The recency and geographical origins of the bat viruses ancestral to SARS-CoV and SARS-CoV-2 (preprint)

The emergence of SARS-CoV in 2002 and SARS-CoV-2 in 2019 has led to increased sampling of related sarbecoviruses circulating primarily in horseshoe bats. These viruses undergo frequent recombination and exhibit spatial structuring across Asia. Employing recombination-aware phylogenetic inference on bat sarbecoviruses, we find that the closest-inferred bat virus ancestors of SARS-CoV and SARS-CoV-2 existed just ~1-3 years prior to their emergence in humans. Phylogeographic analyses examining the movement of related sarbecoviruses demonstrate that they traveled at similar rates to their horseshoe bat hosts and have been circulating for thousands of years in Asia. The closest-inferred bat virus ancestor of SARS-CoV likely circulated in western China, and that of SARS-CoV-2 likely circulated in a region comprising southwest China and northern Laos, both a substantial distance from where they emerged. This distance and recency indicate that the direct ancestors of SARS-CoV and SARS-CoV-2 could not have reached their respective sites of emergence via the bat reservoir alone. Our recombination-aware dating and phylogeographic analyses reveal a more accurate inference of evolutionary history than performing only whole-genome or single gene analyses. These results can guide future sampling efforts and demonstrate that viral genomic fragments extremely closely related to SARS-CoV and SARS-CoV-2 were circulating in horseshoe bats, confirming their importance as the reservoir species for SARS viruses.

A quantitative evaluation of the impact of vaccine roll-out rate and coverage on reducing deaths from COVID-19: a counterfactual study on the impact of the delayed vaccination programme in Iran (preprint)

Vaccination has been a crucial factor in the fight against COVID-19 because of its effectiveness in suppressing virus circulation, lowering the risk of severe disease, and ultimately saving lives. Many countries with an early and rapid distribution of COVID-19 vaccines performed much better in reducing their total number of deaths than those with lower coverage and slower roll-out pace. However, we still do not know how many more deaths could have been averted if countries with slower vaccine roll-outs followed the same rate as countries with earlier and faster distribution of vaccines. Here, we investigated counterfactual scenarios for the number of avertable COVID-19 deaths in a given country based on other countries vaccine roll-out rates. As a case study, we compared Iran to eight model countries with similar income brackets and dominant COVID-19 vaccine types. Our analysis revealed that faster roll-outs were associated with higher numbers of averted deaths. While Irans percentage of fully vaccinated individuals would have been similar to Bangladesh, Nepal, Sri Lanka, and Turkey under counterfactual roll-out rates, adopting Turkeys rates could have averted up to 50,000 more deaths, whereas following Bangladeshs rates could have led to up to 52,800 additional losses of lives in Iran. Notably, a counterfactual scenario based on Argentinas early but slow roll-out rate resulted in a smaller number of averted deaths in Iran, up to 12,600 more individuals. Following Montenegros or Bolivias model of faster per capita roll-out rates for Iran could have resulted in more averted deaths in older age groups, particularly during the Alpha and Delta waves, despite their lower overall coverage. Also, following Bahrains model as an upper bound benchmark, Iran could have averted 75,300 deaths throughout the pandemic, primarily in the >50 age groups. This study provides insights into future decisions on the management of infectious disease epidemics through vaccination strategies by comparing the relative performance of different countries in terms of their timing, pace, and coverage of vaccination in preventing COVID-19 deaths.

High number of SARS-CoV-2 persistent infections uncovered through genetic analysis of samples from a large community-based surveillance study (preprint)

Persistent severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections may act as viral reservoirs that could seed future outbreaks 1-5, give rise to highly divergent lineages 6-8, and contribute to cases with post-acute Coronavirus disease 2019 (COVID-19) sequelae (Long Covid) 9,10. However, the population prevalence of persistent infections, their viral load kinetics, and evolutionary dynamics over the course of infections remain largely unknown. We identified 381 infections lasting at least 30 days, of which 54 lasted at least 60 days. These persistently infected individuals had more than 50% higher odds of self-reporting Long Covid compared to the infected controls, and we estimate that 0.09-0.5% of SARS-CoV-2 infections can become persistent and last for at least 60 days. In nearly 70% of the persistent infections we identified, there were long periods during which there were no consensus changes in virus sequences, consistent with prolonged presence of non-replicating virus. Our findings also suggest reinfections with the same major lineage are rare and that many persistent infections are characterised by relapsing viral load dynamics. Furthermore, we found a strong signal for positive selection during persistent infections, with multiple amino acid substitutions in the Spike and ORF1ab genes emerging independently in different individuals, including mutations that are lineage-defining for SARS-CoV-2 variants, at target sites for several monoclonal antibodies, and commonly found in immunocompromised patients 11-14. This work has significant implications for understanding and characterising SARS-CoV-2 infection, epidemiology, and evolution.

Investigating the evolutionary origins of the first three SARS-CoV-2 variants of concern (preprint)

The emergence of Variants of Concern (VOCs) of SARS-CoV-2 with increased transmissibility, immune evasion properties, and virulence poses a great challenge to public health. Despite unprecedented efforts to increase genomic surveillance, fundamental facts about the evolutionary origins of VOCs remain largely unknown. One major uncertainty is whether the VOCs evolved during transmission chains of many acute infections or during long-term infections within single individuals. We test the consistency of these two possible paths with the observed dynamics, focusing on the clustered emergence of the first three VOCs, Alpha, Beta, and Gamma, in late 2020, following a period of relative evolutionary stasis. We consider a range of possible fitness landscapes, in which the VOC phenotypes could be the result of single mutations, multiple mutations that each contribute additively to increasing viral fitness, or epistatic interactions among multiple mutations that do not individually increase viral fitness--a "fitness plateau". Our results suggest that the timing and dynamics of the VOC emergence, together with the observed number of mutations in VOC lineages, are in best agreement with the VOC phenotype requiring multiple mutations and VOCs having evolved within single individuals with long-term infections.

A framework for reconstructing SARS-CoV-2 transmission dynamics using excess mortality data (preprint)

Detailed reconstruction of the SARS-CoV-2 transmission dynamics and assessment of its burden in several parts of the world has still remained largely unknown due to the scarcity of epidemiological analyses and limited testing capacities of different countries to identify cases and deaths attributable to COVID-19 [1-4]. Understanding the true burden of the Iranian COVID-19 epidemic is subject to similar challenges with limited clinical and epidemiological studies at the subnational level [5-9]. To address this, we develop a new quantitative framework that enables us to fully reconstruct the transmission dynamics across the country and assess the level of under-reporting in infections and deaths using province-level, age-stratified all-cause mortality data. We show that excess mortality aligns with seroprevalence estimates in each province and subsequently estimate that as of 2021-10-22, only 48% (95% confidence interval: 43-55%) of COVID-19 deaths in Iran have been reported. We find that in the most affected provinces such as East Azerbaijan, Qazvin, and Qom approximately 0.4% of the population have died of COVID-19 so far. We also find significant heterogeneity in the estimated attack rates across the country with 11 provinces reaching close to or higher than 100% attack rates. Despite a relatively young age structure in Iran, our analysis reveals that the infection fatality rate in most provinces is comparable to high-income countries with a larger percentage of older adults, suggesting that limited access to medical services, coupled with undercounting of COVID-19-related deaths, can have a significant impact on accurate estimation of COVID-19 fatalities. Our estimation of high attack rates in provinces with largely unmitigated epidemics whereby, on average, between 10% to 25% individuals have been infected with COVID-19 at least twice over the course of 20 months also suggests that, despite several waves of infection, herd immunity through natural infection has not been achieved in the population.

Purifying selection determines the short-term time dependency of evolutionary rates in SARS-CoV-2 and pH1N1 influenza (preprint)

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. 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.

Excess deaths associated with the Iranian COVID-19 epidemic: a province-level analysis (preprint)

BackgroundThe number of publicly reported deaths from COVID-19 may underestimate the true death toll from the epidemic as they rely on provisional data that are often incomplete or omit undocumented deaths from COVID-19. In addition, these reports may be subject to significant under-reporting due to a limited testing capacity of a country to identify suspect cases. This study estimated the number of seasonal excess deaths attributable to the COVID-19 epidemic in 31 provinces of Iran. MethodsWe gathered the nationwide and provincial time series of the seasonal all-cause mortality data from spring 2015 to summer 2020 (21 March 2015 to 21 September 2020), in accordance with the Solar Hijri (SH) calendar, from the National Organization for Civil Registration (NOCR). We estimated the expected number of seasonal deaths for each province using a piecewise linear regression model which we established based on the mortality figures for the previous years and considered any significant deviations from the expectation during winter, spring, and summer of 2020 to be directly associated with COVID-19. ResultsOur analysis shows that from the start of winter to the end of summer (from 22 December 2019 to 21 September 2020), there were a total of 58.9K (95%CI: 46.9K - 69.5K) excess deaths across all 31 provinces with 27% (95%CI: 20% - 34%) estimated nationwide exposure to SARS-CoV-2. In particular, 2 provinces in the central and northern Iran, namely Qom and Golestan, had the highest level of exposure with 57% (95%CI: 44% - 69%) and 56% (95%CI: 44% - 69%), respectively, while another 27 provinces had significant levels of excess mortality in at least one season with >20% population-level exposure to the virus. We also detected unexpectedly high levels of excess mortality during fall 2019 (from 23 September to 21 December 2019) across 18 provinces. Our findings suggest that this spike cannot be a result of an early cryptic transmission of COVID-19 across the country and is also inconsistent with the molecular phylogenetics estimates for the start of the pandemic and its arrival to Iran. However, in the absence of appropriate surveillance data for detecting severe acute respiratory infections we were unable to make a determination as to what caused the spike in fall 2019. Conflict of InterestNone.

Ongoing outbreak of COVID-19 in Iran: challenges and signs of concern with under-reporting of prevalence and deaths (preprint)

Many countries with an early outbreak of SARS-CoV-2 struggled to gauge the size and start date of the epidemic mainly due to limited testing capacities and a large proportion of undetected asymptomatic and mild infections. Iran was among the first countries with a major outbreak outside China. Using all genomic sequences collected from patients with a travel link to Iran, we estimate that the epidemic started on 21/01/2020 (95% HPD: 05/12/2019 – 14/02/2020) with a doubling time of 3 days (95% HPD: 1.68 – 16.27). We also show, using air travel data from confirmed exported cases, that from late February to early March the number of active cases across the country were more than a hundred times higher than the reported cases at the time. A detailed province-level analysis of all-cause mortality shows 20,718 (CI 95%: 18,859 – 22,576) excess deaths during winter and spring 2020 compared to previous years, almost twice the number of reported COVID-19-related deaths at the time. Correcting for under-reporting of prevalence and deaths, we use an SEIR model to reconstruct the outbreak dynamics in Iran. Our model forecasted the second epidemic peak and suggests that by 14/07/2020 a total of 9M (CI 95%: 118K – 44M) have recovered from the disease across the country. These findings have profound implications for assessing the stage of the epidemic in Iran and shed light on the dynamics of SARS-CoV-2 transmissions in Iran and central Asia despite significant levels of under-reporting. 

Ongoing outbreak of COVID-19 in Iran: challenges and signs of concern (preprint)

Since the first outbreak in China, the Coronavirus Disease 2019 (COVID-19) has rapidly spread around the world. Iran was one of the first countries outside of China to report infections with COVID-19. With nearly 100 exported cases to various other countries, it has since been the epicentre of the outbreak in the Middle east. By examining the age-stratified COVID-19 case fatality rates across the country and 14 university hospitals in Tehran, we find that, in younger age groups, the reported cases on 13/03/2020 only capture less than 10% of symptomatic cases in the population. This indicates significant levels of under-reporting in Iran. Using the 18 full-genome sequences from cases with a travel history or link to Iran, as well as the one full genome sequence obtained from within the country, we estimate the time to the most recent common ancestor of sequences which suggests the likely start of the outbreak on 21/01/2020 (95% HPD: 05/12/2019 - 14/02/2020) with an approximate doubling time of 3.07 (95% HPD: 1.68 - 16.27). Also, based on known exported cases to Oman, Kuwait, Lebanon, and China, we estimate the outbreak size on 25 February and 6 March to be around 13,700 (95% CI: 7,600 - 33,300) and 60,500 (43,200 - 209,200), respectively. Knowing the size of the outbreak at two time points and the typical doubling times associated with the COVID-19 epidemics in countries across Europe and North America, we can independently verify that the likely start of epidemic in Iran is around 15/01/2020 (27/12/2019 - 24/01/2020). Our assessment of the fate of the epidemic based on current levels of non-pharmaceutical interventions implemented by the government suggests upward of 10 million cases (IQR: 6.7M - 18M) and 100,000 ICU beds required (IQR: 77K - 140K) during the peak of the epidemic with more than 100,000 cumulative deaths (IQR: 180K - 240K). We also predict a peak in demand for ICU beds on 21/04/2020 (IQR: 06/04/2020 - 23/05/2020). The large span of the peak of the ICU demand is a result of two separate peaks, with the first occurring at around 15/4/2020 and the second in approximately a months time. The latter is also expected to last longer and is based on the relatively relaxed social distancing measures in place. The exact magnitude and timing of the peaks strictly depends on levels of interventions and can change significantly upon new information or change of policy. We caution that a lack of, or relaxed, stringent intervention measures, during a period of highly under-reported spread, would likely lead to the healthcare system becoming overwhelmed in the next few months.

Estimating false-negative detection rate of SARS-CoV-2 by RT-PCR (preprint)

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.