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In the face of any pandemic threat, the primary focus of public health officials will be to reduce the attack rate (AR); the number of people who become infected. Unless we are lucky enough to have a large quantity of experimental vaccine already stockpiled, these early efforts will revolve around NPIs, or non pharmaceutical interventions (i.e. Hand washing, social distancing, school closures, etc.).
But eventually a vaccine will probably become available, albeit first in limited quantities, and decisions will have to be made on how best to deploy it. To whom do we give priority?
The elderly, who are historically most at risk from influenza?
Children or pregnant women who often suffer disproportionately during a pandemic?
Doctors, Nurses, and emergency responders who are badly needed, and most often exposed?
Essential workers or students?
As an example, during the summer of 2008 the HHS released their model of a pandemic vaccine allocation plan based on `the most up-to-date scientific information available and directly considers the values of our society and the ethical issues involved in planning a phased approach to pandemic vaccination.’ NOTE: The link to this plan is no longer operative and so it may no longer be part of the HHS’s pandemic playbook .
Their stated goals at that time were:
- Protect persons critical to the pandemic response and who provide care for persons with pandemic illness
- Protect persons who provide essential community services
- Protect persons who are at high risk of infection because of their occupation and
- Protect children
Of course, much will probably depend upon the demographics of the pandemic. If – as we saw in 2009 – it preferentially targets younger people (or any other specific demographic group), then adjustments would likely be made.
While the tier system above was based on `practical’ considerations, if the goal is to reduce the attack rate, this sort of prioritization system might not be the best solution.
Which brings us to a study, just published in PLoS One, conducted by researchers at Japan’s Institute of Statistical Mathematics, that attempts to model different vaccine prioritization schemes in order to determine which allocation system reduces the spread of a pandemic the most.
It should be noted that this model is based on the demographics, population movements, and geography of a simplified subset of suburban Tokyo, and the results are not necessarily applicable to other communities or settings.
Also, the assumptions made on the amount and timing of a vaccine, its effectiveness, and attack rate of the virus are arbitrarily set (albeit with various permutations considered) – and are not necessarily what would be encountered in a genuine pandemic outbreak.
Still, this study does provide some intriguing insights into how a pandemic virus is likely spread in a highly populated area, and provides a strategy to limit its impact.
Research Article
Masaya M. Saito mail, Seiya Imoto, Rui Yamaguchi, Masaharu Tsubokura, Masahiro Kami, Haruka Nakada, Hiroki Sato, Satoru Miyano, Tomoyuki Higuchi
Abstract
Vaccination is a preventive measure against influenza that does not require placing restrictions on social activities. However, since the stockpile of vaccine that can be prepared before the arrival of an emerging pandemic strain is generally quite limited, one has to select priority target groups to which the first stockpile is distributed. In this paper, we study a simulation-based priority target selection method with the goal of enhancing the collective immunity of the whole population. To model the region in which the disease spreads, we consider an urban area composed of suburbs and central areas connected by a single commuter train line. Human activity is modelled following an agent-based approach. The degree to which collective immunity is enhanced is judged by the attack rate in unvaccinated people.
The simulation results show that if students and office workers are given exclusive priority in the first three months, the attack rate can be reduced from 30% in the baseline case down to 1–2%. In contrast, random vaccination only slightly reduces the attack rate. It should be noted that giving preference to active social groups does not mean sacrificing those at high risk, which corresponds to the elderly in our simulation model. Compared with the random administration of vaccine to all social groups, this design successfully reduces the attack rate across all age groups.
Students and office workers are often the most mobile and interactive members of a society, and therefore have greater opportunities to contract and spread a virus. Once infected, they can bring the virus home, spreading it to other vulnerable cohorts. So in many ways, I can see how this analysis makes sense.
For those interested in methods and materials, and a lot of statistical analysis, the entire research article is available online (open access).
The authors summarize their findings in the discussion section:
We have showed that the AR can be reduced to
or less if students and employees are intensively vaccinated in the first 90 days. This is the result of an intervention program that relies solely on vaccinations, and the AR can be further reduced by individual protection efforts (e.g., wearing masks and avoiding crowded places). If the encountered virus is not highly pathogenic, this value is acceptable. In this case, the goal of intervention is to avoid an excess of patients going to medical practitioners. However, early extinction of transmission chains is required in highly pathogenic cases, and vaccination alone is not sufficient. To achieve early extinction by using only the collective immunity induced by vaccinations, administration of the vaccine would need to be carried out at least three times as fast as the typical speed.
The rub to this (and other) pandemic vaccination models is the probable lack of any vaccine during the opening months of a novel pandemic, and the limited supply of vaccine for months after that.
As George E. P. Box, Professor Emeritus of Statistics at the University of Wisconsin, famously declared.
“All models are wrong, but some models are useful.”
So hopefully, even if this model’s assumptions turn out to be wrong, this study is useful and will provide some scientific rationale for vaccine distribution decisions, once a vaccine does become available.
