As I have said a few times over the life of this blog, and more frequently on the flu forums, I believe in what I label the "Viral Tsunami" theory of a pandemic of a wholly avian version of H5N1 that gains the ability to transmit from person to person, H2H for brevity's sake. For my more recent postings supporting this see here and here. Those two posts and this one draw heavily from the UK's recently released National Framework for Responding to an Influenza Pandemic (pdf download here).
In order to qualify as a Viral Tsunami, by my definition, an influenza pandemic would have to have a high Clinical Attack Rate (CAR) and a high Case Fatality Ratio (CFR), as well as encompass a single wave of illness. There is strong belief that the CAR will be between 30-35% of the population and CFR will be no worse than ~2.5%. Because there is strong belief in this scenario as the upper limit of potentials, those figures represent the worst case scenario in so many plans. In addition, I must say, there are many plans that use numbers lower than these as their "upper limits".
Part of what the experts in influenza have been doing since 1997, more feverishly since 2003, is studying H5N1 and what makes it so different from what they are used to seeing and dealing with. I have quoted this statement many times from one of the Reveres of EffectMeasure:
"The depths of our ignorance in this age of sophisticated molecular biology is truly impressive."
A lot of basic research is being conducted, or minimally, dusted off and reviewed afresh in light of human infections of H5N1, and at nearly every turn, we discover something else that they didn't know or have to rewrite what they thought we knew.
Part of this journey of exploration and research is aimed at hypothesizing, as informed and scientifically based as is possible, what a pandemic of H5N1 would be like. Unfortunately, many of our experts, scientific and otherwise, base their "guesses" and assumptions on the pandemic of 1918, the last severe pandemic and the only one we have any semblance of empirical data to review. That is, if said experts even credit the possibility of a severe influenza pandemic at the beginning of the 21st century.
CAR, a major component, utilizes a number known as R0, the basic reproduction number. From Wikipedia:
In epidemiology, the basic reproduction number of an infection is the mean number of secondary cases a typical single infected case will cause in a population with no immunity to the disease in the absence of interventions to control the infection. It is often denoted R0. This metric is useful because it helps determine whether or not an infectious disease will spread through a population.
If an infectious disease has an R0 of less than 1.0 it will burn itself out, if greater than 1 it will continue to spread if not mitigated. The UK PanFlu Framework assumes an R0 than they believe will range from somewhere between 1.4 and 2.2. It is useful to understand that at 2.0 cases will double every one or two days, at 4.0 they would quadruple, etc, on up the scale.
The assumptions of 1.4 – 2.2 R0 are frightful enough, to say nothing of difficult to address. The same assumptions assume community mitigation measures will have a significant effect on the CAR and R0, a very reasonable assumption. Modeling suggests the dramatic difference between a local outbreak with and without mitigation:
A lot rides on our plans, the actions we prepare ourselves to take, the plans our leaders civic and governmental leaders will institute on our behalf.
What if planning assumptions are wrong?
A reasonable question. We are all pretty much flying by the seat of our pants, some with more well informed seats to be sure, but it is always worthwhile to remember that we cannot know with certainty what the next pandemic will look like. Only in hindsight, or at best as we are struggling in the midst of it, will we know the CAR/R0/CFR. However, even though we do not have definitive numbers to plug into the all-important variables we do have an ability to extrapolate the unknowns with what we do know, and just about every week what we do know expands.
Expanding knowledge base case in point:
PLoS ONE. 2007; 2(11): e1220.
Published online 2007 November 28. doi: 10.1371/journal.pone.0001220.
John D. Mathews, Et al.
The clinical attack rate of influenza is influenced by prior immunity and mixing patterns in the host population, and also by the proportion of infections that are asymptomatic. This complexity makes it difficult to directly estimate R0 from the attack rate, contributing to uncertainty in epidemiological models to guide pandemic planning. We have modelled multiple wave outbreaks of influenza from different populations to allow for changing immunity and asymptomatic infection and to make inferences about R0.
Data and Methods
On the island of Tristan da Cunha (TdC), 96% of residents reported illness during an H3N2 outbreak in 1971, compared with only 25% of RAF personnel in military camps during the 1918 H1N1 pandemic. Monte Carlo Markov Chain (MCMC) methods were used to estimate model parameter distributions.
We estimated that most islanders on TdC were non-immune (susceptible) before the first wave, and that almost all exposures of susceptible persons caused symptoms. The median R0 of 6.4 (95% credibility interval 3.7–10.7) implied that most islanders were exposed twice, although only a minority became ill in the second wave because of temporary protection following the first wave. In contrast, only 51% of RAF personnel were susceptible before the first wave, and only 38% of exposed susceptibles reported symptoms. R0 in this population was also lower [2.9 (2.3–4.3)], suggesting reduced viral transmission in a partially immune population.
Our model implies that the RAF population was partially protected before the summer pandemic wave of 1918, arguably because of prior exposure to interpandemic influenza. Without such protection, each symptomatic case of influenza would transmit to between 2 and 10 new cases, with incidence initially doubling every 1–2 days. Containment of a novel virus could be more difficult than hitherto supposed.
The paper, which includes much more valuable information than I have included in this post, demonstrates that a case can be made for an immunologically naïve population the R0 will likely be much higher than the planning assumptions. An R0 greater than 2 will be difficult enough to mitigate and manage, the implications of an R0 greater than 5 are nothing short of staggering. Fair warning, the R0 is not the only staggering gem this paper will yield, but they will have to wait for their own post.
Thanks to a knowledgeable reader who was thoughtful enough to take the time to gather, highlight, and briefly explain implications of research, some reaching back 30 years, and sending it along to me, I have an appreciation of this paper's implications that I did not previously have.
One of the areas this reader is exploring is heterosubtypic immunity, while I do not specifically address this in this post, as it deserves its own, it is the core concept of the above paper. For all you intrepid googlers out there a search on "T-cell heterosubtypic immunity influenza" will yield support for what follows in this post.
I have assumed a significantly high R0 and CAR because H5N1, should it gain pandemic H2H ability as an avian influenza, will be novel to human beings. Furthermore, I have been laboring under the belief that 1918's H1N1 had its lower CAR/CFR and cohort specificity because of prior exposures to it, or a close relative.
The epidemiological evidence, such as it is, supports the theory of prior exposure. Now I have been presented with a new (to me) theory explaining 1918's CAR/CFR and cohort specificity: heterosubtypic immunity. My conundrum is that while I am quite partial to the prior exposure/circulation theory, being comfortable with it, like my favorite chair, this theory is supported by more than epidemiological hints and suggestions, akin to nothing more substantial than circumstantial evidence in a criminal prosecution, it is supported by scientific findings.
Scientific findings that still leave us extrapolating into a future pandemic of a yet unknown pathogen, we are still left guessing, but every once in awhile, our seats get a little better informed as we fly by them.
SZ <grateful to a thoughtful reader laboring to inform not only themselves but also others along the same path>