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Raquel Viana; Sikhulile Moyo; Daniel Gyamfi Amoako; Houriiyah Tegally; Cathrine Scheepers; Richard J Lessells; Jennifer Giandhari; Nicole Wolter; Josie Everatt; Andrew Rambaut; Christian Althaus; Eduan Wilkinson; Adriano Mendes; Amy Strydom; Michaela Davids; Simnikiwe Mayaphi; Simani Gaseitsiwe; Wonderful T Choga; Dorcas Maruapula; Boitumelo Zuze; Botshelo Radibe; Legodile Koopile; Roger Shapiro; Shahin Lockman; Mpaphi B. Mbulawa; Thongbotho Mphoyakgosi; Pamela Smith-Lawrence; Mosepele Mosepele; Mogomotsi Matshaba; Kereng Masupu; Mohammed Chand; Charity Joseph; Lesego Kuate-Lere; Onalethatha Lesetedi-Mafoko; Kgomotso Moruisi; Lesley Scott; Wendy Stevens; Constantinos Kurt Wibmer; Anele Mnguni; Arshad Ismail; Boitshoko Mahlangu; Darren P. Martin; Verity Hill; Rachel Colquhoun; Modisa S. Motswaledi; James Emmanuel San; Noxolo Ntuli; Gerald Motsatsi; Sureshnee Pillay; Thabo Mohale; Upasana Ramphal; Yeshnee Naidoo; Naume Tebeila; Marta Giovanetti; Koleka Mlisana; Carolyn Williamson; Nei-yuan Hsiao; Nokukhanya Msomi; Kamela Mahlakwane; Susan Engelbrecht; Tongai Maponga; Wolfgang Preiser; Zinhle Makatini; Oluwakemi Laguda-Akingba; Lavanya Singh; Ugochukwu J. Anyaneji; Monika Moir; Stephanie van Wyk; Derek Tshiabuila; Yajna Ramphal; Arisha Maharaj; Sergei Pond; Alexander G Lucaci; Steven Weaver; Maciej F Boni; Koen Deforche; Kathleen Subramoney; Diana Hardie; Gert Marais; Deelan Doolabh; Rageema Joseph; Nokuzola Mbhele; Luicer Olubayo; Arash Iranzadeh; Alexander E Zarebski; Joseph Tsui; Moritz UG Kraemer; Oliver G Pybus; Dominique Goedhals; Phillip Armand Bester; Martin M Nyaga; Peter N Mwangi; Allison Glass; Florette Treurnicht; Marietjie Venter; Jinal N. Bhiman; Anne von Gottberg; Tulio de Oliveira.
medrxiv; 2021.
Preprint in English | medRxiv | ID: ppzbmed-10.1101.2021.12.19.21268028


The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemic in southern Africa has been characterised by three distinct waves. The first was associated with a mix of SARS-CoV-2 lineages, whilst the second and third waves were driven by the Beta and Delta variants respectively. In November 2021, genomic surveillance teams in South Africa and Botswana detected a new SARS-CoV-2 variant associated with a rapid resurgence of infections in Gauteng Province, South Africa. Within three days of the first genome being uploaded, it was designated a variant of concern (Omicron) by the World Health Organization and, within three weeks, had been identified in 87 countries. The Omicron variant is exceptional for carrying over 30 mutations in the spike glycoprotein, predicted to influence antibody neutralization and spike function4. Here, we describe the genomic profile and early transmission dynamics of Omicron, highlighting the rapid spread in regions with high levels of population immunity.

Severe Acute Respiratory Syndrome
medrxiv; 2021.
Preprint in English | medRxiv | ID: ppzbmed-10.1101.2021.12.02.21267189


In infectious disease epidemiology, the instantaneous reproduction number R ( t ) is a timevarying metric defined as the average number of secondary infections generated by individuals who are infectious at time t . It is therefore a crucial epidemiological parameter that assists public health decision makers in the management of an epidemic. We present a new Bayesian tool for robust estimation of the time-varying reproduction number. The proposed methodology smooths the epidemic curve and allows to obtain (approximate) point estimates and credible envelopes of R ( t ) by employing the renewal equation, using Bayesian P-splines coupled with Laplace approximations of the conditional posterior of the spline vector. Two alternative approaches for inference are presented: (1) an approach based on a maximum a posteriori argument for the model hyperparameters, delivering estimates of R ( t ) in only a few seconds; and (2) an approach based on a MCMC scheme with underlying Langevin dynamics for efficient sampling of the posterior target distribution. Case counts per unit of time are assumed to follow a Negative Binomial distribution to account for potential excess variability in the data that would not be captured by a classic Poisson model. Furthermore, after smoothing the epidemic curve, a “plug-in” estimate of the reproduction number can be obtained from the renewal equation yielding a closed form expression of R ( t ) as a function of the spline parameters. The approach is extremely fast and free of arbitrary smoothing assumptions. EpiLPS is applied on data of SARS-CoV-1 in Hong-Kong (2003), influenza A H1N1 (2009) in the USA and current SARS-CoV-2 pandemic (2020-2021) for Belgium, Portugal, Denmark and France. Author summary The instantaneous reproduction number R ( t ) is a key metric that provides important insights into an epidemic outbreak. We present a flexible Bayesian approach called EpiLPS (Epidemiological modeling with Laplacian-P-splines) for smooth estimation of the epidemic curve and R ( t ). Computational speed and absence of arbitrary assumptions on smoothing makes EpiLPS an interesting tool for near real-time estimation of the reproduction number. An R software package is available ( ).

Communicable Diseases
medrxiv; 2021.
Preprint in English | medRxiv | ID: ppzbmed-10.1101.2021.09.23.21264018


The Beta variant of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged in South Africa in late 2020 and rapidly became the dominant variant, causing over 95% of infections in the country during and after the second epidemic wave. Here we show rapid replacement of the Beta variant by the Delta variant, a highly transmissible variant of concern (VOC) that emerged in India and subsequently spread around the world. The Delta variant was imported to South Africa primarily from India, spread rapidly in large monophyletic clusters to all provinces, and became dominant within three months of introduction. This was associated with a resurgence in community transmission, leading to a third wave which was associated with a high number of deaths. We estimated a growth advantage for the Delta variant in South Africa of 0.089 (95% confidence interval [CI] 0.084-0.093) per day which corresponds to a transmission advantage of 46% (95% CI 44-48) compared to the Beta variant. These data provide additional support for the increased transmissibility of the Delta variant relative to other VOC and highlight how dynamic shifts in the distribution of variants contribute to the ongoing public health threat.

Coronavirus Infections
authorea preprints; 2021.


Background: . This paper presents, for the first time, the Epidemic Volatility Index (EVI), a conceptually simple, early warning tool for emerging epidemic waves. Methods: . EVI is based on the volatility of the newly reported cases per unit of time, ideally per day, and issues an early warning when the rate of the volatility change exceeds a threshold. Results: . Results from the COVID-19 epidemic in Italy and New York are presented here, while daily updated predictions for all world countries and each of the United States are available online. Interpretation . EVI’s application to data from the current COVID-19 pandemic revealed a consistent and stable performance in terms of detecting oncoming waves. The application of EVI to other epidemics and syndromic surveillance tasks in combination with existing early warning systems will enhance our ability to act fast and optimize containment of outbreaks.

Encephalitis, Arbovirus , Syndrome , COVID-19
medrxiv; 2021.
Preprint in English | medRxiv | ID: ppzbmed-10.1101.2021.03.05.21252520


In December 2020, the United Kingdom (UK) reported a SARS-CoV-2 Variant of Concern (VoC) which is now coined B.1.1.7. Based on the UK data and later additional data from other countries, a transmission advantage of around 40-80% was estimated for this variant. In Switzerland, since spring 2020, we perform whole genome sequencing of SARS-CoV-2 samples obtained from a large diagnostic lab (Viollier AG) on a weekly basis for genomic surveillance. The lab processes SARS-CoV-2 samples from across Switzerland. Based on a total of 7631 sequences obtained from samples collected between 14.12.2020 and 11.02.2021 at Viollier AG, we determine the relative proportion of the B.1.1.7 variant on a daily basis. In addition, we use data from a second lab (Dr Risch) screening all their samples for the B.1.1.7 variant. These two datasets represent 11.5 % of all SARS-CoV-2 confirmed cases across Switzerland during the considered time period. They allow us to quantify the transmission advantage of the B.1.1.7 variant on a national and a regional scale. Taking all our data and estimates together, we propose a transmission advantage of 49-65% of B.1.1.7 compared to the other circulating variants. Further, we estimate the effective reproductive number through time for B.1.1.7 and the other variants, again pointing to a higher transmission rate of B.1.1.7. In particular, for the time period 01.01.2021-17.01.2021, we estimate an average reproductive number for B.1.1.7 of 1.28 [1.07-1.49] while the estimate for the other variants is 0.83 [0.63-1.03], based on the total number of confirmed cases and our Viollier sequencing data. Switzerland tightened measures on 18.01.2021. A comparison of the empirically confirmed case numbers up to 20.02.2021 to a very simple model using the estimates of the reproductive number from the first half of January provides indication that the rate of spread of all variants slowed down recently. In summary, the dynamics of increase in frequency of B.1.1.7 is as expected based on the observations in the UK. Our plots are available online and constantly updated with new data to closely monitor the changes in absolute numbers.