Ineffectiveness of international travel restrictions to contain spread of the SARS-CoV-2 Omicron BA.1 variant: a continent-wide laboratory-based observational study from Africa (preprint)

Background: In mid-November 2021, the SARS-CoV-2 Omicron BA.1 variant was detected in Southern Africa, prompting international travel restrictions of unclear effectiveness that exacted a substantial economic toll. Methods: Amidst the BA.1 wave, we tested 13,294 COVID-19 patients in 24 African countries between mid-2021 to early 2022 for BA.1 and Delta variants using real-time reverse transcription-PCR tests. The diagnostic precision of the assays was evaluated by high-throughput sequencing in four countries. The observed BA.1 spread was compared to mobility-based mathematical simulations. Findings: By November-December 2021, BA.1 had replaced the Delta variant in all African sub-regions following a South-North gradient, with a median Rt of 2.4 up to 30 days before BA.1 became predominant. PCR-based South-North spread was in agreement with phylogeographic reconstructions relying on 939 SARS-CoV-2 genomes from GISAID. PCR-based reconstructions of country-level BA.1 predominance correlated significantly in time with the emergence of BA.1 genomic sequences on GISAID (p=0.0035, cor=0.70). First BA.1 detections in affluent settings beyond Africa were predicted adequately in time by mobility-based mathematical simulations (p<0.0001). BA.1-infected inbound travelers departing from five continents were identified in five Western countries and one Northern African country by late November/early December 2021, highlighting fast global BA.1 spread aided by international travel. Interpretation: Unilateral travel bans were poorly effective because by the time they came into effect, BA.1 was already widespread in Africa and beyond. PCR-based variant typing combined with mobility-based mathematical modelling can inform rapidly and cost-efficiently on Rt, spread to inform non-pharmaceutical interventions.

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

As the national reference laboratory for febrile illness in Madagascar, we processed samples from the first epidemic wave of COVID-19, between March and September 2020. We fit generalized additive models to cycle threshold (Ct) value data from our RT-qPCR platform, demonstrating a peak in high viral load, low-Ct value infections temporally coincident with peak epidemic growth rates estimated in real time from publicly-reported incidence data and retrospectively from our own laboratory testing data across three administrative regions. We additionally demonstrate a statistically significant effect of duration of time since infection onset on Ct value, suggesting that Ct value can be used as a biomarker of the stage at which an individual is sampled in the course of an infection trajectory. As an extension, the population level Ct distribution at a given timepoint can be used to estimate population-level epidemiological dynamics. We illustrate this concept by adopting a recently-developed, nested modeling approach, embedding a within-host viral kinetics model within a population-level Susceptible-Exposed-Infectious-Recovered (SEIR) framework, to mechanistically estimate epidemic growth rates from cross-sectional Ct distributions across three regions in Madagascar. We find that Ct-derived epidemic growth estimates slightly precede those derived from incidence data across the first epidemic wave, suggesting delays in surveillance and case reporting. Our findings indicate that public reporting of Ct values could offer an important resource for epidemiological inference in low surveillance settings, enabling forecasts of impending incidence peaks in regions with limited case reporting.

The COVID-19 Epidemic in Madagascar: clinical description and laboratory results of the first wave, March-September 2020 (preprint)

Background: Following the first detection of SARS-CoV-2 in passengers arriving from Europe on 19 March 2020, Madagascar took several mitigation measures to limit the spread of the virus in the country. Methods: Nasopharyngeal and/or oropharyngeal swabs were collected from travellers to Madagascar, suspected SARS-CoV-2 cases, and contact of confirmed cases. Swabs were tested at the national reference laboratory using real-time RT-PVR. Data collected from patients were entered in an electronic database for subsequent statistical analysis. All distribution of laboratory confirmed cases were mapped and six genomes of viruses were fully sequenced. Results: Overall, 26,415 individuals were tested for SARS-CoV-2 between 18 March and 18 September 2020, of whom 21.0% (5,553/26,145) returned positive. Among laboratory-confirmed SARS-CoV-2 positive patients, the median age was 39 years (CI95%: 28-52), and 56.6% (3,311/5,553) were asymptomatic at the time of sampling. The probability of testing positive increased with age with the highest adjusted odds ratio of 2.2 [95% CI: 1.9-2.5] for individuals aged 49 years and more. Viral strains sequenced belong to clades 19A, 20A, and 20B in favour of several independent introduction of viruses. Conclusions. Our study describes the first wave of the COVID-19 in Madagascar. Despite early strategies in place Madagascar could not avoid the introduction and spread of the virus. More studies are needed to estimate the true burden of disease and make public health recommendations for a better preparation to another wave.