6 <u>(www.worldpop.org) were all resampled to 1km resolution and included as covariates with thin-</u> plate splines used to model non-linear effects. These covariates were chosen due to their plausible association with poverty and access to handwashing facilities. While other covariates could be used, here the goal of the spatial modeling was to produce predictions of handwashing risk as opposed to examining factors associated with handwashing risk in detail. We also included an additional penalty term in the model to allow covariates that were not related to the outcome to essentially be removed from the model (Marra and Wood, 2011). This model was used to predict handwashing risk at 1km resolution. District level handwashing risk was then estimated by calculating the population weighted mean for all cells within a district.

# _1.2 Respiratory illness_

To generate continuous maps of respiratory illness, we calculated the prevalence of respiratory illness symptoms at any time in the two weeks preceding the survey at each DHS cluster, available for individuals younger than age 5 years (Croft, Marshall and Allen, 2020). The prevalence of respiratory illness symptoms was then modeled using a binomial GAM with handwashing risk, distance to road, population density and poverty as covariates in addition to a spatial bivariate smooth. The ward level poverty estimates were rasterized to be on the same 1km resolution grid as all other covariates and included as an additional covariate. This model was then used to predict prevalence of respiratory illness at 1km resolution. District level prevalence of respiratory illness was then estimated by calculating the population weighted mean for all cells within a district.

# _1.3 Comorbidities and factors for the probability of severe COVID-19_

In addition to respiratory illness, we generated estimates of the prevalence of several additional comorbidities and probability factors for severe COVID-19 at the district level. Individual probability factors that are included at this stage are based on existing literature and presently are HIV prevalence (without Viral Load Suppression), anemia prevalence, and obesity prevalence, in addition to respiratory illness. For each of these, prevalence was calculated at the cluster level and the same GAM model was fit to each outcome to generate predicted values at the 1km cell level. Once these gridded surfaces are created at a 1km resolution, estimates are reached at the district level by weighting, using a raster file of population count at the pixel level.

We then calculate the probability of having any of these comorbidities, defined as 1 minus the probability of having none of these comorbidities: <mark>p p =</mark> _<mark>1</mark>_ <mark>− (</mark> _<mark>1</mark>_ <mark>− (</mark> _pri_ ⋅ _pHIV_ ⋅ _pa_ ⋅ _po_ )) _<mark>where pco = proportion with a comorbidity, pri = proportion with respiratory illness, pHIV =</mark>_ _<mark>where pco = proportion with a comorbidity, pri = proportion with respiratory illness, pHIV = proportion with HIV (without viral load suppression), pa = proportion with anaemia and po = proportion with obesity.</mark>_ We apply the same geospatial methods to calculate the proportion of the population that is a smoker based on smoking prevalence within DHS clusters. We additionally calculate the proportion of the population that is over age 50 from WorldPop data.