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Accurate forecasts can enable more effective public health responses during seasonal influenza epidemics. For the 2021-22 and 2022-23 influenza seasons, 26 forecasting teams provided national and jurisdiction-specific probabilistic predictions of weekly confirmed influenza hospital admissions for one-to-four weeks ahead. Forecast skill is evaluated using the Weighted Interval Score (WIS), relative WIS, and coverage. Six out of 23 models outperform the baseline model across forecast weeks and locations in 2021-22 and 12 out of 18 models in 2022-23. Averaging across all forecast targets, the FluSight ensemble is the 2 nd most accurate model measured by WIS in 2021-22 and the 5 th most accurate in the 2022-23 season. Forecast skill and 95% coverage for the FluSight ensemble and most component models degrade over longer forecast horizons. In this work we demonstrate that while the FluSight ensemble was a robust predictor, even ensembles face challenges during periods of rapid change., (© 2024. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

Accurate forecasts could enable more informed public health decisions. Since 2013, CDC has worked with external researchers to improve influenza forecasts by coordinating seasonal challenges for the United States and the 10 Health and Human Service Regions. Forecasted targets for the 2014-15 challenge were the onset week, peak week, and peak intensity of the season and the weekly percent of outpatient visits due to influenza-like illness (ILI) 1-4 weeks in advance. We used a logarithmic scoring rule to score the weekly forecasts, averaged the scores over an evaluation period, and then exponentiated the resulting logarithmic score. Poor forecasts had a score near 0, and perfect forecasts a score of 1. Five teams submitted forecasts from seven different models. At the national level, the team scores for onset week ranged from <0.01 to 0.41, peak week ranged from 0.08 to 0.49, and peak intensity ranged from <0.01 to 0.17. The scores for predictions of ILI 1-4 weeks in advance ranged from 0.02-0.38 and was highest 1 week ahead. Forecast skill varied by HHS region. Forecasts can predict epidemic characteristics that inform public health actions. CDC, state and local health officials, and researchers are working together to improve forecasts., (Published by Elsevier B.V.)

A variety of mechanistic and statistical methods to forecast seasonal influenza have been proposed and are in use; however, the effects of various data issues and design choices (statistical versus mechanistic methods, for example) on the accuracy of these approaches have not been thoroughly assessed. Here, we compare the accuracy of three forecasting approaches-a mechanistic method, a weighted average of two statistical methods and a super-ensemble of eight statistical and mechanistic models-in predicting seven outbreak characteristics of seasonal influenza during the 2016-2017 season at the national and 10 regional levels in the USA. For each of these approaches, we report the effects of real time under- and over-reporting in surveillance systems, use of non-surveillance proxies of influenza activity and manual override of model predictions on forecast quality. Our results suggest that a meta-ensemble of statistical and mechanistic methods has better overall accuracy than the individual methods. Supplementing surveillance data with proxy estimates generally improves the quality of forecasts and transient reporting errors degrade the performance of all three approaches considerably. The improvement in quality from ad hoc and post-forecast changes suggests that domain experts continue to possess information that is not being sufficiently captured by current forecasting approaches., (© 2018 The Authors.)

To determine rates of both symptomatic and asymptomatic infection among ambulatory adults, we collected nasopharyngeal swab specimens, demographic characteristics, and survey information from 1477 adult visitors to a New York City tourist attraction during April-July 2016. Multiplex polymerase chain reaction analysis was used to identify specimens positive for common respiratory viruses. A total of 7.2% of samples tested positive for respiratory viruses; among positive samples, 71.0% contained rhinovirus, and 21.5% contained coronavirus. Influenza virus, respiratory syncytial virus, and parainfluenza virus were also detected. Depending on symptomatologic definition, 57.7%-93.3% of positive samples were asymptomatic. These findings indicate that significant levels of asymptomatic respiratory viral shedding exist during summer among the ambulatory adult population.

Background: Early insights into the timing of the start, peak, and intensity of the influenza season could be useful in planning influenza prevention and control activities. To encourage development and innovation in influenza forecasting, the Centers for Disease Control and Prevention (CDC) organized a challenge to predict the 2013-14 Unites States influenza season., Methods: Challenge contestants were asked to forecast the start, peak, and intensity of the 2013-2014 influenza season at the national level and at any or all Health and Human Services (HHS) region level(s). The challenge ran from December 1, 2013-March 27, 2014; contestants were required to submit 9 biweekly forecasts at the national level to be eligible. The selection of the winner was based on expert evaluation of the methodology used to make the prediction and the accuracy of the prediction as judged against the U.S. Outpatient Influenza-like Illness Surveillance Network (ILINet)., Results: Nine teams submitted 13 forecasts for all required milestones. The first forecast was due on December 2, 2013; 3/13 forecasts received correctly predicted the start of the influenza season within one week, 1/13 predicted the peak within 1 week, 3/13 predicted the peak ILINet percentage within 1 %, and 4/13 predicted the season duration within 1 week. For the prediction due on December 19, 2013, the number of forecasts that correctly forecasted the peak week increased to 2/13, the peak percentage to 6/13, and the duration of the season to 6/13. As the season progressed, the forecasts became more stable and were closer to the season milestones., Conclusion: Forecasting has become technically feasible, but further efforts are needed to improve forecast accuracy so that policy makers can reliably use these predictions. CDC and challenge contestants plan to build upon the methods developed during this contest to improve the accuracy of influenza forecasts.

Importance: Cardiovascular deaths and influenza epidemics peak during winter in temperate regions., Objectives: To quantify the temporal association between population increases in seasonal influenza infections and mortality due to cardiovascular causes and to test if influenza incidence indicators are predictive of cardiovascular mortality during the influenza season., Design, Setting, and Participants: Time-series analysis of vital statistics records and emergency department visits in New York City, among cardiovascular deaths that occurred during influenza seasons between January 1, 2006, and December 31, 2012. The 2009 novel influenza A(H1N1) pandemic period was excluded from temporal analyses., Exposures: Emergency department visits for influenza-like illness, grouped by age (≥0 years and ≥65 years) and scaled by laboratory surveillance data for viral types and subtypes, in the previous 28 days., Main Outcomes and Measures: Mortality due to cardiovascular disease, ischemic heart disease, and myocardial infarction., Results: Among adults 65 years and older, who accounted for 83.0% (73 363 deaths) of nonpandemic cardiovascular mortality during influenza seasons, seasonal average influenza incidence was correlated year to year with excess cardiovascular mortality (Pearson correlation coefficients ≥0.75, P ≤ .05 for 4 different influenza indicators). In daily time-series analyses using 4 different influenza metrics, interquartile range increases in influenza incidence during the previous 21 days were associated with an increase between 2.3% (95% CI, 0.7%-3.9%) and 6.3% (95% CI, 3.7%-8.9%) for cardiovascular disease mortality and between 2.4% (95% CI, 1.1%-3.6%) and 6.9% (95% CI, 4.0%-9.9%) for ischemic heart disease mortality among adults 65 years and older. The associations were most acute and strongest for myocardial infarction mortality, with each interquartile range increase in influenza incidence during the previous 14 days associated with mortality increases between 5.8% (95% CI, 2.5%-9.1%) and 13.1% (95% CI, 5.3%-20.9%). Out-of-sample prediction of cardiovascular mortality among adults 65 years and older during the 2009-2010 influenza season yielded average estimates with 94.0% accuracy using 4 different influenza metrics., Conclusions and Relevance: Emergency department visits for influenza-like illness were associated with and predictive of cardiovascular disease mortality. Retrospective estimation of influenza-attributable cardiovascular mortality burden combined with accurate and reliable influenza forecasts could predict the timing and burden of seasonal increases in cardiovascular mortality., Competing Interests: Disclosures: All authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. In addition to the funding/support listed below, Dr Shaman reported receiving personal fees from JWT and Axon Advisors and reported having partial ownership in SK Analytics. No other disclosures were reported.

The inference of key infectious disease epidemiological parameters is critical for characterizing disease spread and devising prevention and containment measures. The recent emergence of surveillance records mined from big data such as health-related online queries and social media, as well as model inference methods, permits the development of new methodologies for more comprehensive estimation of these parameters. We use such data in conjunction with Bayesian inference methods to study the transmission dynamics of influenza. We simultaneously estimate key epidemiological parameters, including population susceptibility, the basic reproductive number, attack rate, and infectious period, for 115 cities during the 2003-2004 through 2012-2013 seasons, including the 2009 pandemic. These estimates discriminate key differences in the epidemiological characteristics of these outbreaks across 10 y, as well as spatial variations of influenza transmission dynamics among subpopulations in the United States. In addition, the inference methods appear to compensate for observational biases and underreporting inherent in the surveillance data.

Background: There is limited information on differences in the dynamics of influenza transmission during time periods when schools are open compared with periods when they are closed., Methods: Data on school openings, influenza surveillance, and absolute humidity were incorporated into a regression model to estimate the increase in the reproductive number for the 2009 A(H1N1) influenza pandemic associated with the opening of school in 10 US states., Results: The estimate for the average increase in the reproductive number for the 2009 A(H1N1) influenza pandemic associated with the beginning of the school year was 19.5% (95% credible interval = 10%-29%)., Conclusions: Whether schools are open or closed can have a major impact on community transmission dynamics of influenza.

Background: There is evidence that seasonal variation in depressive symptoms is common in the population. However, research is limited by a reliance on longterm retrospective methods., Methods: Seasonal patterns were tested in two samples of community participants recruited in separate prospective studies in the Midwestern (n=556 males/females) and Pacific Northwestern (n=206 males) United States. Participants completed self-report measures of depressive symptoms 10-19 times from ages 14 to 36 years (n=8316 person observations). These data were compared with local meteorological conditions (e.g., solar radiation) recorded across the 2 weeks prior to each self-report., Results: In within-subjects analyses, participants' depressive symptoms and the probability of clinically significant symptoms varied with the time of year, as hypothesized (highest in the weeks of early Winter; lowest in early Fall). However, effect sizes were modest and were not explained by recent sunlight or other meteorological conditions., Limitations: Samples were not nationally representative. Participants did not complete retrospective reports of seasonal depression or measures of current vegetative symptoms., Conclusions: Neither time of the year nor recent seasonally linked meteorological conditions were powerful influences on depressive symptoms experienced by community populations in relevant geographic regions. Prior studies may have overestimated the prevalence and significance of seasonal variation in depressive symptoms for the general population., (© 2013 Elsevier B.V. All rights reserved.)

Influenza recurs seasonally in temperate regions of the world; however, our ability to predict the timing, duration, and magnitude of local seasonal outbreaks of influenza remains limited. Here we develop a framework for initializing real-time forecasts of seasonal influenza outbreaks, using a data assimilation technique commonly applied in numerical weather prediction. The availability of real-time, web-based estimates of local influenza infection rates makes this type of quantitative forecasting possible. Retrospective ensemble forecasts are generated on a weekly basis following assimilation of these web-based estimates for the 2003-2008 influenza seasons in New York City. The findings indicate that real-time skillful predictions of peak timing can be made more than 7 wk in advance of the actual peak. In addition, confidence in those predictions can be inferred from the spread of the forecast ensemble. This work represents an initial step in the development of a statistically rigorous system for real-time forecast of seasonal influenza.

Experimental and epidemiologic evidence indicates that variations of absolute humidity account for the onset and seasonal cycle of epidemic influenza in temperate regions. A role for absolute humidity in the transmission of pandemic influenza, such as 2009 A/H1N1, has yet to be demonstrated and, indeed, outbreaks of pandemic influenza during more humid spring, summer, and autumn months might appear to constitute evidence against an effect of humidity. However, here the authors show that variations of the basic and effective reproductive numbers for influenza, caused by seasonal changes in absolute humidity, are consistent with the general timing of pandemic influenza outbreaks observed for 2009 A/H1N1 in temperate regions, as well as wintertime transmission of epidemic influenza. Indeed, absolute humidity conditions correctly identify the region of the United States vulnerable to a third, wintertime wave of pandemic influenza. These findings suggest that the timing of pandemic influenza outbreaks is controlled by a combination of absolute humidity conditions, levels of susceptibility, and changes in population-mixing and contact rates.

Seasonal variation in serum concentration of the vitamin D metabolite 25(OH) vitamin D [25(OH)D], which contributes to host immune function, has been hypothesized to be the underlying source of observed influenza seasonality in temperate regions. The objective of this study was to determine whether observed 25(OH)D levels could be used to simulate observed influenza infection rates. Data of mean and variance in 25(OH)D serum levels by month were obtained from the Health Professionals Follow-up Study and used to parameterize an individual-based model of influenza transmission dynamics in two regions of the United States. Simulations were compared with observed daily influenza excess mortality data. Best-fitting simulations could reproduce the observed seasonal cycle of influenza; however, these best-fit simulations were shown to be highly sensitive to stochastic processes within the model and were unable consistently to reproduce observed seasonal patterns. In this respect the simulations with the vitamin D forced model were inferior to similar modeling efforts using absolute humidity and the school calendar as seasonal forcing variables. These model results indicate it is unlikely that seasonal variations in vitamin D levels principally determine the seasonality of influenza in temperate regions.

Much of the observed wintertime increase of mortality in temperate regions is attributed to seasonal influenza. A recent reanalysis of laboratory experiments indicates that absolute humidity strongly modulates the airborne survival and transmission of the influenza virus. Here, we extend these findings to the human population level, showing that the onset of increased wintertime influenza-related mortality in the United States is associated with anomalously low absolute humidity levels during the prior weeks. We then use an epidemiological model, in which observed absolute humidity conditions temper influenza transmission rates, to successfully simulate the seasonal cycle of observed influenza-related mortality. The model results indicate that direct modulation of influenza transmissibility by absolute humidity alone is sufficient to produce this observed seasonality. These findings provide epidemiological support for the hypothesis that absolute humidity drives seasonal variations of influenza transmission in temperate regions., Competing Interests: The authors have declared that no competing interests exist.

Mosquito-borne arboviral epidemics tend to strike without warning. The driving force for these epidemics is a combination of biotic (vector, amplification host, and virus) and abiotic (meteorological conditions, especially rainfall and temperature) factors. Abiotic factors that facilitate the synchronization and interaction of vector and amplification host populations favor epidemic amplification and transmission. In Florida, epidemics of St. Louis encephalitis (SLE) virus (family Flaviviridae, genus Flavivirus, SLEV) have been preceded by major freezes one or two winters before the onset of human cases. Here, we analyze the relationship between severe winter freezes and epidemic SLEV transmission in peninsular Florida and show that there is a significant relationship between the transmission of SLEV and these severe freezes. We propose that by killing cold-sensitive understory vegetation in the mid-peninsular region of Florida, freezes enhance the reproductive success of ground-feeding avian amplification hosts, especially mourning doves and common grackles. In conjunction with other appropriate environmental signals, increased avian reproductive success may enhance SLEV and West Nile (WN) virus amplification and result in SLE and WN epidemics during years when all of the biological cycles are properly synchronized. The knowledge that winter freezes in Florida may enhance the amplification and epidemic transmission of SLE and WN viruses facilitates arboviral tracking and prediction of human risk of SLE and WN infection during the transmission season.

Influenza A incidence peaks during winter in temperate regions. The basis for this pronounced seasonality is not understood, nor is it well documented how influenza A transmission principally occurs. Previous studies indicate that relative humidity (RH) affects both influenza virus transmission (IVT) and influenza virus survival (IVS). Here, we reanalyze these data to explore the effects of absolute humidity on IVT and IVS. We find that absolute humidity (AH) constrains both transmission efficiency and IVS much more significantly than RH. In the studies presented, 50% of IVT variability and 90% of IVS variability are explained by AH, whereas, respectively, only 12% and 36% are explained by RH. In temperate regions, both outdoor and indoor AH possess a strong seasonal cycle that minimizes in winter. This seasonal cycle is consistent with a wintertime increase in IVS and IVT and may explain the seasonality of influenza. Thus, differences in AH provide a single, coherent, more physically sound explanation for the observed variability of IVS, IVT and influenza seasonality in temperate regions. This hypothesis can be further tested through future, additional laboratory, epidemiological and modeling studies.

Disease transmission forecasts can help minimize human and domestic animal health risks by indicating where disease control and prevention efforts should be focused. For disease systems in which weather-related variables affect pathogen proliferation, dispersal, or transmission, the potential for disease forecasting exists. We present a seasonal forecast of St. Louis encephalitis virus transmission in Indian River County, Florida. We derive an empiric relationship between modeled land surface wetness and levels of SLEV transmission in humans. We then use these data to forecast SLEV transmission with a seasonal lead. Forecast skill is demonstrated, and a real-time seasonal forecast of epidemic SLEV transmission is presented. This study demonstrates how weather and climate forecast skill-verification analyses may be applied to test the predictability of an empiric disease forecast model.

Since 2013, the Centers for Disease Control and Prevention (CDC) has hosted an annual influenza season forecasting challenge. The 2015-2016 challenge consisted of weekly probabilistic forecasts of multiple targets, including fourteen models submitted by eleven teams. Forecast skill was evaluated using a modified logarithmic score. We averaged submitted forecasts into a mean ensemble model and compared them against predictions based on historical trends. Forecast skill was highest for seasonal peak intensity and short-term forecasts, while forecast skill for timing of season onset and peak week was generally low. Higher forecast skill was associated with team participation in previous influenza forecasting challenges and utilization of ensemble forecasting techniques. The mean ensemble consistently performed well and outperformed historical trend predictions. CDC and contributing teams will continue to advance influenza forecasting and work to improve the accuracy and reliability of forecasts to facilitate increased incorporation into public health response efforts.

Accurate forecasts could enable more informed public health decisions. Since 2013, CDC has worked with external researchers to improve influenza forecasts by coordinating seasonal challenges for the United States and the 10 Health and Human Service Regions. Forecasted targets for the 2014-15 challenge were the onset week, peak week, and peak intensity of the season and the weekly percent of outpatient visits due to influenza-like illness (ILI) 1-4 weeks in advance. We used a logarithmic scoring rule to score the weekly forecasts, averaged the scores over an evaluation period, and then exponentiated the resulting logarithmic score. Poor forecasts had a score near 0, and perfect forecasts a score of 1. Five teams submitted forecasts from seven different models. At the national level, the team scores for onset week ranged from

Ecological and laboratory studies have demonstrated that temperature modulates West Nile virus (WNV) transmission dynamics and spillover infection to humans. Here we explore whether inclusion of temperature forcing in a model depicting WNV transmission improves WNV forecast accuracy relative to a baseline model depicting WNV transmission without temperature forcing. Both models are optimized using a data assimilation method and two observed data streams: mosquito infection rates and reported human WNV cases. Each coupled model-inference framework is then used to generate retrospective ensemble forecasts of WNV for 110 outbreak years from among 12 geographically diverse United States counties. The temperature-forced model improves forecast accuracy for much of the outbreak season. From the end of July until the beginning of October, a timespan during which 70% of human cases are reported, the temperature-forced model generated forecasts of the total number of human cases over the next 3 weeks, total number of human cases over the season, the week with the highest percentage of infectious mosquitoes, and the peak percentage of infectious mosquitoes that on average increased absolute forecast accuracy 5%, 10%, 12%, and 6%, respectively, over the non-temperature forced baseline model. These results indicate that use of temperature forcing improves WNV forecast accuracy and provide further evidence that temperature influences rates of WNV transmission. The findings provide a foundation for implementation of a statistically rigorous system for real-time forecast of seasonal WNV outbreaks and their use as a quantitative decision support tool for public health officials and mosquito control programs.

Background: The impacts of climate change on surface water, waterborne disease, and human health remain a growing area of concern, particularly in Africa, where diarrheal disease is one of the most important health threats to children under 5 years of age. Little is known about the role of surface water and annual flood dynamics (flood pulse) on waterborne disease and human health nor about the expected impact of climate change on surface-water-dependent populations. Methods and findings: Using the Chobe River in northern Botswana, a flood pulse river—floodplain system, we applied multimodel inference approaches assessing the influence of river height, water quality (bimonthly counts of Escherichia coli and total suspended solids [TSS], 2011–2017), and meteorological variability on weekly diarrheal case reports among children under 5 presenting to health facilities (n = 10 health facilities, January 2007–June 2017). We assessed diarrheal cases by clinical characteristics and season across age groups using monthly outpatient data (January 1998–June 2017). A strong seasonal pattern was identified, with 2 outbreaks occurring regularly in the wet and dry seasons. The timing of outbreaks diverged from that at the level of the country, where surface water is largely absent. Across age groups, the number of diarrheal cases was greater, on average, during the dry season. Demographic and clinical characteristics varied by season, underscoring the importance of environmental drivers. In the wet season, rainfall (8-week lag) had a significant influence on under-5 diarrhea, with a 10-mm increase in rainfall associated with an estimated 6.5% rise in the number of cases. Rainfall, minimum temperature, and river height were predictive of E. coli concentration, and increases in E. coli in the river were positively associated with diarrheal cases. In the dry season, river height (1-week lag) and maximum temperature (1- and 4-week lag) were significantly associated with diarrheal cases. During this period, a 1-meter drop in river height corresponded to an estimated 16.7% and 16.1% increase in reported diarrhea with a 1- and 4-week lag, respectively. In this region, as floodwaters receded from the surrounding floodplains, TSS levels increased and were positively associated with diarrheal cases (0- and 3-week lag). Populations living in this region utilized improved water sources, suggesting that hydrological variability and rapid water quality shifts in surface waters may compromise water treatment processes. Limitations include the potential influence of health beliefs and health seeking behaviors on data obtained through passive surveillance. Conclusions: In flood pulse river—floodplain systems, hydrology and water quality dynamics can be highly variable, potentially impacting conventional water treatment facilities and the production of safe drinking water. In Southern Africa, climate change is predicted to intensify hydrological variability and the frequency of extreme weather events, amplifying the public health threat of waterborne disease in surface-water-dependent populations. Water sector development should be prioritized with urgency, incorporating technologies that are robust to local environmental conditions and expected climate-driven impacts. In populations with high HIV burdens, expansion of diarrheal disease surveillance and intervention strategies may also be needed. As annual flood pulse processes are predominantly influenced by climate controls in distant regions, country-level data may be inadequate to refine predictions of climate—health interactions in these systems. [ABSTRACT FROM AUTHOR]

Laboratory and epidemiological evidence indicate that ambient humidity modulates the survival and transmission of influenza. Here we explore whether the inclusion of humidity forcing in mathematical models describing influenza transmission improves the accuracy of forecasts generated with those models. We generate retrospective forecasts for 95 cities over 10 seasons in the United States and assess both forecast accuracy and error. Overall, we find that humidity forcing improves forecast performance (at 1–4 lead weeks, 3.8% more peak week and 4.4% more peak intensity forecasts are accurate than with no forcing) and that forecasts generated using daily climatological humidity forcing generally outperform forecasts that utilize daily observed humidity forcing (4.4% and 2.6% respectively). These findings hold for predictions of outbreak peak intensity, peak timing, and incidence over 2- and 4-week horizons. The results indicate that use of climatological humidity forcing is warranted for current operational influenza forecast. [ABSTRACT FROM AUTHOR]

Recent research has produced a number of methods for forecasting seasonal influenza outbreaks. However, differences among the predicted outcomes of competing forecast methods can limit their use in decision-making. Here, we present a method for reconciling these differences using Bayesian model averaging. We generated retrospective forecasts of peak timing, peak incidence, and total incidence for seasonal influenza outbreaks in 48 states and 95 cities using 21 distinct forecast methods, and combined these individual forecasts to create weighted-average superensemble forecasts. We compared the relative performance of these individual and superensemble forecast methods by geographic location, timing of forecast, and influenza season. We find that, overall, the superensemble forecasts are more accurate than any individual forecast method and less prone to producing a poor forecast. Furthermore, we find that these advantages increase when the superensemble weights are stratified according to the characteristics of the forecast or geographic location. These findings indicate that different competing influenza prediction systems can be combined into a single more accurate forecast product for operational delivery in real time. [ABSTRACT FROM AUTHOR]

Background: Low absolute humidity (AH) has been associated with increased influenza virus survival and transmissibility and the onset of seasonal influenza outbreaks. Humidification of indoor environments may mitigate viral transmission and may be an important control strategy, particularly in schools where viral transmission is common and contributes to the spread of influenza in communities. However, the variability and predictors of AH in the indoor school environment and the feasibility of classroom humidification to levels that could decrease viral survival have not been studied.Methods: Automated sensors were used to measure temperature, humidity and CO2 levels in two Minnesota grade schools without central humidification during two successive winters. Outdoor AH measurements were derived from the North American Land Data Assimilation System. Variability in indoor AH within classrooms, between classrooms in the same school, and between schools was assessed using concordance correlation coefficients (CCC). Predictors of indoor AH were examined using time-series Auto-Regressive Conditional Heteroskedasticity models. Classroom humidifiers were used when school was not in session to assess the feasibility of increasing indoor AH to levels associated with decreased influenza virus survival, as projected from previously published animal experiments.Results: AH varied little within classrooms (CCC >0.90) but was more variable between classrooms in the same school (CCC 0.81 for School 1, 0.88 for School 2) and between schools (CCC 0.81). Indoor AH varied widely during the winter (range 2.60 to 10.34 millibars [mb]) and was strongly associated with changes in outdoor AH (p < 0.001). Changes in indoor AH on school weekdays were strongly associated with CO2 levels (p < 0.001). Over 4 hours, classroom humidifiers increased indoor AH by 4 mb, an increase sufficient to decrease projected 1-hour virus survival by an absolute value of 30% during winter months.Conclusions: During winter, indoor AH in non-humidified grade schools varies substantially and often to levels that are very low. Indoor results are predicted by outdoor AH over a season and CO2 levels (which likely reflects human activity) during individual school days. Classroom humidification may be a feasible approach to increase indoor AH to levels that may decrease influenza virus survival and transmission. [ABSTRACT FROM AUTHOR]