Reverse transcription-polymerase chain reaction (RT-PCR) assays are used to test patients and key workers for infection with the causative SARS-CoV-2 virus. RT-PCR tests are highly specific and the probability of false positives is low, but false negatives can occur if the sample contains insufficient quantities of the virus to be successfully amplified and detected. The amount of virus in a swab is likely to vary between patients, sample location (nasal, throat or sputum) and through time as infection progresses. Here, we analyse publicly available data from patients who received multiple RT-PCR tests and were identified as SARS-CoV-2 positive at least once. We identify that the probability of a positive test decreases with time after symptom onset, with throat samples less likely to yield a positive result relative to nasal samples. Empirically derived distributions of the time between symptom onset and hospitalisation allowed us to comment on the likely false negative rates in cohorts of patients who present for testing at different clinical stages. We further estimate the expected numbers of false negative tests in a group of tested individuals and show how this is affected by the timing of the tests. Finally, we assessed the robustness of these estimates of false negative rates to the probability of false positive tests. This work has implications both for the identification of infected patients and for the discharge of convalescing patients who are potentially still infectious.

Background Reverse-transcription PCR (RT-PCR) assays are used to test for infection with the SARS-CoV-2 virus. RT-PCR tests are highly specific and the probability of false positives is low, but false negatives are possible depending on swab type and time since symptom onset. Aim To determine how the probability of obtaining a false-negative test in infected patients is affected by time since symptom onset and swab type. Methods We used generalised additive mixed models to analyse publicly available data from patients who received multiple RT-PCR tests and were identified as SARS-CoV-2 positive at least once. Results The probability of a positive test decreased with time since symptom onset, with oropharyngeal (OP) samples less likely to yield a positive result than nasopharyngeal (NP) samples. The probability of incorrectly identifying an uninfected individual due to a false-negative test was considerably reduced if negative tests were repeated 24 hours later. For a small false-positive test probability (<0.5%), the true number of infected individuals was larger than the number of positive tests. For a higher false-positive test probability, the true number of infected individuals was smaller than the number of positive tests. Conclusion NP samples are more sensitive than OP samples. The later an infected individual is tested after symptom onset, the less likely they are to test positive. This has implications for identifying infected patients, contact tracing and discharging convalescing patients who are potentially still infectious.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused millions of deaths and substantial morbidity worldwide. Intense scientific effort to understand the biology of SARS-CoV-2 has resulted in daunting numbers of genomic sequences. We witnessed evolutionary events that could mostly be inferred indirectly before, such as the emergence of variants with distinct phenotypes, for example transmissibility, severity and immune evasion. This Review explores the mechanisms that generate genetic variation in SARS-CoV-2, underlying the within-host and population-level processes that underpin these events. We examine the selective forces that likely drove the evolution of higher transmissibility and, in some cases, higher severity during the first year of the pandemic and the role of antigenic evolution during the second and third years, together with the implications of immune escape and reinfections, and the increasing evidence for and potential relevance of recombination. In order to understand how major lineages, such as variants of concern (VOCs), are generated, we contrast the evidence for the chronic infection model underlying the emergence of VOCs with the possibility of an animal reservoir playing a role in SARS-CoV-2 evolution, and conclude that the Nature Reviews Microbiology Review articlevirus (HIV; ~10 -4 × 10 -6 mutations per nucleotide per replication cycle), which, unlike coronaviruses, lack a 3′ exonuclease proofreading mechanism in their replication machinery 8,[10][11][12] . Insertions and deletions result from replication errors and can also generate diversity, such as the deletion at position 69-70 of the spike gene responsible for the S-gene dropout that was instrumental in detecting the SARS-CoV-2 Alpha variant, and has been reported to be associated with increased infectivity 13 .In addition to RNA replication errors, host-mediated genome editing by innate cell defence mechanisms may introduce substantial numbers of directed mutations into the SARS-CoV-2 genome, and thus may influence its evolutionary rate. Cellular mutational drivers include members of the apolipoprotein B mRNA editing enzyme, catalytic polypeptide-like (APOBEC) family [14][15][16] , including APOBEC1, APOBEC3A and APOBEC3G that demonstrate editing activity for numerous DNA and RNA virus and retroviral genomes 17,18 , including SARS- . APOBEC activity has been inferred bioinformatically through observations of a substantial excess of C → U transitions over all other mutations 18,20,21 . SARS-CoV-2 genomes may also be edited by different cellular antiviral proteins (adenosine deaminases that act on RNA 1 (ADAR1)), leading to A → G mutations (and U → C mutations in opposite genomic strands) 21,22 .The potential editing-associated C → U mutations in the SARS-CoV-2 genome sequences introduce complexities to SARS-CoV-2 evolutionary genomic analysis. C → U mutations account, in part, for the strikingly high ratio of non-synonymous changes in SARS-CoV-2 genomes compared with those at synonymous sites; the mean dN/dS ratio ...

Estimating viral timescales is fundamental in understanding the evolutionary biology of viruses. Molecular clocks are widely used to reveal the recent evolutionary histories of viruses but may severely underestimate their longer-term origins because of the inverse correlation between inferred rates of evolution and the timescale of their measurement. Here, we provide a predictive mechanistic model that readily explains the rate decay phenomenon over a wide range of timescales and recapitulates the ubiquitous power-law rate decay with a slope of À0.65. We show that standard substitution models fail to correctly estimate divergence times once the most rapidly evolving sites saturate, typically after hundreds of years in RNA viruses and thousands of years in DNA viruses. Our model successfully recreates the observed pattern of decay and explains the evolutionary processes behind the time-dependent rate phenomenon. We then apply our model to re-estimate the date of diversification of genotypes of hepatitis C virus to 423,000 (95% highest posterior density [HPD]: 394,000-454,000) years before present, a time preceding the dispersal of modern humans out of Africa, and show that the most recent common ancestor of sarbecoviruses dates back to 21,000 (95% HPD: 19,000-22,000) years ago, nearly thirty times older than previous estimates. This creates a new perspective for our understanding of the origins of these viruses and also suggests that a substantial revision of evolutionary timescales of other viruses can be similarly achieved.

High throughput sequencing enables rapid genome sequencing during infectious disease outbreaks and provides an opportunity to quantify the evolutionary dynamics of pathogens in near real-time. One difficulty of undertaking evolutionary analyses over short timescales is the dependency of the inferred evolutionary parameters on the timespan of observation. Crucially, there is an increasing number of molecular clock analyses using external evolutionary rate priors to infer evolutionary parameters. However, it is not clear which rate prior is appropriate for a given time window of observation due to the time-dependent nature of evolutionary rate estimates. Here, we characterise the molecular evolutionary dynamics of SARS-CoV-2 and 2009 pandemic H1N1 (pH1N1) influenza during the first 12 months of their respective pandemics. We use Bayesian phylogenetic methods to estimate the dates of emergence, evolutionary rates, and growth rates of SARS-CoV-2 and pH1N1 over time and investigate how varying sampling window and dataset sizes affects the accuracy of parameter estimation. We further use a generalised McDonald-Kreitman test to estimate the number of segregating non-neutral sites over time. We find that the inferred evolutionary parameters for both pandemics are time-dependent, and that the inferred rates of SARS-CoV-2 and pH1N1 decline by ∼50% and ∼100%, respectively, over the course of one year. After at least 4 months since the start of sequence sampling, inferred growth rates and emergence dates remain relatively stable and can be inferred reliably using a logistic growth coalescent model. We show that the time-dependency of the mean substitution rate is due to elevated substitution rates at terminal branches which are 2-4 times higher than those of internal branches for both viruses. The elevated rate at terminal branches is strongly correlated with an increasing number of segregating non-neutral sites, demonstrating the role of purifying selection in generating the time-dependency of evolutionary parameters during pandemics.

At the end of 2020, the Network for Genomic Surveillance in South Africa (NGS-SA) detected a SARS-CoV-2 variant of concern (VOC) in South Africa (501Y.V2 or PANGO lineage B.1.351)1. 501Y.V2 is associated with increased transmissibility and resistance to neutralizing antibodies elicited by natural infection and vaccination2,3. 501Y.V2 has since spread to over 50 countries around the world and has contributed to a significant resurgence of the epidemic in southern Africa. In order to rapidly characterize the spread of this and other emerging VOCs and variants of interest (VOIs), NGS-SA partnered with the Africa Centres for Disease Control and Prevention and the African Society of Laboratory Medicine through the Africa Pathogen Genomics Initiative to strengthen SARS-CoV-2 genomic surveillance across the region. Here, we report the first genomic surveillance results from Angola, which has had 21 500 reported cases and around 500 deaths from COVID-19 up to March 2021 (Supplemental Fig S1). On 15 January 2021, in response to the international spread of VOCs, the government instituted compulsory rapid antigen testing of all passengers arriving at the main international airport, in addition to the existing requirement to present a negative PCR test taken within 72 hours of travel. All individuals with a positive antigen test are isolated in a government facility for a minimum of 14 days and require two negative RT-PCR tests at least 48 hours apart for de-isolation, whilst all travelers with a negative test on arrival proceed to mandatory self-quarantine for 10 days followed by a repeat test. In March 2021, we received 118 nasopharyngeal swab samples collected between June 2020 and February 2021, a number of which were from incoming air travelers (Supplemental Fig S1). From these, we produced 73 high quality genomes (>80% coverage), 14 of which were known VOCs/VOIs (seven 501Y.V2/B.1.351, six B.1.1.7, one B.1.525), 44 of which were C.16 (a common lineage circulating in Portugal), and twelve of which were other lineages (Supplemental Fig S2). In addition, we detected a new VOI in three incoming travelers from Tanzania who were tested together at the airport in mid-February. The three genomes from these passengers were almost identical and presented highly divergent sequences within the A lineage (Figure 1A & 1B). The GISAID database contains nine other sequences reported to be sampled from cases involving travel from Tanzania, two of which are basal to the three sampled in Angola (Figure 1A, Supplemental Table S1). This new VOI, temporarily designated A.VOI.V2, has 31 amino acid substitutions (11 in spike) and three deletions (all in spike) (Figure 1C & 1D). The spike mutations include three substitutions in the receptor-binding domain (R346K, T478R and E484K); five substitutions and three deletions in the N-terminal domain, some of which are within the antigenic supersite (Y144Δ, R246M, SYL247-249Δ and W258L)4; and two substitutions adjacent to the S1/S2 cleavage site (H655Y and P681H). Several of these mutations are present in other VOCs/VOIs and are evolving under positive selection.

There has been no province-level data on the number of coronavirus disease 2019 (COVID-19)related deaths in Iran since the start of the pandemic. This study was performed to estimate the number of COVID-19 deaths and population-level exposure per province using seasonal all-cause mortality data. Methods: Time-series data were collected from the National Organization for Civil Registration on the seasonal all-cause mortality from spring 2015 to summer 2020 (from March 21, 2015 to September 21, 2020), in accordance with the Solar Hijri (SH) calendar, to estimate the expected number of seasonal deaths for each province using a piecewise linear regression model. A population-weighted infection fatality ratio was then applied to estimate the level of exposure per province during this period. Results: From the start of winter to the end of summer (from December 22, 2019 to September 21, 2020), there were a total of 58 900 (95% confidence interval 46 900-69 500) excess deaths across all 31 provinces, with 27% (95% confidence interval 20-34%) estimated nationwide exposure to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In particular, Qom and Golestan were among the hardest-hit provinces, with nearly 57% exposure, while another 27 provinces showed significant levels of excess mortality in at least one season with >20% population-level exposure to the virus. Unexpectedly high levels of excess mortality were also detected during fall 2019 (from September 23 to December 21, 2019) across 18 provinces, unrelated and prior to the start of the COVID-19 pandemic. Conclusions: This study quantified the pattern of spread of COVID-19 across the country and identified areas with the largest epidemic growth requiring the most immediate interventions.

The success of public health interventions is highly dependent on the compliance of the general population. State authorities often implement policies without consulting representatives of faith-based communities, thereby overlooking potential implications of public health measures for these parts of society. Although ubiquitous, these challenges are more readily observable in highly religious states. Romania serves as an illustrative example for this, as recent data identify it as the most religious country in Europe. In this paper, we discuss the contributions of the Romanian Orthodox Church (ROC), the major religious institution in the country, to the national COVID-19 mitigation efforts. We present not only the positive outcomes of productive consultations between public health authorities and religious institutions but also the detrimental impact of unidirectional communication. Our work highlights that an efficient dialogue with faith-based communities can greatly enhance the results of public health interventions. As the outlined principles apply to a variety of contexts, the lessons learned from this case study can be generalized into a set of policy recommendations for the betterment of future public health initiatives worldwide.