5 adults within the same household (Longini _et al._ , 1982; MacIntyre _et al._ , 2012), and thus we have used this indicator as a proxy for respiratory illness prevalence in adults at the household level. Anemia, overweight prevalence and HIV prevalence were also taken from this source.

There are 400 clusters in the DHS distributed across the ten provinces (60 districts) of the country. Though the data is originally collected to be representative at the province level, the application of spatial modeling to such data allows for higher resolution predictions of outcomes (Diggle _et al._ , 2013; DHS Spatial Interpolation Working Group, 2014) such as malaria prevalence or vaccine coverage e.g. (Takahashi _et al._ , 2017; Utazi _et al._ , 2020, 2020).<sup>2</sup> Spatial modeling approaches leveraged are described in more detail in the methods section.

The odds ratios for risk of severe disease from different clinical risk factors were taken from a meta-analysis and systematic review study of incidence and clinical characteristics and prognostic factors of patients with COVID-19 across 53,000 patients (C. Ma _et al._ , 2020) and the age specific probabilities of hospitalization are taken from _Estimates of the severity of coronavirus disease 2019: a model-based analysis_ (Verity _et al._ , 2020) _._

# Methods

<mark>The methodology outlined here includes three parts. First we calculate the prevalence of risk factors and comorbidities for severe COVID-19 at the district level. This includes the prevalence of respiratory illness modeled as a function of handwashing risk. Second, we use these prevalence estimates to calculate the probability of developing severe COVID-19 disease. Third, we modify the level of handwashing risk according to different counterfactual scenarios in order to see the impact it has on reducing prevalence of respiratory illness and severe COVID-19 risk across the country.</mark>

1. Predicting prevalence of risk factors and comorbidities

## _1.1 Handwashing risk_

Using the three variables related to handwashing in the DHS data, we assign each household a hand washing ‘risk’ score. For households with a hand washing facility with water and soap, this was considered equivalent to a hand washing risk of 0. With water but no soap, there was a risk of 0.5, while with no water or no facility (even if soap was present) the risk was set to 1. The mean household level observation was then calculated for each cluster.

Cluster level georeferenced handwashing risk scores were used to predict handwashing risk across the whole country and generate a continuous map using a beta Generalized Additive Model (GAM) which includes a bivariate smooth on latitude and longitude. Population density, night-time light intensity for 2015 and distance to nearest Open Street Map (OSM) road obtained via WorldPop

> 2 Though DHS clusters are intentionally displaced by 0-2km in urban areas and 0-5 km in rural clusters, with a further, randomly-selected 1% (every 100th) of rural clusters displaced by up to 10km (Burgert, 2013), this is taken into account in the interpolation process. The application of interpolation techniques specifically to DHS data is described in the referenced publication (DHS Spatial Interpolation Working Group, 2014).