Philippe Lemoine writes:
I [Lemoine] just published a blog post in which I explore what impact population structure might have on the transmission of an infectious disease such as COVID-19, which I thought might be of interest to you and your readers. It’s admittedly speculative, but I like to think it’s the kind of speculation that might be fruitful. Perhaps of particular interest to you is my discussion of how, if the population has the sort of structure my simulations assume, it would bias the estimates of causal effects of interventions. This illustrates a point I made before, such as in my discussion of Chernozhukov et al. (2021), namely that any study that purports to estimate the causal effect of interventions must — implicitly or explicitly — assume a model of the transmission process, which makes this tricky because I don’t think we understand it very well. My hope is that it will encourage more discussion of the effect population structure might have on transmission, a topic which I think has been under-explored, although other people have mentioned the sort of possibility I explore in my post before. I’m copying the summary of the post below.
– Standard epidemiological models predict that, in the absence of behavioral changes, the 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.
– I argue that, 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.
– I show with simulations 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.
– I argue that, 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, such as the effective reproduction number and the herd immunity threshold, 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.
– I conclude that 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.
I agree with Lemoine that we don’t understand well what is going on with covid, or with epidemics more generally. I agree, and, as many people have recognized, there are several difficulties here, including data problems (most notably, not knowing who has covid or even the rates of exposure etc. among different groups); gaps in our scientific understanding regarding modes of transmission, mutations, etc.; and, as Trisha Greenhalgh has discussed, a lack of integration of data analysis with substantive theory.
All these are concerns, even without getting to the problems of overconfident public health authorities, turf-protecting academic or quasi-academic organizations, ignorant-but-well-connected pundits, idiotic government officials, covid deniers, and trolls. It’s easy to focus on all the bad guys out there, but even in world where people are acting with intelligence, common sense, and good faith, we’d have big gaps in our understanding.
Lemoine makes the point that the spread of coronavirus along the social network represents another important area of uncertainty in our understanding. That makes sense, and I like that he approaches this problem using simulation. The one thing I don’t really buy—but maybe it doesn’t matter for his simulation—is Lemoine’s statement that fluctuations in the epidemic’s spread “as far as we can tell, can’t be explained by behavioral changes.” I mean, sure, we can’t tell, but behaviors change a lot, and it seems clear that even small changes in behavior can have big effects in transmission. The reason this might not matter so much in the modeling is that it can be hard to distinguish between a person changing his or her behavior over time, or a correlation of different people’s behaviors with their positions in the transmission network. Either way, you have variation in behavior and susceptibility that is interacting with the spread of the disease.
In his post, Lemoine gives several of examples of countries and states where the recorded number of infections went up for no apparent reason, or where you might expect it to have increased exponentially but it didn’t. One way to think about this is to suppose the epidemic is moving through different parts of the network and reaching pockets where it will travel faster or slower. As noted above, this could be explained my some mixture of variation across people and variation over time (that is, changing behaviors). It makes sense that we shouldn’t try to explain this behavior using the crude categories of exponential growth and herd immunity. I’m not sure where this leads us going forward, but in any case I like this approach of looking carefully at data, not just to fit models but to uncover anomalies that aren’t explained by existing models.