Outbreak Of Influenza-like Illness Research Articles

Factors Associated with Influenza-like Illness among Inmates in an All-male Prison, Songkhla Province, Thailand, 2023

On 9 Feb 2023, the Office of Disease Prevention and Control Region 12Songkhlawas informed of an influenza-like illness (ILI) cluster in an all-male prison in Songkhla Province. We investigated to identify the causative agent, possible sources and risk factors, and provide control measures. We conducted an active case finding by interviewing inmates and officers. Prison facilities and hygiene behaviors of inmates and officers were inspected. A retrospective cohort study was performed. Basic and effective reproductive numbers were estimated. The overall attack rate was 12.6% (474/3,648). Most cases were inmates from Wing C (80.4%). There were no severe cases or deaths. Of ten specimens tested, all were positive for influenza B/Victoria lineage, V1a.3a.2. Fifty-four percent of inmates had received influenza vaccine within the last 12 months. A mismatch between the viral strain in the vaccine and the one causing this outbreak likely contributed to the outbreak as the vaccine provided to the inmates was manufactured during the previous year's influenza season. Vaccine effectiveness was 36.2% against ILI. Having high-risk conditions (adjusted odds ratios (AOR) 2.83, 95% confidence interval (CI) 1.27–6.32) and sharing drinking glasses (AOR 2.02, 95% CI 1.21–3.36) were significant risk factors. The basic reproductive number for ILI in this outbreak was 1.36. The effective reproductive number ranged from 0.18–3.69. For effective management of ILI outbreaks in prisons, a continuous program of influenza vaccination following updated World Health Organization recommendations and a comprehensive surveillance system with rigorous respiratory illness management practices are suggested.

The impact of COVID-19 pandemic on surveillance of influenza and influenza-like viruses: a single center experience

Aims: Influenza and the agents responsible for influenza-like illness (ILI) are a significant cause of upper and lower respiratory tract infections, with a notable pattern of seasonal incidence. The surveillance of influenza and agents causing ILI is important for the developmentof a prophylaxis approach and infection control. In the present study, we utilize the Reverse transcription-polymerase chain reaction (RT-PCR) method to identify the presence of influenza virus and other agents associated with ILI in nasopharyngeal smears obtained from patients exhibiting symptoms of influenza and ILI. The study sample included patients who were admitted to multiple clinics and intensive care units (ICU) of the Health Sciences University Ankara Training and Research Hospital in the period preceding, and in the initial months and first year following the coronavirus disease 2019 (COVID-19) outbreak. Throughthe comparisons made in the study, the intention is to gain a better understanding of the influence of the COVID-19 pandemic on other viral infectious agents transmitted via the respiratory tract.
 Methods: Included in the study were 257 admitted to different wards and ICUs of our hospital due to symptoms of upper or lower respiratory tract infection and ILI between 2015 and 2021 (excluding data from 2017–2018). The study was conducted prospectively within the scope of the Global Influenza Hospital Surveillance Network project. Using sterile swabs, nasopharyngeal swab samples were collected from inpatients who provided informed consent for their participation in the study, and the samples were placed in a viral transport medium. The presence of influenza viruses (Influenza A, subtypes of Influenza A and Influenza B) and agents causing ILI (i.e. respiratory syncytial virus, coronavirus) was investigated with an RT-PCR approach.
 Results: The most common symptoms among the 257 patients included in the study with pre-diagnoses of influenza and ILI were cough (82.2%), fever (67.7%), shortness of breath (66.1%) and myalgia (40%). The RT-PCR detected a viral agent in 60 (23.3%) of the 257 patients, whereas no agent could be detected in 197 (76.6%) patients. Furthermore, 51 (18.5%) tested positive for influenza virus, five (1.9%) for respiratory syncytial virus (RSV), and four (1.5%) for SARS-CoV-2. An analysis of the results within two distinct time frames, namely prior to the COVID-19 pandemic (2016–2017, 2019–prior to December 10, 2020) and during the COVID-19 pandemic (between December 11, 2020 and 2021), influenza viruses (influenza A H1N1 and influenza A H3N2), RSV and influenza type B were identified as the dominant viruses before the COVID-19 pandemic, while the predominant viruses were a single influenza strain and four SARS-CoV-2 variants during the COVID-19 pandemic.A significant difference was noted in the distribution of viruses between the two time frames – prior to the pandemic and during the pandemic. Of the patients, 199 (77.4%) were discharged with full recovery while 58 patients died (22.6%). Of the 58 patients that died, 25 were female (25/131 females) and 33 were male (33/126 males), 11 had tested positive for influenza virus andone for RSV, while no infectious agent could be identified in 46 patients. 
 Conclusion: The implementation of molecular testing methods for the identification of viral infectious agents among inpatients during influenza and ILI outbreaks, administering antiviral and prophylactic treatments targeting influenza, RSV and SARS-CoV-2 infections, and adopting infection control measures, could significantly decrease mortality and morbidity rates while mitigating the complications associated with these infectious agents.

Institutional outbreaks of influenza-like illnesses in the state of São Paulo: an analysis of the epidemiological profile during the COVID-19 pandemic

ObjectivesThis study aimed to spatiotemporally analyze the profile of influenza-like illness (ILI) outbreaks in the state of São Paulo, Brazil, between 2020 and 2022. Study designThis was a cross-sectional retrospective study. MethodsOutbreaks of ILI with final diagnoses of COVID-19, influenza, or other respiratory viruses (ORVs) recorded between January 2020 and November 2022, obtained from the Notifiable Diseases Information System (SINAN NET) Outbreak module, were analyzed. Kernel density estimates and Getis-Ord Gi∗ statistics were performed to identify spatial clusters. ResultsA total of 13,314 ILI outbreaks were identified, involving 130,568 cases and 2649 deaths. Of these, 104,399 (80%) were confirmed as COVID-19, 15,861 (12%) were confirmed as ORV, and 10,308 (8%) were confirmed as influenza. The year 2021 had the highest number of outbreaks and cases. Schools recorded the most outbreaks and cases, followed by long-term care facilities for older adults (LTCs). The highest average number of cases per outbreak and the highest attack rates occurred at social gatherings and prisons. Prisoners were three times more likely to contract COVID-19 during outbreaks than people in other institutions. The highest hospitalization and mortality rates for all virus types occurred in the LTC group. The occurrence and intensity of outbreaks were highly heterogeneous among the different institutions after the introduction of new SARS-CoV-2 variants in the state. ConclusionsILI outbreaks were not randomly distributed; they clustered in specific areas. Transmissibility varied among different institutions with different responses to the COVID-19 pandemic. These results can be used as a basis for prioritizing actions and allocating resources during future pandemics.

Change from low to out-of-season epidemics of influenza in China during the COVID-19 pandemic: A time series study.

Nonpharmaceutical interventions to limit the coronavirus disease 2019 (COVID-19) pandemic might reduce the transmission of influenza viruses and disrupt the typical seasonality of influenza. However, changes in epidemiology and seasonal patterns of influenza remain unknown in China during the COVID-19 pandemic. Data on influenza-like illness (ILI) and influenza cases between surveillance Week 14 in 2010 and Week 6 in 2023 and ILI outbreaks between Week 14 in 2013 and Week 6 in 2023 were collected from the weekly reports of the Chinese National Influenza Center. A total of 32 10 735 ILI specimens were tested between Week 14 in 2010 and Week 6 in 2023 in China, with 12.4% of specimens positive for influenza. The influenza-positive percentage ranged from 11.8% to 21.1% in southern China and 9.5% to 19.5% in northern China between the 2010/2011 and 2019/2020 influenza seasons. The influenza-positive percentage was 0.7% in southern China and 0.2% in northern China in the 2020/2021 season. An increasing trend in influenza-positive percentage was observed in southern China in Weeks 18-27 in the 2022/2023 season, with a peak of 37.3%. A total of 768 ILI outbreaks reported in southern China in Weeks 14-26 in the 2022/2023 season were much more than those in the same period in the 2020/2021 and 2021/2022 seasons. In summary, seasonal influenza shifted from low to out-of-season epidemics during the COVID-19 pandemic in China, especially in southern China. Influenza vaccination and everyday preventive actions, such as mask wearing, appropriate air exchange, and good hand hygiene practices, are essential for the prevention of influenza virus infection during the COVID-19 pandemic.

Analysis of the epidemiological characteristics and genetic characteristics of influenza in the surveillance-year of 2021 to 2022 in Shandong Province, China

Objective: To analyze the epidemiological characteristics, etiology and hemagglutinin (HA) gene characteristics of prevalent strains in Shandong Province from 2021 to 2022. Methods: The sentinel surveillance data of influenza-like illness (ILI) were collected in Shandong Province from 2021 to 2022. ILI specimens were detected with Real-Time PCR and virus isolation to explore the distribution of influenza viruses in different months. Three virus strains of each city were selected for gene sequencing, and the HA phylogenetic analysis was carried out. Results: In the surveillance-year from 2021 to 2022, 528 263 ILI cases were totally reported in 54 sentinel hospitals for influenza surveillance in Shandong Province. ILI visiting ratio (ILI%) was 4.07%, with the largest number in 0-4 age group (45.86%). The highly frequent season for ILI was in winter and spring, with a peak in the 52nd week, 2021 (6.62%). Totally, nucleic acid was detected in 26 754 specimens, with a positive rate of 27.10%, all of which were type B Victoria influenza. The positive rate reached a peak in the 49th week, 2021 (63.78%). A total of 295 outbreaks of ILI had been reported, in which 269 were positive for influenza virus. Most of outbreaks occurred in the primary school, with a peak in December. Gene evolution analysis showed that the HA gene in Shandong possessed high homology, 98.6% to 99.5%, with the recommended vaccine strains in 2020-2023, which was divided into two branches, V1A.3a.1 and V1A.3a.2. Conclusion: In the surveillance-year of 2021-2022, influenza is prevalent in December in Shandong Province, with a single circulating strain type. The positive rate of influenza virus and outbreak are higher than those in the previous surveillance-year. The circulating strain possesses high HA gene homology with those of the WHO vaccine recommended strains. However, the overall immune barrier of influenza virus is weak.

Outbreaks of Human Metapneumovirus in Western Sydney Aged-Care Facilities in 2018.

Human metapneumovirus (hMPV), first described in 2001, is a cause of acute respiratory tract infection in the elderly, with symptoms ranging from mild to severe, including pneumonia, but outbreaks are rarely described. Between 1 July and 31 December 2018, there were three outbreaks of Influenza-like Illness (ILI) where hMPV was the primary pathogen observed, among 64 aged-care facilities (ACFs) in Western Sydney. These outbreaks were investigated by the Western Sydney Local Health District (WSLHD) Public Health Unit (PHU); multiplex polymerase chain reaction (PCR) testing was performed on nasopharyngeal swabs collected by the ACF. hMPV was the main causative pathogen in three outbreaks (27, 28, and 15 symptomatic cases, respectively) in late winter and early spring. Fifty-five residents and 15 staff cases (70 total cases) were identified; hMPV was detected in 12 of 63 specimens submitted. Of the cases in residents, eight were hospitalised (8/63; 15%), including five with confirmed hMPV and a further one epidemiologically linked to a positive case. Six residents died (6/63; 11%) during the hMPV outbreaks; four of these had laboratory-confirmed hMPV, with a further case epidemiologically linked to a hMPV case, with a primary diagnosis of pneumonia/viral pneumonia. The sixth death was not epidemiologically linked, nor did this case exhibit any respiratory symptoms during the outbreak; however, it was reported in line with public health guidelines. A major challenge in 2018 was the incompleteness of testing for, and awareness of, hMPV as a viral cause of ILI by facilities, laboratories, and emergency departments, which generally opted for rapid testing for influenza and RSV only. There is no licensed vaccine or approved treatment for hMPV, so efficient infection control measures are most important.

Epidemiological and Clinical Characteristics of Influenza Outbreaks Among Children in Chongqing, China.

Influenza is a global serious public health threat. Seasonal influenza among children in Chongqing has been a heavy health burden. To date, few studies have examined the spatial and temporal characteristics of influenza. This research sheds new light on correlating them with influenza outbreaks with data of over 5 years (2014–2018). All cluster outbreaks among preschool and school-age children reported in Chongqing were collected through the Public Health Emergency Management Information System. The demographical, epidemiological, and clinical data of the cases were analyzed. From 2014 to 2018, a total of 111 preschool- and school-based influenza-like illness outbreaks involving 3,549 cases were identified. Several clinical symptoms that were analyzed in this study showed significant contrast between influenza A and B. Spatial autocorrelation analysis over the 5-year data detected Xiushan district being the most likely cluster. The exploration of the spatial distribution and clinical characteristics of influenza cluster of children in Chongqing could help the effective implementation of health policies. Future studies should be conducted to monitor the outbreaks of influenza among children.

A deep learning framework with convolutional long short‐term memory for influenza‐like illness trend estimation

AbstractForecasting and modeling real‐world time series problems have been challenging for researchers due to nonlinear and complicated patterns. With the recent developments in deep learning, more accurate and efficient forecasting can be provided by learning the relationships between input and output data. This article has introduced a novel hybrid approach, called the GA‐ConvLSTM‐CNN, based on the genetic algorithm (GA), convolutional long short‐term memory (ConvLSTM) and convolutional neural network (CNN) to capture complex and sophisticated features from historical data. The GA‐ConvLSTM‐CNN approach is constructed with two‐stacked ConvLSTM networks, two‐stacked CNN, and a fully connected layer to alleviate the complexity of the real‐world time series problems with multivariate and multistep. In order to increase the accuracy of the GA‐ConvLSTM‐CNN, the genetic algorithm is adopted to simultaneously optimize the network's hyperparameters. With the aim of making comparisons, the weekly influenza‐like illness (ILI) and the weather data have been handled. The GA‐ConvLSTM‐CNN framework compared different techniques comprising machine learning and deep learning approaches. The results have indicated that the GA‐ConvLSTM‐CNN has the ability to accomplish more effective prediction accuracy and provide remarkable support for ILI outbreaks than the benchmark techniques.

Evaluation of IBM Watson Natural Language Processing Service to predict influenza-like illness outbreaks from Twitter data

In this work we evaluate whether Watson NLP service can be used to reliably predict infectious disease such as influenza-like illness (ILI) outbreaks using Twitter data. Watson’s performance is evaluated by computing Pearson correlation coefficient between the number of tweets classified by Watson as ILI and the number of ILI occurrences recovered from traditional epidemic surveillance system of the Centers for Disease Control and Prevention (CDC). Achieved correlation was 0.55. Furthermore, a 12-week discrepancy was found between peak occurrences of ILI predicted by Watson and CDC reported data. Additionally, we developed a scoring method for ILI prediction from a Twitter post using a simple formula with the ability to predict ILI two weeks ahead of ILI data as reported by CDC. The obtained results suggest that data found within social media can be used to supplement the traditional surveillance in epidemics of infectious diseases such as influenza or more recently COVID-19 with the help of intelligent computations

Influenza A imported from Comoros to France in the summer months of 2021.

Three children <4 years were diagnosed with influenza A/H3N2 on returning from Comoros to Marseille, France in summer 2021. An outbreak of influenza-like illness is currently affecting children in Comoros. Sentinel travellers returning to developed countries allow identifying pathogens circulating in tropical areas where diagnostic tools are lacking.

Influenza-Like Illness Among Personnel Responding to U.S. Quarantine of Cruise Ship Passengers Exposed to SARS-CoV-2.

Objectives:Before community transmission of COVID-19 was recognized in the United States, cruise ship passengers with high risk for exposure to SARS-CoV-2 were repatriated and quarantined. We describe cases of influenza-like illness (ILI) among responders.Methods:We reviewed situation reports and responder illness reports to characterize ill responders, including illness onset date, symptoms, fever, diagnostic tests, potential breaches in PPE use, and return to work status.Results:Among 339 responders, nine (3%) reported ILI. No breaches in PPE were reported. Three responders with ILI were tested for both SARS-CoV-2 infection and influenza A; none tested positive for SARS-CoV-2 infection and two tested positive for influenza A.Conclusions:Despite an outbreak of ILI among responders, none were diagnosed with COVID-19, suggesting preventive measures in place might have been sufficient to prevent responders from SARS-CoV-2 exposure.

Sodium Toxicity in the Nutritional Epidemiology and Nutritional Immunology of COVID-19.

Dietary factors in the etiology of COVID-19 are understudied. High dietary sodium intake leading to sodium toxicity is associated with comorbid conditions of COVID-19 such as hypertension, kidney disease, stroke, pneumonia, obesity, diabetes, hepatic disease, cardiac arrhythmias, thrombosis, migraine, tinnitus, Bell’s palsy, multiple sclerosis, systemic sclerosis, and polycystic ovary syndrome. This article synthesizes evidence from epidemiology, pathophysiology, immunology, and virology literature linking sodium toxicological mechanisms to COVID-19 and SARS-CoV-2 infection. Sodium toxicity is a modifiable disease determinant that impairs the mucociliary clearance of virion aggregates in nasal sinuses of the mucosal immune system, which may lead to SARS-CoV-2 infection and viral sepsis. In addition, sodium toxicity causes pulmonary edema associated with severe acute respiratory syndrome, as well as inflammatory immune responses and other symptoms of COVID-19 such as fever and nasal sinus congestion. Consequently, sodium toxicity potentially mediates the association of COVID-19 pathophysiology with SARS-CoV-2 infection. Sodium dietary intake also increases in the winter, when sodium losses through sweating are reduced, correlating with influenza-like illness outbreaks. Increased SARS-CoV-2 infections in lower socioeconomic classes and among people in government institutions are linked to the consumption of foods highly processed with sodium. Interventions to reduce COVID-19 morbidity and mortality through reduced-sodium diets should be explored further.

Epidemiological characteristics of influenza-like illness outbreaks in long-term care facilities of the state of São Paulo, Brazil

Long-term care facilities for older adults present a high risk of outbreaks since they concentrate often more frail and vulnerable individuals. OBJECTIVE: To describe the epidemiological characteristics of influenza-like illness outbreaks and cases among older people in long-term care facilities in the state of São Paulo, Brazil. METODS: The analysis was performed through an exploratory and descriptive approach, with records from the outbreak module of the National System of Notifiable Diseases between January 2020 and June 2021. RESULTS: Outbreaks of influenza-like illness in this department represented 24.93% of all notifications. The highest concentration was seen in the state capital and metropolitan area. A total of 1 018 confirmed outbreaks were observed, involving 6 110 cases and 1 240 deaths among older people. Of these cases, 71.67% were confirmed for coronavirus disease 19 (COVID-19), 12.77% for the influenza virus, and 15.56% for other respiratory viruses. The percentages regarding death outcomes were similar, with a 20.29% lethality of influenza-like illness. Within the studied group, the older adults were the most affected. A statistical difference was observed between cases and deaths. CONCLUSIONS: Owing to the current scenario and the known vulnerabilities of these facilities, there is an urgent need for joint and articulated action by various administrative levels in order to minimize the devastating effects of influenza-like illness outbreaks (especially of COVID- 19) in older adults at long-term care homes. The strengthening of information systems and their interoperability are considered of utmost importance in order to improve the quality of information on outbreaks, which is essential during a pandemic.

Influenza-like illness outbreaks in China during 2017-2018 surveillance season

Objective: To analyze epidemiological characteristics of influenza-like illness outbreaks in mainland China during 2017-2018 surveillance season, and to provide scientific evidence for developing influenza prevention and control strategies. Methods: We collected the data on reported influenza outbreaks in 2017-2018 surveillance season from China Influenza Surveillance Information System and China Public Health Emergency Management Information System and analyzed the data of laboratory-confirmed influenza-like illness outbreaks by descriptive epidemiological methods. Results: During the surveillance season, a total of 2 398 influenza-like illness outbreaks (with 10 or more incidences in an outbreak) in mainland China were reported, involving 87 084 patients, of which 2 323 were influenza outbreaks, involving 85 531 patients. The reported influenza-like illness outbreaks occurred most frequently from November 2017 to January 2018 in both the southern and northern regions and the highest peaks were in December 2017. During the period 1 850 influenza-like illness outbreaks (77.15%) were reported in the southern region, and 548 influenza-like illness outbreaks (22.85%) were reported in the northern region. The most of the outbreaks occurred in primary, secondary schools and nursery care schools, with a total of 2 210 reports (92.16%). And the majority of the outbreaks involved 10-29 incident cases. The dominant isolated virus strains for the outbreaks were influenza B (1 505 outbreaks, 62.76% of all the outbreaks). Conclusion: Seasonality of influenza outbreaks were observed in mainland China during 2017-2018 surveillance season and the reported influenza outbreaks were most frequently occurred in autumn-winter season and in southern China. Primary, secondary schools and nursery care schools are high-risk places for outbreaks, and the dominant isolated virus strains for the outbreaks were influenza B.

Streamlined Development of Analytic Fusion Capability for Health Surveillance

Streamlined Development of Analytic Fusion Capability for Health Surveillance

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 >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.

Outbreak detection of influenza-like illness in Prey Veng Province, Cambodia: a community-based surveillance.

ABSTRACTOn June 4, 2016, the Prey Veng Provincial Health Department reported a total of 107 patients with influenza-like illness (ILI) from Chakhlanh village to the Cambodian Ministry of Health. To confirm the outbreak and evaluate its clinical and epidemiological characteristics, the investigation team visited the village and reviewed the case-based surveillance (CBS) data on severe respiratory infection (SRI) and patients’ records in health facilities. The team interviewed all households in the village from May 1 to June 5, 2016 and obtained the following data: age, medical history, date of onset, treatment, symptoms, and history of contact with patients or dead poultry. Nasal swab samples were collected from suspected ILI cases to test for influenza virus by RT-PCR. The investigation detected 498 suspected ILI cases, including 288 females. Although the incidence of suspected ILI cases who visited health centers was 63.0 per 1,000 persons per month, the attack rate was 27.1 per 100 population. The major age group was 5–14 years followed by 0–4 years. Major symptoms were cough, fever, runny nose, and headache. Six of seven nasal swab samples were positive for influenza A/H1N1 pdm09 virus. Most children with flu symptoms had contact with previous cases. This study showed that the ILI outbreak might be caused by seasonal influenza A/H1N1 pdm09 spread from person to person. Poor living conditions and poor hygiene and sanitation practices were environmental factors that caused the outbreak. As the CBS system was unable to identify this epidemic, it needs to be improved.

Predicting the spread of influenza epidemics by analyzing twitter messages

Seasonal influenza epidemics affect millions of people with respiratory illnesses and cause 250,000 to 500,000 deaths worldwide each year. Rapidly predicting the outbreak of epidemics leads to an earlier detection and control. In this study, we predicted an influenza-like illness (ILI) based on social media data derived from Twitter. Tweets and patients do not always have a linear correlation; therefore, we employed nonlinear methods including autoregressive with exogenous inputs (ARX), autoregressive-moving-average with exogenous inputs (ARMAX), nonlinear autoregressive exogenous (NARX), deep multilayer perceptron (DeepMLP), and a convolutional neural network (CNN). Two new features employed to significantly reduce the prediction errors are products of the tweets and Centers for Disease Control and Prevention (CDC) data and of the tweets and Google data. Furthermore, we introduced a new method based on entropy that decreased the errors as well as time complexity. Among the available methods and features, the best results were obtained with the newly developed features in the deep neural network methods and the entropy-based method that decreased the mean average error by up to 25%. The entropy method also reduced the time complexity. Applying the above-mentioned methods to the Twitter datasets from 2009 to 2010 and 2011–2014 revealed that the ILI outbreak can be predicted 2–4 weeks earlier than by the CDC.

Outbreak of influenza A in a boarding school in South Africa, 2016.

IntroductionWe investigated an outbreak of influenza-like illness (ILI) at a boarding school in Eastern Cape Province, South Africa. We aimed to confirm the etiological agent, estimate attack rates and identify risk factors for illness.MethodsWe conducted a retrospective cohort study including senior school boarders (n=308). Students with ILI (cough and fever) were identified through school medical records. We also conducted a questionnaire-based cross-sectional study among senior students including boarders (n=107) and day students (n=45). We collected respiratory specimens for respiratory pathogen testing by real-time polymerase chain reaction from a subset of symptomatic students. We calculated attack rates of medically attended ILI (medILI) and identified factors associated with medILI using logistic regression. We calculated seasonal influenza vaccine effectiveness (VE) against medILI.ResultsInfluenza A (H3N2) virus was detected in 61% (23/38) of specimens. Attack rate for medILI was 13% among boarders (39/308) in the cohort study and 20% in both day students (9/45) and boarders (21/107) in the cross-sectional study. Playing squash was associated with medILI (aOR 5.35, 95% confidence interval [95% CI]: 1.68-17.07). Of the boarders, 19% (57/308) were vaccinated before the outbreak. The adjusted VE against medILI was 18% (aOR 0.82, 95% CI 0.38-1.78). The outbreak led to cancellation of several events and the need for academic remedial sessions.ConclusionWe confirmed an influenza A (H3N2) virus outbreak with a high attack rate. The outbreak affected academic and sports activities. Participation in sports and social gatherings while experiencing ILI should be discouraged to reduce viral transmission and impact on school activities.

Outbreaks of influenza-like illness in care homes inthe East of England: impact of variations in neuraminidase inhibitor provision.

To examine the differences in outcomes related to variable provision of antivirals in care home respiratory outbreaks. This is a retrospective observational study. Routinely collected outbreak surveillance data reported from care home staff was recorded using a standard template and extracted from the Public Health England health protection electronic records. Data included numbers of people affected, provision of oseltamivir, hospitalization, and deaths during the outbreak in the care home. Oseltamivir provision was categorized by proportion of eligible residents prescribed it on advice. Additional data on microbiological diagnoses were obtained directly from the hospital laboratories. Logistic regression was used to examine associations between oseltamivir provision and hospitalizations and deaths in care homes. One hundred and sixty-eight outbreaks were reported from 28th July 2016 until 27th March 2017, affecting 1459 residents and 347 staff. There were 76 hospital admissions and 37 deaths overall. Although deaths and hospital admissions also occurred in outbreaks caused by other respiratory viruses, outbreaks caused by influenza had the highest median number of people affected and a greater proportion of hospital admissions and deaths. Of the 56 outbreaks caused by influenza, there was a significant increase in the odds of the care home reporting deaths when oseltamivir was not used (odds ratio=8.15, 95% confidence interval=1.38-48.20, P=0.02). There were also non-significant reductions in duration of outbreak and hospital admissions in care home outbreaks with oseltamivir treatment. Partial or no provision of oseltamivir was associated with poorer outcomes in laboratory-confirmed influenza outbreaks.