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John R. La Montagne Memorial Symposium on Pandemic Influenza Research: Meeting Proceedings
PROF. FERGUSON: We are very interested in looking at the latter phenomenon. I did not use my model to predict the impact of social distance measures because we regard past pandemics through the filter of intrinsic behavioral adjustments in the population. That is, people decrease their social distance spontaneously, whether by closing schools or through fear, and that was particularly true for 1918.
One of my hypotheses for why three waves occurred in some but not all locations in 1918 was that density-dependent adjustment of contact rates occurred. People did not contact each other during the pandemic, and that reduced transmission rates. The disease went away temporarily and came back multiple times. Other hypotheses to explain the waves exist, but we are trying to correlate transmission and mortality patterns with quantitative data on social distance measures such as school closures.
Our models of international travel do account for journey time and numbers of people, which is why I talked about effective epidemiological contact rates rather than absolute numbers of individuals traveling. But I agree: a 6-week journey with 50 people is a bit of a paradox.
PARTICIPANT: Is it possible that the H5 we now observe in poultry reflects better surveillance in Asia? I am referring to a paper by Ken Shortridge from 1990, which showed that about 7 percent of the rural population had H5 antibodies. If 500 million people live in rural China, this would mean that 35 million people had been infected by H5 before the 1997 outbreaks. Is the situation today truly different, or do higher infection rates simply reflect the fact that we have better ways of detecting the virus?
Dr. Webster: Surveillance in Hong Kong has certainly improved. However, if deaths had previously occurred, they would have been noticed. This virus has undoubtedly changed. Before 1997, the H5N1 viruses did not have the capacity to infect humans, as they do today. That is a learned capacity, with incremental changes then allowing the virus to kill the duck population, to transmit to cats, and so on. This virus is continuing to evolve, and we are probably creating the conditions that allow that to happen. This epidemic began just as Asian countries began to move from backyard flocks to huge chicken houses and pig-raising facilities. So no—current infection rates do not simply reflect better skills.
PARTICIPANT: Could you apply your models to try to block the annual epidemic, using antivirals, vaccines, and measures to reduce social distance, instead of waiting for a pandemic? That is, can we test the modeling and predictions in real time, rather than continue to model until we have a pandemic?
PROF. FERGUSON: I think that would be feasible, and one of my research priorities is to initiate another community study, perhaps equivalent to the Tecumseh study, where we attempt to influence transmission in isolated communities. Blocking seasonal influenza epidemics is probably infeasible, but we don't need to do that. We simply need to demonstrate the impact of control measures on transmission to be fairly sure we are exerting some influence.
Complete blocking is impractical because of the sheer weight of infectious burden on a particularly community. We are not going to treat the whole country, so the infection is constantly challenging the treated population from outside. But we certainly could use well-designed studies to refine the values of our model’s parameters in advance.
Participant: Some of the studies that Ian Stevenson and the group at CDC have done lately, with neutralization antibodies to the Duck Singapore vaccine, suggest significant