UKRI GCRF/Newton Fund : Building an Early Warning System for community-wide infectious disease spread: SARS-CoV-2 tracking in Africa via environment fingerprinting- COVID-19

Mitigating the rapid global spread of Covid-19 requires real-time data on community infection prevalence in order to guide targeted intervention measures on regional, national and global scales. Individual diagnostic testing is of paramount importance for short- and long-term management of the pandemic, but limits on capacity (both of kits and trained workers) mean that healthcare settings are prioritised over the community.
Here we propose a novel supplemental low-resource approach for broad community-wide surveillance of SARS-CoV-2 infection prevalence. We aim for a real-time Covid-19 risk prediction platform for community-wide diagnostics via wastewater-based epidemiology (Figure 1). Disease markers present in domestic wastewater can reveal the health status of contributing population, and we propose that this includes the infection prevalence by SARS-CoV-2. Real-time spatiotemporal estimation of this novel coronavirus in urban water across several sites in South Africa (Cape Town) and Nigeria (Lagos) will provide a broad picture of community infection prevalence, even for asymptomatic cases, as well as the level of acquired immunity, thus identifying hotspots for priority testing, contact-tracing and quarantine and will provide more accurate projections of the spread of the virus and the infection fatality rate. As communities contribute directly to wastewater, we will be able to estimate true infection rate at the community level, including also asymptomatic and pre-symptomatic people. The virus loading levels will be used to establish status and time trends. This would enable rapid identification of hot spots for management via targeted intervention measures and potentially support important decisions regarding entry into and exit from 'lockdown' periods as well as focussed screening of selected communities.