Seasonal Influenza Disease Burden Estimator
Our tool supports countries in estimating seasonal influenza burden of disease, so that decisionmakers may better prepare for seasonal influenza outbreaks and future influenza pandemics.
We use a multiplier-based approach, allowing our tool to provide burden estimates even with minimal data inputs. This is ideal for countries who do not have comprehensive influenza surveillance systems.
To use the tool, users must input some data, specifically the number of hospitalized cases and/or deaths in an influenza season by age group, which can be derived from hospital and/or mortality surveillance.
This tool estimates seasonal influenza burden by using multipliers, which were derived from data published in the peer-reviewed literature.
The tool uses these data in 4 ways: (1) to determine multipliers for calculating deaths or hospitalizations (depending on user data entered), hospitalized critically ill cases, and mild/moderate cases; (2) to determine expansion factors (multipliers to account for underdetection) for deaths and hospitalizations; (3) to determine the proportion of influenza-associated deaths by syndrome; and (4) to determine the range (minimum and maximum estimates) in the literature for these assumptions to help bound uncertainty.
Our tool uses baseline assumptions that support our final estimates of seasonal influenza-related burden at 4 levels (deaths, hospitalizations, critically ill cases, and mild/moderate nonhospitalized cases). Our tool also uses published data to calculate the proportion of deaths occurring due to respiratory disease, cardiovascular disease, or other syndromes.
The research team obtained these data by conducting a review of peer-reviewed literature for all 6 WHO regions. Using PubMed, we applied standardized search terms for each region and examined literature published between January 1, 2000 and May 1, 2021, which resulted in a total of 1,991 studies. After reviewing titles and abstracts, we removed duplicates and studies that did not fit our inclusion criteria. We conducted a full content review of the remaining 411 studies. Studies were included if they provided data for multiple burden levels (eg, hospitalizations and deaths); were stratified by age; and were of high quality with clear and well-accepted methods and results that are generalizable to other populations. Based on the inclusion criteria, a total of 66 studies were selected for data extraction and reviewed for data to support developing the tool’s assumptions.
Most countries with influenza surveillance systems collect and report data for hospitalizations and, sometimes, deaths, but less often for mild, moderate, or critically ill hospitalized cases. In addition, most countries do not have data on the proportion of deaths from respiratory, cardiovascular, or other syndromes associated with influenza. Our methodology applies multipliers to hospitalizations or deaths in order to calculate these other components of burden and more fully account for the impact of influenza at all levels of disease severity and for different age groups.
Deaths Multiplier: This multiplier was derived from studies with data on seasonal influenza-associated hospitalized cases and deaths. Our tool includes multipliers for each age group, which are calculated as the average number of deaths divided by the average number of hospitalized cases for the age group. This multiplier is also used to calculate hospitalized cases if the user enters deaths into the tool.
Critical Illness Multiplier: This multiplier was derived from studies with data on hospitalized cases and critically ill (intensive care unit [ICU] hospitalized) cases. The tool includes multipliers for each age group, which are calculated as the average number of critically ill cases divided by the average number of hospitalized cases for the age group.
Mild/Moderate (Nonhospitalized) Case Multiplier: This multiplier was derived from studies with data on mild and moderate influenza cases and hospitalizations. This multiplier represents all symptomatic, nonhospitalized influenza cases, including both those that are medically attended and not medically attended. The tool includes multipliers for each age group, which are calculated as the average number of mild/moderate cases divided by the average number of hospitalized cases for the age group.
Expansion Factors for Hospitalized Cases and Deaths: In addition to the multipliers above, the tool includes an option to apply an expansion factor to user-entered case data for hospitalizations or deaths. Expansion factors are intended to correct for underdetection of hospitalizations or deaths. These expansion factors were derived from the literature, specifically from studies that examined underdetection of influenza-associated hospitalizations and deaths and estimated the percentage of cases or deaths not detected in hospitals.
Estimate of Deaths by Syndrome: Our tool includes an assumption about the proportion of deaths attributed to different syndromes. It calculates the percentage of deaths occurring with respiratory disease, circulatory disease, and other nonrespiratory/noncirculatory disease. The data for this estimate were derived from a single unpublished study by Tempia et al (2018). Because the data on the proportion of deaths occurring due to these syndromes are limited, this assumption is included but will be refined over time as additional data become available.
Data from the literature were taken from each included influenza study to inform assumptions in the tool by extracting information by the age groups reported in each study. To standardize data into the age groups recommended by WHO, the ungroup package in R (Pascariu et al, 2018), was used to ungroup the bins originally reported in the primary sources into single year bins. This statistical approach uses a penalized composite link model—a method for ungrouping histograms that assumes only that the underlying distribution is smooth—to estimate the latent distribution of data within coarsely grouped age bins. This approach allows for estimating open-ended age groups (eg, 65 years of age and older). As many data sources reported a top age bin of 65 years and older or 85 years and older and did not report a maximum age, we imposed an artificial maximum age of 95 years for all data sources.
For the deaths and critically ill multipliers, data were ungrouped for both total hospitalizations as well as the number of hospitalized patients who died or the number of hospitalized patients admitted to the ICU, respectively. For these 2 multipliers, the number of deaths or ICU cases was divided by the total number of hospitalizations for each year of age for each original paper. For the mild/moderate multipliers a similar approach was taken, but in this case, the number of mild/moderate cases was divided by the total number of hospitalizations for each year of age for the original paper.
Once all data for each tool assumption were disaggregated into single years for each original source, all years from all sources were regrouped into WHO-recommended age groups. The mean value for each age group was used as the point estimate for the respective multiplier and 95% confidence intervals were calculated to bound uncertainty.
In the case of expansion factors, data did not facilitate unbinning and rebinning by WHO-recommended age groups. Instead, we combined the data into 3 age bins (under 5, 5 to 64, and 65 and over) and took the mean expansion factor included in the age ranges as point estimates. The minimum and maximum values from included studies are included to show how much variability (uncertainty) there is in the data.
The tool allows users to choose the year/influenza season for which to estimate the burden of disease and compare results between seasons. The format of the season (single year vs. multiyear season) is determined based on the reporting format used for the WHO Global Influenza Programme FluNET database. Our tool shows the proportions of flu strains by season from country-reported data in FluNET. Not all countries report this information fully each season, so flu subtypes are shown only when data are available. The flu subtype proportions do not currently influence the multipliers in the tool because there are insufficient data on influenza strain-specific disease severity and on how differences in circulating strains impact the proportion of mild/moderate cases versus hospitalizations and deaths. This is an area for future research.
Uncertainty is handled in 3 ways. First, the tool provides ranges for case and death estimates, which represent the range of values found in the literature. For age-stratified multipliers, we also show 95% confidence intervals in the tables above. Second, the tool includes a sensitivity table to show how results change if some of the assumptions differ from the tool default. This sensitivity table allows a user to explore uncertainty and how varied assumptions alter the burden outputs from the tool. Third, the tool accounts for uncertainty by allowing users to modify some of the assumptions. Users enter their own data into the tool for hospitalizations and/or deaths, and they can customize expansion factors for those data. In addition, users can customize the mild/moderate multiplier and the assumptions in the sensitivity table to better reflect their own data or assumptions.
Deaths Multiplier Data Sources
Abdel-Hady DM, Al Balushi RM, Al Abri BA, et al. Estimating the burden of influenza-associated hospitalization and deaths in Oman (2012-2015). Influenza Other Respir Viruses. 2018;12(1):146-152. doi:10.1111/irv.12500
Cohen C, Moyes J, Tempia S, et al. Mortality amongst patients with influenza-associated severe acute respiratory illness, South Africa, 2009–2013. PLoS One. 2015;10(3):e0118884. doi:10.1371/journal.pone.0118884
Cromer D, Jan van Hoek A, Jit M, Edmunds WJ, Fleming D, Miller E. The burden of influenza in England by age and clinical risk group: a statistical analysis to inform vaccine policy. J Infect. 2014;68(4):363-371. doi:10.1016/j.jinf.2013.11.013
Descalzo MA. Wilfrido C, Guzmán G, et al. Estimating the burden of influenza‐associated hospitalizations and deaths in Central America. Influenza Other Respir Viruses. 2016;10(4):340-345. doi:10.1111/irv.12385
Matias G, Taylor RJ, Haguinet F, et al. Modelling estimates of age-specific influenza-related hospitalisation and mortality in the United Kingdom. BMC Public Health. 2016;16:481. doi:10.1186/s12889-016-3128-4
Olivia J, Delgado-Sanz C, Larrauri A; Spanish Influenza Surveillance System. Estimating the burden of seasonal influenza in Spain from surveillance of mild and severe influenza disease, 2010‐2016. Influenza Other Respir Viruses. 2018;12(1):161-170. doi:10.1111/irv.12499
Pivette M, Nicolay N, de Lauzun V, Hubert B. Characteristics of hospitalizations with an influenza diagnosis, France, 2012-2013 to 2016-2017 influenza seasons. Influenza Other Respir Viruses. 2020;14(3):340-348. doi:10.1111/irv.12719
Reed C, Chaves SS, Kirley PD, et al. Estimating influenza disease burden from population-based surveillance data in the United States. PLoS One. 2015;10(3):e0118369. doi:10.1371/journal.pone.0118369
Rolfes MA, Foppa IM, Garg S, et al. Annual estimates of the burden of seasonal influenza in the United States: a tool for strengthening influenza surveillance and preparedness. Influenza Other Respir Viruses. 2018;12(1):132-137. doi:10.1111/irv.12486
Russell K, Herrick K, Venkat H, et al. Utility of state-level influenza disease burden and severity estimates to investigate an apparent increase in reported severe cases of influenza A(H1N1) pdm09 – Arizona, 2015–2016. Epidemiol Infect. 2018;146(11):1359-1365. doi:10.1017/S0950268818001516
Sotomayor V, Fasce RA, Vergara N, De la Fuente F, Loayza S, Palekar R. Estimating the burden of influenza-associated hospitalizations and deaths in Chile during 2012-2014. Influenza Other Respir Viruses. 2018;12(1):138-145. doi:10.1111/irv.12502
Tempia S, Walaza S, Moyes J, et al. Quantifying how different clinical presentations, levels of severity, and healthcare attendance shape the burden of influenza-associated illness: a modeling study from South Africa. Clin Infect Dis. 2019;69(6):1036-1048. doi:10.1093/cid/ciy1017
Critical Illness Multiplier Data Sources
Olivia J, Delgado-Sanz C, Larrauri A; Spanish Influenza Surveillance System. Estimating the burden of seasonal influenza in Spain from surveillance of mild and severe influenza disease, 2010‐2016. Influenza Other Respir Viruses. 2018;12(1):161-170. doi:10.1111/irv.12499
Ortiz JR, Neuzil KM, Cooke CR, Neradilek MB, Goss CH, Shay DK. Influenza pneumonia surveillance among hospitalized adults may underestimate the burden of severe influenza disease. PLoS One. 2014;9(11):e113903. doi:10.1371/journal.pone.0113903
Pivette M, Nicolay N, de Lauzun V, Hubert B. Characteristics of hospitalizations with an influenza diagnosis, France, 2012-2013 to 2016-2017 influenza seasons. Influenza Other Respir Viruses. 2020;14(3):340-348. doi:10.1111/irv.12719
Reed C, Chaves SS, Kirley PD, et al. Estimating influenza disease burden from population-based surveillance data in the United States. PLoS One. 2015;10(3):e0118369. doi:10.1371/journal.pone.0118369
Mild/Moderate (Nonhospitalized) Case Multiplier Data Sources
Hughes MM, Carmack AE, McCaffrey K, et al. Estimating the incidence of influenza at the state level – Utah, 2016-17 and 2017-18 influenza seasons. MMWR Morb Mortal Wkly Rep. 2019;68(50):1158-1161. doi:10.15585/mmwr.mm6850a2
Olivia J, Delgado-Sanz C, Larrauri A; Spanish Influenza Surveillance System. Estimating the burden of seasonal influenza in Spain from surveillance of mild and severe influenza disease, 2010‐2016. Influenza Other Respir Viruses. 2018;12(1):161-170. doi:10.1111/irv.12499
Rolfes MA, Foppa IM, Garg S, et al. Annual estimates of the burden of seasonal influenza in the United States: a tool for strengthening influenza surveillance and preparedness. Influenza Other Respir Viruses. 2018;12(1):132-137. doi:10.1111/irv.12486
Russell K, Herrick K, Venkat H, et al. Utility of state-level influenza disease burden and severity estimates to investigate an apparent increase in reported severe cases of influenza A(H1N1) pdm09 – Arizona, 2015–2016. Epidemiol Infect. 2018;146(11):1359-1365. doi:10.1017/S0950268818001516
Tempia S, Walaza S, Moyes J, et al. Quantifying how different clinical presentations, levels of severity, and healthcare attendance shape the burden of influenza-associated illness: a modeling study from South Africa. Clin Infect Dis. 2019;69(6):1036-1048. doi:10.1093/cid/ciy1017
Deaths Expansion Factor (Under Detection Multiplier) Data Sources
Ahmed M, Aleem MA, Roguski K, et al. Estimates of seasonal influenza-associated mortality in Bangladesh, 2010-2012. Influenza Other Respir Viruses. 2018;12(1):65-71. doi:10.1111/irv.12490
Reed C, Chaves SS, Kirley PD, et al. Estimating influenza disease burden from population-based surveillance data in the United States. PLoS One. 2015;10(3):e0118369. doi:10.1371/journal.pone.0118369
Rolfes MA, Foppa IM, Garg S, et al. Annual estimates of the burden of seasonal influenza in the United States: a tool for strengthening influenza surveillance and preparedness. Influenza Other Respir Viruses. 2018;12(1):132-137. doi:10.1111/irv.12486
Hospitalization Expansion Factor (Under Detection Multiplier) Data Sources
Hughes MM, Carmack AE, McCaffrey K, et al. Estimating the incidence of influenza at the state level – Utah, 2016-17 and 2017-18 influenza seasons. MMWR Morb Mortal Wkly Rep. 2019;68(50):1158-1161. doi:10.15585/mmwr.mm6850a2
Reed C, Chaves SS, Kirley PD, et al. Estimating influenza disease burden from population-based surveillance data in the United States. PLoS One. 2015;10(3):e0118369. doi:10.1371/journal.pone.0118369
Schanzer DL, Saboui M, Lee L, Nwosu, Bancej C. Burden of influenza, respiratory syncytial virus, and other respiratory viruses and the completeness of respiratory viral identification among respiratory inpatients, Canada, 2003‐2014. Influenza Other Respir Viruses. 2018;12(1):113-121. doi:10.1111/irv.12497
Estimate of Percentages of Deaths by Syndrome
Tempia S. Estimating the burden of influenza-associated illness in South Africa: approach, considerations and generalizability of estimates. Presented at: WHO Workshop on a Tool to Calculate the Influenza Disease Burden Pyramid; April 2018; Geneva, Switzerland.
Death and Hospitalization Range Sources
Abdel-Hady DM, Al Balushi RM, Al Abri BA, et al. Estimating the burden of influenza-associated hospitalization and deaths in Oman (2012-2015). Influenza Other Respir Viruses. 2018;12(1):146-152. doi:10.1111/irv.12500
Choi WS, Cowling BJ, Noh JY, et al. Disease burden of 2013-2014 seasonal influenza in adults in Korea. PLoS One. 2017;12(3):e0172012. doi:10.1371/journal.pone.0172012
Cohen C, Moyes J, Tempia S, et al. Mortality amongst patients with influenza-associated severe acute respiratory illness, South Africa, 2009–2013. PLoS One. 2015;10(3):e0118884. doi:10.1371/journal.pone.0118884
Cromer D, Jan van Hoek A, Jit M, Edmunds WJ, Fleming D, Miller E. The burden of influenza in England by age and clinical risk group: a statistical analysis to inform vaccine policy. J Infect. 2014;68(4):363-371. doi:10.1016/j.jinf.2013.11.013
Drăgănescu A, Săndulescu O, Florea D, et al. The 2017–2018 influenza season in Bucharest, Romania: epidemiology and characteristics of hospital admissions for influenza-like illness. BMC Infect Dis. 2019;19:967. doi:10.1186/s12879-019-4613-z
Descalzo MA. Wilfrido C, Guzmán G, et al. Estimating the burden of influenza‐associated hospitalizations and deaths in Central America. Influenza Other Respir Viruses. 2016;10(4):340-345. doi:10.1111/irv.12385
Gefenaite G, Pistol A, Popescu R, et al. Estimating burden of influenza-associated influenza-like illness and severe acute respiratory infection at public healthcare facilities in Romania during the 2011/12-2015/16 influenza seasons. Influenza Other Respir Viruses. 2018;12(1):183-192. doi:10.1111/irv.12525
Matias G, Taylor RJ, Haguinet F, et al. Modelling estimates of age-specific influenza-related hospitalisation and mortality in the United Kingdom. BMC Public Health. 2016;16:481. doi:10.1186/s12889-016-3128-4
Naudion P, Lepiller Q, Bouiller K. Risk factors and clinical characteristics of patients with nosocomial influenza A infection. J Med Virol. 2020;92(8):1047-1052. doi:10.1002/jmv.25652
Olivia J, Delgado-Sanz C, Larrauri A; Spanish Influenza Surveillance System. Estimating the burden of seasonal influenza in Spain from surveillance of mild and severe influenza disease, 2010‐2016. Influenza Other Respir Viruses. 2018;12(1):161-170. doi:10.1111/irv.12499
Pivette M, Nicolay N, de Lauzun V, Hubert B. Characteristics of hospitalizations with an influenza diagnosis, France, 2012-2013 to 2016-2017 influenza seasons. Influenza Other Respir Viruses. 2020;14(3):340-348. doi:10.1111/irv.12719
Reed C, Chaves SS, Kirley PD, et al. Estimating influenza disease burden from population-based surveillance data in the United States. PLoS One. 2015;10(3):e0118369. doi:10.1371/journal.pone.0118369
Rolfes MA, Foppa IM, Garg S, et al. Annual estimates of the burden of seasonal influenza in the United States: a tool for strengthening influenza surveillance and preparedness. Influenza Other Respir Viruses. 2018;12(1):132-137. doi:10.1111/irv.12486
Russell K, Herrick K, Venkat H, et al. Utility of state-level influenza disease burden and severity estimates to investigate an apparent increase in reported severe cases of influenza A(H1N1) pdm09 – Arizona, 2015–2016. Epidemiol Infect. 2018;146(11):1359-1365. doi:10.1017/S0950268818001516
Saborio GG, Clara A, Garcia A, et al. Influenza-associated hospitalizations and deaths, Costa Rica, 2009–2012. Emerg Infect Dis. 2014;20(5):878-881. doi:10.3201/eid2005.131775
Sotomayor V, Fasce RA, Vergara N, De la Fuente F, Loayza S, Palekar R. Estimating the burden of influenza-associated hospitalizations and deaths in Chile during 2012-2014. Influenza Other Respir Viruses. 2018;12(1):138-145. doi:10.1111/irv.12502
Tempia S, Walaza S, Moyes J, et al. Quantifying how different clinical presentations, levels of severity, and healthcare attendance shape the burden of influenza-associated illness: a modeling study from South Africa. Clin Infect Dis. 2019;69(6):1036-1048. doi:10.1093/cid/ciy1017
Wood T, Huang S. A full influenza burden pyramid from the Southern Hemisphere Influenza and Vaccine Effectiveness Research and Surveillance (SHIVERS) project, New Zealand. Presented to: World Health Organization; June 21, 2019; Geneva, Switzerland.
Wu P, Presanis AM, Bond HS, et al. A joint analysis of influenza-associated hospitalizations and mortality in Hong Kong, 1998–2013. Sci Rep. 2017;7(1):929. doi:10.1038/s41598-017-01021-x
Critical Illness Case Range Sources
Bassetti M, Peghin M, Gallo T, et al. The burden of severe cases of influenza disease: the Friuli Venezia Giulia Region experience. J Prev Med Hyg. 2019;60(3):E163-E170. doi:10.15167/2421-4248/jpmh2019.60.3.1314
Bolge SC, Kariburyo F, Uyce H, Fleischhackl R. Predictors and outcomes of hospitalization for influenza: real-world evidence from the United States Medicare population. Infect Dis Ther. 2021;10(1):213-228. doi:10.1007/s40121-020-00354-x
Choi WS, Cowling BJ, Noh JY, et al. Disease burden of 2013-2014 seasonal influenza in adults in Korea. PLoS One. 2017;12(3):e0172012. doi:10.1371/journal.pone.0172012
Drăgănescu A, Săndulescu O, Florea D, et al. The 2017–2018 influenza season in Bucharest, Romania: epidemiology and characteristics of hospital admissions for influenza-like illness. BMC Infect Dis. 2019;19:967. doi:10.1186/s12879-019-4613-z
Olivia J, Delgado-Sanz C, Larrauri A; Spanish Influenza Surveillance System. Estimating the burden of seasonal influenza in Spain from surveillance of mild and severe influenza disease, 2010‐2016. Influenza Other Respir Viruses. 2018;12(1):161-170. doi:10.1111/irv.12499
Ortiz JR, Neuzil KM, Cooke CR, Neradilek MB, Goss CH, Shay DK. Influenza pneumonia surveillance among hospitalized adults may underestimate the burden of severe influenza disease. PLoS One. 2014;9(11):e113903. doi:10.1371/journal.pone.0113903
Pivette M, Nicolay N, de Lauzun V, Hubert B. Characteristics of hospitalizations with an influenza diagnosis, France, 2012-2013 to 2016-2017 influenza seasons. Influenza Other Respir Viruses. 2020;14(3):340-348. doi:10.1111/irv.12719
Rasul CH, Bakar MA, Mamun AA, Siraz MS, Zaman RU. Burden and outcome of human influenza in a tertiary care hospital of Bangladesh. Asian Pac J Trop Med. 2011;4(6):478-481. doi:10.1016/S1995-7645(11)60130-2
Reed C, Chaves SS, Kirley PD, et al. Estimating influenza disease burden from population-based surveillance data in the United States. PLoS One. 2015;10(3):e0118369. doi:10.1371/journal.pone.0118369
Wood T, Huang S. A full influenza burden pyramid from the Southern Hemisphere Influenza and Vaccine Effectiveness Research and Surveillance (SHIVERS) project, New Zealand. Presented to: World Health Organization; June 21, 2019; Geneva, Switzerland.
Mild/Moderate Case Range Data
Hughes MM, Carmack AE, McCaffrey K, et al. Estimating the incidence of influenza at the state level – Utah, 2016-17 and 2017-18 influenza seasons. MMWR Morb Mortal Wkly Rep. 2019;68(50):1158-1161. doi:10.15585/mmwr.mm6850a2
Olivia J, Delgado-Sanz C, Larrauri A; Spanish Influenza Surveillance System. Estimating the burden of seasonal influenza in Spain from surveillance of mild and severe influenza disease, 2010‐2016. Influenza Other Respir Viruses. 2018;12(1):161-170. doi:10.1111/irv.12499
Rolfes MA, Foppa IM, Garg S, et al. Annual estimates of the burden of seasonal influenza in the United States: a tool for strengthening influenza surveillance and preparedness. Influenza Other Respir Viruses. 2018;12(1):132-137. doi:10.1111/irv.12486
Russell K, Herrick K, Venkat H, et al. Utility of state-level influenza disease burden and severity estimates to investigate an apparent increase in reported severe cases of influenza A(H1N1) pdm09 – Arizona, 2015–2016. Epidemiol Infect. 2018;146(11):1359-1365. doi:10.1017/S0950268818001516
Tempia S, Walaza S, Moyes J, et al. The effects of the attributable fraction and the duration of symptoms on burden estimates of influenza‐associated respiratory illnesses in a high HIV prevalence setting, South Africa, 2013‐2015. Influenza Other Respir Viruses. 2018;12(3):360-373. doi:10.1111/irv.12529
Tempia S, Walaza S, Moyes J, et al. Quantifying how different clinical presentations, levels of severity, and healthcare attendance shape the burden of influenza-associated illness: a modeling study from South Africa. Clin Infect Dis. 2019;69(6):1036-1048. doi:10.1093/cid/ciy1017
Wood T, Huang S. A full influenza burden pyramid from the Southern Hemisphere Influenza and Vaccine Effectiveness Research and Surveillance (SHIVERS) project, New Zealand. Presented to: World Health Organization; June 21, 2019; Geneva, Switzerland.