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In 2013 to 2017, avian influenza A(H7N9) virus has caused five severe epidemic waves of human infections in China. The role of live bird markets (LBMs) in the transmission dynamics of H7N9 remains unclear. Using a Bayesian phylodynamic approach, we shed light on past H7N9 transmission events at the human-LBM interface that were not directly observed using case surveillance data-based approaches. Our results reveal concurrent circulation of H7N9 lineages in Yangtze and Pearl River Delta regions, with evidence of local transmission during each wave. Our results indicate that H7N9 circulated in humans and LBMs for weeks to months before being first detected. Our findings support the seasonality of H7N9 transmission and suggest a high number of underreported infections, particularly in LBMs. We provide evidence for differences in virus transmissibility between low and highly pathogenic H7N9. We demonstrate a regional spatial structure for the spread of H7N9 among LBMs, highlighting the importance of further investigating the role of local live poultry trade in virus transmission. Our results provide estimates of avian influenza virus (AIV) transmission at the LBM level, providing a unique opportunity to better prepare surveillance plans at LBMs for response to future AIV epidemics., Proceedings of the National Academy of Sciences of the United States of America, 120 (17), ISSN:0027-8424, ISSN:1091-6490

In winter 2016-7, Europe was severely hit by an unprecedented epidemic of highly pathogenic avian influenza viruses (HPAIVs), causing a significant impact on animal health, wildlife conservation, and livestock economic sustainability. By applying phylodynamic tools to virus sequences collected during the epidemic, we investigated when the first infections occurred, how many infections were unreported, which factors influenced virus spread, and how many spillover events occurred. HPAIV was likely introduced into poultry farms during the autumn, in line with the timing of wild birds' migration. In Germany, Hungary, and Poland, the epidemic was dominated by farm-to-farm transmission, showing that understanding of how farms are connected would greatly help control efforts. In the Czech Republic, the epidemic was dominated by wild bird-to-farm transmission, implying that more sustainable prevention strategies should be developed to reduce HPAIV exposure from wild birds. Inferred transmission parameters will be useful to parameterize predictive models of HPAIV spread. None of the predictors related to live poultry trade, poultry census, and geographic proximity were identified as supportive predictors of HPAIV spread between farms across borders. These results are crucial to better understand HPAIV transmission dynamics at the domestic-wildlife interface with the view to reduce the impact of future epidemics., Virus Evolution, 8 (2), ISSN:2057-1577

The multi-type birth–death model with sampling is a phylodynamic model which enables the quantification of past population dynamics in structured populations based on phylogenetic trees. The BEAST 2 package bdmm implements an algorithm for numerically computing the probability density of a phylogenetic tree given the population dynamic parameters under this model. In the initial release of bdmm, analyses were computationally limited to trees consisting of up to approximately 250 genetic samples. We implemented important algorithmic changes to bdmm which dramatically increased the number of genetic samples that could be analyzed and which improved the numerical robustness and efficiency of the calculations. Including more samples led to the improved precision of parameter estimates, particularly for structured models with a high number of inferred parameters. Furthermore, we report on several model extensions to bdmm, inspired by properties common to empirical datasets. We applied this improved algorithm to two partly overlapping datasets of the Influenza A virus HA sequences sampled around the world—one with 500 samples and the other with only 175—for comparison. We report and compare the global migration patterns and seasonal dynamics inferred from each dataset. In this way, we show the information that is gained by analyzing the bigger dataset, which became possible with the presented algorithmic changes to bdmm. In summary, bdmm allows for the robust, faster, and more general phylodynamic inference of larger datasets.

Background: The effective reproductive number, Re, is a critical indicator to monitor disease dynamics, inform regional and national policies, and estimate the effectiveness of interventions. It describes the average number of new infections caused by a single infectious person through time. To date, Re estimates are based on clinical data such as observed cases, hospitalizations, and/or deaths. These estimates are temporarily biased when clinical testing or reporting strategies change. Objectives: We show that the dynamics of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA in wastewater can be used to estimate Re in near real time, independent of clinical data and without the associated biases. Methods: We collected longitudinal measurements of SARS-CoV-2 RNA in wastewater in Zurich, Switzerland, and San Jose, California, USA. We combined this data with information on the temporal dynamics of shedding (the shedding load distribution) to estimate a time series proportional to the daily COVID-19 infection incidence. We estimated a wastewater-based Re from this incidence. Results: The method to estimate Re from wastewater worked robustly on data from two different countries and two wastewater matrices. The resulting estimates were as similar to the Re estimates from case report data as Re estimates based on observed cases, hospitalizations, and deaths are among each other. We further provide details on the effect of sampling frequency and the shedding load distribution on the ability to infer Re Discussion: To our knowledge, this is the first time Re has been estimated from wastewater. This method provides a low-cost, rapid, and independent way to inform SARS-CoV-2 monitoring during the ongoing pandemic and is applicable to future wastewater-based epidemiology targeting other pathogens. https://doi.org/10.1289/EHP10050 ISSN:1552-9924 ISSN:0091-6765

(JSH); Abstract The effective reproductive number Re is a key indicator of the growth of an epidemic. Since the start of the SARS- CoV- 2 pandemic, many methods and online dashboards have sprung up to monitor this number through time. However, these methods are not always thoroughly tested, correctly placed in time, or are overly confident during high incidence periods. Here, we present a method for timely estimation of Re, applied to COVID- 19 epidemic data from 170 countries. We thoroughly evaluate the method on simulated data, and present an intuitive web interface for interactive data exploration. We show that, in early 2020, in the majority of countries the estimated Re dropped below 1 only after the introduction of major non-pharmaceutical interventions. For Europe the implementation of non-pharmaceutical interventions was broadly associated with reductions in the estimated Re. Globally though, relaxing non-pharmaceutical interventions had more varied effects on subsequent Re estimates. Our framework is useful to inform governments and the general public on the status of epidemics in their country, and is used as the official source of Re estimates for SARS- CoV- 2 in Switzerland. It further allows detailed comparison between countries and in relation to covariates such as implemented public health policies, mobility, behaviour, or weather data., eLife, 11, ISSN:2050-084X

Recent outbreaks of highly pathogenic avian influenza H5N8 virus in Europe have caused severe damage to animal health, wildlife conservation and livestock economic sustainability. While epidemiological and phylogenetic studies have generated important clues about the virus spread in Europe, they remained opaque to the specific role of poultry farms and wild birds. Using a phylodynamic framework, we inferred the H5N8 virus transmission dynamics among poultry farms and wild birds in four severely affected countries and investigated drivers of spread between farms across borders during the 2016-17 epidemic. Based on existing genetic data, we showed that the virus was likely introduced into poultry farms during the autumn, in line with the timing of arrival of migratory wild birds. Then, transmission was mainly driven by farm-to-farm transmission in Germany, Hungary and Poland, suggesting that better understanding of how infected farms are connected in those countries would greatly help control efforts. In contrast, the epidemic was dominated by wild bird-to-farm transmission in Czech Republic, meaning that more sustainable prevention strategies should be developed to reduce virus exposure from wild birds. We inferred effective reproduction number Re estimates among poultry farms and wild birds. We expect those estimates being useful to parameterize predictive models of virus spread aiming at optimising control strategies. None of the investigated predictors related to live poultry trade, poultry census and geographic proximity were identified as supportive predictors of the viral spread between farms across borders, suggesting that other drivers should be considered in future studies.Significance statementIn winter 2016-17, Europe was severely hit by an unprecedented epidemic of highly pathogenic avian influenza (HPAI) H5N8 virus, causing significant impact on animal health, wildlife conservation and livestock economic sustainability. By applying phylodynamic tools to H5N8 sequence data collected from poultry farms and wild birds during the epidemic, we quantified how effectively the first infections were detected, how fast the virus spread, how many infections were missed and how many transmission events occurred at the wildlife-domestic interface. Also, we investigated predictors of the virus spread between farms across borders. These results are crucial to better understand the virus transmission dynamics, with the view to inform policy decision-making and reduce the impact of future epidemics of HPAI viruses.

The effective reproductive number, Re, is a critical indicator to monitor disease dynamics, inform regional and national policies, and estimate the effectiveness of interventions. It describes the average number of new infections caused by a single infectious person through time. To date, Re estimates are based on clinical data such as observed cases, hospitalizations, and/or deaths. Here we show that the dynamics of SARS-CoV-2 RNA in wastewater can be used to estimate Re in near real-time, independent of clinical data and without associated biases stemming from clinical testing and reporting strategies. The method to estimate Re from wastewater is robust and applicable to data from different countries and wastewater matrices. The resulting estimates are as similar to the Re estimates from case report data as Re estimates based on observed cases, hospitalizations, and deaths are among each other. We further provide details on the effect of sampling frequency and the shedding load distribution on the ability to infer Re. To our knowledge, this is the first time Re has been estimated from wastewater. This method provides a low cost, rapid, and independent way to inform SARS-CoV-2 monitoring during the ongoing pandemic and is applicable to future wastewater-based epidemiology targeting other pathogens. Significance statementThe effective reproductive number, Re, is widely used during the COVID-19 pandemic to track disease dynamics, inform regional and national policies, and estimate the effectiveness of interventions. Re is typically estimated from clinical case data, and can be biased by e.g. changes in testing and reporting. We show longitudinal measurements of SARS-CoV-2 RNA in wastewater can be used to estimate Re, across different regions, and provide an independent assessment of the dynamics of COVID-19. Given widespread wastewater sampling during this pandemic, these Re estimates are directly applicable as a rapid, low-cost method to inform public health policy. The method can be adapted to other pathogens, including those for which clinical data is not available.

Significance We estimate the origin and spread of SARS-CoV-2 in Europe prior to spring 2020 border closures based on viral genome sequences using a phylodynamic model with geographic structure. We confirm that the predominant European outbreak most likely started in Italy and spread from there. This outbreak was probably seeded by a transmission event in either Hubei, China or Germany. In particular, we find that before the first border closures in Europe, the rate of new cases occurring from within-country transmission was within or exceeded the estimated bounds on the rate of new migration cases., The investigation of migratory patterns during the SARS-CoV-2 pandemic before spring 2020 border closures in Europe is a crucial first step toward an in-depth evaluation of border closure policies. Here we analyze viral genome sequences using a phylodynamic model with geographic structure to estimate the origin and spread of SARS-CoV-2 in Europe prior to border closures. Based on SARS-CoV-2 genomes, we reconstruct a partial transmission tree of the early pandemic and coinfer the geographic location of ancestral lineages as well as the number of migration events into and between European regions. We find that the predominant lineage spreading in Europe during this time has a most recent common ancestor in Italy and was probably seeded by a transmission event in either Hubei, China or Germany. We do not find evidence for preferential migration paths from Hubei into different European regions or from each European region to the others. Sustained local transmission is first evident in Italy and then shortly thereafter in the other European regions considered. Before the first border closures in Europe, we estimate that the rate of occurrence of new cases from within-country transmission was within the bounds of the estimated rate of new cases from migration. In summary, our analysis offers a view on the early state of the epidemic in Europe and on migration patterns of the virus before border closures. This information will enable further study of the necessity and timeliness of border closures.

The investigation of migratory patterns during the SARS-CoV-2 pandemic before spring 2020 border closures in Europe is a crucial first step toward an in-depth evaluation of border closure policies. Here we analyze viral genome sequences using a phylodynamic model with geographic structure to estimate the origin and spread of SARS-CoV-2 in Europe prior to border closures. Based on SARS-CoV-2 genomes, we reconstruct a partial transmission tree of the early pandemic and coinfer the geographic location of ancestral lineages as well as the number of migration events into and between European regions. We find that the predominant lineage spreading in Europe during this time has a most recent common ancestor in Italy and was probably seeded by a transmission event in either Hubei, China or Germany. We do not find evidence for preferential migration paths from Hubei into different European regions or from each European region to the others. Sustained local transmission is first evident in Italy and then shortly thereafter in the other European regions considered. Before the first border closures in Europe, we estimate that the rate of occurrence of new cases from within-country transmission was within the bounds of the estimated rate of new cases from migration. In summary, our analysis offers a view on the early state of the epidemic in Europe and on migration patterns of the virus before border closures. This information will enable further study of the necessity and timeliness of border closures. © 2021 National Academy of Sciences, Proceedings of the National Academy of Sciences of the United States of America, 118 (9), ISSN:0027-8424, ISSN:1091-6490

The effective reproductive number Re is a key indicator of the growth of an epidemic. Since the start of the SARS-CoV-2 pandemic, many methods and online dashboards have sprung up to monitor this number through time. However, these methods are not always thoroughly tested, correctly placed in time, or are overly confident during high incidence periods. Here, we present a method for timely estimation of Re, applied to COVID-19 epidemic data from 170 countries. We thoroughly evaluate the method on simulated data, and present an intuitive web interface for interactive data exploration. We show that, in early 2020, in the majority of countries the estimated Re dropped below 1 only after the introduction of major non-pharmaceutical interventions. For Europe the implementation of non-pharmaceutical interventions was broadly associated with reductions in the estimated Re. Globally though, relaxing non-pharmaceutical interventions had more varied effects on subsequent Re estimates. Our framework is useful to inform governments and the general public on the status of epidemics in their country, and is used as the official source of Re estimates for SARS-CoV-2 in Switzerland. It further allows detailed comparison between countries and in relation to covariates such as implemented public health policies, mobility, behaviour, or weather data.

We estimate the basic reproductive number and case counts for 15 distinct SARS-CoV-2 outbreaks, distributed across 10 countries and one cruise ship, based solely on phylodynamic analyses of genomic data. Our results indicate that, prior to significant public health interventions, the reproductive numbers for a majority (10) of these outbreaks are similar, with median posterior estimates ranging between 1.4 and 2.8. These estimates provide a view which is complementary to that provided by those based on traditional line listing data. The genomic-based view is arguably less susceptible to biases resulting from differences in testing protocols, testing intensity, and import of cases into the community of interest. In the analyses reported here, the genomic data primarily provides information regarding which samples belong to a particular outbreak. We observe that once these outbreaks are identified, the sampling dates carry the majority of the information regarding the reproductive number. Finally, we provide genome-based estimates of the cumulative case counts for each outbreak, which allow us to speculate on the amount of unreported infections within the populations housing each outbreak. These results indicate that for the majority (7) of the populations studied, the number of recorded cases is much bigger than the estimated cumulative case counts, suggesting the presence of unsequenced pathogen diversity in these populations.Significance StatementSince the beginning of the COVID-19 outbreak in late 2019, researchers around the globe have sought to estimate the rate at which the disease spread through populations prior to public health intervention, as quantified by the parameter R0. This is often estimated based on case count data and may be biased due to the presence of import cases. To overcome this, we estimate R0 by applying Bayesian phylodynamic methods to SARS-CoV-2 genomes which have been made available by laboratories worldwide. We provide R0 and absolute infection count estimates for 15 distinct outbreaks. These estimates contribute to our understanding of the baseline transmission dynamics of the disease, which will be critical in guiding future public health responses to the pandemic.

Estimation of the effective reproductive number Rt is important for detecting changes in disease transmission over time. During the Coronavirus Disease 2019 (COVID-19) pandemic, policy makers and public health officials are using Rt to assess the effectiveness of interventions and to inform policy. However, estimation of Rt from available data presents several challenges, with critical implications for the interpretation of the course of the pandemic. The purpose of this document is to summarize these challenges, illustrate them with examples from synthetic data, and, where possible, make recommendations. For near real-time estimation of Rt, we recommend the approach of Cori and colleagues, which uses data from before time t and empirical estimates of the distribution of time between infections. Methods that require data from after time t, such as Wallinga and Teunis, are conceptually and methodologically less suited for near real-time estimation, but may be appropriate for retrospective analyses of how individuals infected at different time points contributed to the spread. We advise caution when using methods derived from the approach of Bettencourt and Ribeiro, as the resulting Rt estimates may be biased if the underlying structural assumptions are not met. Two key challenges common to all approaches are accurate specification of the generation interval and reconstruction of the time series of new infections from observations occurring long after the moment of transmission. Naive approaches for dealing with observation delays, such as subtracting delays sampled from a distribution, can introduce bias. We provide suggestions for how to mitigate this and other technical challenges and highlight open problems in Rt estimation., PLoS Computational Biology, 16 (12), ISSN:1553-734X, ISSN:1553-7358

The multi-type birth-death model with sampling is a phylodynamic model which enables quantification of past population dynamics in structured populations, based on phylogenetic trees. The BEAST 2 package bdmm implements an algorithm for numerically computing the probability density of a phylogenetic tree given the population dynamic parameters under this model. In the initial release of bdmm, analyses were limited to trees consisting of up to approximately 250 genetic samples for numerical reasons. We implemented important algorithmic changes to bdmm which dramatically increase the number of genetic samples that can be analyzed, and improve the numerical robustness and efficiency of the calculations. Being able to use bigger datasets leads to improved precision of parameter estimates. Furthermore, we report on several model extensions to bdmm, inspired by properties common to empirical datasets. We apply this improved algorithm to two partly overlapping datasets of Influenza A virus HA sequences sampled around the world, one with 500 samples, the other with only 175, for comparison. We report and compare the global migration patterns and seasonal dynamics inferred from each dataset.AvailabilityThe latest release with our updates, bdmm 0.3.5, is freely available as an open access package of BEAST 2. The source code can be accessed at https://github.com/denisekuehnert/bdmm.

The World Health Organization (WHO) declared the COVID-19 outbreak a “Public Health Emergency of International Concern” on 30 January 2020, after rapid spread from a few initial cases to thousands of cases across China and introductions into several other countries. On 11 March 2020, the WHO classified the outbreak as a pandemic. The first cases in Switzerland, Basel-Stadt, and Basel-Landschaft were confirmed on 25 February, 27 February and 28 February 2020. As of 22 April 2020, there are 28,154 confirmed cases in Switzerland, including 933 and 811 in the Cantons of Basel-Stadt and Basel-Landschaft, respectively. The rapid increase of confirmed cases in March suggests considerable community transmission. We estimated the reproductive number through time for the whole of Switzerland and its cantons for which sufficient data are available. For this estimation, we used publicly available data on the number of confirmed cases and COVID-related deaths through time, as well as additional data directly obtained from the University Hospital of Basel and the Cantonal Office of Public Health, Economics and Health Directorate of Basel-Landschaft. If the reproductive number is below 1, the epidemic is overall under control for that specific location, with the number of new infections per day decreasing through time. If this number is above 1, the epidemic is exponentially increasing in size. We found that the reproductive number in Switzerland was between 1.5 and 2 during the first third of March, and has consistently decreased to around 1. After the announcement of the latest strict measure on 20 March 2020, namely that gatherings of more than five people in public spaces are prohibited, the reproductive number dropped significantly below 1, we estimated the reproductive number to be between 0.6 and 0.8 in the first third of April. Our sensitivity analyses addressed the concern of a decreasing reproductive number being merely an artifact of less intense testing through time. In summary, our results suggest that from the last week of March onwards, the reproductive number was significantly below 1 in Switzerland and thus the epidemic was declining. However, our analyses do not allow us to identify a cause for this decline. From now on, we will provide daily estimates for the reproductive number on our webpage. Important to note in this respect is that estimates of the reproductive number lag about 10 days behind the last date of data collection since confirmation of the diagnosis occurs around 10 days after infection., Swiss Medical Weekly, 150 (19-20), ISSN:1424-7860, ISSN:1424-3997