On This Page
- National Estimates of Economic Impact
- State and Local-level Influenza Pandemic Plans
- Estimates of Impact: Methodology
- What This Model Does Not Estimate
- Age and Risk Groups
- Gross Attack Rates
- Impact of Pandemic Influenza on the Healthcare System
- Data Sources
- Modeling Philosophy: Sensitivity Analyses and Overall Objectives
- Additional Information
FluAid is a test version of software created by programmers at the Centers for Disease Control and Prevention (CDC). It is designed to assist state and local level planners in preparing for the next influenza pandemic by providing estimates of potential impact specific to their locality. FluAid provides only a range of estimates of impact in terms of deaths, hospitalizations, and outpatients visits due to pandemic influenza. The software cannot describe when or how people will become ill, nor how a pandemic may spread through a society over time.
FluSurge and FluAid Questions and Answers
Can FluSurge and FluAid be used to estimate impact on a pediatric facility?
You can use FluSurge to consider plans for pediatric facilities. You may wish to alter the rates of health outcomes. In FluSurge and FluAid, the rates of health outcomes for children are averaged over the entire 0-18 years of age. Please see instructions on how to use FluAid and FluSurge to estimate the impact of next pandemic by using 1968 and 1918 scenarios [467 KB, 42 pages]. The instructions also explain how to alter the rate of outcomes in FluSurge and FluAid.
Can FluSurge and FluAid be used to accurately estimate the impact of H5N1?
Estimates from FluSurge are really illustrations: for a given scenario; therefore, accuracy cannot be guaranteed. FluSurge should be used as a starting point for planning. Given the large number of unknowns for a possible pandemic, any plan has to be flexible.
As of mid May 2006, there is not a human-adapted strain of H5N1. The reported human cases of H5N1 have not been associated with sustained human-to-human transmission, and most of the cases appear to be the result of direct infection from bird to human. Therefore, we do not have any data regarding any aspect related to the potential impact of such a strain in humans. However, you can follow the instructions on how to alter FluSurge and FluAid outputs to reflect different rates of health outcomes [1.2 MB, 42 pages].
Influenza pandemics have occurred four times in the 20th century: 1918, 1957, 1968, and 2009. Experts predict that another influenza pandemic is highly likely, if not inevitable. The impact of an influenza pandemic can be devastating. For example, it has been estimated that over 20 million people died during the pandemic of 1918. Pre-pandemic planning, therefore, is essential if influenza pandemic-related morbidity, mortality, and social disruption are to be minimized. Unfortunately, no one can predict when the next pandemic will occur, nor can they accurately forecast who will become ill and suffer adverse health outcomes such as death and hospitalization.
To help overcome uncertainty about the effects of an influenza pandemic, a national plan was prepared by the U.S. Department of Health and Human Services. As part of the plan, a paper has been published which provides a range of national estimates of the numbers of deaths, hospitalizations, outpatient visits, and those who will become ill but not seek medical care (Meltzer, Cox, and Fukuda, 1999a). The authors then use the estimates to evaluate the potential economic impact of the next pandemic, and discuss the implications of various options for intervention.
Part of the national influenza pandemic plan calls for each state to develop its own state-specific plan to deal with an influenza pandemic. To develop such plans, state and local level public health planners need to have estimates of the potential impact of a pandemic in their state or locality. National level estimates of impact may not be useful when creating state or local level plans. FluAid was developed to provide state and local level planners with estimates of potential impact specific to their localities.
FluAid is designed to provide a range of estimates of impact in terms of deaths, hospitalizations, and outpatient visits due to pandemic influenza. The methodology used to design the software is similar to that used to calculate national level estimates of impact (Meltzer, Cox and Fukuda, 1999a, 1999b). The one notable difference is that, unlike the model used to calculate national level estimates, the software does not use Monte-Carlo methodologies to provide ranges of estimates. Instead, the software requires that the user supply minimum, most likely, and maximum estimates of some inputs (e.g., rates of death per 1,000 population). These data are then used by the program to provide estimates of the minimum, most likely, and maximum impact of an influenza pandemic.
Another important difference between the state and local level model and the national level model is that the latter included a predefined age distribution of cases. For simplicity, this assumption was omitted from the state and local level model.
This software model provides only estimates of the total impact (i.e., after-the-event estimates). The model is not an epidemiologic model and cannot describe when or how persons will become ill. That is, FluAid cannot provide any description of how a pandemic may spread through a geographic region over time. This is due to the difficulty of mathematically modeling the epidemiology of influenza (for a discussion of these difficulties, see Cliff and Haggett, 1993).
As in the model used to generate national level estimates of impact, FluAid also distributes the defined state or local population into three age groups (0-18 years, 19-64 years, and 65+ years), and two risk categories: high risk and non-high risk. Individuals categorized as high risk are those who have a preexisting medical condition (e.g., asthma, diabetes mellitus) that makes them more susceptible to developing medical complications due to influenza. High risk does not mean that those persons are more likely to contract a case of influenza. It means that if they do have a case of influenza, then they are more likely to have an adverse health outcome (e.g., outpatient visit, hospitalization) than those considered non-high risk. Note that age by itself was not used as a high risk condition. The software, however, allows the health care planner to input higher rates of adverse health outcomes for those aged 65 years and older.
As in the model used to estimate the potential national level impact, the state and local level model uses different levels of gross attack rates. Gross attack rate is the percentage of population that becomes clinically ill due to influenza. Clinical illness is defined as a case of influenza that causes some measurable economic impact, such as one-half day of work lost or a visit to a physician’s office.
This software contains elements of estimated impact not included in the national level model. These elements are designed to help the public health planner begin to estimate the potential impact on pandemic influenza on the state and local health care systems. The results are intended to answer questions such as: Will there be sufficient hospital beds? Will there be enough health care providers to deal with the estimated number of outpatients?
Not all of the information required to run this model is readily available. You will need to conduct research to find the necessary data specific to the state or locality of interest, such as the number of health care providers, number of hospital beds available for influenza-related illness, etc. Please refer to the list of suggested data sources for help in this process.
Much of what will define the impact of the next influenza pandemic is unknown. For example, one can only guess, based on existing data, what the rate of outpatient visits will be among the non-high risk 19-64 year olds. The existing data often relate to non-pandemic situations. Even those data obtained from pandemics may not be reliable predictors of the impact of the next pandemic. Therefore, planners are encouraged to be realistic when interpreting the results obtained from this software.
Given this uncertainty, it is advisable to run this model several times. Once you have become adept at using FluAid, you may wish to consider a plan wherein you systematically alter the values of input variables. You may alter one variable at a time (univariate sensitivity analysis), or alter the values of two or more variables simultaneously (multivariate sensitivity analysis). Different results due to different values for the input variables will help you obtain a sense of the relative importance of each variable in determining the size of the estimated impacts.
Given the inherent uncertainty associated with trying to estimate the potential impact of the next influenza pandemic, it is recommended that you avoid the temptation of using the software to obtain a single set of estimates describing the potential impact. Rather, FluAid should be used to obtain a range of estimates of potential impact. Although decision makers, the media, and the public may expect a single estimate of impact, the interest of public health may be better served if the degree of uncertainty is at least partially explained.
Please keep in mind that this is a test version of the software and a draft version of the manual. The numbers generated through use of FluAid should not be considered predictions of what will actually occur during a pandemic. Rather, they should be treated as estimates of what could happen.
Users are encouraged to download the FluAid 2.0 User’s Manual to obtain additional information regarding the use and interpretation of results, as well as comments on the general modeling philosophy used in designing FluAid. In addition to the resources listed in the References section, two scientific papers that address the economic impact of an influenza pandemic can be found at the CDC’s Web site, or by clicking on the links provided below:
- Meltzer MI, Cox NJ, Fukuda K. The economic impact of pandemic influenza in the United States: Implications for setting priorities for interventions. Emerg Infect Dis 1999:5(5); 659-671.
- Meltzer MI, Cox NJ, Fukuda K. Modeling the economic impact of pandemic influenza in the United States: Implications for setting priorities for intervention. Background paper.
- CDC. Prevention and Control of Influenza: Recommendations of the CDC Advisory Committee on Immunization Practices (ACIP). MMWR 1999;48(RR-04):1-28.
- Cliff AD, Haggett P. Statistical modeling of measles and influenza outbreaks. Statl Methods Med Res 1993;2:43-73.
- Meltzer MI, Cox NJ, Fukuda K. 1999a. The economic impact of pandemic influenza in the United States: Implications for setting priorities for intervention. Emerg Infect Dis 1999:5(5).
- Meltzer MI, Cox NJ, Fukuda K. 1999b. Modeling the economic impact of pandemic influenza in the United States: Implications for setting priorities for intervention. Background paper.
- Simonsen L, Clarke MJ, Williamson GD, et al. The impact of influenza epidemics on mortality: Introducing a severity index. Am J Public Health 1997;87:1944-1950.
- U.S. Bureau of the Census. 1999a. International database, Table -094 Midyear population, by age and sex, 1997. [online database] Feb 1999: Available from the Census Bureau Home Page.
- U.S. Bureau of the Census. 1999b. Estimates of the population of the U.S., regions, and states, by selected age groups and sex: Annual time series, July 1, 1990 to July 1, 1997. [online database] Feb 1999: Available from the Census Bureau Home Page.
- Download Stand-alone FluAid 2.0 Application [EXE, 8.3 MB]
- Download FluAid 2.0 User’s Manual [EXE, 606 KB]
- Operating System: Windows 95/98 or NT
- Disk Space: 12 MB
- RAM (recommended): 16 MB
The Online FluAid Calculator is unavailable. Please refer to downloadable application above.
- Page last reviewed: August 22, 2016
- Page last updated: August 22, 2016
- Content source:
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- Centers for Disease Control and Prevention, National Center for Immunization and Respiratory Diseases (NCIRD)
- Page maintained by: Office of the Associate Director for Communication, Digital Media Branch, Division of Public Affairs