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Table 2 Contextual parameters and their uncertainty ranges

From: Quantifying the potential value of antigen-detection rapid diagnostic tests for COVID-19: a modelling analysis

Parameter Value References
  Hospital setting Community setting  
Prevalence of current or recent SARS-CoV-2 infection (%) * 25 5 Assumption
Proportion amongst those tested who are in acute phase 0.5–1.00 0.5–1.00 Assumption
Of those in acute phase, number of infectious days remaining (days) 5–15 5–15 [16]
Case fatality rate amongst hospitalised COVID-19 patients 0.20–0.30 N/A [7]
Case fatality reduction amongst COVID-19 patients on dexamethasone (1—risk ratio) 0.07–0.25 N/A [7]
NAT performance  
NAT sensitivity (for current or recent SARS-CoV-2) 0.85–0.95 0.85–0.95 [17,18,19,20,21,22,23]
NAT specificity 0.99–1 0.99–1 [17,18,19,20]
NAT availability (proportion able to access NAT test) 0.1–1 0.1–1 Assumption
Cost per NAT test ($) 20–70 20–70  
NAT turnaround time (days) 1–10 5–15 [10], Expert consultation
Confirm Ag-RDT negative results with NAT Y/N Y/N  
Confirm Ag-RDT positive results with NAT ** Y/N Y/N  
Isolate and initiate treatment (if indicated) whilst awaiting NAT result Y N  
Ag-RDT performance (assumed fixed)  
Ag-RDT sensitivity for current infection, relative to NAT (%, assumed only amongst acute cases)* 0.80 0.80 [14, 15]
Ag-RDT specificity, relative to NAT (%)* 0.98 0.98 [14, 15]
Cost per Ag-RDT test ($) 5 5 [14]
Clinical judgement and management  
Sensitivity of clinical judgement in absence of NAT 0.45–0.99 0.45–0.99 [24,25,26,27]
Specificity of clinical judgement in absence of NAT 0.20–0.70 0.20–0.50 [24,25,26,27]
Proportion of hospitalised patients with a negative COVID-19 test result (true and false negatives) that are initiated onto dexamethasone 0.05–0.15 N/A Assumption
Duration of isolation (days) 10 10 [16]
Duration of dexamethasone treatment (days) 10 N/A [7]
Cost of isolation per day ($) 50–350 N/A [28,29,30]
Cost of dexamethasone per day ($) 0.13–3.5 N/A [31]
  1. Ranges define limits on uniform distributions, chosen to capture plausible parameter ranges that may apply across a variety of low- and middle-income settings. As described in the main text, the main analysis is a systematic uncertainty analysis, structured to identify which of these uniform distributions is most influential for model outcomes. *We performed sensitivity analyses on these fixed parameters, with results presented in the supporting information. We varied prevalence in the hospital setting between 10 and 30% and in the community setting between 1 and 10% (Additional file 7: Fig.S4). We varied Ag-RDT sensitivity and specificity between 75-95% and 98–100%, respectively, relative to NAT (Additional file 8: Fig.S5). **We exclude any parameter draws involving NAT confirmation of both Ag-RDT negative and Ag-RDT positive results.