Cross-sectional cycle threshold values reflect epidemic dynamics of COVID-19 in Madagascar

Age and Sex Differences Among Mildly Symptomatic and Asymptomatic Patients with Omicron Infection in 2022 in Shanghai, China

“…When the sample size is large enough, the overall mean Ct value can assist in the identification of the epidemic stage, including the beginning, the peak, and the end of the epidemic. 22 Therefore, the findings in this study can provide basic disease duration patterns of patients with different features, which can provide a reference for decision-making under the dynamic epidemic strategy in China.…”

Systematic Review on the Correlation Between SARS-CoV-2 Real-Time PCR Cycle Threshold Values and Epidemiological Trends

“…[ 18 ] 2022 France To explore the possibility of using Ct values from SARS-CoV-2 screening tests to better understand the spread of an epidemic and to better understand the biology of the infection Retrospective National National The main factors affecting Ct values of SARS-CoV-2 RT–PCR in this multivariate linear model were the assay type; the laboratory; the level of positivity; the days post-symptom onset; the sample type; age (per 20 years older); whether target gene was N, ORF1, or S; or the date (per 71 days later) Andriamandimby et al. [ 22 ] 2022 Madagascar To estimate COVID-19 epidemic growth rates at the national level and in two major administrative regions of Madagascar, and evaluate the robustness of this Ct-based method in comparison with epidemic growth rates derived from more traditional case-count methods applied to the same regions and at the national level Real-time and retrospective Local/regional National Public reporting of Ct values could enable forecasting of impending incidence peaks in regions with limited case reporting Avadhanula et al. [ 15 ] 2021 USA To determine the potential of the superspreader by examining the viral load of SARS-CoV-2 in adults during the first and second wave of coronavirus disease 2019 pandemic at the local level Cross-sectional observational cohort Single unit/local Single unit/local The median Ct of the weekly viral load from nasopharyngeal samples of hospitalized patients on admission at an individual level may help to predict epidemic trend at the population level.…”

“…[ 18 ] Public 793,479 tests Only tests with a Ct value were included, meaning that negative results were less represented in the final database as negative samples do not usually have any reported Ct value PerkinElmer, Alinity, Abbott Laboratories, Abbott Park, IL, USA, Allplex, Seegene, Argene, BioMerieux, BGI, CNR Paris, Cobas 6800, Roche, Cobas 8800, Roche, Daan Gene, Appolon Biotek, Rhone, France, Genefinder, Technique Charite, Thermo Fisher Scientific, Waltham, MA, USA E, N, N and O, targeted together, ORF1, S Nasopharyngeal, lower respiratory tract, feces, saliva Known for 9%: 0–4 days (reference), 4–7 days, 8–14 days, > 14 days NR The R t of the epidemic (measured via national hospital admission data and the EpiEstim method) NR ~ 1 week The R t (based on hospitalization and screening data) on the date of sampling was not significantly associated with Ct values Using an ARIMA predictive model to estimate whether the Ct data improves short-term predictions of the disease epidemiology, 6–7 days appeared to be the most significant time lag for cross-correlation between Ct and R t (tested between 0 and 20 days) The mean absolute percentage error in predicting R t using the ARIMA model improved when including Ct quartiles and Ct skewness Andriamandimby et al. [ 22 ] Public 5310 Ct cutoffs for positive results from different PCR assays: Charity Berlin: ≤ 38; Hong Kong: ≤ 40; Da An: ≤ 40; LightMix SarbeCoV/SarbeCoV TibMolBiol ≤ 38; TaqPath ≤ 37 for 2 of 3 targets; GeneXpert: ≤ 40) To control for extensive variation in qPCR test and target a TaqPath N-corrected Ct value was calculated with a Ct cutoff of ≤ 37 Seven WHO recommended kits and corresponding protocols: Charity Berlin kit, Hong Kong University kit, Da An gene kit, Da An Gene Co. Ltd. Sun Yatsen University, Guangzhou, China, LightMix SarbeCoV E-gene plus EAV control, TIB MolBiolBiolmo Berlin, Germany SarbeCoV TibMolBiol, TIB Biolmol, Berlin, Germany TaqPath COVID-19 Combo kit, Life Technologies Ltd, Paisley, UK GeneXpert, Cepheid, Sunnyvale CA, USA E, N, Orf1a/b or S gene Nasopharyngeal and oropharyngeal NR NR Real-time estimates of COVID-19 prevalence taken from publicly reported incidence data and retrospective results from the National Influenza Centre across three administrative ...…”

“…The studies were conducted in the USA [ 2 , 13 – 17 ], Europe [ 18 – 21 ], Africa [ 22 ], Australia [ 23 ], China [ 24 ], India [ 25 ], the Kingdom of Bahrain [ 3 ], and Lebanon [ 26 ]. RT–PCR Ct values were measured from public samples only (national or local), or from hospital and public samples in ten studies [ 3 , 14 , 18 – 25 ], and from hospital only or hospital and care home samples only in six studies [ 2 , 13 , 15 – 17 , 26 ].…”

Epidemic SI COVID-19 modeling in LMICs: Accompanying commentary

“…The first theme of these papers, that of methods developments, highlights one of the key ways in which the field of epidemiological modeling benefits from more work and a wider appreciation of work being undertaken globally; more people working on methods that can be of use to us all. In this special issue we include one of the early applications using Ct values as extra information to understand epidemics dynamics ( Andriamandimby et al, 2022 ), and a novel Bayesian approach to combining disease forecasts ( Daza-Torres et al, 2022 ). There were two approaches to understanding spatial patterns using new approaches and/or new combinations of data ( Ramiadantsoa et al, 2022 , Saba et al, 2022 ) as well as a novel approach to modelling household transmission and itnerventions ( Franco et al, 2022 ).…”