Modeling the Pandemic

Philippe Lemoine has a new post up on the CSPI website, “Have we been thinking about the pandemic wrong? The effect of population structure on transmission.” See Twitter thread here.

The main highlights are the following:

  • Standard epidemiological models predict that, in the absence of behavioral changes, an epidemic should continue to grow until herd immunity has been reached and the dynamic of the epidemic is determined by people’s behavior.

  • However, during the COVID-19 pandemic, there have been plenty of cases where the effective reproduction number of the pandemic underwent large fluctuations that, as far as we can tell, can’t be explained by behavioral changes.

  • While everybody admits that other factors, such as meteorological variables, can also affect transmission, it doesn’t look as though they can explain the large fluctuations of the effective reproduction number that often took place in the absence of any behavioral changes.

  • While standard epidemiological models, which assume a homogeneous or quasi-homogeneous mixing population, can’t make sense of those fluctuations, they can be explained by population structure.

  • Through simulations, this report shows that if the population can be divided into networks of quasi-homogeneous mixing populations that are internally well-connected but only loosely connected to each other, the effective reproduction number can undergo large fluctuations even in the absence of behavioral changes.

  • While there is no evidence that can bear directly on this hypothesis, it could explain several phenomena beyond the cyclical nature of the pandemic and the disconnect between transmission and behavior – why the transmission advantage of variants is so variable, why waves are correlated across regions, why even places with a high prevalence of immunity can experience large waves – that are difficult to explain within the traditional modeling framework.

  • If the population has that kind of structure, then some of the quantities we have been obsessing over during the pandemic are essentially meaningless at the aggregate level.

  • Moreover, in the presence of complex population structure, the methods that have been used to estimate the impact of non-pharmaceutical interventions are totally unreliable. Thus, even if this hypothesis turned out to be false, we should regard many widespread claims about the pandemic with the utmost suspicion since we have good reasons to think it might be true.

  • We should try to find data about the characteristics of the networks on which the virus is spreading and make sure that we have such data when the next pandemic hits so that modeling can properly take population structure into account.

Click here to read the whole thing.