# 1 Priors on Model Parameters

epidemia aims to give the user a high degree of control over setting prior distributions. It does this by leveraging the functionality provided by rstanarm, which defines functions representing a number of different prior families. These include for example student-t, laplace, and hierarchical shrinkage families. In this article, we provide a brief introduction to the available families, and discuss some important quirks to be aware of when defining priors. We use the same mathematical notation as in the model description article.

Please do not rely on the default priors in epidemia. Although these have been designed to be weakly informative, they are not guaranteed to be appropriate for your particular model. Please adjust prior distributions as required.

Priors must be defined for all parameters in each of the three model components: transmission, infection, and observations. In the transmission model, priors must be set for all effects appearing in the linear predictor $$\eta$$. In the infection model, a prior must be set on $$\tau$$, but also on the dispersion parameter $$d$$ in the extended version of the model. In each observational model, priors must be set for effects defining the ascertainment rates, but also for the auxiliary parameter for the sampling distribution, $$\phi$$.

In general, primitive model parameters are either intercepts, fixed effects, a covariance matrix, an auxiliary parameter, or the error term in a random walk. We discuss each in turn, in particular highlighting where they appear in the model, and what distributions are available for them.

## 1.1 Priors on Intercepts

Intercepts can appear in the linear predictor $$\eta$$ for the reproduction numbers $$R$$ and in the linear predictors for ascertainment rates $$\alpha$$. The prior distribution is specified using an argument prior_intercept. This appears in both epirt() and epiobs(). prior_intercept must be a call to an rstanarm function that represents a student-t family: i.e. one of rstanarm::normal, rstanarm::student_t or rstanarm::cauchy. prior_intercept is of course only used if the formula specifies an intercept. Please note that the interpretation of prior_intercept depends on the center argument to epirt() and epiobs(). Please see Section 2.1 for more details.

## 1.2 Priors on Regression Coefficients

In addition to intercepts, the predictors for $$R$$ and $$\alpha$$ may also contain fixed effects. In the regression for $$R$$ this corresponds to the parameter vector $$\beta$$. The prior distribution is set using the prior argument, which, similarly to prior_intercept, appears in both epirt() and epiobs(). Note that this does not set the prior for the group-specific effects $$b$$, which are instead controlled by prior_covariance.

prior can be a call to one of rstanarm’s prior functions. These can be broadly grouped into four families: student-t, hierarchical shrinkage, laplace and the product normal family. Note that all effects must follow the same family; for example, it is not possible for $$\beta_1$$ to have a normal prior while $$\beta_2$$ has a Cauchy prior. Nonetheless, different hyperparameters can be set for each effect.

As an example, suppose the following formula is used to model $$R$$, where cov1 and cov2 are some covariates.

R(group, date) ~ 1 + cov1 + cov2

Using prior = rstanarm::normal(location=0,scale=1) in the call to epirt() would give a standard normal prior to both covariate effects. To give different prior locations and or scales for each covariate, we simply pass numeric vectors instead of scalars. For example, prior = rstanarm::normal(location=c(0,1),scale=c(1,2)) would set priors $$\beta_1 \sim N(0,1)$$ and $$\beta_2 \sim N(1,2)$$, where $$\beta_1$$ and $$\beta_2$$ are the effects for cov1 and cov2 respectively.

The interpretation of prior depends on whether covariates are being centered, and whether automatic scale adjustments are occurring. Please see 2 for more details.

### 1.2.1 Additional Priors

In addition to rstanarm’s prior functions, epidemia offers additional priors for regression coefficients. Currently, there is just one, shifted_gamma. This represents a gamma distribution that can be shifted so that it has support other than on $$[0, \infty)$$. Specifically, $$$\beta_i \sim \text{Gamma}(\alpha_i, \theta_i) - a_i,$$$ where $$\alpha_i$$ and $$\theta_i$$ are shape and scale parameters, and $$a_i$$ is a shift. This prior is used in Flaxman et al. (2020) to model the prior effect of non-pharmaceutical interventions (NPIs). Intuitively, it is unlikely that an NPI designed to reduce transmission rates ends up increasing transmission significantly. This implies that a symmetric prior may not be appropriate for these effects: it makes sense to put low mass on large positive effect sizes. In addition, this prior can help to improve identifiability when multiple NPIs occur in quick succession - as is often the case during the early stages of an epidemic.

## 1.3 Priors on Auxiliary Parameters

Auxiliary parameters can appear in the sampling distributions for observations. This corresponds to the parameter $$\phi$$ in the model-description vignette. The interpretation of this parameter depends on the chosen distribution: Poisson has no auxiliary parameter as it is fully defined by its mean. For the negative binomial distribution (specified by using family = "neg_binom" in the call to epiobs()), $$\phi$$ represents the reciprocal dispersion. An auxiliary parameter $$d$$ also exists in the extended version of the infection model (when using latent = TRUE in the call to epiinf()). This represents the coefficient of variation of the offspring distribution. Auxiliary parameters are always non-negative in epidemia.

Priors for auxiliary parameters are set using the prior_aux argument in the epiobs() and epiinf() modeling functions. It is not used when family = "poisson in the call to epiobs() or when latent = FALSE in the call to epiinf(). prior_aux can be a call to one of rstanarm::normal(), rstanarm::student_t(), rstanarm::cauchy() or rstanarm::exponential().

## 1.4 Priors on Covariance Matrices

Recall that partial pooling can be used in the regression for $$R$$. The partially pooled parameters $$b$$ are characterized as zero mean multivariate normal with an unknown covariance matrix, which must itself be assigned a prior. The precise model for these parameters is described in detail in the partial pooling article. The prior on the covariance matrix can be set using the prior_covariance argument in epirt().

Although the Inverse-Wishart prior is a popular prior for covariance matrices, it does not cleanly separate shape and scale (Tokuda et al. 2011). A general approach is to decompose the prior on the covariance matrix into a prior on the correlation matrix and a vector of variances. This is the approach taken by rstanarm, which has functions rstanarm::decov and rstanarm::lkj which represent priors for covariance matrices. These are also used by epidemia for the same purpose.

We briefly describe rstanarm’s decov prior, as it applies to partially pooled parameters in the regression for $$R$$. For a comprehensive account, please refer to this article.

Before reading on, it helps to review the partial pooling vignette. Suppose the formula for $$R$$ contains a term of the form (expr | factor), and that expr evaluated to a model matrix with $$p$$ columns, and factor has $$L$$ levels. Letting $$\theta_l$$ denote the $$p$$-vector of paramaters for the $$l$$th group, recall from the partial pooling vignette that this is modeled as $$$\theta_{l} \sim N(0, \Sigma),$$$ where $$\Sigma$$ is a $$p \times p$$ covariance matrix. The decov prior decomposes $$\Sigma$$ into a vector of variances $$(\sigma^2_1, \ldots \sigma^2_p)$$ and a correlation matrix $$\Omega$$, which is given an LKJ prior. The variance vector is decomposed into the product of a simplex vector $$s$$ and the trace of $$\Omega$$, which is just the sum of the individual variances. Specifically, $$$\sigma^2_i = s_i \text{tr}\left(\Sigma\right).$$$ The simplex vector is given a symmetric dirichlet prior, while the trace is decomposed into $$tr(\Sigma) = p \kappa^2$$, where $$p$$ is the order of the matrix (i.e. the number of correlated effects), and $$\kappa$$ is a parameter which is assigned a scale invariant prior; specifically a Gamma with given shape and scale hyperparameters. When $$p = 1$$, for example with (1 | factor), the prior simplifies considerably. $$\Sigma$$ simply reduces to $$\kappa^2$$, which has a Gamma prior.

# 2 Caveats

There are several important caveats to be aware of when using prior distributions in epidemia.

## 2.1 Covariate Centering

By default, covariates in the regressions for $$R$$ and $$\alpha$$ are not centered automatically by epidemia. This can, however, be done by using center = TRUE in the call to epirt() and epiobs() respectively. It is important to note that if center = TRUE, the prior_intercept and prior set the priors on the intercept and coefficients after centering the covariates.

Covariates are not centered automatically because often the intercept has an intuitive interpretation in the model. For example, if all covariates are zero at the beginning of the epidemic, then the intercept can be seen as specifying the $$R_0$$ of the disease. If center = TRUE, then the intercept no longer has an easily intuited interpretation.

### 2.1.1 Autoscaling

rstanarm’s prior functions have an argument called autoscale. If autoscale = TRUE, then epidemia automatically adjusts the prior scale to account for the scale of the covariates. This only applies to priors on fixed effects, and not to the intercepts. epidemia rescales according to the following rules.

• If a predictor has only one unique value, no rescaling occurs.
• If it has two unique values, the original scale is divided by the range of the values.
• For more than two unique values, the original scale is divided by the standard deviation of the predictor.

If you are unsure whether rescaling has occurred, call prior_summary on a fitted model object. This gives details on the original priors specified, and the priors that were actually used after rescaling.

# 3 References

Flaxman, Seth, Swapnil Mishra, Axel Gandy, H Juliette T Unwin, Thomas A Mellan, Helen Coupland, Charles Whittaker, et al. 2020. “Estimating the effects of non-pharmaceutical interventions on COVID-19 in Europe.” Nature. https://doi.org/10.1038/s41586-020-2405-7.

Tokuda, Tomoki, Ben Goodrich, I Van Mechelen, Andrew Gelman, and F Tuerlinckx. 2011. “Visualizing Distributions of Covariance Matrices.” Columbia Univ., New York, USA, Tech. Rep, 18–18.