitor for new ILI episodes (cough with fever) through school visits and by phone if a child was absent. When an illness episode was identified, medical technologists collected nasal swabs to test for influenza viruses. During the 10-week follow-up period, the incidence of ILI per 1,000 student-weeks was 22 in the intervention group versus 27 in the control group (P-value = 0.4). The incidence of laboratory-confirmed influenza was 53% lower in the intervention schools (3/1,000 person-weeks) than in the control schools (6/1,000 person-weeks) (P-value = 0.01). Hand sanitizer and respiratory hygiene education can help to reduce the risk of influenza virus transmission in schools.

Forecasting dengue and influenza incidences using a sparse representation of Google trends, electronic health records, and time series data.

Dengue and influenza-like illness (ILI) are two of the leading causes of viral infection in the world and it is estimated that more than half the world’s population is at risk for developing these infections. It is therefore important to develop accurate methods for forecasting dengue and ILI incidences. Since data from multiple sources (such as dengue and ILI case counts, electronic health records and frequency of multiple internet search terms from Google Trends) can improve forecasts, standard time series analysis methods are inadequate to estimate all the parameter values from the limited amount of data available if we use multiple sources. In this paper, we use a computationally efficient implementation of the known variable selection method that we call the Autoregressive Likelihood Ratio (ARLR) method. This method combines sparse representation of time series data, electronic health records data (for ILI) and Google Trends data to forecast dengue and ILI incidences. This sparse representation method uses an algorithm that maximizes an appropriate likelihood ratio at every step. Using numerical experiments, we demonstrate that our method recovers the underlying sparse model much more accurately than the lasso method. We apply our method to dengue case count data from five countries/states: Brazil, Mexico, Singapore, Taiwan, and Thailand and to ILI case count data from the United States. Numerical experiments show that our method outperforms existing time series forecasting methods in forecasting the dengue and ILI case counts. In particular, our method gives a 18 percent forecast error reduction over a leading method that also uses data from multiple sources. It also performs better than other methods in predicting the peak value of the case count and the peak time.

Latitudes mediate the association between influenza activity and meteorological factors: A nationwide modelling analysis in 45 Japanese prefectures from 2000 to 2018

BackgroundCold and dry conditions were well-documented as a major determinant of influenza seasonality in temperate countries but the association may not be consistent when the climate in temperate areas is closer to that in sub-tropical areas. We hypothesized latitudes may mediate the association between influenza activity and meteorological factors in 45 Japanese prefectures. MethodsWe used the weekly incidence of influenza-like illness of 45 prefectures from 2000 to 2018 as a proxy for influenza activity in Japan, a temperate country lying off the east coast of Asia. A combination of generalized additive model and distributed lag nonlinear model was adopted to investigate the associations between meteorological factors (average temperature, relative humidity, total rainfall, and actual vapour pressure, a proxy for absolute humidity) and the influenza incidence. Kendall’s tau b (τ) and Spearman correlation coefficient (rs) between latitude and the adjusted relative risk (ARR) of each meteorological factor were also assessed. ResultsA higher vapour pressure was significantly associated with a lower influenza risk but the ARR strongly weakened along with a lower latitude (τ = −0.23, p-value = 0.02; rs = −0.33, p-value = 0.03). Lower temperature and lower relatively humidity were significantly associated with higher influenza risks in over 65% and around 40% of the prefectures respectively but the strength and significance of the correlations between their ARRs and latitude were weaker than that from vapour pressure. ConclusionEven though the range of latitudes in Japan is small (26°N–43°N), the relationships between meteorological factors and influenza activity were mediated by the latitude. Our study echoed absolute humidity played a more important role in relating influenza risk, but we on the other hand showed its effect on influenza activity could be hampered in a low-latitude temperate region, which have a warmer climate. These findings thus offer a high-resolution characterization of the role of meteorological factors on influenza seasonality.

Evaluating impact of school outreach vaccination programme in Hong Kong influenza season 2018 – 2019

ABSTRACT School-based vaccination, as a means to mitigate seasonal influenza outbreak, depends on attaining adequate coverage rate. We evaluated the potential of a fully subsidized school outreach vaccination (SOV) program to achieve epidemic prevention potential in Hong Kong. The purpose of this study was to evaluate the impact of SOV program 2018–2019 on influenza vaccination rates and influenza-like illness (ILI) in the primary school students and their household members during the influenza season. The vaccination rate was significantly higher in the schools offering SOV (intervention schools) (69.2% vs. 34.3%) than those not offering SOV (control schools) (p < .0001). The ILI rate was significantly reduced from 14.1% among non-vaccinated to 7.7% among vaccinated students (p < .0001). Influenza vaccine effectiveness against ILI was 45.3%. The vaccination rates of the household members were the same in both intervention and control schools except in the sub-group of preschool household members with the intervention significantly higher than the control group (43.8% vs. 32%, p < .0001). SOV program significantly improved influenza vaccine coverage, and the vaccine reduced ILI incidence. Extension of SOV program to all primary schools as well as kindergartens in Hong Kong could achieve epidemic prevention potential and should be evaluated.

2756. Pragmatic Assessment of Influenza Vaccine Effectiveness in the DoD (PAIVED), Influenza-Like-Illnesses (ILIs) Sub-Study at the Marine Corps Recruit Depot-San Diego, CA (MCRD-SD) During the 2018–2019 Influenza Season

BackgroundMilitary recruits suffer high rates of influenza and influenza-like illness (ILI) during training. ILIs may lead to morbidity, lost training time, and hospitalization. We evaluated the incidence and clinical outcomes of ILI among recruits at Marine Corps Recruit Depot San Diego (MCRD-SD) in a prospective trial of influenza vaccine efficacy.MethodsRecruits at MCRD-SD were enrolled to compare the effectiveness of 3 types of FDA approved influenza vaccine: Afluria®, an egg-based vaccine; Flucelvax®, a cell-culture-derived vaccine; and Flublok®, a recombinant vaccine. Four companies of recruits were enrolled sequentially from 28 November 2018 to 19 December 2018, then randomized in a 1:1:1 ratio. Participants were followed for 18 weeks at MCRD-SD and Camp Pendleton. All participants who presented with ILI symptoms at medical care sites underwent viral diagnostic testing in addition to immunologic studies. Recruits were excluded from participation if <18 years of age, if previously vaccinated in the 2018–2019 season, or if reporting allergy to the vaccines.ResultsOf 1338 recruits approached, 771 (57.6%) participants consented for enrollment. All recruits were men between 18 and 28 years. There were 182 ILIs amongst 177 recruits (23% of 771 recruits). Nasal swabs were obtained in 180/182 cases (99%). Mean duration of ILI symptoms was 7 days. Mean days of fever was 4. Subjects reported a total 168 days of reduced training (range 0–14 days; mean 0.9 days). There were 47 total days of missed training for all subjects (range of 0–4 days; mean 0.3 days/subject). There were no hospitalizations related to ILIs. Approximately 82% (148/182) of ILIs presented within the first 3 weeks of training; 44% (80/182) of ILIs occurred during the second week of training. PCR- nasal swabs results; race/ethnicity data, and frequency of ILI mapped to week of training are illustrated below.ConclusionILIs can negatively impact training effectiveness. Days lost to training from ILIs and hospitalizations can prevent successful completion of training with impact on military readiness. PAIVED may inform the DoD on future strategies to minimize influenza and other respiratory threats in recruit military populations. Influenza vaccine effectiveness will be reported separately. Disclosures All authors: No reported disclosures.

2757. Pragmatic Assessment of Influenza Vaccine Effectiveness in the DoD (PAIVED): Immunogenicity Sub-Study

BackgroundDespite greater than 90% of US active duty (AD) military personnel receiving influenza vaccination annually, vaccine effectiveness (VE) among AD members has been substantially lower than in groups with less vaccine uptake. The substrate used in vaccine production may impact immunogenicity and thus VE. The PAIVED study is investigating VE of 3 different influenza vaccine formulations; a sub-study assesses immunogenicity. This analysis compares demographic characteristics and influenza-like illness (ILI) experience among main and sub-study participants for the first year of PAIVED.MethodsDuring the 2018–2019 influenza season, PAIVED enrolled participants at 5 military medical centers, recruiting sub-study subjects from the main cohort excluding marine recruits. All participants were randomized (1:1:1) to receive either egg-based, cell-culture based or recombinant influenza vaccine. At enrollment, participants provided key demographic and behavioral data. Weekly surveillance for ILI symptoms was performed electronically. Sub-study volunteers underwent an additional blood draw prior to and at 21–35 days post vaccination ± an optional buccal swab.Results200 (23.5%) of 852 non-recruit PAIVED participants enrolled in the immunogenicity sub-study. Similar to the main cohort, 46% of sub-study volunteers were female, 85% were physically active, and 6% smoked tobacco. Sub-study participants were younger (47 ± 16 years vs. 51 ± 17 years, P = 0.004) and more likely to be AD (34% vs. 22%, P = 0.001). Although 70% of both groups identified as White, the percent African American (20% sub-study; 13% main), Asian (3%; 7%), multi-racial (2%; 5%), and unknown (6%; 4%) differed (P = 0.02). More sub-study participants developed an ILI (19% vs. 12%, P = 0.02).ConclusionThe convenience sampling method used for recruitment into the sub-study was effective. The younger age and higher AD status in the sub-study group may be informative for evaluation of military readiness issues. The greater incidence of ILI in the sub-study increases the chance differences in immune response by vaccine type may be interpretable in the context of circulating influenza strains. Targeted efforts to enhance recruitment of a racially diverse sub-study cohort may be warranted. Disclosures All authors: No reported disclosures.

The short-term effects of air pollutants on influenza-like illness in Jinan, China

BackgroundThere is valid evidence that air pollution is associated with respiratory disease. However, few studies have quantified the short-term effects of six air pollutants on influenza-like illness (ILI). This study explores the potential relationship between air pollutants and ILI in Jinan, China.MethodsDaily data on the concentration of particulate matters < 2.5 μm (PM 2.5), particulate matters < 10 μm (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), and ozone (O3) and ILI counts from 2016 to 2017 were retrieved. The wavelet coherence analysis and generalized poisson additive regression model were employed to qualify the relationship between air pollutants and ILI risk. The effects of air pollutants on different age groups were investigated.ResultsA total of 81,459 ILI counts were collected, and the average concentrations of PM2.5, PM10, O3, CO, SO2 and NO2 were 67.8 μg/m3, 131.76 μg/ m3, 109.85 μg/ m3, 1133 μg/ m3, 33.06 μg/ m3 and 44.38 μg/ m3, respectively. A 10 μg/ m3 increase in concentration of PM2.5, PM10, CO at lag0 and SO2 at lag01, was positively associated with a 1.0137 (95% confidence interval (CI): 1.0083–1.0192), 1.0074 (95% CI: 1.0041–1.0107), 1.0288 (95% CI: 1.0127–1.0451), and 1.0008 (95% CI: 1.0003–1.0012) of the relative risk (RR) of ILI, respectively. While, O3 (lag5) was negatively associated with ILI (RR 0.9863; 95%CI: 0.9787–0.9939), and no significant association was observed with NO2, which can increase the incidence of ILI in the two-pollutant model. A short-term delayed impact of PM2.5, PM10, SO2 at lag02 and CO, O3 at lag05 was also observed. People aged 25–59, 5–14 and 0–4 were found to be significantly susceptible to PM2.5, PM10, CO; and all age groups were significantly susceptible to SO2; People aged ≥60 year, 5–14 and 0–4 were found to be significantly negative associations with O3.ConclusionAir pollutants, especially PM2.5, PM10, CO and SO2, can increase the risk of ILI in Jinan. The government should create regulatory policies to reduce the level of air pollutants and remind people to practice preventative and control measures to decrease the incidence of ILI on pollution days.

High Public-Health Impact in an Influenza-B-Mismatch Season in Southern Italy, 2017-2018.

Background Yearly influenza epidemics have considerable effects on public health worldwide. The 2017-2018 influenza season in Italy was of greater severity than previous seasons. The aim of this study was to describe the 2017-2018 influenza season in Southern Italy and the molecular characteristics of the circulating viral strains. Methods The incidence of influenza-like illness (ILI) was analysed. Nasopharyngeal swabs collected from patients with ILI from week 46/2017 to week 17/2018 were tested to identify influenza A viruses (IAV) and influenza B viruses (IBV). Sequencing and phylogenetic analysis of haemagglutinin genes were also performed on 73 positive samples (35 IBV, 36 IAV H1, and 2 IAV H3 strains). Results During the 2017-2018 season, the peak incidence was 14.32 cases per 1,000 inhabitants. IBV strains were identified in 71.0% of cases. The 35 characterised IBV strains belonged to Yamagata lineage clade 3, the 36 A/H1N1pdm09 strains clustered with the genetic subgroup 6B.1, and the 2 A/H3N2 strains clustered with the genetic subgroup 3C.2a. Intensive-care unit (ICU) admission was required in 50 cases of acute respiratory distress syndrome (ARDS). Among the >64-year age group, 18 out of 26 ICU-ARDS cases (69.2%) were caused by IBV, and 14 of these (77.8%) were B/Yamagata lineage. Conclusions The 2017-2018 influenza season was one of the most severe in a decade in Southern Italy. IBV mismatch between the trivalent vaccine and the circulating strains occurred. The high number of ICU-ARDS cases caused by B/Yamagata strains in the >64-year age group suggests that further data on the effectiveness of the available influenza vaccines are needed to determine the best way to protect the elderly against both IBV lineages.

Assessment of two complementary influenza surveillance systems: sentinel primary care influenza-like illness versus severe hospitalized laboratory-confirmed influenza using the moving epidemic method

BackgroundMonitoring seasonal influenza epidemics is the corner stone to epidemiological surveillance of acute respiratory virus infections worldwide.This work aims to compare two sentinel surveillance systems within the Daily Acute Respiratory Infection Information System of Catalonia (PIDIRAC), the primary care ILI and Influenza confirmed samples from primary care (PIDIRAC-ILI and PIDIRAC-FLU) and the severe hospitalized laboratory confirmed influenza system (SHLCI), in regard to how they behave in the forecasting of epidemic onset and severity allowing for healthcare preparedness.MethodsEpidemiological study carried out during seven influenza seasons (2010–2017) in Catalonia, with data from influenza sentinel surveillance of primary care physicians reporting ILI along with laboratory confirmation of influenza from systematic sampling of ILI cases and 12 hospitals that provided data on severe hospitalized cases with laboratory-confirmed influenza (SHLCI-FLU). Epidemic thresholds for ILI and SHLCI-FLU (overall) as well as influenza A (SHLCI-FLUA) and influenza B (SHLCI-FLUB) incidence rates were assessed by the Moving Epidemics Method.ResultsEpidemic thresholds for primary care sentinel surveillance influenza-like illness (PIDIRAC-ILI) incidence rates ranged from 83.65 to 503.92 per 100.000 h. Paired incidence rate curves for SHLCI –FLU / PIDIRAC-ILI and SHLCI–FLUA/ PIDIRAC-FLUA showed best correlation index’ (0.805 and 0.724 respectively). Assessing delay in reaching epidemic level, PIDIRAC-ILI source forecasts an average of 1.6 weeks before the rest of sources paired. Differences are higher when SHLCI cases are paired to PIDIRAC-ILI and PIDIRAC-FLUB although statistical significance was observed only for SHLCI-FLU/PIDIRAC-ILI (p-value Wilcoxon test = 0.039).ConclusionsThe combined ILI and confirmed influenza from primary care along with the severe hospitalized laboratory confirmed influenza data from PIDIRAC sentinel surveillance system provides timely and accurate syndromic and virological surveillance of influenza from the community level to hospitalization of severe cases.

Use of ambulance dispatch calls for surveillance of severe acute respiratory infections?

ObjectiveWe aim to assess whether influenza circulation, as measured through influenza-like-illness (ILI) in primary care, is reflected in ambulance dispatch (AD) calls.IntroductionSurveillance of severe influenza infections is lacking in the Netherlands. Ambulance dispatch (AD) data may provide information about severity of the influenza epidemic and its burden on emergency services.The current gold standard, primary care-based surveillance of influenza-like-illness (ILI), mainly captures mild to moderate influenza cases, and does not provide adequate information on severe disease.Monitoring the severity of the annual epidemic, particularly among groups most at risk of complications, is of importance for the planning of health services and the public health response.MethodsWe analysed all calls from four ambulance dispatch centers serving 4.3 million people in the Netherlands, between January 2014 and December 2016. The main complaint and urgency level is recorded during triage; those possibly caused by respiratory infections were grouped as respiratory syndrome calls (RSC). We modelled the proportion of all RSC calls against the weekly ILI incidence (we allowed up to 4-week lags and leads), from sentinel primary-care surveillance. We used binomial regression with identity link to obtain differences in proportions. We built separate models by age group, urgency level and time of day. We tested heterogeneity of effects by season.ResultsWe included 289,307 calls; 6.7% were RSC. Overall, proportion of RSC increased by 0.114 percentage points for each increase of 1/10,000 population in ILI incidence. In our study population, this translated into 550 ambulance calls attributable to influenza (as measured by ILI) per year. Association was stronger in the models including only out-of-office hours, children (&lt;15 years) and highest urgency level calls. In the latter two, the effect varied by season. RSC was best associated with ILI from the previous 1-3 weeks in all models, except in children where RSC preceded ILI by 1 week.ConclusionsOur results demonstrate the potential usefulness of ambulance dispatch data to complement existing influenza surveillance by providing information on the volume and timing of severe cases attributable to influenza within the yearly epidemics.

Using the MCRISP Network to Study Acute Gastroenteritis and Influenza-Like Illness Outbreaks in Child Care Centers Compared to Statewide Epidemics

Introduction:Biosurveillance is critical for early detection of disease outbreaks and resource mobilization. Child care center (CCC) attendance has long been recognized as a significant independent predictor for respiratory and gastrointestinal diseases, but CCC surveillance is currently not part of the statewide disease surveillance system. The Michigan Child Care Related Infections Surveillance Program (MCRISP) is an independent, online reporting network with &gt;30 local CCCs that was created to fill this surveillance gap.Aim:To describe the capability of a novel CCC biosurveillance system (MCRISP) to report pediatric Influenza-Like Illness (ILI) and Acute Gastroenteritis (AGE) illness over three years to (i) assess both the timing and magnitude of epidemics in CCCs and (ii) compare CCC outbreak patterns with those of the state database.Methods:MCRISP collates real-time syndromic reports of illness from local county CCCs. The statewide Michigan Disease Surveillance System (MDSS) collects reports of diagnosed illness from designated laboratories, clinics, and hospitals statewide. We assessed epidemic curves based on MCRISP incidence rates and MDSS case counts for ILI and AGE over three seasons (2014-7).Results:A total of 4,627 MCRISP cases (2,425 ILI and 2,202 AGE reports) were reported during the three years of study surveillance. Epidemic patterns (seasonal peaks, troughs, and breadth) for both ILI and AGE in CCCs mirrored those reported at county and state levels, respectively. Two distinguishing features of CCC ILI outbreaks were noted in all three seasons: MCRISP ILI rates remained elevated after MDSS influenza counts abated, and MCRISP rates consistently peaked prior to MDSS influenza peaks. Neither of these phenomena were observed in comparing AGE outbreaks between surveillance systems.Discussion:ILI and AGE incidence rates from the MCRISP network appeared to broadly mirror epidemics from the established state surveillance system. MCRISP may act as a sentinel system for larger community outbreaks of respiratory disease.

Real-time prediction of influenza outbreaks in Belgium

Seasonal influenza is a worldwide public health concern. Forecasting its dynamics can improve the management of public health regulations, resources and infrastructure, and eventually reduce mortality and the costs induced by influenza-related absenteism. In Belgium, a network of Sentinel General Practitioners (SGPs) is in place for the early detection of the seasonal influenza epidemic. This surveillance network reports the weekly incidence of influenza-like illness (ILI) cases, which makes it possible to detect the epidemic onset, as well as other characteristics of the epidemic season. In this paper, we present an approach for predicting the weekly ILI incidence in real-time by resorting to a dynamically calibrated compartmental model, which furthermore takes into account the dynamics of other influenza seasons. In order to validate the proposed approach, we used data collected by the Belgian SGPs for the influenza seasons 2010–2016. In spite of the great variability among different epidemic seasons, providing weekly predictions makes it possible to capture variations in the ILI incidence. The confidence region becomes more representative of the epidemic behavior as ILI data from more seasons become available. Since the SIR model is then calibrated dynamically every week, the predicted ILI curve gets rapidly tuned to the dynamics of the ongoing season. The results show that the proposed method can be used to characterize the overall behavior of an epidemic.

Unsupervised extraction of epidemic syndromes from participatory influenza surveillance self-reported symptoms.

Seasonal influenza surveillance is usually carried out by sentinel general practitioners (GPs) who compile weekly reports based on the number of influenza-like illness (ILI) clinical cases observed among visited patients. This traditional practice for surveillance generally presents several issues, such as a delay of one week or more in releasing reports, population biases in the health-seeking behaviour, and the lack of a common definition of ILI case. On the other hand, the availability of novel data streams has recently led to the emergence of non-traditional approaches for disease surveillance that can alleviate these issues. In Europe, a participatory web-based surveillance system called Influenzanet represents a powerful tool for monitoring seasonal influenza epidemics thanks to aid of self-selected volunteers from the general population who monitor and report their health status through Internet-based surveys, thus allowing a real-time estimate of the level of influenza circulating in the population. In this work, we propose an unsupervised probabilistic framework that combines time series analysis of self-reported symptoms collected by the Influenzanet platforms and performs an algorithmic detection of groups of symptoms, called syndromes. The aim of this study is to show that participatory web-based surveillance systems are capable of detecting the temporal trends of influenza-like illness even without relying on a specific case definition. The methodology was applied to data collected by Influenzanet platforms over the course of six influenza seasons, from 2011-2012 to 2016-2017, with an average of 34,000 participants per season. Results show that our framework is capable of selecting temporal trends of syndromes that closely follow the ILI incidence rates reported by the traditional surveillance systems in the various countries (Pearson correlations ranging from 0.69 for Italy to 0.88 for the Netherlands, with the sole exception of Ireland with a correlation of 0.38). The proposed framework was able to forecast quite accurately the ILI trend of the forthcoming influenza season (2016-2017) based only on the available information of the previous years (2011-2016). Furthermore, to broaden the scope of our approach, we applied it both in a forecasting fashion to predict the ILI trend of the 2016-2017 influenza season (Pearson correlations ranging from 0.60 for Ireland and UK, and 0.85 for the Netherlands) and also to detect gastrointestinal syndrome in France (Pearson correlation of 0.66). The final result is a near-real-time flexible surveillance framework not constrained by any specific case definition and capable of capturing the heterogeneity in symptoms circulation during influenza epidemics in the various European countries.

“Epidemiology and aetiology of influenza-like illness among households in metropolitan Vientiane, Lao PDR”: A prospective, community-based cohort study

Respiratory diseases are a major contributor to morbidity and mortality in many tropical countries, including Lao PDR. However, little has been published regarding viral or bacterial pathogens that can contribute to influenza-like illness (ILI) in a community setting. We report on the results of a community-based surveillance that prospectively monitored the incidence of ILI and its causative pathogens in Vientiane capital in Lao PDR. A cohort of 995 households, including 4885 study participants, were followed-up between May 2015 and May 2016. Nasopharyngeal swabs, throat swabs, and sputum specimens were collected from ILI cases identified through active case-finding. Real-Time PCR was used to test nasopharyngeal swabs for 21 respiratory pathogens, while throat and sputum samples were subjected to bacterial culture. Generalized linear mixed models were used to assess potential risk factors for associations with ILI. In total, 548 episodes of ILI were reported among 476 (9.7%) of the study participants and 330 (33.2%) of the study households. The adjusted estimated incidence of ILI within the study area was 10.7 (95%CI: 9.4–11.9) episodes per 100 person-years. ILI was significantly associated with age group (p<0.001), sex (p<0.001), and number of bedrooms (p = 0.04) in multivariate analysis. In 548 nasopharyngeal swabs, the most commonly detected potential pathogens were Streptococcus pneumoniae (17.0%), Staphylococcus aureus (11.3%), influenza A (11.1%; mostly subtype H3N2), rhinovirus (7.5%), and influenza B (8.0%). Streptococci were isolated from 42 (8.6%) of 536 throat swabs, most (27) of which were Lancefield Group G. Co-infections were observed in 132 (24.1%) of the 548 ILI episodes. Our study generated valuable data on respiratory disease burden and patterns of etiologies associated with community-acquired acute respiratory illness Laos. Establishment of a surveillance strategy in Laos to monitor trends in the epidemiology and burden of acute respiratory infections is required to minimize their impact on human health.

An Integrated Influenza Surveillance Framework Based on National Influenza-Like Illness Incidence and Multiple Hospital Electronic Medical Records for Early Prediction of Influenza Epidemics: Design and Evaluation.

BackgroundInfluenza is a leading cause of death worldwide and contributes to heavy economic losses to individuals and communities. Therefore, the early prediction of and interventions against influenza epidemics are crucial to reduce mortality and morbidity because of this disease. Similar to other countries, the Taiwan Centers for Disease Control and Prevention (TWCDC) has implemented influenza surveillance and reporting systems, which primarily rely on influenza-like illness (ILI) data reported by health care providers, for the early prediction of influenza epidemics. However, these surveillance and reporting systems show at least a 2-week delay in prediction, indicating the need for improvement.ObjectiveWe aimed to integrate the TWCDC ILI data with electronic medical records (EMRs) of multiple hospitals in Taiwan. Our ultimate goal was to develop a national influenza trend prediction and reporting tool more accurate and efficient than the current influenza surveillance and reporting systems.MethodsFirst, the influenza expertise team at Taipei Medical University Health Care System (TMUHcS) identified surveillance variables relevant to the prediction of influenza epidemics. Second, we developed a framework for integrating the EMRs of multiple hospitals with the ILI data from the TWCDC website to proactively provide results of influenza epidemic monitoring to hospital infection control practitioners. Third, using the TWCDC ILI data as the gold standard for influenza reporting, we calculated Pearson correlation coefficients to measure the strength of the linear relationship between TMUHcS EMRs and regional and national TWCDC ILI data for 2 weekly time series datasets. Finally, we used the Moving Epidemic Method analyses to evaluate each surveillance variable for its predictive power for influenza epidemics.ResultsUsing this framework, we collected the EMRs and TWCDC ILI data of the past 3 influenza seasons (October 2014 to September 2017). On the basis of the EMRs of multiple hospitals, 3 surveillance variables, TMUHcS-ILI, TMUHcS-rapid influenza laboratory tests with positive results (RITP), and TMUHcS-influenza medication use (IMU), which reflected patients with ILI, those with positive results from rapid influenza diagnostic tests, and those treated with antiviral drugs, respectively, showed strong correlations with the TWCDC regional and national ILI data (r=.86-.98). The 2 surveillance variables—TMUHcS-RITP and TMUHcS-IMU—showed predictive power for influenza epidemics 3 to 4 weeks before the increase noted in the TWCDC ILI reports.ConclusionsOur framework periodically integrated and compared surveillance data from multiple hospitals and the TWCDC website to maintain a certain prediction quality and proactively provide monitored results. Our results can be extended to other infectious diseases, mitigating the time and effort required for data collection and analysis. Furthermore, this approach may be developed as a cost-effective electronic surveillance tool for the early and accurate prediction of epidemics of influenza and other infectious diseases in densely populated regions and nations.

A collaborative multiyear, multimodel assessment of seasonal influenza forecasting in the United States.

Influenza infects an estimated 9-35 million individuals each year in the United States and is a contributing cause for between 12,000 and 56,000 deaths annually. Seasonal outbreaks of influenza are common in temperate regions of the world, with highest incidence typically occurring in colder and drier months of the year. Real-time forecasts of influenza transmission can inform public health response to outbreaks. We present the results of a multiinstitution collaborative effort to standardize the collection and evaluation of forecasting models for influenza in the United States for the 2010/2011 through 2016/2017 influenza seasons. For these seven seasons, we assembled weekly real-time forecasts of seven targets of public health interest from 22 different models. We compared forecast accuracy of each model relative to a historical baseline seasonal average. Across all regions of the United States, over half of the models showed consistently better performance than the historical baseline when forecasting incidence of influenza-like illness 1 wk, 2 wk, and 3 wk ahead of available data and when forecasting the timing and magnitude of the seasonal peak. In some regions, delays in data reporting were strongly and negatively associated with forecast accuracy. More timely reporting and an improved overall accessibility to novel and traditional data sources are needed to improve forecasting accuracy and its integration with real-time public health decision making.