Comparing Observed with Predicted Weekly Influenza-Like Illness Rates during the Winter Holiday Break,United States, 2004-2013

In the United States, influenza season typically begins in October or November, peaks in February, and tapers off in April. During the winter holiday break, from the end of December to the beginning of January, changes in social mixing patterns, healthcare-seeking behaviors, and surveillance reporting could affect influenza-like illness (ILI) rates. We compared predicted with observed weekly ILI to examine trends around the winter break period. We examined weekly rates of ILI by region in the United States from influenza season 2003–2004 to 2012–2013. We compared observed and predicted ILI rates from week 44 to week 8 of each influenza season using the auto-regressive integrated moving average (ARIMA) method. Of 1,530 region, week, and year combinations, 64 observed ILI rates were significantly higher than predicted by the model. Of these, 21 occurred during the typical winter holiday break period (weeks 51–52); 12 occurred during influenza season 2012–2013. There were 46 observed ILI rates that were significantly lower than predicted. Of these, 16 occurred after the typical holiday break during week 1, eight of which occurred during season 2012–2013. Of 90 (10 HHS regions x 9 seasons) predictions during the peak week, 78 predicted ILI rates were lower than observed. Out of 73 predictions for the post-peak week, 62 ILI rates were higher than observed. There were 53 out of 73 models that had lower peak and higher post-peak predicted ILI rates than were actually observed. While most regions had ILI rates higher than predicted during winter holiday break and lower than predicted after the break during the 2012–2013 season, overall there was not a consistent relationship between observed and predicted ILI around the winter holiday break during the other influenza seasons.     

Wikipedia Usage Estimates Prevalence of Influenza-Like Illness in the United States in Near Real-Time

Circulating levels of both seasonal and pandemic influenza require constant surveillance to ensure the health and safety of the population. While up-to-date information is critical, traditional surveillance systems can have data availability lags of up to two weeks. We introduce a novel method of estimating, in near-real time, the level of influenza-like illness (ILI) in the United States (US) by monitoring the rate of particular Wikipedia article views on a daily basis. We calculated the number of times certain influenza- or health-related Wikipedia articles were accessed each day between December 2007 and August 2013 and compared these data to official ILI activity levels provided by the Centers for Disease Control and Prevention (CDC). We developed a Poisson model that accurately estimates the level of ILI activity in the American population, up to two weeks ahead of the CDC, with an absolute average difference between the two estimates of just 0.27% over 294 weeks of data. Wikipedia-derived ILI models performed well through both abnormally high media coverage events (such as during the 2009 H1N1 pandemic) as well as unusually severe influenza seasons (such as the 2012–2013 influenza season). Wikipedia usage accurately estimated the week of peak ILI activity 17% more often than Google Flu Trends data and was often more accurate in its measure of ILI intensity. With further study, this method could potentially be implemented for continuous monitoring of ILI activity in the US and to provide support for traditional influenza surveillance tools.     

Effective Detection of the 2009 H1N1 Influenza Pandemic in U.S. Veterans Affairs Medical Centers Using a National Electronic Biosurveillance System

Background

The 2008–09 influenza season was the time in which the Department of Veterans Affairs (VA) utilized an electronic biosurveillance system for tracking and monitoring of influenza trends. The system, known as ESSENCE or Electronic Surveillance System for the Early Notification of Community-based Epidemics, was monitored for the influenza season as well as for a rise in influenza cases at the start of the H1N1 2009 influenza pandemic. We also describe trends noted in influenza-like illness (ILI) outpatient encounter data in VA medical centers during the 2008–09 influenza season, before and after the recognition of pandemic H1N1 2009 influenza virus.

Methodology/Principal Findings

We determined prevalence of ILI coded visits using VA''s ESSENCE for 2008–09 seasonal influenza (Sept. 28, 2008–April 25, 2009 corresponding to CDC 2008–2009 flu season weeks 40–16) and the early period of pandemic H1N1 2009 (April 26, 2009–July 31, 2009 corresponding to CDC 2008–2009 flu season weeks 17–30). Differences in diagnostic ICD-9-CM code frequencies were analyzed using Chi-square and odds ratios. There were 649,574 ILI encounters captured representing 633,893 patients. The prevalence of VA ILI visits mirrored the CDC''s Outpatient ILI Surveillance Network (ILINet) data with peaks in late December, early February, and late April/early May, mirroring the ILINet data; however, the peaks seen in the VA were smaller. Of 31 ILI codes, 6 decreased and 11 increased significantly during the early period of pandemic H1N1 2009. The ILI codes that significantly increased were more likely to be symptom codes. Although influenza with respiratory manifestation (487.1) was the most common code used among 150 confirmed pandemic H1N1 2009 cases, overall it significantly decreased since the start of the pandemic.

Conclusions/Significance

VA ESSENCE effectively detected and tracked changing ILI trends during pandemic H1N1 2009 and represents an important temporal alerting system for monitoring health events in VA facilities.     

Estimated Number of Patients with Influenza A(H1)pdm09, or Other Viral Types,from 2010 to 2014 in Japan

Infectious disease surveillance systems provide information crucial for protecting populations from influenza epidemics. However, few have reported the nationwide number of patients with influenza-like illness (ILI), detailing virological type. Using data from the infectious disease surveillance system in Japan, we estimated the weekly number of ILI cases by virological type, including pandemic influenza (A(H1)pdm09) and seasonal-type influenza (A(H3) and B) over a four-year period (week 36 of 2010 to week 18 of 2014). We used the reported number of influenza cases from nationwide sentinel surveillance and the proportions of virological types from infectious agents surveillance and estimated the number of cases and their 95% confidence intervals. For the 2010/11 season, influenza type A(H1)pdm09 was dominant: 6.48 million (6.33–6.63), followed by types A(H3): 4.05 million (3.90–4.21) and B: 2.84 million (2.71–2.97). In the 2011/12 season, seasonal influenza type A(H3) was dominant: 10.89 million (10.64–11.14), followed by type B: 5.54 million (5.32–5.75). In conclusion, close monitoring of the estimated number of ILI cases by virological type not only highlights the huge impact of previous influenza epidemics in Japan, it may also aid the prediction of future outbreaks, allowing for implementation of control and prevention measures.     

Recording of Influenza-Like Illness in UK Primary Care 1995-2013: Cohort Study

Background

There is a lack of recent studies examining recording of influenza-like illness (ILI) in primary care in the UK over time and according to population characteristics. Our aim was to determine time trends and socio-demographic patterns of ILI recorded consultations in primary care.

Methods

We used The Health Improvement Network (THIN) UK primary care database and extracted data on all ILI consultations between 1995 and 2013. We estimated ILI recorded consultation rates per 100,000 person-weeks (pw) by age, gender, deprivation and winter season. Negative binomial regression models were used to examine time trends and the effect of socio-demographic characteristics. Trends in ILI recorded consultations were compared to trends in consultations with less specific symptoms (cough or fever) recorded.

Results

The study involved 7,682,908 individuals in 542 general practices. The ILI consultation rate decreased from 32.5/100,000 pw (95% confidence interval (CI) 32.1, 32.9) in 1995–98 to 15.5/100,000 pw (95% CI 15.4, 15.7) by 2010–13. The decrease occurred prior to 2002/3, and rates have remained largely stable since then. Declines were evident in all age groups. In comparison, cough or fever consultation rates increased from 169.4/100,000 pw (95% CI 168.6, 170.3) in 1995–98 to 237.7/100,000 pw (95% CI 237.2, 238.2) in 2010–13. ILI consultation rates were highest among individuals aged 15–44 years, higher in women than men, and in individuals from deprived areas.

Conclusion

There is substantial variation in ILI recorded consultations over time and by population socio-demographic characteristics, most likely reflecting changing recording behaviour by GPs. These results highlight the difficulties in using coded information from electronic primary care records to measure the severity of influenza epidemics across time and assess the relative burden of ILI in different population subgroups.     

Evaluation of an Internet-Based Monitoring System for Influenza-Like Illness in Sweden

To complement traditional influenza surveillance with data on disease occurrence not only among care-seeking individuals, the Swedish Institute for Communicable Disease Control (SMI) has tested an Internet-based monitoring system (IMS) with self-recruited volunteers submitting weekly on-line reports about their health in the preceding week, upon weekly reminders. We evaluated IMS acceptability and to which extent participants represented the Swedish population. We also studied the agreement of data on influenza-like illness (ILI) occurrence from IMS with data from a previously evaluated population-based system (PBS) with an actively recruited random sample of the population who spontaneously report disease onsets in real-time via telephone/Internet, and with traditional general practitioner based sentinel and virological influenza surveillance, in the 2011–2012 and 2012–2013 influenza seasons. We assessed acceptability by calculating the participation proportion in an invited IMS-sample and the weekly reporting proportion of enrolled self-recruited IMS participants. We compared distributions of socio-demographic indicators of self-recruited IMS participants to the general Swedish population using chi-square tests. Finally, we assessed the agreement of weekly incidence proportions (%) of ILI in IMS and PBS with cross-correlation analyses. Among 2,511 invited persons, 166 (6.6%) agreed to participate in the IMS. In each season, 2,552 and 2,486 self-recruited persons participated in the IMS respectively. The weekly reporting proportion among self-recruited participants decreased from 87% to 23% (2011–2012) and 82% to 45% (2012–2013). Women, highly educated, and middle-aged persons were overrepresented among self-recruited IMS participants (p<0.01). IMS (invited and self-recruited) and PBS weekly incidence proportions correlated strongest when no lags were applied (r = 0.71 and r = 0.69, p<0.05). This evaluation revealed socio-demographic misrepresentation and limited compliance among the self-recruited IMS participants. Yet, IMS offered a reasonable representation of the temporal ILI pattern in the community overall during the 2011–2012 and 2012–2013 influenza seasons and could be a simple tool for collecting community-based ILI data.     

Evaluation of Two Influenza Surveillance Systems in South Africa

Background

The World Health Organisation recommends outpatient influenza-like illness (ILI) and inpatient severe acute respiratory illness (SARI) surveillance. We evaluated two influenza surveillance systems in South Africa: one for ILI and another for SARI.

Methodology

The Viral Watch (VW) programme has collected virological influenza surveillance data voluntarily from patients with ILI since 1984 in private and public clinics in all 9 South African provinces. The SARI surveillance programme has collected epidemiological and virological influenza surveillance data since 2009 in public hospitals in 4 provinces by dedicated personnel. We compared nine surveillance system attributes from 2009–2012.

Results

We analysed data from 18,293 SARI patients and 9,104 ILI patients. The annual proportion of samples testing positive for influenza was higher for VW (mean 41%) than SARI (mean 8%) and generally exceeded the seasonal threshold from May to September (VW: weeks 21–40; SARI: weeks 23–39). Data quality was a major strength of SARI (most data completion measures >90%; adherence to definitions: 88–89%) and a relative weakness of the VW programme (62% of forms complete, with limited epidemiologic data collected; adherence to definitions: 65–82%). Timeliness was a relative strength of both systems (e.g. both collected >93% of all respiratory specimens within 7 days of symptom onset). ILI surveillance was more nationally representative, financially sustainable and expandable than the SARI system. Though the SARI programme is not nationally representative, the high quality and detail of SARI data collection sheds light on the local burden and epidemiology of severe influenza-associated disease.

Conclusions

To best monitor influenza in South Africa, we propose that both ILI and SARI should be under surveillance. Improving ILI surveillance will require better quality and more systematic data collection, and SARI surveillance should be expanded to be more nationally representative, even if this requires scaling back on information gathered.     

Age Distribution of Influenza Like Illness Cases during Post-Pandemic A(H3N2): Comparison with the Twelve Previous Seasons,in France

In France, the 2011–2012 influenza epidemic was characterized by the circulation of antigenically drifted influenza A(H3N2) viruses and by an increased disease severity and mortality among the elderly, with respect to the A(H1N1)pdm09 pandemic and post-pandemic outbreaks. Whether the epidemiology of influenza in France differed between the 2011–2012 epidemic and the previous outbreaks is unclear. Here, we analyse the age distribution of influenza like illness (ILI) cases attended in general practice during the 2011–2012 epidemic, and compare it with that of the twelve previous epidemic seasons. Influenza like illness data were obtained through a nationwide surveillance system based on sentinel general practitioners. Vaccine effectiveness was also estimated. The estimated number of ILI cases attended in general practice during the 2011–2012 was lower than that of the past twelve epidemics. The age distribution was characteristic of previous A(H3N2)-dominated outbreaks: school-age children were relatively spared compared to epidemics (co-)dominated by A(H1N1) and/or B viruses (including the 2009 pandemic and post-pandemic outbreaks), while the proportion of adults over 30 year-old was higher. The estimated vaccine effectiveness (54%, 95% CI (48, 60)) was in the lower range for A(H3N2) epidemics. In conclusion, the age distribution of ILI cases attended in general practice seems to be not different between the A(H3N2) pre-pandemic and post-pandemic epidemics. Future researches including a more important number of ILI epidemics and confirmed virological data of influenza and other respiratory pathogens are necessary to confirm these results.     

Case Fatality Rates Based on Population Estimates of Influenza-Like Illness Due to Novel H1N1 Influenza: New York City,May–June 2009

Background

The public health response to pandemic influenza is contingent on the pandemic strain''s severity. In late April 2009, a potentially pandemic novel H1N1 influenza strain (nH1N1) was recognized. New York City (NYC) experienced an intensive initial outbreak that peaked in late May, providing the need and opportunity to rapidly quantify the severity of nH1N1.

Methods and Findings

Telephone surveys using rapid polling methods of approximately 1,000 households each were conducted May 20–27 and June 15–19, 2009. Respondents were asked about the occurrence of influenza-like illness (ILI, fever with either cough or sore throat) for each household member from May 1–27 (survey 1) or the preceding 30 days (survey 2). For the overlap period, prevalence data were combined by weighting the survey-specific contribution based on a Serfling model using data from the NYC syndromic surveillance system. Total and age-specific prevalence of ILI attributed to nH1N1 were estimated using two approaches to adjust for background ILI: discounting by ILI prevalence in less affected NYC boroughs and by ILI measured in syndromic surveillance data from 2004–2008. Deaths, hospitalizations and intensive care unit (ICU) admissions were determined from enhanced surveillance including nH1N1-specific testing. Combined ILI prevalence for the 50-day period was 15.8% (95% CI:13.2%–19.0%). The two methods of adjustment yielded point estimates of nH1N1-associated ILI of 7.8% and 12.2%. Overall case-fatality (CFR) estimates ranged from 0.054–0.086 per 1000 persons with nH1N1-associated ILI and were highest for persons ≥65 years (0.094–0.147 per 1000) and lowest for those 0–17 (0.008–0.012). Hospitalization rates ranged from 0.84–1.34 and ICU admission rates from 0.21–0.34 per 1000, with little variation in either by age-group.

Conclusions

ILI prevalence can be quickly estimated using rapid telephone surveys, using syndromic surveillance data to determine expected “background” ILI proportion. Risk of severe illness due to nH1N1 was similar to seasonal influenza, enabling NYC to emphasize preventing severe morbidity rather than employing aggressive community mitigation measures.     

Carriage of Streptococcus pneumoniae in Aged Adults with Influenza-Like-Illness

Incidence of pneumococcal disease is disproportionally high in infants and elderly. Nasopharyngeal colonisation by Streptococcus pneumoniae is considered a prerequisite for disease but unlike in children, carriage in elderly is rarely detected. Here, we tested for S. pneumoniae in nasopharyngeal and saliva samples collected from community-dwelling elderly with influenza-like-illness (ILI). Trans-nasal nasopharyngeal, trans-oral nasopharyngeal and saliva samples (n = 270 per sample type) were collected during winter/spring 2011/2012 from 135 persons aged 60–89 at onset of ILI and 7–9 weeks later following recovery. After samples were tested for pneumococci by conventional culture, all plate growth was collected. DNA extracted from plate harvests was tested by quantitative-PCRs (qPCR) specific for S. pneumoniae and serotypes included in the 13-valent pneumococcal conjugated vaccine (PCV13). Pneumococci were cultured from 14 of 135 (10%) elderly with none of the sampled niches showing superiority in carriage detection. With 76/270 (28%) saliva, 31/270 (11%) trans-oral and 13/270 (5%) trans-nasal samples positive by qPCR, saliva was superior to nasopharyngeal swabs (p<0.001) in qPCR-based carriage detection. Overall, from all methods used in the study, 65 of 135 (48%) elderly carried pneumococci at least once and 26 (19%) at both study time points. The difference between carriage prevalence at ILI (n = 49 or 36%) versus recovery (n = 42 or 31%) was not significant (p = 0.38). At least 23 of 91 (25%) carriage events in 19 of 65 (29%) carriers were associated with PCV13-serotypes. We detected a large reservoir of pneumococci in saliva of elderly, with PCV13-serotype distribution closely resembling the contemporary carriage of serotypes reported in the Netherlands for PCV-vaccinated infants.     

School-Located Influenza Vaccination Reduces Community Risk for Influenza and Influenza-Like Illness Emergency Care Visits

Background

School-located influenza vaccination (SLIV) programs can substantially enhance the sub-optimal coverage achieved under existing delivery strategies. Randomized SLIV trials have shown these programs reduce laboratory-confirmed influenza among both vaccinated and unvaccinated children. This work explores the effectiveness of a SLIV program in reducing the community risk of influenza and influenza-like illness (ILI) associated emergency care visits.

Methods

For the 2011/12 and 2012/13 influenza seasons, we estimated age-group specific attack rates (AR) for ILI from routine surveillance and census data. Age-group specific SLIV program effectiveness was estimated as one minus the AR ratio for Alachua County versus two comparison regions: the 12 county region surrounding Alachua County, and all non-Alachua counties in Florida.

Results

Vaccination of ∼50% of 5–17 year-olds in Alachua reduced their risk of ILI-associated visits, compared to the rest of Florida, by 79% (95% confidence interval: 70, 85) in 2011/12 and 71% (63, 77) in 2012/13. The greatest indirect effectiveness was observed among 0–4 year-olds, reducing AR by 89% (84, 93) in 2011/12 and 84% (79, 88) in 2012/13. Among all non-school age residents, the estimated indirect effectiveness was 60% (54, 65) and 36% (31, 41) for 2011/12 and 2012/13. The overall effectiveness among all age-groups was 65% (61, 70) and 46% (42, 50) for 2011/12 and 2012/13.

Conclusion

Wider implementation of SLIV programs can significantly reduce the influenza-associated public health burden in communities.     

Influenza and Other Respiratory Viruses Detected by Influenza-Like Illness Surveillance in Leyte Island,the Philippines, 2010–2013

This study aimed to determine the role of influenza-like illness (ILI) surveillance conducted on Leyte Island, the Philippines, including involvement of other respiratory viruses, from 2010 to 2013. ILI surveillance was conducted from January 2010 to March 2013 with 3 sentinel sites located in Tacloban city, Palo and Tanauan of Leyte Island. ILI was defined as fever ≥38°C or feverish feeling and either cough or running nose in a patient of any age. Influenza virus and other 5 respiratory viruses were searched. A total of 5,550 ILI cases visited the 3 sites and specimens were collected from 2,031 (36.6%) cases. Among the cases sampled, 1,637 (75.6%) were children aged <5 years. 874 (43.0%) cases were positive for at least one of the respiratory viruses tested. Influenza virus and respiratory syncytial virus (RSV) were predominantly detected (both were 25.7%) followed by human rhinovirus (HRV) (17.5%). The age distributions were significantly different between those who were positive for influenza, HRV, and RSV. ILI cases were reported throughout the year and influenza virus was co-detected with those viruses on approximately half of the weeks of study period (RSV in 60.5% and HRV 47.4%). In terms of clinical manifestations, only the rates of headache and sore throat were significantly higher in influenza positive cases than cases positive to other viruses. In conclusion, syndromic ILI surveillance in this area is difficult to detect the start of influenza epidemic without laboratory confirmation which requires huge resources. Age was an important factor that affected positive rates of influenza and other respiratory viruses. Involvement of older age children may be useful to detect influenza more effectively.     

Assessing Google flu trends performance in the United States during the 2009 influenza virus A (H1N1) pandemic

Background

Google Flu Trends (GFT) uses anonymized, aggregated internet search activity to provide near-real time estimates of influenza activity. GFT estimates have shown a strong correlation with official influenza surveillance data. The 2009 influenza virus A (H1N1) pandemic [pH1N1] provided the first opportunity to evaluate GFT during a non-seasonal influenza outbreak. In September 2009, an updated United States GFT model was developed using data from the beginning of pH1N1.

Methodology/Principal Findings

We evaluated the accuracy of each U.S. GFT model by comparing weekly estimates of ILI (influenza-like illness) activity with the U.S. Outpatient Influenza-like Illness Surveillance Network (ILINet). For each GFT model we calculated the correlation and RMSE (root mean square error) between model estimates and ILINet for four time periods: pre-H1N1, Summer H1N1, Winter H1N1, and H1N1 overall (Mar 2009–Dec 2009). We also compared the number of queries, query volume, and types of queries (e.g., influenza symptoms, influenza complications) in each model. Both models'' estimates were highly correlated with ILINet pre-H1N1 and over the entire surveillance period, although the original model underestimated the magnitude of ILI activity during pH1N1. The updated model was more correlated with ILINet than the original model during Summer H1N1 (r = 0.95 and 0.29, respectively). The updated model included more search query terms than the original model, with more queries directly related to influenza infection, whereas the original model contained more queries related to influenza complications.

Conclusions

Internet search behavior changed during pH1N1, particularly in the categories “influenza complications” and “term for influenza.” The complications associated with pH1N1, the fact that pH1N1 began in the summer rather than winter, and changes in health-seeking behavior each may have played a part. Both GFT models performed well prior to and during pH1N1, although the updated model performed better during pH1N1, especially during the summer months.     

Age-Related Differences in the Accuracy of Web Query-Based Predictions of Influenza-Like Illness

BackgroundWeb queries are now widely used for modeling, nowcasting and forecasting influenza-like illness (ILI). However, given that ILI attack rates vary significantly across ages, in terms of both magnitude and timing, little is known about whether the association between ILI morbidity and ILI-related queries is comparable across different age-groups. The present study aimed to investigate features of the association between ILI morbidity and ILI-related query volume from the perspective of age.MethodsSince Google Flu Trends is unavailable in Italy, Google Trends was used to identify entry terms that correlated highly with official ILI surveillance data. All-age and age-class-specific modeling was performed by means of linear models with generalized least-square estimation. Hold-out validation was used to quantify prediction accuracy. For purposes of comparison, predictions generated by exponential smoothing were computed.ResultsFive search terms showed high correlation coefficients of > .6. In comparison with exponential smoothing, the all-age query-based model correctly predicted the peak time and yielded a higher correlation coefficient with observed ILI morbidity (.978 vs. .929). However, query-based prediction of ILI morbidity was associated with a greater error. Age-class-specific query-based models varied significantly in terms of prediction accuracy. In the 0–4 and 25–44-year age-groups, these did well and outperformed exponential smoothing predictions; in the 15–24 and ≥ 65-year age-classes, however, the query-based models were inaccurate and highly overestimated peak height. In all but one age-class, peak timing predicted by the query-based models coincided with observed timing.ConclusionsThe accuracy of web query-based models in predicting ILI morbidity rates could differ among ages. Greater age-specific detail may be useful in flu query-based studies in order to account for age-specific features of the epidemiology of ILI.     

Assessing the impact of ambient temperature on the risk of hand,foot, and mouth disease in Guangdong,China: New insight from the disease severity and burden

BackgroundThe association between the incidence of hand, foot, and mouth disease (HFMD) and ambient temperature has been well documented. Although the severity of symptoms is an important indicator of disease burden and varies significantly across cases, it usually was ignored in previous studies, potentially leading to biased estimates of the health impact of temperature.MethodsWe estimated the disability-adjusted life year (DALY) by considering the severity of symptoms for each HFMD case reported during 2010–2012 in Guangdong and used distributed lag-nonlinear models to estimate the association between the daily average temperature and daily DALY of HFMD cases at the city-level. We investigated the potential effect modifiers on the pathway between temperature and DALY and pooled city-specific estimates to a provincial association using a meta-regression. The overall impact of temperature was further evaluated by estimates of DALYs that could be attributed to HFMD.ResultsThe overall cumulative effect of daily mean temperature on the DALY of HFMD showed an inverse-U shape, with the maximum effect estimated to be β = 0.0331 (95%CI: 0.0199–0.0463) DALY at 23.8°C. Overall, a total of 6.432 (95%CI: 3.942–8.885) DALYs (attributable fraction = 2.721%, 95%CI: 1.660–3.759%) could be attributed to temperature exposure. All the demographic subgroups had a similar trend as the main analysis, while the magnitude of the peak of the temperature impact tended to be higher among the males, those aged ≥3yrs or from the Pear-River Delta region. Additionally, the impact of temperature on DALY elevated significantly with the increasing population density, per capita GDP, and per capita green space in parks.ConclusionsTemperature exposure was associated with increased burden of HFMD nonlinearly, with certain groups such as boys and those from areas with greater population density being more vulnerable.     

Forecasting the 2013–2014 Influenza Season Using Wikipedia

Infectious diseases are one of the leading causes of morbidity and mortality around the world; thus, forecasting their impact is crucial for planning an effective response strategy. According to the Centers for Disease Control and Prevention (CDC), seasonal influenza affects 5% to 20% of the U.S. population and causes major economic impacts resulting from hospitalization and absenteeism. Understanding influenza dynamics and forecasting its impact is fundamental for developing prevention and mitigation strategies. We combine modern data assimilation methods with Wikipedia access logs and CDC influenza-like illness (ILI) reports to create a weekly forecast for seasonal influenza. The methods are applied to the 2013-2014 influenza season but are sufficiently general to forecast any disease outbreak, given incidence or case count data. We adjust the initialization and parametrization of a disease model and show that this allows us to determine systematic model bias. In addition, we provide a way to determine where the model diverges from observation and evaluate forecast accuracy. Wikipedia article access logs are shown to be highly correlated with historical ILI records and allow for accurate prediction of ILI data several weeks before it becomes available. The results show that prior to the peak of the flu season, our forecasting method produced 50% and 95% credible intervals for the 2013-2014 ILI observations that contained the actual observations for most weeks in the forecast. However, since our model does not account for re-infection or multiple strains of influenza, the tail of the epidemic is not predicted well after the peak of flu season has passed.     

Influenza-Like-Illness and Clinically Diagnosed Flu: Disease Burden,Costs and Quality of Life for Patients Seeking Ambulatory Care or No Professional Care at All

This is one of the first studies to (1) describe the out-of-hospital burden of influenza-like-illness (ILI) and clinically diagnosed flu, also for patients not seeking professional medical care, (2) assess influential background characteristics, and (3) formally compare the burden of ILI in patients with and without a clinical diagnosis of flu. A general population sample with recent ILI experience was recruited during the 2011–2012 influenza season in Belgium. Half of the 2250 respondents sought professional medical care, reported more symptoms (especially more often fever), a longer duration of illness, more use of medication (especially antibiotics) and a higher direct medical cost than patients not seeking medical care. The disease and economic burden were similar for ambulatory ILI patients, irrespective of whether they received a clinical diagnosis of flu. On average, they experienced 5–6 symptoms over a 6-day period; required 1.6 physician visits and 86–91% took medication. An average episode amounted to €51–€53 in direct medical costs, 4 days of absence from work or school and the loss of 0.005 quality-adjusted life-years. Underlying illness led to greater costs and lower quality-of-life. The costs of ILI patients with clinically diagnosed flu tended to increase, while those of ILI patients without clinically diagnosed flu tended to decrease with age. Recently vaccinated persons experienced lower costs and a higher quality-of-life, but this was only the case for patients not seeking professional medical care. This information can be used directly to evaluate the implementation of cost-effective prevention and control measures for influenza. In particular to inform the evaluation of more widespread seasonal influenza vaccination, including in children, which is currently considered by many countries.     

Hygiene Behaviors Associated with Influenza-Like Illness among Adults in Beijing,China: A Large,Population-Based Survey

The objective of this study was to identify possible hygiene behaviors associated with the incidence of ILI among adults in Beijing. In January 2011, we conducted a multi-stage sampling, cross-sectional survey of adults living in Beijing using self-administered anonymous questionnaires. The main outcome variable was self-reported ILI within the past year. Multivariate logistic regression was used to identify factors associated with self-reported ILI. A total of 13003 participants completed the questionnaires. 6068 (46.7%) of all participants reported ILI during the past year. After adjusting for demographic characteristics, the variables significantly associated with a lower likelihood of reporting ILI were regular physical exercise (OR 0.80; 95% CI 0.74–0.87), optimal hand hygiene (OR 0.87; 95% CI 0.80–0.94), face mask use when going to hospitals (OR 0.87; 95% CI 0.80–0.95), and not sharing of towels and handkerchiefs (OR 0.68; 95% CI 0.63–0.73). These results highlight that personal hygiene behaviors were potential preventive factors against the incidence of ILI among adults in Beijing, and future interventions to improve personal hygiene behaviors are needed in Beijing.