|Year : 2020 | Volume
| Issue : 1 | Page : 55-65
Seasonal influenza surveillance (2009–2017) for pandemic preparedness in the WHO South-East Asia Region
Members of the WHO South-East Asia Region Global Influenza Surveillance and Response System
Members of the WHO South-East Asia Region Global Influenza Surveillance and Response System are listed in the acknowledgements
|Date of Web Publication||26-Apr-2020|
Background Influenza causes seasonal outbreaks each year and periodically causes a pandemic. The World Health Organization (WHO) Global Influenza Surveillance and Response System (GISRS) has contributed to global understanding of influenza patterns, but limited regional analysis has occurred. This study describes the virological patterns and influenza surveillance systems in the 11 countries of the WHO South-East Asia Region.
Methods Virological data were extracted in January 2018 from FluNet, GISRS’s web-based reporting tool, for 10 of the 11 countries that had data available for the years 2009 to 2017. Descriptive data for 2017 on influenza surveillance systems, including the number of sentinel sites, case definitions and reporting frequency, were collected through an annual questionnaire.
Results Data on surveillance systems were available for all 11 Member States, and 10 countries reported virological data to FluNet between 2009 and 2017. Influenza surveillance in the region and national participation increased over the 8 years. Seasons varied between countries, with some experiencing two peak seasons and others having one main predominant season. Bangladesh, Indonesia and Myanmar have only one season: Bangladesh and Myanmar have a mid-year pattern and Indonesia an end-year pattern. Influenza A was the predominant circulating type for all years except 2012 and 2016, when A and B co-circulated. Influenza A(H1N1)pdm09 was dominant in 2009 and 2010 (77% and 76%, respectively), 2015 (72%) and 2017 (54%); influenza A(H3) accounted for approximately half of the positive specimens in 2011 (46%), 2013 (51%) and 2014 (47%); and influenza B (lineage not determined) made up over 49% of positive specimens in 2012.
Conclusion Although the timings of peaks varied from country to country, the viruses circulating within the region were similar. Influenza surveillance remains a challenge in the region. However, timely reporting and regional sharing of information about influenza may help countries that have later peaks to allow them to prepare for the potential severity and burden associated with prevailing strains.
Keywords: influenza, pandemic influenza preparedness, seasonal influenza, South-East Asia, surveillance
|How to cite this article:|
Members of the WHO South-East Asia Region Global Influenza Surveillance and Response System. Seasonal influenza surveillance (2009–2017) for pandemic preparedness in the WHO South-East Asia Region. WHO South-East Asia J Public Health 2020;9:55-65
|How to cite this URL:|
Members of the WHO South-East Asia Region Global Influenza Surveillance and Response System. Seasonal influenza surveillance (2009–2017) for pandemic preparedness in the WHO South-East Asia Region. WHO South-East Asia J Public Health [serial online] 2020 [cited 2022 Jan 22];9:55-65. Available from: http://www.who-seajph.org/text.asp?2020/9/1/55/282999
Correspondence to: Dr Philip L Gould ([email protected]), Ms Leila Bell ([email protected])
| Background|| |
Seasonal influenza viruses are an often underappreciated public health threat. With an estimated annual attack rate of 5–10% in adults and between 20% and 30% in children, it is estimated that influenza causes between 290 000 and 650 000 respiratory-related deaths annually., Accurate surveillance data are important, as they can be used to predict seasonal trends to inform public health action, to identify unusual severity or incidence and to provide important information for vaccine composition. Vaccines remain the best public health intervention against influenza, and the frequent mutations to circulating viruses mean that accurate and timely information is particularly important. Seasonal influenza trends are well established in temperate climates, with yearly peaks in activity occurring in winter and spring. Trends are less well defined in the tropics and subtropics, owing in part to the variety in trends and also to the lack of surveillance data. Recently, the amount of available data has increased, particularly as more tropical and subtropical countries contribute surveillance data to global databases. Surveillance data have shown evidence that, although there is no globally unifying trend in these areas, tropical countries may exhibit predictable yearly trends, with dual or single peaks in activity either with or without year-round background activity.,,,
The World Health Organization (WHO) South-East Asia Region is home to approximately one quarter of the global population, and most of the 11 Member States are at least partly located in the tropics. Recent improvements in surveillance systems and laboratory capacity have produced valuable surveillance data from the region. Preliminary evidence suggests that seasonal peaks in activity often occur during countries’ respective monsoon seasons. Virological data are collected by laboratories within the Global Influenza Surveillance and Response System (GISRS) and analysed to determine recommendations for vaccine composition. There are 10 WHO-recognized national influenza centres (NICs) located in eight countries of the region, as well as one WHO reference laboratory for influenza A(H5).
Many Member States in the region have been contributing valuable data to GISRS for nearly a decade. These include data on type, subtype and lineage of influenza-positive specimens, numbers of specimens processed by NICs or other national public health laboratories, and information on influenza-like illness (ILI) and severe acute respiratory infection (SARI) consultation and admission rates. However, as yet there has been no regional analysis that describes the characteristics of influenza in the WHO South-East Asia Region. Understanding the larger context of influenza in the region will help the global community to better utilize surveillance data from the region, as well as enabling regional and national prioritization of influenza work. This paper contributes key knowledge for the understanding of seasonal influenza in the WHO South-East Asia Region.
| Methods|| |
Virological and epidemiological data were extracted in January 2018 from the WHO FluNet and FluID databases for Member States of the WHO South-East Asia Region that had contributed influenza surveillance data between 2009 and 2017. Although there were countries that had contributed data before 2009, the reporting was less consistent and therefore these data are not included in this analysis. FluNet and FluID are GISRS databases coordinated by WHO, with data submitted periodically by Member State representatives.
Descriptive data on influenza surveillance systems, including number of sentinel sites, case definitions and reporting frequency, were derived from the data from the 2017 annual questionnaire that the WHO Regional Office for South-East Asia sends to WHO country office and government colleagues. A literature review using the search terms “Member State” + “influenza” was conducted to review relevant publications to compare the seasonal trends identified from FluNet sentinel surveillance data with those identified from data collected using other methods.
Descriptive data, including on number and type of surveillance sites and methodology for sample collection, were summarized from the 2017 annual survey for each Member State.
Virological data for each Member State were graphed and analysed. In order to convert weekly FluNet data into monthly data, the epidemiological week was multiplied by 12 months/53 weeks. This was then divided into months with products < 1 allocated to January, products between 1 and < 2 allocated to February and so forth. Monthly percentage positive was calculated by dividing total number of influenza-positive specimens by total number of specimens processed.
Epidemiological data, which varied by Member State depending on individual reporting formats, were converted to month as described above and then graphed and analysed. Analyses included the percentage of total outpatient consultations that were for ILI and the percentage of total inpatient admissions that were for SARI.
The data used were aggregated and individual patient-level data were not involved. This research was done without patient involvement. Patients were not invited to comment on the study design and were not consulted to develop patient-relevant outcomes or on the interpretation of the results, nor were they invited to contribute to the writing or editing of this document for readability or accuracy.
| Results|| |
Descriptive data on surveillance systems were available for all 11 Member States of the region through the annual questionnaire. Ten of the 11 countries reported virological data to FluNet between 2009 and 2017, namely Bangladesh, Bhutan, India, Indonesia, Maldives, Myanmar, Nepal, Sri Lanka, Thailand and Timor-Leste. Epidemiological data for the reporting period were submitted by five countries: Bangladesh, Bhutan, Indonesia, Maldives and Thailand.
The types and numbers of surveillance systems, case definitions and sampling methodologies in 2017 varied by country (see [Table 1]). In total, nine countries reported use of the 2011 WHO ILI case definition of acute respiratory infection with onset during the past 7 days with measured temperature of ≥ 38 °C and cough, while seven reported use of the 2011 WHO SARI case definition of acute respiratory infection with a history of fever or measured fever of ≥ 38 °C and cough, with onset within the past 7 days, requiring hospitalization. In addition, two countries reported the use of the 2014 WHO ILI and 2014 WHO SARI case definitions, both of which increase sensitivity by extending the definition to include cases with onset during the past 10 days (see [Table 1]). Those countries reporting use of a different case definition varied with regard to the number of days since symptom onset or required a specific diagnosis of pneumonia or acute respiratory distress syndrome. All the sentinel surveillance systems included at least some collection of samples although information was not collected on specific methodologies.
|Table 1. Surveillance systems and ILI and SARI case definitions in WHO South-East Asia Region countries, 2017|
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The number of countries of the region submitting data to FluNet increased over the reporting period, from three in 2009 to ten in 2017 (see [Table 2]). In 2008, a total of 10 892 samples were processed by three countries, namely India, Sri Lanka and Thailand. In 2009, 30 683 samples were processed by these same countries, a nearly threefold increase that was driven primarily by an increase in samples from India. Percentage positive also increased between these years, from 12% in 2008 to 21% in 2009.
|Table 2. Specimens tested and specimens found positive for influenza by type/subtype/lineage in the WHO South-East Asia Region, 2009–2017|
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During the reporting period, a total of 313 432 specimens were processed in the region, with 66 965 specimens (21%) being influenza positive (see Table 2). The total number of specimens processed annually fluctuated between 25 401 in 2011 and 45 249 in 2017, with variation between years. India contributed the largest number of specimens, accounting for 43% of samples processed, followed by Thailand, Sri Lanka and Bangladesh, all contributing 12% of data on virus detections (see [Figure 1]). Percentage positive for the region ranged from 16% to 25% in 2011 and 2017, respectively.
|Figure 1: Total ILI and SARI samples processed and reported to FluNet in the WHO South-East Asia Region, 2009–2017a|
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Influenza A was the predominant circulating influenza virus for all years except 2012 and 2016, when influenza A and B co-circulated in the region. In years in which influenza A made up the majority of viruses, it accounted for between 64% and 99% of viruses identified (see Table 2). In 2009 and 2010, A(H1N1)pdm09 was the predominant subtype, accounting for 77% and 76% of total influenza-positive samples, respectively. The first A(H1N1)pdm09 viruses detected in the region were in May 2009 in Thailand, before India and Sri Lanka reported the pandemic strain in June 2009 (see [Figure 2]). Much of the A(H1N1)pdm09 activity in the region during those years was related to large peaks in August in 2009 and 2010 in India. A(H1N1)pdm09 was also predominant throughout the region in 2015 (72%) and 2017 (54%). Influenza A(H3) accounted for approximately half of positive specimens in 2011 (46%), 2013 (51%) and 2014 (47%), while influenza B (lineage not determined) accounted for 49% of influenza positive specimens in 2012.
Bangladesh started reporting virological data in 2010. The first positive specimens of A(H1N1)pdm09 during the pandemic were identified in February 2010; however, influenza B and A(H1N1)pdm09 predominated that year (see [Figure 2]a and [Figure 3]). In general, there was a consistent trend towards higher activity between approximately April and September of each year. This trend did not hold in 2012, when there were two peaks, in April and September, and a decrease in percentage positive between these months. Influenza A(H3) and B viruses co-circulated in 2011, with the B viruses mainly identified slightly later in the year. In 2012, 2015 and 2017, A(H1N1)pdm09 predominated during the first half of the year, with influenza B activity increasing later in the year.
|Figure 3. Proportion of in-uenza viruses by type/subtype by Member State, by year, 2009–2017|
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Bhutan began reporting to FluNet in 2011 and in general the data during the reporting period show two peaks of activity, although the timing of these peaks was not always consistent across years. In 2011, A(H1N1)pdm09 and A(H3) accounted for most influenza activity, whereas in 2012 influenza B circulated more widely, along with A(H1N1). In 2013 and 2014, influenza A(H3) predominated, with influenza B co-circulating in 2014. In 2015 and 2017, A(H1N1)pdm09 activity was high again, although influenza B and A(H3) co-circulated. In 2016, there was variable activity with several peaks, primarily of A(H3) and B.
India has reported data to FluNet since 1997, but the number and consistency of reports has increased since the 2009 pandemic. In general, peak influenza activity is observed during July to September, with a secondary peak in November to January. In 2009 and 2010, A(H1N1)pdm09 accounted for the majority of influenza viruses reported and peak activity was in August for both years. In 2011, fewer positive samples were reported than for the two previous years, and A(H3) and B co-circulated during the second half of the year, with influenza B becoming more predominant from September. In 2012, 2013, 2015 and 2017, outbreaks were reported in interseasonal periods. In 2012, influenza activity was observed in March and April, with A(H1N1)pdm09 and influenza B co-circulating; A(H1N1)pdm09 and A(H3) co-circulated in July to April of 2013. In 2015 and 2017, A(H1N1)pdm09 was the predominant circulating subtype. The A(H3) activity in 2013 continued into 2014, with a peak in activity in August. During March 2015, there was a large number of A(H1N1)pdm09-positive specimens resulting in a substantially higher peak in influenza positives than in any other year.
In general, since Indonesia began reporting data in 2010, influenza activity has tended to be highest at the beginning and end of the year, with lower activity in the middle of the year. In addition, peaks in influenza A virus activity often occurred before peaks in influenza B virus activity. In 2010, influenza A(H1N1)pdm09 was not circulating widely until late in the year, with A(H3) predominant at the beginning of the year and influenza B activity increasing in the second half of the year. Most years saw co-circulation of influenza B, A(H3) and A(H1N1)pdm09, with substantial overlap.
Since Maldives started to report in 2015, the number of positives has been low; peaks in percentage positive have been variable, with no clear trend. In 2017, a large number of A(H1N1)pdm09-positive samples were reported, with the outbreak starting in February and peaking in March. Influenza B activity increased in September and the virus continued to circulate until the end of 2017.
Since reporting began in 2016, data from Myanmar have shown an increased number of positive samples from June to September each year. Peaks in percentage positive do not correspond directly to these increased positive samples, and the numbers of samples processed by month vary substantially throughout the year. In 2016, influenza B and A(H3) co-circulated, while most influenza-positive specimens in 2017 were A(H1N1)pdm09.
Nepal has routinely reported data since mid-2011, and a consistent trend has emerged from these data, with two peaks in activity for most reporting years. In 2012, influenza B and A(H1N1)pdm09 co-circulated, both peaking in activity in September. In each year from 2013 to 2016, a peak in February or March was due primarily to influenza A(H3) and A(H1N1)pdm09 activity, while a peak later in the year, in July to September, mainly reflected increased influenza B activity, although A(H3) also circulated. Influenza A(H1N1)pdm09 activity was relatively low, except in early 2015 and again in August 2017, when large numbers of positive specimens were detected.
Virological data from Sri Lanka have been routinely reported since 2009. Influenza B and A subtypes co-circulated and there were generally two peak seasons, from November to February and then from April to July. In 2009, the first A(H1N1)pdm09 viruses were detected in June and activity peaked in December of that year. In 2010, activity was low at the beginning of the year but that year again saw a peak in A(H1N1)pdm09 activity in December. This corresponded to peaks in A(not subtyped) and B. Activity was low from mid-2013 to 2014, before a dramatic increase in the number of influenza-positive samples identified in early 2015. In 2015 and 2017, a large number of viruses was identified, primarily A(not subtyped), in June and March, respectively.
Thailand has been regularly reporting sentinel surveillance data to FluNet since 1997. Influenza activity between 2009 and 2017 was variable in Thailand but tended to show two general increases in activity. Influenza A(H1N1)pdm09, A(H3) and B co-circulated during most years, with several peaks of activity in A(H1N1)pdm09 and A(H3), for example in late 2009 to early 2010, July to October 2010 and 2011, and August to October 2017.
Timor-Leste recently began collecting sentinel surveillance data and reporting them to global platforms, and clear trends are not yet visible from the available data.
Epidemiological data were reported by five countries: Bhutan and Thailand provided data for the period from 2011 to 2017, Indonesia for the period from 2015 to 2017, and Bangladesh and Maldives for the period from 2016 to 2017. The numbers of ILI presentations and total outpatients at sentinel sites were provided by all five countries reporting. In addition, data on SARI cases, total inpatients and SARI-associated deaths were reported by Bangladesh, Bhutan and Maldives. Bangladesh also provided data on numbers of ILI and SARI samples tested and found positive for influenza. In general, increased proportions of ILI consultations and SARI admissions corresponded to increased percentages of samples found positive for influenza. This trend was not always seen, however, and varied across countries and years.
| Discussion|| |
Influenza surveillance was reported as ongoing in each of the 11 Member States of the WHO South-East Asia Region. Based on self-reporting, during 2017, 9 countries of the region used the 2011 WHO case definition for ILI and 7 used the 2011 WHO case definition for SARI, making comparison between countries theoretically possible. In addition, assuming a consistent definition is used over time, comparisons can be made across years. However, any comparisons must be approached with caution owing to differences in surveillance and sample collection methodology, geographical distribution of sentinel sites and consistency of reporting, all of which may affect comparability.
The trends observed from the FluNet surveillance data were consistent with those observed from data previously reported for many countries of the region. In the case of Bangladesh, several studies have been carried out that demonstrate that influenza activity tends to be higher from May to September, which is consistent with the trends found in this analysis., Previous evidence from both Bhutan and Indonesia also supports the data presented here, with Bhutan exhibiting a bimodal trend in influenza activity, and activity in Indonesia being higher at the beginning and end of the year.,, A previous analysis of influenza trends in Myanmar found heightened activity from June to September, which is also consistent with the trends found here. A study was carried out on the antigenic characteristics of influenza circulating in Nepal in 2014; the authors found that influenza A accounted for the majority (71.5%) of influenza-positive specimens and that influenza activity was reported year round. This is consistent with the surveillance data, according to which 70% of positive specimens were influenza A and influenza activity was bimodal during 2014.
In countries with large geographical and climatic diversity, aggregation of data may mean loss of clear trends. For example, previous studies have shown the impact of latitude and environmental factors in India on trends in seasonal influenza activity, with substantial variation by latitude based primarily on regional monsoon seasons., Because FluNet data are aggregated by country, in the case of India there are no clear seasonal patterns across years. The same applies to a study based on data from the southern region of Sri Lanka, which found clear seasonality of influenza activity, with peaks in March to June. This trend was not observed using the FluNet data, possibly because of data aggregation and geographical differences in influenza trends within the country. Similarly, a study restricted to Bangkok, Thailand, found a seasonal trend in influenza activity during the rainy season, from July to September. Given the large area and diverse geographical characteristics of the country, the fact that this is not consistent with data from this analysis is not surprising. Other studies from Thailand found period peaks in influenza-associated mortality and activity; however, the timing of these peaks was not consistent from year to year, similar to what was observed in the FluNet data., This presents an interesting challenge for national immunization policies. For example, a study based in India demonstrated that influenza seasonality in the country was so variable that vaccination policies should be tailored to subregions of the country. However, there is also evidence from a recent vaccine trial among pregnant women in Nepal that in subtropical areas maternal influenza immunization delivered year round may lead to reductions in maternal ILI, in infant influenza and in low birthweight.
The 2009 influenza pandemic and subsequent influenza epidemics, such as the A(H1N1)pdm09 outbreaks in India in 2015 and in Maldives and Myanmar in 2017, highlight the importance of constant vigilance with regard to influenza surveillance. Although sentinel surveillance data, such as those reported to FluNet and FluID, are unlikely to identify an outbreak of unusual influenza, the identification of unusual clusters of severe disease by health-care workers, including laboratory workers, is important for early detection of severe seasonal influenza epidemics or possible outbreaks of novel influenza viruses with pandemic potential. The 2009 pandemic was particularly influential and provided a poignant reminder that novel influenza viruses have the potential to spread globally rapidly. For example, in the case of Bhutan, the virus took only 3 months from first detection in Mexico to cross into the country despite geographical isolation and few air and road routes into the country. Before the pandemic, the influenza surveillance system in Bhutan was limited, but during and after the pandemic the number of sites increased from three in 2008 to 11 in 2010, and now the surveillance system is consistently reporting both virological and epidemiological data.
Trends in ILI and SARI consultation and admission by age group and overall were difficult to assess from the data provided, in part because of the number of data available. However, general trends were consistent with those observed previously in other regions, with higher proportions of ILI consultations and SARI admissions among all consultations and admissions during periods of increased influenza virus circulation. As additional data become available, further analyses of epidemiological trends, including by age group and influenza type/subtype/lineage, will further understanding of influenza trends in the region.
There are several limitations of this study that must be noted. First, the number of data submitted from each country varied month to month and year to year. Some countries submitted hundreds of samples for processing one month and then very few the next month, introducing uncertainty into observed seasonal trends in both percentage positive and total number of positive samples. Second, the number of sentinel sites submitting data per week varied and the national representativeness of the sentinel sites is unknown. Although ideally sentinel sites will be representative of the country, this may not be the case and trends observed may be representative of only a subset of the national population. Differences in demographic characteristics, health-care-seeking behaviours and geographic characteristics of sentinel catchment populations may all impact the trends observed. Third, virological data are aggregated by age, despite known variation in morbidity and mortality by age and circulating subtype. Some countries provided age-disaggregated data, but the available data were insufficient for age-specific analysis. Finally, changes to surveillance systems, including case definition, sample collection methods, and number and location of sentinel sites, may all impact the trends observed.
| Conclusions and next steps|| |
The importance of accurate and timely influenza surveillance data is well understood, and data from countries in the WHO South-East Asia Region have contributed to global estimates of influenza trends and the burden of influenza.,, However, continued efforts to gather data from all countries are needed. Moving forward, we identify three main priorities in relation to influenza surveillance in the region. The first is to ensure that all countries are regularly testing virological samples for influenza and contributing these data, in addition to epidemiological data, to GISRS databases. This will ensure that vaccine composition recommendations continue to incorporate all available data and that any changes to circulating viruses are quickly identified. In addition, by linking virological and epidemiological data, vaccination and other public health interventions can be appropriately prioritized. The second priority is to ensure a functional and representative sentinel site network in each country. Through a network of high-quality, representative sites that routinely report data, information on influenza-associated morbidity and mortality will be available to support policy-makers and health-care professionals to ensure that the most appropriate actions are taken. The third priority is to increase understanding of the impact of influenza on hospitalizations with severe respiratory infections in the health-care and policy fields. Previous studies have shown that influenza is a major contributor to respiratory-related hospitalizations, particularly among children and older adults.,, Member States are moving towards improved surveillance of influenza, which will support public health action to reduce the burden of influenza, as well as enhance preparedness efforts for the next influenza pandemic.
Acknowledgements: We would like to thank many contributors for their continued support in collecting, reporting and analysing influenza data in the WHO South-East Asia Region. At the time of writing, members of the WHO South-East Asia Region Global Influenza Surveillance and Response System included the following: National Influenza Centre, Institute of Epidemiology, Disease Control and Research, Bangladesh; Kunzang Dorji, Binay Thapa, Dorji Wangchuk, Sonam Wangchuk, Pema Yuden and Sangay Zangmo (Royal Centre for Disease Control, Bhutan); Mandeep Chadha and Varsha Potdar (Ministry of Health and Family Welfare, India); National Institute of Health Research and Development, Indonesia; Ibrahim Nishan Ahmed (Ministry of Health, Maldives); Latt Latt Kyaw, Win Thein, Htay Htay Tin and Ommar Swe Tin (National Influenza Centre, National Health Laboratory, Myanmar); Runa Jha, Alisha Sapkota, Bishnu Prasad Upadhyay and Harish Chandra Upreti (National Public Health Laboratory, Nepal); Kedar Baral (Patan Academy of Health Sciences, Nepal); Sanjaya K Shrestha (Walter Reed/AFRIMS Research Unit, Nepal); Samitha Ginige and Paba Palihawadana (Ministry of Health, Nutrition and Indigenous Medicine, Sri Lanka); Jude Jayamaha and Geethani Wickramasinghe (National Influenza Centre, Medical Research Institute, Sri Lanka); National Institute of Health, Department of Medical Sciences, Ministry of Public Health, Thailand; Surveillance and Epidemiology Department, Ministry of Health, Timor-Leste; Leila Bell, Richard Brown, Ritu Singh Chauhan, Mohamed Hammam El Sakka, Prakash Ghimire, Philip L Gould, Faiha Ibrahim, Maja Lievre, Bikram Maharjan, Sudhansh Malhotra, Pavana Murthy, Roderico H Ofrin, Shushil Pant, Wagawatta Liyanage Sugandhika Padmini Perera, Rajan Rayamajhi, Kwang Il Rim, Reuben Samuel, Suraj Man Shrestha, Pushpa Ranjan Wijesinghe, Endang Widuri Wulandari, Mya Yee Mon and Dongbao Yu (World Health Organization).
Source of support: The United States of America Centers for Disease Control and Prevention have a cooperative agreement on influenza surveillance and response activities with the WHO Regional Office for South-East Asia and Member States.
Conflict of interest: None declared.
Authorship: Both corresponding authors contributed equally to this paper. All members of the South-East Asia Region Global Influenza Surveillance and Response System contributed equally to this paper.
| References|| |
Iuliano AD, Roguski KM, Chang HH, Muscatello DJ, Palekar R, Tempia S, et al.; Global Seasonal Influenza-associated Mortality Collaborator Network. Estimates of global seasonal influenza-associated respiratory mortality: a modelling study. Lancet. 2018 Mar;391(10127):1285–300. https://doi.org/10.1016/S0140-6736(17)33293-2 PMID:29248255
Saha S, Chadha M, Al Mamun A, Rahman M, Sturm-Ramirez K, Chittaganpitch M, et al. Influenza seasonality and vaccination timing in tropical and subtropical areas of southern and south-eastern Asia. Bull World Health Organ. 2014 May;92(5):318–30. https://doi.org/ 10.2471/BLT.13.124412 PMID:24839321
Saha S, Chadha M, Shu Y, Lijie W, Chittaganpitch M, Waicharoen S, et al.; Group of Asian Researchers on Influenza (GARI). Divergent seasonal patterns of influenza types A and B across latitude gradient in tropical Asia. Influenza Other Respir Viruses. 2016 May;10(3):176–84. https://doi.org/10.1111/irv.12372 PMID:26781162
Centers for Disease Control and Prevention. Case definitions for infectious conditions under public health surveillance. MMWR Recomm Rep. 1997 May;46 RR-10:1–55. PMID:9148133
Ahmed M, Aleem MA, Roguski K, Abedin J, Islam A, Alam KF, et al. Estimates of seasonal influenza-associated mortality in Bangladesh, 2010–2012. Influenza Other Respir Viruses. 2018 Jan;12(1):65–71. https://doi.org/10.1111/irv.12490 PMID:29197174
Caini S, Andrade W, Badur S, Balmaseda A, Barakat A, Bella A, et al.; Global Influenza B Study. Temporal patterns of influenza A and B in tropical and temperate countries: what are the lessons for influenza vaccination? PLoS One. 2016 Mar;11(3):e0152310. https://doi.org/ 10.1371/journal.pone.0152310 PMID:27031105
Shapiro D, Bodinayake CK, Nagahawatte A, Devasiri V, Kurukulasooriya R, Hsiang J, et al. Burden and seasonality of viral acute respiratory tract infections among outpatients in southern Sri Lanka. Am J Trop Med Hyg. 2017 Jul;97(1):88–96. https://doi.org/ 10.4269/ajtmh.17-0032 PMID:28719323
Prachayangprecha S, Makkoch J, Suwannakarn K, Vichaiwattana P, Korkong S, Theamboonlers A, et al. Epidemiology of seasonal influenza in Bangkok between 2009 and 2012. J Infect Dev Ctries. 2013 Oct;7(10):734–40. https://doi.org/10.3855/jidc.2929 PMID:24129626
Aungkulanon S, Cheng PY, Kusreesakul K, Bundhamcharoen K, Chittaganpitch M, Margaret M, et al. Influenza-associated mortality in Thailand, 2006–2011. Influenza Other Respir Viruses. 2015 Nov;9(6):298–304. https://doi.org/10.1111/irv.12344 PMID:26283569
Cooper BS, Kotirum S, Kulpeng W, Praditsitthikorn N, Chittaganpitch M, Limmathurotsakul D, et al. Mortality attributable to seasonal influenza A and B infections in Thailand, 2005–2009: a longitudinal study. Am J Epidemiol. 2015 Jun;181(11):898–907. https://doi.org/10.1093/aje/kwu360 PMID:25899091
Western Pacific Region Global Influenza Surveillance and Response System. Epidemiological and virological characteristics of influenza in the Western Pacific Region of the World Health Organization, 2006–2010. PLoS One. 2012;7(5):e37568.https://doi.org/10.1371/journal.pone.0037568 PMID:22675427
Menec VH, Black C, MacWilliam L, Aoki FY. The impact of influenza-associated respiratory illnesses on hospitalizations, physician visits, emergency room visits, and mortality. Can J Public Health. 2003 Jan-Feb;94(1):59–63. https://doi.org/10.1007/BF03405054 PMID:12583681
Lafond KE, Nair H, Rasooly MH, Valente F, Booy R, Rahman M, et al.; Global Respiratory Hospitalizations—Influenza Proportion Positive (GRIPP) working group. Global role and burden of influenza in pediatric respiratory hospitalizations, 1982–2012: a systematic analysis. PLoS Med. 2016 Mar;13(3):e1001977. https://doi.org/ 10.1371/journal.pmed.1001977 PMID:27011229
[Figure 1], [Figure 2], [Figure 3]
[Table 1], [Table 2]