key: cord-0940036-1upu1noh authors: Hjorleifsson, Kristjan E.; Rognvaldsson, Solvi; Jonsson, Hakon; Agustsdottir, Arna B.; Andresdottir, Margret; Birgisdottir, Kolbrun; Eiriksson, Ogmundur; Eythorsson, Elias S.; Fridriksdottir, Run; Georgsson, Gudmundur; Gudmundsson, Kjartan R.; Gylfason, Arnaldur; Haraldsdottir, Gudbjorg; Jensson, Brynjar O.; Jonasdottir, Adalbjorg; Jonasdottir, Aslaug; Josefsdottir, Kamilla S.; Kristinsdottir, Nina; Kristjansdottir, Borghildur; Kristjansson, Thordur; Magnusdottir, Droplaug N.; Palsson, Runolfur; le Roux, Louise; Sigurbergsdottir, Gudrun M.; Sigurdsson, Asgeir; Sigurdsson, Martin I.; Sveinbjornsson, Gardar; Thorarensen, Emil Aron; Thorbjornsson, Bjarni; Thordardottir, Marianna; Helgason, Agnar; Holm, Hilma; Jonsdottir, Ingileif; Jonsson, Frosti; Magnusson, Olafur T.; Masson, Gisli; Norddahl, Gudmundur L.; Saemundsdottir, Jona; Sulem, Patrick; Thorsteinsdottir, Unnur; Gudbjartsson, Daniel F.; Melsted, Pall; Stefansson, Kari title: Reconstruction of a large-scale outbreak of SARS-CoV-2 infection in Iceland informs vaccination strategies date: 2022-02-17 journal: Clin Microbiol Infect DOI: 10.1016/j.cmi.2022.02.012 sha: 20b03793e4b408eabbc3a6761ebd62dd8247977f doc_id: 940036 cord_uid: 1upu1noh OBJECTIVES: The spread of SARS-CoV-2 is dependent on several factors, both biological and behavioral. The effectiveness of non-pharmaceutical interventions can largely be attributed to changes in human behavior, but quantifying this effect remains challenging. Reconstructing the transmission tree of the third wave of SARS-CoV-2 infections in Iceland using contact tracing and viral sequence data from 2522 cases enables us to directly compare the infectiousness of distinct groups of persons. METHODS: The transmission tree enables us to model the effect that a given population prevalence of vaccination would have had on the third wave had one of three different vaccination strategies been implemented before that time. This allows us to compare the effectiveness of the strategies in terms of minimizing the number of cases, deaths, critical cases and severe cases. RESULTS: We find that people diagnosed outside of quarantine [Formula: see text] were 89% more infectious than those diagnosed while in quarantine [Formula: see text] and that infectiousness decreased as a function of time spent in quarantine before diagnosis, with people diagnosed outside of quarantine being 144% more infectious than those diagnosed after three or more days in quarantine [Formula: see text]. People of working age, 16-66 years old [Formula: see text] , were 46% more infectious than those outside that age range [Formula: see text]. CONCLUSIONS: We find that vaccinating the population in order of ascending age or uniformly at random would have prevented more infections per vaccination than vaccinating in order of descending age, without significantly affecting the expected number of deaths, critical cases, or severe cases. Over 160 million cases of SARS-CoV-2 have been diagnosed globally, resulting in over 3.3 million deaths [1] . The first case of SARS-CoV-2 infection in Iceland was confirmed on February 28, 2020, and as of May 14, 2021, a total of 6526 people have been diagnosed in the country. The third wave of SARS-CoV-2 in Iceland consisted of 2783 confirmed cases and was characterized by a single genetic clade, colloquially referred to as the blue clade (Supplementary table 2) , traced back to a person who entered the country in August 2020. It was contained by January 2021 through non-pharmaceutical interventions [2] (Supplementary methods). Every diagnosed case was contact traced and recent contacts placed in quarantine. Every PCR positive sample was sequenced (Supplementary methods) within 36-48 hours of the sample collection and the results fed back to the contact tracing team [3] . Understanding of the differences between distinct groups of persons in epidemic outbreaks is a key to employing targeted containment measures. By reconstructing the chain of events in an entire outbreak, we can observe these differences directly. Furthermore, case-by-case replay of outbreaks enable us to model the effect vaccinations would have had on them, had they been administered beforehand. In this study, we construct a model of the third wave of SARS-CoV-2 in Iceland. This constitutes the largest study to date reconstructing a single outbreak with complete contact tracing and sequence data. Any outbreak of a viral disease has a single progenitor, who infects a number of persons, each of whom infects other persons and so forth until the disease is contained, or everyone has been infected. These transmissions from person to person form a tree of transmissions with the progenitor as its root. The third wave in Iceland consisted of a single subtree of the global transmission tree of the SARS-CoV-2 pandemic. Despite the extensive data collected on each case, the true transmission tree of the third wave cannot be determined from them with certainty ( Figure 1 .B). We extended the Outbreaker2 model [4, 5] to infer the transmission tree using data from contact tracing, viral genome sequences, household membership, and times of onset of symptoms, quarantine, and diagnosis ( Figure 1 .C, Supplementary methods). The effective reproduction number of a disease outbreak denotes how many persons each diagnosed person infects on average. The at a given time is denoted by , the time-varying reproduction number. A variety of methods have been proposed to estimate [6] [7] [8] [9] [10] , all of which attribute the number of cases at time to cases diagnosed in the preceding days weighted with the assumed generation time distribution. Reconstruction of transmission trees has been explored previously [4, [11] [12] [13] , most recently with the Outbreaker2 model which infers the transmission tree of an outbreak using contact data, sequence data and times of symptom onset. In a transmission tree model, is calculated by averaging the out-degree, i.e. the number of persons they infected, of everyone in the tree at time (Supplementary methods). Since the data are available on an individual level, we can estimate the reproduction number for distinct groups of people, and compare their relative infectiousness. In this study, we expand upon the size of transmission trees reconstructed in previous studies by analyzing an entire epidemic on a national scale [4, 12] . This enables us to quantify the efficacy of quarantine measures and compare the infectiousness of different age groups at different times. Current methods do not consider much of the information we possess, e.g. quarantine times, household data and the singleintroduction nature of the outbreak. Vaccination has two distinct goals. Firstly, to protect those at risk, such as the elderly, those with underlying diseases, and front-line workers. Secondly, to protect the community from future outbreaks. Once the former has been attained, the order in which vaccines should be distributed to the remaining population needs to be decided. Some efforts have been made at simulating the effect vaccination has on the spread of the disease [14] [15] [16] . These models construct a theoretical wave of infections assuming a compartmental model (e.g. SIR and variations), and rely on multiple epidemiological constants. However, by using transmission trees, we can use realworld data to simulate what would have happened if certain persons in the tree had been immune at the time. Thus, we can simulate the effect different vaccine distribution strategies would have had on the third wave. J o u r n a l P r e -p r o o f The study was approved by the National Bioethics Committee of Iceland (approval no. VSN-20-070), after review by the Icelandic Data Protection Authority (DPA). Consent was not required from subjects in the study. In order to make use of our extensive data on each case we extended the Outbreaker2 [4] model as detailed in the supplementary methods. We selected adults, 16 years and older, to be immune and removed them and all their downstream transmissions from the tree. By counting the remaining persons in the tree, we obtained a measure of the size of the third wave in Iceland given a particular vaccination distribution, all non-pharmaceutical interventions being identical. This assumes all transmissions remain the same, except some persons have been immunized and therefore break the chain of transmission. We considered three strategies: vaccinating in order of descending age, in order of ascending age, and uniformly at random. The adult population was segmented into ten-year age brackets. A person was selected to be immune based on the proportion of their age group vaccinated in the simulation, and a given vaccine efficacy, 60% for the former dose and 90% for the latter [19] [20] [21] . The outbreak size point estimates were obtained by averaging the mean outbreak size over all simulations and 95% confidence intervals by taking the 2.5% and 97.5% quantiles. We performed 1,000 simulations. We also simulated the expected number of deaths, critical cases, and severe cases, using the log-linear fits from Herrera-Esposito et al. [22] and Levin et al. [23] (Supplementary methods). These simulations were not sensitive to the initial cases in the tree (Supplementary methods). Furthermore, we simulated the effect the actual distribution of vaccines in at-risk groups and front-line workers, at the time of writing, would have had on the third wave (Supplementary methods). We estimated of a particular group of persons by averaging the out-degree of everyone in the group over all transmission trees. Confidence intervals were obtained by iteratively calculating ̂ with bootstrapping of the persons in the data set. We calculated the ratio of ̂ between distinct groups of persons to estimate the effect size of the difference in infectiousness. Significance was tested by taking the log difference of the bootstrapped ̂ values for the two groups and performing a z-test, using the bootstrapped values to estimate the standard deviation. In addition to bootstrapping, we performed jackknife and permutation tests, with identical results. We estimated to be the mean out-degree per group of everyone diagnosed in a four-day sliding window ( − 4, ], such that each person contributed to ̂ on four days. We obtained the 95% confidence interval with bootstrapping. All sequences used in this analysis are available in the European Nucleotide Archive (ENA) under accession number PRJEB44803 (https://www.ebi.ac.uk/ena/browser/view/PRJEB44803) Source code for model construction and analysis is available at https://github.com/DecodeGenetics/COVID19_reconstruction_iceland In the third wave of SARS-CoV-2 infections in Iceland, 89% of diagnosed cases shared a single haplotype, traced back to a person who entered the country in August 2020 ( Figure 1 .A). It accumulated 2783 cases of the same or derived haplotype over a period of five months before being contained. Other clades did not gain foothold during this time and those cases are not included in this analysis. Vaccinations against SARS-CoV- ii. Quarantine, diagnosis and dates of symptom onset make some people more likely than others, assuming specific incubation time and generation time distributions. iii. Contact tracing data make certain transmissions very likely but do not enable us to disregard others. iv. Given the viral haplotypes, we can disregard transmissions where the haplotypes are incompatible, i.e. neither is derived from the other, and in some cases determine the direction of the transmission, in cases where de novo mutations occur between generations. C. We use the real-world data and the tree structure to infer the latent data for each diagnosed case. The "<"-symbol represents that the date on the left needs to precede the date on the right. For each diagnosed person we infer the ancestor, i.e. the person who infected them, the date of infection, and the number of transmissions separating the ancestor and the person, . D. One instance of a reconstructed transmission tree for the third wave in Iceland. We inferred a transmission tree using data on every person in the third wave diagnosed before December 1st, 2020, a total of 2522 people (Figure 1.D We calculated ̂, stratified by whether people were in quarantine at the time of diagnosis as seen in Figure 2 .A. It shows three peaks in ̂ outside of quarantine, corresponding to two superspreading events and one outbreak in a hospital. Any outbreak has at least one growth phase and one decline phase. The mean outdegree during a growth phase is greater than one, and less than one during a decline phase. The ̂ of different groups during the decline and growth phase of the third wave are shown in Table 1 . All comparisons reported above remain significant in the growth phase and decline phase, except there is no significant difference between the infectiousness of those of working age and those outside working age in the decline phase (23.8%, 95%-CI: -3.3%-56.3%, p=0.08 We modeled three vaccination strategies on the adult population, 16 years and older, to investigate the difference in infectiousness between age groups: vaccinating by order of descending age, order of ascending age, and uniformly at random. We then estimated what the size of the third wave would have been for different levels of vaccination. For each strategy we iteratively increased the proportion of the adult population vaccinated, both starting at 0% and assuming a starting point of 29%. The Table 2 shows the lowest proportion of adults who would have needed to be vaccinated such that the final size of the third wave would have been 100 persons (4% of the observed outbreak) on average. Simulations of the estimated final size of the third wave at a given population prevalence of vaccination. Solid lines show the mean size of the outbreak, shaded areas represent 2.5%-97.5% quantiles. As a benchmark we compare the vaccination strategies by the lowest proportion of adults who would have needed to be vaccinated such that the final size of the third wave would have been 100 persons (4% of the observed outbreak) on average. A. Using the actual vaccination scheme for at-risk groups and front-line workers, up to 29% of the adult population, and using three separate vaccination strategies from 29% to 100%: age-descending, age-ascending and uniformly at random. Modeled vaccinations beyond the 29% mark are assumed to have an efficacy of 60%. B. Simulations of the size of the third wave, assuming 60% vaccine efficacy, under the three different vaccination strategies, starting with no vaccinations and concluding with 100% of the adult population vaccinated. C. Same simulation as in A, but all vaccinations are assumed to have an efficacy of 90% (both first and second dose administered). D. Same simulation as in B, but assuming 90% vaccine efficacy. Table 2 . The lowest proportion of adults who would have needed to be vaccinated such that the final size of the third wave would have been 100 persons on average. The former two models use actual vaccination numbers up to the 29% mark and extrapolate from there using the three strategies. The latter two models start from zero. Quarantine has been assumed to slow the spread of infectious diseases but the extent to which it is effective has been difficult to quantify because it requires data on the individual level. We found that mandated quarantine significantly decreased the spread of the third wave of SARS-CoV-2 infections in Iceland, with persons diagnosed outside of quarantine being 89% more infectious than those diagnosed while in quarantine. Furthermore, we observed that contact tracing is time critical, by comparing the infectiousness of people diagnosed after a short quarantine to that of those diagnosed after a longer quarantine. Lastly, we found that people of working age played a key role in the generation of the third wave in Iceland, most likely resulting from more frequent contact among this age group, when compared to older persons who may be retired. We found that vaccinating persons in order of ascending age or uniformly at random would have prevented more transmissions per vaccination than vaccinating in descending order of age, in the third wave in Iceland. Our estimates of the final size of the outbreak are sensitive to the assumed vaccine efficacy. However, the relative difference between the modeled vaccination strategies is independent of the efficacy. Recent studies suggest that vaccinated persons who become infected have a lower viral load [24] and may be less likely to infect others [25] . This is not taken into account here. The effect of vaccination on the spread of the disease has been studied with classical modeling approaches, based on SIR models and variations thereof. These models can yield insights, but uncertainty remains as to how contacts, dependency between age of contacts, and variability due to superspreading events should be modeled. By reconstructing the third wave from real-world data we have circumvented these limitations by removing the behavioral modeling assumptions and simulating vaccinations directly on the transmission tree. Our results show no significant difference in the expected number of deaths, critical cases, or severe cases between the modeled strategies. This implies it is possible to minimize the number of cases without increasing the mortality or hospitalization rates. One possible explanation is that while older persons are more likely to develop severe disease, the vaccination of younger persons prevents transmission to older people. The effectiveness of non-pharmaceutical interventions can largely be attributed to changes in human behavior. Quantifying this effect remains challenging. By leveraging the extensive data collected for diagnosed persons in the third wave of SARS-CoV-2 infections in Iceland, we created a model that allowed us to observe the differences in infectiousness of distinct groups of people. Although the data collected are extensive, some cases went undiagnosed. Serological measurements following the first wave of SARS-CoV-2 infections in Iceland [3] estimated that diagnosed cases were 56% of the total and another 14% were quarantined but undiagnosed. 95% (21,225) of people quarantined in the third wave were PCR tested upon leaving quarantine. Due to this and higher availability of PCR tests, we expect at least 70% of the cases in the third wave were diagnosed and therefore included in the transmission tree. Outbreaker2 estimated that 87% of cases were diagnosed, but this estimate does not include undiagnosed persons who did not infect others. The vaccination of a population serves two distinct purposes: firstly, to prevent death and severe illness in groups at high risk, and secondly to curb the spread of the virus in the population. We simulated the effect of three vaccination strategies measured using four different metrics: the number of infections, severe cases, critical cases, and deaths. Our results demonstrate negligible difference between the vaccination strategies for the latter three metrics, but a significant difference in the number of infections ( Figure 3 ). While our results for the third wave indicate that vaccinating in order of ascending age would have curtailed the outbreak sooner, this may reflect the age composition of this particular outbreak. Vaccinating the remaining adult population uniformly at random, once high-risk groups have been fully vaccinated, is a more robust strategy, since it removes the dependency between who is vaccinated and their age. When interpreting the results, it is important to keep in mind that they only provide a lower bound on the so-called herd immunity threshold. 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No external funding.