Deviations in influenza seasonality: odd coincidence or obscure consequence?

Molecular characterization of influenza virus circulating in Nepal in the year 2019

Influenza (sometimes referred to as "flu") is a contagious viral infection of the airways in the lungs that affects a significant portion of the world's population. Clinical symptoms of influenza virus infections can range widely, from severe pneumonia to moderate or even asymptomatic sickness. If left untreated, influenza can have more severe effects on the heart, brain, and lungs than on the respiratory tract and can necessitate hospitalization. This study was aimed to investigate and characterize all types of influenza cases prevailing in Nepal and to analyze seasonal occurrence of Influenza in Nepal in the year 2019. A cross sectional, retrospective and descriptive study was carried out at National Influenza Center (NIC), National Public Health Laboratory Kathmandu Nepal for the period of one year (Jan-Dec 2019). A total of 3606 throat swab samples from various age groups and sexes were processed at the NIC. The specimens were primarily stored at 4 °C and processed using ABI 7500 RT PCR system for the identification of Influenza virus types and subtypes. Data accessed for research purpose were retrieved from National Influenza Centre (NIC) on 1st Jan 2020. Of the total 3606 patients suspected of having influenza infection, influenza viruses were isolated from 1213 (33.6%) patients with male predominance. The highest number of infection was caused by Influenza A/Pdm09 strain 739 (60.9%) followed by Influenza B 304 (25.1%) and Influenza A/H3 169 (13.9%) and most remarkable finding of this study was the detection of H5N1 in human which is the first ever case of such infection in human from Nepal. Similar to other tropical nations, influenza viruses were detected year-round in various geographical locations of Nepal. The influenza virus type and subtypes that were in circulation in Nepal were comparable to vaccine candidate viruses, which the currently available influenza vaccine may prevent.

A Retrospective Database Analysis of Before and After Social Distancing in Relation to Pediatric Infection Rate and Healthcare Services Usage During the Coronavirus Disease 2019 Pandemic

BACKGROUND

Social distancing policy was introduced in Israel in 2020 to reduce the spread of coronavirus disease 2019 (COVID-19). The aim of this study was to analyze the effect of social distancing on other infections in children, by comparing disease rate and healthcare utilization before and after social distancing.

METHODS

This was a before-and-after study. Within this retrospective database analysis of parallel periods in 2019 (periods 1 and 2) and 2020 (periods 3 [prelockdown period] and 4 [lockdown period]) we included all pediatric population registered in the electronic medical records of the Maccabi Healthcare Services, Israel, looking at the occurrence of non-COVID-19 infections, antibiotic purchasing, physician visits, ambulatory emergency care center visits, emergency department visits, and hospitalizations.

RESULTS

A total of 776 828 children were included from 2019, and 777 729 from 2020. We found a lower infection rate in 2020 versus 2019. We did not find a difference in infection rate between periods 1 and 2, while there was a significant difference between periods 3 and 4. We found a significant difference between periods 2 and 4, with a higher RR than for the comparison between periods 1 and 3. There was a modest decrease in ambulatory emergency care center visits in 2020, and lower increases in emergency department visits and hospital admissions. We found decreases in antibiotic purchasing between periods 1 and 3 and between periods 2 and 4, more pronounced in 2020 than in 2019.

CONCLUSIONS

Analysis of findings before and after social distancing and masking showed reduced prevalence of non-COVID-19 pediatric infections and reduced consumption of healthcare services and antibiotics related with the lockdown period.

Global seasonal and pandemic patterns in influenza: An application of longitudinal study designs

The confluence of growing analytic capacities and global surveillance systems for seasonal infections has created new opportunities to further develop statistical methodology and advance the understanding of the global disease dynamics. We developed a framework to characterise the seasonality of infectious diseases for publicly available global health surveillance data. Specifically, we aimed to estimate the seasonal characteristics and their uncertainty using mixed effects models with harmonic components and the δ-method and develop multi-panel visualisations to present complex interplay of seasonal peaks across geographic locations. We compiled a set of 2 422 weekly time series of 14 reported outcomes for 173 Member States from the World Health Organization's (WHO) international influenza virological surveillance system, FluNet, from 02 January 1995 through 20 June 2021. We produced an analecta of data visualisations to describe global travelling waves of influenza while addressing issues of data completeness and credibility. Our results offer directions for further improvements in data collection, reporting, analysis and development of statistical methodology and predictive approaches.

Role of cross-reactivity in cellular immune targeting of influenza A M158-66 variant peptide epitopes

The immunologic significance of cross-reactivity of TCR recognition of peptide:MHC complexes is still poorly understood. We have described TCR cross-reactivity in a system involving polyclonal CD8 T cell recognition of the well characterized influenza viral M158-66 epitope. While M158-66 is generally conserved between influenza A isolates, error-prone transcription generates stable variant RNA during infection which could act as novel epitopes. If packaged and viable, variant genomic RNA generates an influenza quasispecies. The stable RNA variants would generate a new transmissible epitope that can select a specific repertoire, which itself should have cross-reactive properties. We tested two candidate peptides in which Thr65 is changed to Ala (A65) or Ser (S65) using recall responses to identify responding T cell clonotypes. Both peptides generated large polyclonal T cell repertoires of their own with repertoire characteristics and cross-reactivity patterns like that observed for the M158-66 repertoire. Both substitutions could be present in viral genomes or mRNA at sufficient frequency during an infection to drive immunity. Peptides from the resulting protein would be a target for CD8 cells irrespective of virus viability or transmissibility. These data support the hypothesis that cross-reactivity is important for immunity against RNA virus infections.

Dynamic mapping of cholera outbreak during the Yemeni Civil War, 2016-2019

Widespread destruction from the Yemeni Civil War (2014-present) triggered the world's largest cholera outbreak. We compiled a comprehensive health dataset and created dynamic maps to demonstrate spatiotemporal changes in cholera infections and war conflicts. We aligned and merged daily, weekly, and monthly epidemiological bulletins of confirmed cholera infections and daily conflict events and fatality records to create a dataset of weekly time series for Yemen at the governorate level (subnational regions administered by governors) from 4 January 2016 through 29 December 2019. We demonstrated the use of dynamic mapping for tracing the onset and spread of infection and manmade factors that amplify the outbreak. We report curated data and visualization techniques to further uncover associations between infectious disease outbreaks and risk factors and to better coordinate humanitarian aid and relief efforts during complex emergencies.

How Drivers of Seasonality in Respiratory Infections May Impact Vaccine Strategy: A Case Study in How Coronavirus Disease 2019 (COVID-19) May Help Us Solve One of Influenza's Biggest Challenges

Vaccines against seasonal infections like influenza offer a recurring testbed, encompassing challenges in design, implementation, and uptake to combat a both familiar and ever-shifting threat. One of the pervading mysteries of influenza epidemiology is what causes the distinctive seasonal outbreak pattern. Proposed theories each suggest different paths forward in being able to tailor precision vaccines and/or deploy them most effectively. One of the greatest challenges in contrasting and supporting these theories is, of course, that there is no means by which to actually test them. In this communication we revisit theories and explore how the ongoing coronavirus disease 2019 (COVID-19) pandemic might provide a unique opportunity to better understand the global circulation of respiratory infections. We discuss how vaccine strategies may be targeted and improved by both isolating drivers and understanding the immunological consequences of seasonality, and how these insights about influenza vaccines may generalize to vaccines for other seasonal respiratory infections.

Signatures of Cholera Outbreak during the Yemeni Civil War, 2016-2019

The Global Task Force on Cholera Control (GTFCC) created a strategy for early outbreak detection, hotspot identification, and resource mobilization coordination in response to the Yemeni cholera epidemic. This strategy requires a systematic approach for defining and classifying outbreak signatures, or the profile of an epidemic curve and its features. We used publicly available data to quantify outbreak features of the ongoing cholera epidemic in Yemen and clustered governorates using an adaptive time series methodology. We characterized outbreak signatures and identified clusters using a weekly time series of cholera rates in 20 Yemeni governorates and nationally from 4 September 2016 through 29 December 2019 as reported by the World Health Organization (WHO). We quantified critical points and periods using Kolmogorov–Zurbenko adaptive filter methodology. We assigned governorates into six clusters sharing similar outbreak signatures, according to similarities in critical points, critical periods, and the magnitude of peak rates. We identified four national outbreak waves beginning on 12 September 2016, 6 March 2017, 28 May 2018, and 28 January 2019. Among six identified clusters, we classified a core regional hotspot in Sana’a, Sana’a City, and Al-Hudaydah—the expected origin of the national outbreak. The five additional clusters differed in Wave 2 and Wave 3 peak frequency, timing, magnitude, and geographic location. As of 29 December 2019, no governorates had returned to pre-Wave 1 levels. The detected similarity in outbreak signatures suggests potentially shared environmental and human-made drivers of infection; the heterogeneity in outbreak signatures implies the potential traveling waves outwards from the core regional hotspot that could be governed by factors that deserve further investigation.

Impact of China's COVID-19 prevention and control efforts on outbreaks of influenza

The COVID-19 pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in a serious public health burden. As the COVID-19 epidemic in China would coincide with a seasonal outbreak of influenza, there were serious concerns about whether influenza would be aggravated by the SARS-CoV-2 infection and COVID-19 pandemic. This article provides a brief overview of the impacts of the COVID-19 epidemic on influenza activity in China. The percentage of positive influenza tests decreased during the COVID-19 pandemic. During the first stage of the COVID-19 outbreak, the percentage of positive influenza tests reached to a peak of 47.7%. At the second stage, the percentage of positive influenza tests was dramatically decreased from 40.4% to 14.0%. Thereafter, it remains at a low level of less than 6.2%. In addition, the possible causes of this phenomenon have been summarized, including prevention and control measures and ecological competition. Lastly, this article suggests that the public health approach to preventing COVID-19 may also help to control other respiratory infectious diseases. Public health measures need to be maintained even in the later stages of the COVID-19 epidemic.

Impact of biometeorological conditions and air pollution on influenza-like illnesses incidence in Warsaw

In order to assess the influence of atmospheric conditions and particulate matter (PM) on the seasonally varying incidence of influenza-like illnesses (ILI) in the capital of Poland-Warsaw, we analysed time series of ILI reported for the about 1.75 million residents in total and for different age groups in 288 approximately weekly periods, covering 6 years 2013-2018. Using Poisson regression, we predicted ILI by the Universal Thermal Climate Index (UTCI) as biometeorological indicator, and by PM2.5 and PM10, respectively, as air quality measures accounting for lagged effects spanning up to 3 weeks. Excess ILI incidence after adjusting for seasonal and annual trends was calculated by fitting generalized additive models. ILI morbidity increased with rising PM concentrations, for both PM2.5 and PM10, and with cooler atmospheric conditions as indicated by decreasing UTCI. While the PM effect focused on the actual reporting period, the atmospheric influence exhibited a more evenly distributed lagged effect pattern over the considered 3-week period. Though ILI incidence adjusted for population size significantly declined with age, age did not significantly modify the effect sizes of both PM and UTCI. These findings contribute to better understanding environmental conditionings of influenza seasonality in a temperate climate. This will be beneficial to forecasting future dynamics of ILI and to planning clinical and public health resources under climate change scenarios.

Environmental factors, winter respiratory infections and the seasonal variation in heart failure admissions

Seasonal cycles of AHF are causally attributed to the seasonal pattern of respiratory tract infections. However, this assumption has never been formally validated. We aimed to determine whether the increase in winter admissions for acute heart failure (AHF) can be explained by seasonal infectious diseases. We studied 12,147 patients admitted for AHF over a period of 11 years (2005–2015). Detailed virology and bacteriology data were collected on each patient. Meteorological information including daily temperature and relative humidity was obtained for the same period. The peak-to-low ratio, indicating the intensity of seasonality, was calculated using negative binomial regression-derived incidence rate ratios (IRR). AHF admissions occurred with a striking annual periodicity, peaking in winter (December-February) and were lowest in summer (June–August), with a seasonal amplitude (January vs. August) of 2.00 ([95% CI 1.79–2.24]. Occurrence of confirmed influenza infections was low (1.59%). Clinical diagnoses of respiratory infections, confirmed influenza infections, and influenza-like infections also followed a strong seasonal pattern (P < 0.0001; Peak/low ratio 2.42 [95% CI 1.394–3.03]). However, after exclusion of all respiratory infections, the seasonal variation in AHF remained robust (Peak/low ratio January vs. August, 1.81 [95% CI 1.60–2.05]; P < 0.0001). There was a strong inverse association between AHF admissions and average monthly temperature (IRR 0.95 per 1℃ increase; 95% CI 0.94 to 0.96). In conclusion, these is a dominant seasonal modulation of AHF admissions which is only partly explained by the incidence of winter respiratory infections. Environmental factors modify the susceptibility of heart failure patients to decompensation.

The role of respiratory droplet physicochemistry in limiting and promoting the airborne transmission of human coronaviruses: A critical review

Whether virulent human pathogenic coronaviruses (SARS-CoV, MERS-CoV, SARS-CoV-2) are effectively transmitted by aerosols remains contentious. Transmission modes of the novel coronavirus have become a hot topic of research with the importance of airborne transmission controversial due to the many factors that can influence virus transmission. Airborne transmission is an accepted potential route for the spread of some viral infections (measles, chickenpox); however, aerosol features and infectious inoculum vary from one respiratory virus to another. Infectious virus-laden aerosols can be produced by natural human respiratory activities, and their features are vital determinants for virus carriage and transmission. Physicochemical characteristics of infectious respiratory aerosols can influence the efficiency of virus transmission by droplets. This critical review identifies studies reporting instances of infected patients producing airborne human pathogenic coronaviruses, and evidence for the role of physical/chemical characteristics of human-generated droplets in altering embedded viruses' viability. We also review studies evaluating these viruses in the air, field studies and available evidence about seasonality patterns. Ultimately the literature suggests that a proportion of virulent human coronaviruses can plausibly be transmitted via the air, even though this might vary in different conditions. Evidence exists for respirable-sized airborne droplet nuclei containing viral RNA, although this does not necessarily imply that the virus is transmittable, capable of replicating in a recipient host, or that inoculum is sufficient to initiate infection. However, evidence suggests that coronaviruses can survive in simulated droplet nuclei for a significant time (>24 h). Nevertheless, laboratory nebulized virus-laden aerosols might not accurately model the complexity of human carrier aerosols in studying airborne viral transport. In summary, there is disagreement on whether wild coronaviruses can be transmitted via an airborne path and display seasonal patterns. Further studies are therefore required to provide supporting evidence for the role of airborne transmission and assumed mechanisms underlying seasonality.

The burden of influenza in older adults: meeting the challenge

Influenza is an acute respiratory infection for which vaccination is our best prevention strategy. Small seasonal changes in circulating influenza viruses (antigenic drift) result in the need for annual influenza vaccination, in which the vaccine formulation is updated to better match the predominant circulating influenza viruses that have undergone important antigenic changes. Although the burden of influenza infection and its complications is the highest in older adults, vaccine effectiveness is the lowest in this vulnerable population. This is largely due to waning of the immune response with age known as "immune senescence", and presents an important, unmet challenge. Possible strategies to tackle this include adjuvant and high-dose vaccines, and herd immunity induced by greater vaccine uptake.

Completeness of open access FluNet influenza surveillance data for Pan-America in 2005-2019

For several decades, the World Health Organization has collected, maintained, and distributed invaluable country-specific disease surveillance data that allow experts to develop new analytical tools for disease tracking and forecasting. To capture the extent of available data within these sources, we proposed a completeness metric based on the effective time series length. Using FluNet records for 29 Pan-American countries from 2005 to 2019, we explored whether completeness was associated with health expenditure indicators adjusting for surveillance system heterogeneity. We observed steady improvements in completeness by 4.2–6.3% annually, especially after the A(H1N1)-2009 pandemic, when 24 countries reached > 95% completeness. Doubling in decadal health expenditure per capita was associated with ~ 7% increase in overall completeness. The proposed metric could navigate experts in assessing open access data quality and quantity for conducting credible statistical analyses, estimating disease trends, and developing outbreak forecasting systems.

Abrupt Subsidence of Seasonal Influenza after COVID-19 Outbreak, Hong Kong, China

The onset of the 2019-20 winter influenza season in Hong Kong coincided with the emergence of the coronavirus disease epidemic in neighboring mainland China. After widespread adoption of large-scale social distancing interventions in response to the impending coronavirus disease outbreak, the influenza season ended abruptly with a decrease to a low trough.

Characteristics of Seasonal Influenza Virus Activity in a Subtropical City in China, 2013-2019

BACKGROUND

To optimize seasonal influenza vaccination programs in regions with potentially complicated seasonal patterns, the epidemiological characteristics of seasonal influenza activity in a subtropical city of China were explored.

MATERIALS AND METHODS

Influenza virus data of patients with influenza-like illness (ILI) during 2013-2019 were collected from two sentinel hospitals in a subtropical region of China, Yichang city. The influenza virus positive rate among sampled ILI cases served as a proxy to estimate influenza seasonal characteristics, including periodicity, duration, peaks, and predominant subtypes/lineages. Epidemiological features of different years, seasons and age groups were analyzed, and vaccine mismatches were identified.

RESULTS

In total, 8693 ILI cases were included; 1439 (16.6%) were laboratory-confirmed influenza cases. The influenza A positive rate (10.6%) was higher than the influenza B positive rate (5.9%). There were three influenza circulation patterns in Yichang: (1) annual periodicity (in 2013-2014, 2015-2016 and 2018-2019), (2) semiannual periodicity (in 2014-2015), and (3) year-round periodicity (in 2016-2017 and 2017-2018). Summer epidemics existed in two of the six years and were dominated by influenza A/H3N2. Winter and spring epidemics occurred in five of the six years, and A/H1N1, A/H3N2, B/Victoria, and B/Yamagata were codominant. During the study period, the predominant lineages, B/Victoria in 2015-16 and B/Yamagata in 2017-2018, were both mismatched with the influenza B component of the trivalent vaccine. Children 5-14 years old (26.4%) and individuals over 60 years old (16.9%) had the highest influenza positive rates.

CONCLUSIONS

The seasonal epidemic period and the predominant subtype/lineage of influenza viruses in Yichang city are complex. Influenza vaccination timing and strategies need to be optimized according to the local features of influenza virus activity.

Assessing Seasonality Variation with Harmonic Regression: Accommodations for Sharp Peaks

The use of the harmonic regression model is well accepted in the epidemiological and biostatistical communities as a standard procedure to examine seasonal patterns in disease occurrence. While these models may provide good fit to periodic patterns with relatively symmetric rises and falls, for some diseases the incidence fluctuates in a more complex manner. We propose a two-step harmonic regression approach to improve the model fit for data exhibiting sharp seasonal peaks. To capture such specific behavior, we first build a basic model and estimate the seasonal peak. At the second step, we apply an extended model using sine and cosine transform functions. These newly proposed functions mimic a quadratic term in the harmonic regression models and thus allow us to better fit the seasonal spikes. We illustrate the proposed method using actual and simulated data and recommend the new approach to assess seasonality in a broad spectrum of diseases manifesting sharp seasonal peaks.

Incorporating calendar effects to predict influenza seasonality in Milwaukee, Wisconsin

Social outings can trigger influenza transmission, especially in children and elderly. In contrast, school closures are associated with reduced influenza incidence in school-aged children. While influenza surveillance modelling studies typically account for holidays and mass gatherings, age-specific effects of school breaks, sporting events and commonly celebrated observances are not fully explored. We examined the impact of school holidays, social events and religious observances for six age groups (all ages, ⩽4, 5–24, 25–44, 45–64, ⩾65 years) on four influenza outcomes (tests, positives, influenza A and influenza B) as reported by the City of Milwaukee Health Department Laboratory, Milwaukee, Wisconsin from 2004 to 2009. We characterised holiday effects by analysing average weekly counts in negative binomial regression models controlling for weather and seasonal incidence fluctuations. We estimated age-specific annual peak timing and compared influenza outcomes before, during and after school breaks. During the 118 university holiday weeks, average weekly tests were lower than in 140 school term weeks (5.93 vs. 11.99 cases/week, P < 0.005). The dampening of tests during Winter Break was evident in all ages and in those 5–24 years (RR = 0.31; 95% CI 0.22–0.41 vs. RR = 0.14; 95% CI 0.09–0.22, respectively). A significant increase in tests was observed during Spring Break in 45–64 years old adults (RR = 2.12; 95% CI 1.14–3.96). Milwaukee Public Schools holiday breaks showed similar amplification and dampening effects. Overall, calendar effects depend on the proximity and alignment of an individual holiday to age-specific and influenza outcome-specific peak timing. Better quantification of individual holiday effects, tailored to specific age groups, should improve influenza prevention measures.

Incidence and seasonality of respiratory viruses causing acute respiratory infections in the Northern United Arab Emirates

Background

The data on the seasonality of respiratory viruses helps to ensure the optimal vaccination period and to monitor the possible outbreaks of variant type.

Objectives

This study was designed to describe the molecular epidemiology and seasonality of acute respiratory infection (ARI)‐related respiratory viruses in the United Arab Emirates (UAE).

Methods

Both upper and lower respiratory specimens were collected for the analysis from all the patients who visited the Sheikh Khalifa Specialty Hospital (SKSH) with ARI for over 2 years. The multiplex real‐time reverse transcription polymerase chain reaction (rRT‐PCR) test was used to detect respiratory viruses, which include human adenovirus, influenza virus (FLU) A and B, respiratory syncytial virus, parainfluenza viruses, human rhinovirus (HRV), human metapneumovirus, human enterovirus, human coronavirus, and human bocavirus.

Results

A total of 1,362 respiratory samples were collected from 733 (53.8%) male and 629 (46.2%) female patients with ARI who visited the SKSH between November 2015 and February 2018. The rRT‐PCR test revealed an overall positivity rate of 37.2% (507/1362). The positive rate increased during winter; it was highest in December and lowest in September. FLU was the most frequently detected virus (273/1362 [20.0%]), followed by human rhinovirus (146/1362 [10.7%]). The FLU positivity rate showed two peaks, which occurred in August and December. The peak‐to‐low ratio for FLU was 2.26 (95% confidence interval: 1.52‐3.35).

Conclusions

The pattern of FLU in the UAE parallels to that of temperate countries. The trend of the small peak of FLU in the summer suggests a possibility of semi‐seasonal pattern in the UAE.

Deploying digital health data to optimize influenza surveillance at national and local scales

The surveillance of influenza activity is critical to early detection of epidemics and pandemics and the design of disease control strategies. Case reporting through a voluntary network of sentinel physicians is a commonly used method of passive surveillance for monitoring rates of influenza-like illness (ILI) worldwide. Despite its ubiquity, little attention has been given to the processes underlying the observation, collection, and spatial aggregation of sentinel surveillance data, and its subsequent effects on epidemiological understanding. We harnessed the high specificity of diagnosis codes in medical claims from a database that represented 2.5 billion visits from upwards of 120,000 United States healthcare providers each year. Among influenza seasons from 2002-2009 and the 2009 pandemic, we simulated limitations of sentinel surveillance systems such as low coverage and coarse spatial resolution, and performed Bayesian inference to probe the robustness of ecological inference and spatial prediction of disease burden. Our models suggest that a number of socio-environmental factors, in addition to local population interactions, state-specific health policies, as well as sampling effort may be responsible for the spatial patterns in U.S. sentinel ILI surveillance. In addition, we find that biases related to spatial aggregation were accentuated among areas with more heterogeneous disease risk, and sentinel systems designed with fixed reporting locations across seasons provided robust inference and prediction. With the growing availability of health-associated big data worldwide, our results suggest mechanisms for optimizing digital data streams to complement traditional surveillance in developed settings and enhance surveillance opportunities in developing countries.

Influenza contributes substantially to global morbidity and mortality each year, and epidemiological surveillance for influenza is typically conducted by sentinel physicians and health care providers recruited to report cases of influenza-like illness. While population coverage and representativeness, and geographic distribution are considered during sentinel provider recruitment, systems cannot always achieve these standards due to the administrative burdens of data collection. We present spatial estimates of influenza disease burden across United States counties by leveraging the volume and fine spatial resolution of medical claims data, and existing socio-environmental hypotheses about the determinants of influenza disease disease burden. Using medical claims as a testbed, this study adds to literature on the optimization of surveillance system design by considering conditions of limited reporting and spatial aggregation. We highlight the importance of considering sampling biases and reporting locations when interpreting surveillance data, and suggest that local mobility and regional policies may be critical to understanding the spatial distribution of reported influenza-like illness.

Pilot study to harmonize the reported influenza intensity levels within the Spanish Influenza Sentinel Surveillance System (SISSS) using the Moving Epidemic Method (MEM)

The intensity of annual Spanish influenza activity is currently estimated from historical data of the Spanish Influenza Sentinel Surveillance System (SISSS) using qualitative indicators from the European Influenza Surveillance Network. However, these indicators are subjective, based on qualitative comparison with historical data of influenza-like illness rates. This pilot study assesses the implementation of Moving Epidemic Method (MEM) intensity levels during the 2014-2015 influenza season within the 17 sentinel networks covered by SISSS, comparing them to historically reported indicators. Intensity levels reported and those obtained with MEM at the epidemic peak of the influenza wave, and at national and regional levels did not show statistical difference (P = 0·74, Wilcoxon signed-rank test), suggesting that the implementation of MEM would have limited disrupting effects on the dynamic of notification within the surveillance system. MEM allows objective influenza surveillance monitoring and standardization of criteria for comparing the intensity of influenza epidemics in regions in Spain. Following this pilot study, MEM has been adopted to harmonize the reporting of intensity levels of influenza activity in Spain, starting in the 2015-2016 season.

Influenza seasonality and vaccination timing in tropical and subtropical areas of southern and south-eastern Asia

OBJECTIVE

To characterize influenza seasonality and identify the best time of the year for vaccination against influenza in tropical and subtropical countries of southern and south-eastern Asia that lie north of the equator.

METHODS

Weekly influenza surveillance data for 2006 to 2011 were obtained from Bangladesh, Cambodia, India, Indonesia, the Lao People's Democratic Republic, Malaysia, the Philippines, Singapore, Thailand and Viet Nam. Weekly rates of influenza activity were based on the percentage of all nasopharyngeal samples collected during the year that tested positive for influenza virus or viral nucleic acid on any given week. Monthly positivity rates were then calculated to define annual peaks of influenza activity in each country and across countries.

FINDINGS

Influenza activity peaked between June/July and October in seven countries, three of which showed a second peak in December to February. Countries closer to the equator had year-round circulation without discrete peaks. Viral types and subtypes varied from year to year but not across countries in a given year. The cumulative proportion of specimens that tested positive from June to November was > 60% in Bangladesh, Cambodia, India, the Lao People's Democratic Republic, the Philippines, Thailand and Viet Nam. Thus, these tropical and subtropical countries exhibited earlier influenza activity peaks than temperate climate countries north of the equator.

CONCLUSION

Most southern and south-eastern Asian countries lying north of the equator should consider vaccinating against influenza from April to June; countries near the equator without a distinct peak in influenza activity can base vaccination timing on local factors.

Rotavirus seasonality and age effects in a birth cohort study of southern India

INTRODUCTION

Understanding the temporal patterns in disease occurrence is valuable for formulating effective disease preventive programs. Cohort studies present a unique opportunity to explore complex interactions associated with emergence of seasonal patterns of infectious diseases.

METHODS

We used data from 452 children participating in a birth cohort study to assess the seasonal patterns of rotavirus diarrhea by creating a weekly time series of rotavirus incidence and fitting a Poisson harmonic regression with biannual peaks. Age and cohort effects were adjusted for by including the weekly counts of number of children in the study and the median age of cohort in a given week. Weekly average temperature, humidity and an interaction term to reflect the joint effect of temperature and humidity were included to consider the effects of meteorological variables.

RESULTS

In the overall rotavirus time series, two significant peaks within a single year were observed--one in winter and the other in summer. The effect of age was found to be the most significant contributor for rotavirus incidence, showing a strong negative association. Seasonality remained a significant factor, even after adjusting for meteorological parameters, and the age and cohort effects.

CONCLUSIONS

The methodology for assessing seasonality in cohort studies is not yet developed. This is the first attempt to explore seasonal patterns in a cohort study with a dynamic denominator and rapidly changing immune response on individual and group levels, and provides a highly promising approach for a better understanding of the seasonal patterns of infectious diseases, tracking emergence of pathogenic strains and evaluating the efficacy of intervention programs.