Iâve been thinking about this approach since last year, and havenât had time to prioritize it to do detailed work on a framework, but I have some initial thoughts. I think youâre right that, if youâre comfortable with that sort of cluelessness, this kind of thing is relatively safe to do (although as @Michael St Jules đ¸ notes, Iâd also want to do some proper population modeling in a highly studied ecosystem to get some grounding for the idea).
But I think you can actually do better than focusing on only the very worst diseases depending on population parameters. For example, in populations that are top-down regulated (i.e., the population size is held under the carrying capacity of the resource by an external factor), you would not expect increases in starvation as a result of removing a disease (caveat: if that disease *is* the top down regulator, than you would have a problemâwhich unfortunately is the case in many CWD contexts). So then the disease doesnât need to be worse than both starvation and predation, say, but rather just worse than predation. The population size would equilibriate somewhere a bit higher, but the top-down regulation creates a buffer between population size and resource carrying capacity, and at high enough predation pressures you might reasonably expect almost no population increase.
So I think in an ideal case, youâd identify (1) a high suffering disease that (2) affects a population primarily controlled by intense predation pressure in (3) a predator that mainly eats the target population (so the increases in predator population sizes donât affect other animals, who arenât having a disease treated and for whom this would just represent an increase in suffering).
Of course, if you have a population with high predation pressure, the target population probably dies very quickly after getting the disease, so the suffering caused by the disease might not be very long in duration. But if its a really awful disease that could still be a lot of suffering.
I donât think anyoneâs done a scan of the literature for diseases with these properties, and I doubt youâd easily find a perfect caseâmost populations are a mixture of top-down and bottom-up regulation. But I also think that probably my few hours of playing around with these ideas on the side of my other work are not likely to be the final word on the question :) so Iâm optimistic someone spending a lot more time with this could identify other âecological profilesâ of diseases that make them âsaferâ in indirect effects terms to work on than others (I think thereâs some things to say about bottom-up regulated populations as well, for exampleâprobably there you would want a disease that is a lot worse than starvation).
Re: your footnote: I think this depends heavily on how severe we are talking. I donât have a strong opinion, because I really think no one has looked at it, about how much more severe things can get from disease than from something like keel bone fractures. A priori it doesnât seem unreasonable to assume that the artificial conditions of factory farming enable a chicken to live in pain much longer, and therefore have higher overall suffering, than we would ever see in the wildâbut Iâm not that confident in that idea, so it would be good to look at more diseases. The point being that a severe enough disease could still be worth working on in dalys/âdollar terms even if it doesnât affect that many individuals, and that would also make it more ecologically inert in many cases (since changing the circumstances of very large numbers of animals seems riskier).
WAI facilitated a grant from Coefficient (then OP) years ago to look at disease severity; they came out with a few papers recently here and here. As is perhaps unsurprising, but disappointing, much of the research on disease in wildlife doesnât provide enough info to do a good job estimating the welfare burden. But the high scoring bacterial zoonoses in the first paper could be a good place to start a research project attempting to better assess the severity and numerosity compared to FAW conditions (as a cost effectiveness bar).