Variance in biting risk is an important epidemiological parameter.
A recent paper(Cooper et al 2019) suggested that biting risk follows a roughly Pareto distribution, with 20% of households receiving 80% of the bites.
Current heterogeneous biting scheme that most people use is an exponential distribution with scale factor = 1
Bottom line: new distribution better fits data for low and moderate transmission settings, and this distribution is more resistant to elimination.
Methods
Took raw data of mosquito counts from 3 sites in Uganda from Cooper et al. 2019. This contains number of mosquitos collected at each household in the study, for each month.
Generated model data for different heterogeneous biting distributions to find best fit.
Fitting raw data
First, took each month of data and normalized household-level counts by the monthly average. This allows us to focus on the monthly variance of risk, ignoring seasonal differences
Then, compute normalized, Euclidean distance between observed distribution and model distribution
Log-normal is provide better fit for lower transmission area(Jinja, Kanungu)
Exponential is better fit for very high transmission area(Tororo)
How different is this new distribution?
How does this show up in the simulations?
(constant transmission sims, for clarity)
Uniform biting tends to have higher prevalence than heterogeneous biting
Uniform biting explores the antigen space faster because people are less likely to be capped out on their allowed infections.
The more heterogeneous the biting, the lower the prevalence in general
HOWEVER, the more heterogeneous biting is more resistant to elimination:
Takeaways
Uniform biting risk is probably never what you want, except for debugging.
In other words, make sure Enable_Demographics_Risk is set to 1, and, that you are setting a distribution of risk in the demographics file.
In low and moderate settings, a log-normal distribution of risk with sigma ~1.6 is better fit to data than exponential distribution.
The new distribution has a more extended tail of high-risk individuals
These high-risk individuals make the overall population more resistant to elimination
Background
Methods
Fitting raw data
How different is this new distribution?
How does this show up in the simulations?
Takeaways