Standardizing scenarios to assess the need to respond to an influenza pandemic.

An outbreak of human infections with an avian influenza A(H7N9) virus was first reported in eastern China by the World Health Organization on 1 April 2013 [1]. This novel influenza virus was fatal in approximately one-third of the 135 confirmed cases detected in the 4 months following its initial identification [2], and limited human-to-human H7N9 virus transmission could not be excluded in some Chinese clusters of cases [3, 4]. There was, and still is, the possibility that the virus would mutate to the point where there would be sustained human-to-human transmission. Given that most of the human population has no prior immunity (either due to natural challenge or vaccine induced), such a strain presents the danger of starting an influenza pandemic.

In response to such a threat, the Joint Modeling Unit at the Centers for Disease Control and Prevention (CDC) was asked to conduct a rapid assessment of both the potential burden of unmitigated disease and the possible impacts of different mitigation measures. We were tasked to evaluate the 6 following interventions: invasive mechanical ventilators, influenza antiviral drugs for treatment (but not large-scale prophylaxis), influenza vaccines, respiratory protective devices for healthcare workers and surgical face masks for patients, school closings to reduce transmission, and airport-based screening to identify those ill with novel influenza virus entering the United States. This supplement presents reports on the methods and estimates for the first 5 listed interventions, and in this introduction we outline the general approach and standardized epidemiological assumptions used in all the articles.

METHODS

Approach to Modeling

Given that there had not yet been (and subsequently has not been to date) a pandemic caused by the H7N9 virus, there are no relevant large-population data concerning transmission and clinical impacts of H7N9. We therefore had to consider the potential impacts of disease and interventions for a not fully defined pandemic (ie, a pandemic caused by a generic influenza strain HxNy). Thus, any model that we built had to allow for a wide range in virus transmissibility and resulting clinical impact. The models had to also fully consider a range of effectiveness of interventions—for example, influenza antiviral drugs could be less effective against the next influenza strain causing a pandemic.

Given these uncertainties, and the need for a rapid assessment of a large number of factors, the models produced had to meet a number of specifications: had to be produced in a manner that would allow the models to be easily transferred to other units in government and to public health officials, and subsequently used by people who did not build them; had to provide easy identification of all input variables, their values, and ability to rapidly change those values; can be easily stored and resurrected for future use and reference at some unspecified time in the future; and, the results from each model can be readily compared to each other. In response to these specifications, we decided to require that each model be built in a spreadsheet format, and that we would essentially have 1 model for each intervention considered.

Meeting these specifications had the added value of producing models that readily fit into the existing CDC Emergency Operations response structure. In this structure, groups called Task Forces are formed to focus on particular aspects of a response to a public health emergency. For example, for an influenza pandemic response, there are usually Task Forces that focus on vaccines (eg, recommendations regarding prioritization of vaccine supplies, issues related to distribution), medical countermeasures (eg, recommendations regarding use of drugs for treatment and prophylaxis, use of personal protective equipment such as face masks), and nonpharmaceutical interventions (eg, recommendations regarding school closures, border security, and screening).

Standardized Epidemiological Scenarios

To allow easy comparison between results (a specification), we standardized a risk space defined by using ranges of transmission and clinical severity from a previously published influenza severity assessment framework (Figure 1) [5]. The framework can be used to plot, and compare to historical data, the relative severity of an influenza pandemic (or nonpandemic influenza season). The framework uses 2 scales: a scale of clinical severity, and a scale of transmissibility. The severity scale has a number of components in it, including case-fatality ratio and case-to-hospitalization ratio (Table 1) [5]. The transmissibility scale is assessed by considering factors such as the clinical (symptomatic) attack rate in various locales, such as school, community, and workplace (Table 1) [5].

Figure 1.

Framework for assessing the impact of an influenza pandemic, with examples of past pandemics and influenza seasons plotted as examples. The assumed possible risk space is the range of possible transmissibility and clinical severity standardized for use in all models used to assess the possible impact of studied interventions. For the actual range of values used in each of the models, see Table 1. See main text for additional details. Note that the 1977–1978, 2006–2007, and 2007–2008 seasons were nonpandemic seasons. They are included to provide reference points regarding the impact of nonpandemic seasons. Adapted from Reed et al [5].

Table 1.

Measures of Transmissibility and Clinical Severity for the Defined Pandemic Impact Assessmenta

		Scaleb
Parameter		1	2	3	4	5	6	7
Transmissibility
1	Symptomatic attack rate, community	≤10%	11%–15%	16%–20%	21%–24%	≥25%
2	Symptomatic attack rate, school	≤20%	21%–25%	26%–30%	31%–35%	≥36%
3	Symptomatic attack rate, workplace	≤10%	11%–15%	16%–20%	21%–24%	≥25%
4	Household secondary attack rate, symptomatic	≤5%	6%–10%	11%–15%	16%–20%	≥21%
5	R0: basic reproductive number	≤1.1	1.2–1.3	1.4–1.5	1.6–1.7	≥1.8
6	Peak % outpatient visits for influenza-like illness	1%–3%	4%–6%	7%–9%	10%–12%	≥13%
Clinical severity
1	Case-fatality ratio	<0.02%	0.02%–0.05%	0.05%–0.1%	0.1%–0.25%	0.25%–0.5%	0.5%–1%	>1%
2	Case-hospitalization ratio	<0.5%	0.5%–0.8%	0.8%–1.5%	1.5%–3%	3%–5%	5%–7%	>7%
3	Ratio, deaths: hospitalization	≤3%	4%–6%	7%–9%	10%–12%	13%–15%	16%–18%	>18%

For case-fatality ratio and case-hospitalized ratio, scale 3 shows low severity, and scale 5 shows high severity (in bold).

Source: Adapted from Reed et al [5].

a These estimates related to the framework for assessing the impact of influenza pandemics, shown in Figure 1.

b Italics represent the measures of transmissibility included in the defined risk space, shown in Figure 1.

Possible Risk Space

We defined and chose a risk space that has a transmission scale that runs from approximately a scale of 3 (eg, comparable to a community attack rate of 11%–15%) to a scale of 5 (community attack rate of >25%) (Figure 1, Table 1). Our defined risk space has a low-end clinical severity scale of 3, with a case-fatality ratio of 0.05%–0.1% and a death-to-hospitalization ratio of 7%–9% (Table 1). The upper range of severity in our risk space was defined as a scale of 5, with a case-fatality rate of 0.25%–0.5%, and a death-to-hospitalization ratio of 13%–15% (Table 1). Note that the defined risk space encloses the 1968 and 1957 pandemics (Figure 1).

It is essential to note that this chosen risk space is illustrative, not definitive. Until there are data defining the epidemiological elements of the next pandemic, such as rate of transmission, and case-fatality rate, other risk spaces could be chosen for planning purposes. The models presented in this collection, built to the specifications listed here, allow for rapid alterations in input values.

Epidemic Curves

The size and shape of the epidemic curve could impact the effectiveness of interventions. For example, the impact of influenza vaccines depends upon the start of deliveries of large amounts of vaccine compared to the timing of the pandemic peak. Thus, we included in the standardized epidemiological scenario 4 epidemic curves, produced using a simple simulation model (see below). We configured the model using 2 clinical attack rates of approximately 20% and 30%. These clinical attack rates represent the aggregated attack rate across the entire US population. Within the population, subpopulations will typically experience different attack rates (eg, children will experience a higher attack rate than adults 20–64 years old—see description later in paper). Furthermore, for each attack rate, we assumed 2 starting (seeding) scenarios. We used one scenario in which the pandemic started with the arrival of 10 infectious cases and the other when the pandemic started with 100 infectious cases (Figure 2).

Figure 2.

Standardized attack rates and epidemic curves used in the models: 2 clinical attack rates and 2 initial seedings. Clinical attack rates of 20% or 30% represent the aggregated attack rate across the entire US population. A 30% clinical attack rate results in approximately 94 million persons becoming ill, and a 20% clinical attack rate causes approximately 64 million to become ill. Within the population, subpopulations will typically experience different attack rates (see Table 3). Seeding refers to the number of infectious cases, either 10 or 100, that arrives near-simultaneously in the United States to start the pandemic.

Table 3.

Age-Specific Number of Clinical Cases and Attack Rates by Total Population Attack Rate Scenarios

Total Clinical Attack Rate	0–10 y		11–20 y		21–60 y		≥61 y		Total
	Million Cases	Age-Specific Attack Rate	Million Cases	Age-Specific Attack Rate	Million Cases	Age-Specific Attack Rate	Million Cases	Age-Specific Attack Rate	Million Cases
30%	13.1	31.9%	16.9	39.0%	52.7	31.0%	11.3	20.0%	94.0
20%	8.9	21.7%	12.7	29.3%	35.2	20.7%	6.9	12.2%	63.7

To model the 4 epidemic curves, we built a simple, nonprobabilistic (ie, deterministic) model that simulates the spread of influenza through a population by moving the population into groups of susceptible, exposed, infectious, and recovered or death (Table 2 provides values used). We divided the population into 4 age groups (0–10, 11–20, 21–60, or ≥61 years of age). We modeled the probabilities of daily contact (and thus risk of disease transmission) by constructing a contact matrix using data from the United Kingdom (see Table A1 in Technical Appendix A).

Table 2.

Assumed Values Used to Model the Standardized Influenza Epidemiological Curves

Model Parameter	Value
No. of persons infected per infectious person: for clinical attack rate of 20%a	1.3
No. of persons infected per infectious person: for clinical attack rate of 30%a	1.65
Average duration of incubation of infection	1.5 d
Average duration of infectious period	2 d
Proportion of population asymptomatic	50%
Contact mixing matrix	See Technical Appendix A
Initial population immunity	Zero for all age groups

a Average number of persons infected per infectious person is often, in modeling terms, referred to as R0. This number represents the number infected when all, or almost all, of the population is susceptible to infection.

Table A1.

Contact Matrix Used to Model Probabilities of Contact and Potential Onward Transmission Between Age Groups (Contacts per Day Between Each Age Group)

Age group, y	No. of Contacts per Day
	Age Groups, y
	0–10	11–20	21–60	≥61
0–10	4.962	1.235	5.029	0.743
11–20	1.197	8.063	5.640	1.018
21–60	1.102	1.275	7.582	1.488
≥61	0.389	0.55	3.556	2.254

Source: Adapted from Mossong et al [10] (Supplementary Table 8.4: contact data from Great Britain).

We thus produced 4 notably different epidemic curves (Figure 2). For example, the two 30% attack rate scenarios peak in weeks 12 and 14, whereas the 20% attack rate scenarios peak in weeks 13 and 22 (Figure 2). The clinical attack rates by age group are presented in Table 3. Obviously, the largest numbers of cases occur in the largest age group of 21- to 60-year-olds; however, children in both the 0–10 and 11–20 age groups have the highest attack rates, indicating a potentially greater degree of vulnerability (Table 3).

Strengths and Limitations

Perhaps one of the greatest strengths of the simple models presented in this collection of articles is that they highlight what is and is not known about the burden of disease and the potential impact of a planned intervention. To find the weaknesses of what is currently known, a reader need only consult Table 1 in each article. These tables list inputs, their assumed values, and data sources. An example of an important unknown is as follows: When estimating the number of respiratory protection devices (eg, face and surgical masks) needed by first responders (police officers, firefighters, emergency medical technicians), one could assume that first responders will need 1 mask per person whom they encounter with influenza-like illness. The problem is that there are no readily available data that report on the measurement of such [6]. Similarly, when considering the potential use and impact of influenza antiviral drugs, O'Hagan et al had to assume that existing influenza antiviral drugs would have the same level of effectiveness against the strain causing the next influenza pandemic as they do with existing influenza strains [7]. Despite these limitations, these simple models make it fairly straightforward to rapidly assess the relative importance of each of the input variables.

One assumption that may not be readily appreciated is the impact of the shape of the standardized epidemiological curves used in all the models (Figure 2). Previous influenza pandemics have produced different shapes of deaths over time (Figure 3). Such differences in deaths over time can greatly influence the success of some of the interventions. For example, when considering the number of mechanical ventilators needed at the peak of the pandemic, Meltzer et al initially assumed that the peak demand for ventilators would equal approximately 13% of all patients needing mechanical ventilation [8]. However, in the 30% attack rate epidemiological curve (Figure 2), the number of cases that occur in the peak 10 days is approximately 30% of all cases. Thus, the authors of the ventilator study conducted a sensitivity analysis by changing from 13% to 30% the assumed number of mechanically ventilated patients that occurs at the peak of a pandemic.

Figure 3.

Standardized plots of deaths over time from different influenza pandemics compared to the epidemic curves used in the model. The different curves illustrate that influenza pandemics can have different pattern of deaths (and, by extension, cases) over time. When the peak occurs and the shape of the curve can greatly influence the success of some of the interventions. See main text and Technical Appendix B for further details. These curves were standardized to the approximate 2014 US population of 310 million persons. The standardized curves of 20% and 30% attack rate (AR) refer to the curves built for this exercise. The 2 standardized curves plotted here are those assuming an introduction of 100 infectious persons (cf, Figure 2). Note that the data for 1957 were recorded once every 2 weeks, whereas all other plots used weekly data. See Technical Appendix B for further details.

The articles in this supplement also incorporate other important implicit assumptions. One of the more important is that each article essentially assumes that the healthcare system can absorb and/ or successfully execute any of the interventions so modeled. For example, Biggerstaff et al provide some estimates of the impact of influenza vaccination in which it was assumed that 30 million persons could be vaccinated each week [9]. The US private and public health systems, collectively or separately, have never previously achieved such a rate (though the authors clearly demonstrate that achieving such a rate would have very positive public health outcomes).

Furthermore, the successful deployment and ultimate impact of each intervention is likely to have a wide variation. Schools can close for different lengths of time, antiviral drug prescription and distribution may not be equally efficient in all areas, and healthcare workers and patients may have different levels of compliance in wearing protective gear.

Finally, readers will note that there are no reports in this collection that consider the simultaneous deployment of ≥2 interventions. It is realistic to assume that, during the next influenza pandemic, public health officials, healthcare providers, and other policy makers are likely to enact several interventions at once (eg, close schools, start dispensing antiviral medications, recommend use of protective personal gear). The problem arises in that such multi-intervention models become very scenario specific. For example, different locales are likely to face different unmitigated epidemic curves (Figure 3). Thus, researchers who estimate the potential impact of combining several interventions at once have to make a very large increase in the number of assumptions. This makes it more difficult to both generalize the results and to rapidly understand what assumptions are relatively more important.

Despite these limitations, we believe that the benefits of using these models outweigh the limitations. This assessment is based on our experience of using the models and results produced to help public health leadership reassess US influenza pandemic planning and preparedness. In the 2013 response to the H7N9 threat, the most important outcome from policy makers seeing the results from these models was the intense debate concerning the inputs and assumptions. We thus believe that the methodology used here to develop and guide the building of the models in this collection, and the subsequent interpretations and use of the results, can be a useful part of future public health responses.

Supplementary Data

Supplementary materials are available at Clinical Infectious Diseases online (http://cid.oxfordjournals.org). Supplementary materials consist of data provided by the author that are published to benefit the reader. The posted materials are not copyedited. The contents of all supplementary data are the sole responsibility of the authors. Questions or messages regarding errors should be addressed to the author.

Click here for additional data file.

1.9	0.7	0.4	0.2	0.5	0.7	0.7	0.8	0.2	0.2	0.4	0.2	0.2	0.3	0.1
1.0	6.6	1.1	0.7	0.6	0.8	1.0	1.4	0.9	0.2	0.3	0.2	0.5	0.5	0.2
0.5	1.3	6.9	1.5	0.3	0.3	0.5	0.8	1.0	0.7	0.3	0.4	0.3	0.4	0.3
0.3	0.3	1.0	6.7	1.6	0.7	0.4	0.6	0.9	1.2	0.7	0.3	0.2	0.5	0.6
0.5	0.3	0.2	0.9	2.6	1.5	0.8	0.6	0.8	0.9	0.9	0.6	0.5	0.4	0.3
0.8	0.7	0.4	0.7	1.3	1.8	1.0	0.7	0.7	0.9	0.9	0.9	0.7	0.7	0.3
1.0	1.1	0.6	0.5	0.9	1.2	1.7	0.9	1.0	0.9	0.9	0.6	0.8	0.6	0.3
1.0	1.0	1.3	1.1	0.8	1.0	1.5	1.5	1.3	1.1	0.8	0.7	1.0	1.0	0.2
0.6	1.0	1.1	0.9	0.7	0.9	0.8	1.2	1.4	1.3	0.9	0.7	0.9	0.8	0.8
0.3	0.5	0.6	0.8	1.0	0.9	0.6	0.8	1.3	1.9	0.6	0.8	0.6	0.6	0.6
0.3	0.4	0.4	0.4	0.4	0.9	0.6	0.6	0.7	1.0	0.7	1.1	0.6	0.6	0.6
0.3	0.2	0.3	0.3	0.4	0.5	0.7	0.5	0.6	0.5	0.8	1.2	0.9	0.9	0.3
0.3	0.3	0.2	0.2	0.2	0.3	0.4	0.4	0.5	0.6	0.4	0.8	0.7	0.9	0.6
0.1	0.1	0.1	0.2	0.2	0.2	0.1	0.3	0.2	0.1	0.2	0.3	0.4	0.7	0.6
0.1	0.2	0.2	0.1	0.2	0.2	0.2	0.4	0.5	0.7	0.5	0.8	0.5	0.7	1.5

2.6	0.7	3.7	0.6
7.6	1.8	5.3	1.2
1.8	8.4	4.3	1.0
0.7	7.7	6.3	1.3
0.8	1.2	8.6	1.2
1.5	1.2	8.1	1.7
2.0	1.1	8.1	1.7
2.0	2.4	8.7	2.2
1.6	2.1	7.8	2.6
0.8	1.3	7.9	1.8
0.7	0.8	6.0	1.7
0.5	0.6	5.2	2.0
0.5	0.4	3.5	2.1
0.2	0.3	1.7	1.7
0.3	0.3	3.5	2.7

36 753 010	8 945 040	33 090 700	6 459 320
9 245 500	61 646 310	40 847 730	8 941 760
42 080 560	45 753 440	257 630 820	63 160 700
3 260 150	3 367 050	27 090 960	19 494 940

4.9	1.2	5.1	0.7
1.2	8.0	5.6	0.8
1.1	1.3	7.6	1.3
0.4	0.7	4.9	2.1