ABSTRACT
Diagnostic assays for various infectious diseases, including COVID-19, have been challenged for their utility as standalone point-of-care diagnostic tests due to suboptimal accuracy, complexity, high cost or long turnaround times for results. It is therefore critical to optimise their use to meet the needs of users. We used a simulation approach to estimate diagnostic outcomes, number of tests required and average turnaround time of using two-test algorithms compared with singular testing;the two tests were reverse transcription polymerase chain reaction (RT-PCR) and an antigen-based rapid diagnostic test (Ag-RDT). A web-based application of the model was developed to visualise and compare diagnostic outcomes for different disease prevalence and test performance characteristics (sensitivity and specificity). We tested the model using hypothetical prevalence data for COVID-19, representing low- and high-prevalence contexts and performance characteristics of RT-PCR and Ag-RDTs. The two-test algorithm when RT-PCR was applied to samples negative by Ag-RDT predicted gains in sensitivity of 27% and 7%, respectively, compared with Ag-RDT and RT-PCR alone. Similarly, when RT-PCR was applied to samples positive by Ag-RDT, specificity gains of 2.9% and 1.9%, respectively, were predicted. The algorithm using Ag-RDT followed by RT-PCR as a confirmatory test for positive patients limited the requirement of RT-PCR testing resources to 16,400 and 3,034 tests when testing a population of 100,000 with an infection prevalence of 20% and 0.05%, respectively. A two-test algorithm comprising a rapid screening test followed by confirmatory laboratory testing can reduce false positive rate, produce rapid results and conserve laboratory resources, but can lead to large number of missed cases in high prevalence setting. The web application of the model can identify the best testing strategies, tailored to specific use cases and we also present some examples how it was used as part of the Access to Covid-19 Tools (ACT) Accelerator Diagnostics Pillar.
ABSTRACT
Diagnostic assays for various infectious diseases, including COVID-19, have been challenged for their utility as standalone point-of-care diagnostic tests due to suboptimal accuracy, complexity, high cost or long turnaround times for results. It is therefore critical to optimise their use to meet the needs of users. We used a simulation approach to estimate diagnostic outcomes, number of tests required and average turnaround time of using two-test algorithms compared with singular testing; the two tests were reverse transcription polymerase chain reaction (RT-PCR) and an antigen-based rapid diagnostic test (Ag-RDT). A web-based application of the model was developed to visualise and compare diagnostic outcomes for different disease prevalence and test performance characteristics (sensitivity and specificity). We tested the model using hypothetical prevalence data for COVID-19, representing low- and high-prevalence contexts and performance characteristics of RT-PCR and Ag-RDTs. The two-test algorithm when RT-PCR was applied to samples negative by Ag-RDT predicted gains in sensitivity of 27% and 7%, respectively, compared with Ag-RDT and RT-PCR alone. Similarly, when RT-PCR was applied to samples positive by Ag-RDT, specificity gains of 2.9% and 1.9%, respectively, were predicted. The algorithm using Ag-RDT followed by RT-PCR as a confirmatory test for positive patients limited the requirement of RT-PCR testing resources to 16,400 and 3,034 tests when testing a population of 100,000 with an infection prevalence of 20% and 0.05%, respectively. A two-test algorithm comprising a rapid screening test followed by confirmatory laboratory testing can reduce false positive rate, produce rapid results and conserve laboratory resources, but can lead to large number of missed cases in high prevalence setting. The web application of the model can identify the best testing strategies, tailored to specific use cases and we also present some examples how it was used as part of the Access to Covid-19 Tools (ACT) Accelerator Diagnostics Pillar.
ABSTRACT
BACKGROUND: In 2008, Somalia introduced an electronic based Early Warning Alert and Response Network (EWARN) for real time detection and response to alerts of epidemic prone diseases in a country experiencing a complex humanitarian situation. EWARN was deactivated between 2008 to 2016 due to civil conflict and reactivated in 2017 during severe drought during a cholera outbreak. We present an assessment of the performance of the EWARN in Somalia from January 2017 to December 2020, reflections on the successes and failures, and provide future perspectives for enhancement of the EWARN to effectively support an Integrated Disease Surveillance and Response strategy. METHODS: We described geographical coverage of the EWARN, system attributes, which included; sensitivity, flexibility, timeliness, data quality (measured by completeness), and positive predictive value (PPV). We tested for trends of timeliness of submission of epidemiological reports across the years using the Cochran-Mantel-Haenszel stratified test of association. RESULTS: By December 2020, all 6 states and the Banadir Administrative Region were implementing EWARN. In 2017, only 24.6% of the records were submitted on time, but by 2020, 96.8% of the reports were timely (p < 0.001). Completeness averaged < 60% in all the 4 years, with the worst-performing year being 2017. Overall, PPV was 14.1%. Over time, PPV improved from 7.1% in 2017 to 15.4% in 2019 but declined to 9.7% in 2020. Alert verification improved from 2.0% in 2017 to 52.6% by 2020, (p < 0.001). In 2020, EWARN was enhanced to facilitate COVID-19 reporting demonstrating its flexibility to accommodate the integration of reportable diseases. CONCLUSIONS: During the past 4 years of implementing EWARN in Somalia, the system has improved significantly in timeliness, disease alerts verification, and flexibility in responding to emerging disease outbreaks, and enhanced coverage. However, the system is not yet optimal due to incompleteness and lack of integration with other systems suggesting the need to build additional capacity for improved disease surveillance coverage, buttressed by system improvements to enhance data quality and integration.