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With the covid-19 pandemic still ongoing and an enormous amount of test dataavailable, the lessons learned over the last two years need to be developed toa point where they can provide understanding for tackling new variants andfuture diseases. The SIR-model commonly used to model disease spread, predictsexponential initial growth, which helps establish the infectiousness of adisease in the early days of an outbreak. Unfortunately, the exponential growthbecomes muddied by spatial, finite-size, and non-equilibrium effects inrealistic systems, and robust estimates that may be used in prediction anddescription are still lacking. I here establish a second quantization frameworkthat allows introduction of arbitrarily complicated spatial behavior, and Ishow that a simplified version of this model is in good agreement with both thegrowth of different covid-19 variants in Denmark and analytical results fromthe theory of branched polymers. Denmark is well-suited for comparison, becausethe number of tests with variant information in early December 2021 is veryhigh, so the spread of a single variant can be followed. I expect this model tobuild bridges between the epidemic modeling and solid state communities. Thelong-term goal of the particular analysis in this paper is to establish priorsthat allow better early estimates for the infectiousness of a new disease.

On the Role of Spatial Effects in Early Estimates of Disease Infectiousness: A Second Quantization Approach