Potential impact of annual vaccination with reformulated COVID-19 vaccines: Lessons from the US COVID-19 scenario modeling hub

BACKGROUND

Coronavirus Disease 2019 (COVID-19) continues to cause significant hospitalizations and deaths in the United States. Its continued burden and the impact of annually reformulated vaccines remain unclear. Here, we present projections of COVID-19 hospitalizations and deaths in the United States for the next 2 years under 2 plausible assumptions about immune escape (20% per year and 50% per year) and 3 possible CDC recommendations for the use of annually reformulated vaccines (no recommendation, vaccination for those aged 65 years and over, vaccination for all eligible age groups based on FDA approval).

METHODS AND FINDINGS

The COVID-19 Scenario Modeling Hub solicited projections of COVID-19 hospitalization and deaths between April 15, 2023 and April 15, 2025 under 6 scenarios representing the intersection of considered levels of immune escape and vaccination. Annually reformulated vaccines are assumed to be 65% effective against symptomatic infection with strains circulating on June 15 of each year and to become available on September 1. Age- and state-specific coverage in recommended groups was assumed to match that seen for the first (fall 2021) COVID-19 booster. State and national projections from 8 modeling teams were ensembled to produce projections for each scenario and expected reductions in disease outcomes due to vaccination over the projection period. From April 15, 2023 to April 15, 2025, COVID-19 is projected to cause annual epidemics peaking November to January. In the most pessimistic scenario (high immune escape, no vaccination recommendation), we project 2.1 million (90% projection interval (PI) [1,438,000, 4,270,000]) hospitalizations and 209,000 (90% PI [139,000, 461,000]) deaths, exceeding pre-pandemic mortality of influenza and pneumonia. In high immune escape scenarios, vaccination of those aged 65+ results in 230,000 (95% confidence interval (CI) [104,000, 355,000]) fewer hospitalizations and 33,000 (95% CI [12,000, 54,000]) fewer deaths, while vaccination of all eligible individuals results in 431,000 (95% CI: 264,000-598,000) fewer hospitalizations and 49,000 (95% CI [29,000, 69,000]) fewer deaths.

CONCLUSIONS

COVID-19 is projected to be a significant public health threat over the coming 2 years. Broad vaccination has the potential to substantially reduce the burden of this disease, saving tens of thousands of lives each year.

Evaluation of the US COVID-19 Scenario Modeling Hub for informing pandemic response under uncertainty

Our ability to forecast epidemics far into the future is constrained by the many complexities of disease systems. Realistic longer-term projections may, however, be possible under well-defined scenarios that specify the future state of critical epidemic drivers. Since December 2020, the U.S. COVID-19 Scenario Modeling Hub (SMH) has convened multiple modeling teams to make months ahead projections of SARS-CoV-2 burden, totaling nearly 1.8 million national and state-level projections. Here, we find SMH performance varied widely as a function of both scenario validity and model calibration. We show scenarios remained close to reality for 22 weeks on average before the arrival of unanticipated SARS-CoV-2 variants invalidated key assumptions. An ensemble of participating models that preserved variation between models (using the linear opinion pool method) was consistently more reliable than any single model in periods of valid scenario assumptions, while projection interval coverage was near target levels. SMH projections were used to guide pandemic response, illustrating the value of collaborative hubs for longer-term scenario projections.

Potential impact of annual vaccination with reformulated COVID-19 vaccines: lessons from the U.S. COVID-19 Scenario Modeling Hub

Importance

COVID-19 continues to cause significant hospitalizations and deaths in the United States. Its continued burden and the impact of annually reformulated vaccines remain unclear.

Objective

To project COVID-19 hospitalizations and deaths from April 2023-April 2025 under two plausible assumptions about immune escape (20% per year and 50% per year) and three possible CDC recommendations for the use of annually reformulated vaccines (no vaccine recommendation, vaccination for those aged 65+, vaccination for all eligible groups).

Design

The COVID-19 Scenario Modeling Hub solicited projections of COVID-19 hospitalization and deaths between April 15, 2023-April 15, 2025 under six scenarios representing the intersection of considered levels of immune escape and vaccination. State and national projections from eight modeling teams were ensembled to produce projections for each scenario.

Setting

The entire United States.

Participants

None.

Exposure

Annually reformulated vaccines assumed to be 65% effective against strains circulating on June 15 of each year and to become available on September 1. Age and state specific coverage in recommended groups was assumed to match that seen for the first (fall 2021) COVID-19 booster.

Main outcomes and measures

Ensemble estimates of weekly and cumulative COVID-19 hospitalizations and deaths. Expected relative and absolute reductions in hospitalizations and deaths due to vaccination over the projection period.

Results

From April 15, 2023-April 15, 2025, COVID-19 is projected to cause annual epidemics peaking November-January. In the most pessimistic scenario (high immune escape, no vaccination recommendation), we project 2.1 million (90% PI: 1,438,000-4,270,000) hospitalizations and 209,000 (90% PI: 139,000-461,000) deaths, exceeding pre-pandemic mortality of influenza and pneumonia. In high immune escape scenarios, vaccination of those aged 65+ results in 230,000 (95% CI: 104,000-355,000) fewer hospitalizations and 33,000 (95% CI: 12,000-54,000) fewer deaths, while vaccination of all eligible individuals results in 431,000 (95% CI: 264,000-598,000) fewer hospitalizations and 49,000 (95% CI: 29,000-69,000) fewer deaths.

Conclusion and Relevance

COVID-19 is projected to be a significant public health threat over the coming two years. Broad vaccination has the potential to substantially reduce the burden of this disease.

Echinoderm radial glia in adult cell renewal, indeterminate growth, and regeneration

Echinoderms are a phylum of marine deterostomes with a range of interesting biological features. One remarkable ability is their impressive capacity to regenerate most of their adult tissues, including the central nervous system (CNS). The research community has accumulated data that demonstrates that, in spite of the pentaradial adult body plan, echinoderms share deep similarities with their bilateral sister taxa such as hemichordates and chordates. Some of the new data reveal the complexity of the nervous system in echinoderms. In terms of the cellular architecture, one of the traits that is shared between the CNS of echinoderms and chordates is the presence of radial glia. In chordates, these cells act as the main progenitor population in CNS development. In mammals, radial glia are spent in embryogenesis and are no longer present in adults, being replaced with other neural cell types. In non-mammalian chordates, they are still detected in the mature CNS along with other types of glia. In echinoderms, radial glia also persist into the adulthood, but unlike in chordates, it is the only known glial cell type that is present in the fully developed CNS. The echinoderm radial glia is a multifunctional cell type. Radial glia forms the supporting scaffold of the neuroepithelium, exhibits secretory activity, clears up dying or damaged cells by phagocytosis, and, most importantly, acts as a major progenitor cell population. The latter function is critical for the outstanding developmental plasticity of the adult echinoderm CNS, including physiological cell turnover, indeterminate growth, and a remarkable capacity to regenerate major parts following autotomy or traumatic injury. In this review we summarize the current knowledge on the organization and function of the echinoderm radial glia, with a focus on the role of this cell type in adult neurogenesis.

Informing pandemic response in the face of uncertainty. An evaluation of the U.S. COVID-19 Scenario Modeling Hub

Our ability to forecast epidemics more than a few weeks into the future is constrained by the complexity of disease systems, our limited ability to measure the current state of an epidemic, and uncertainties in how human action will affect transmission. Realistic longer-term projections (spanning more than a few weeks) may, however, be possible under defined scenarios that specify the future state of critical epidemic drivers, with the additional benefit that such scenarios can be used to anticipate the comparative effect of control measures. Since December 2020, the U.S. COVID-19 Scenario Modeling Hub (SMH) has convened multiple modeling teams to make 6-month ahead projections of the number of SARS-CoV-2 cases, hospitalizations and deaths. The SMH released nearly 1.8 million national and state-level projections between February 2021 and November 2022. SMH performance varied widely as a function of both scenario validity and model calibration. Scenario assumptions were periodically invalidated by the arrival of unanticipated SARS-CoV-2 variants, but SMH still provided projections on average 22 weeks before changes in assumptions (such as virus transmissibility) invalidated scenarios and their corresponding projections. During these periods, before emergence of a novel variant, a linear opinion pool ensemble of contributed models was consistently more reliable than any single model, and projection interval coverage was near target levels for the most plausible scenarios (e.g., 79% coverage for 95% projection interval). SMH projections were used operationally to guide planning and policy at different stages of the pandemic, illustrating the value of the hub approach for long-term scenario projections.

Phylogeny, ancestral ranges and reclassification of sand dollars

Classification of the Class Echinoidea is under significant revision in light of emerging molecular phylogenetic evidence. In particular, the sister-group relationships within the superorder Luminacea (Echinoidea: Irregularia) have been considerably updated. However, the placement of many families remains largely unresolved due to a series of incongruent evidence obtained from morphological, paleontological, and genetic data for the majority of extant representatives. In this study, we investigated the phylogenetic relationships of 25 taxa, belonging to eleven luminacean families. We proposed three new superfamilies: Astriclypeoidea, Mellitoidea, and Taiwanasteroidea (including Dendrasteridae, Taiwanasteridae, Scutellidae, and Echinarachniidae), instead of the currently recognized superfamily Scutelloidea Gray, 1825. In light of the new data obtained from ten additional species, the historical biogeography reconstructed shows that the tropical western Pacific and eastern Indian Oceans are the cradle for early sand dollar diversification. Hothouse conditions during the late Cretaceous and early Paleogene were coupled with diversification events of major clades of sand dollars. We also demonstrate that Taiwan fauna can play a key role in terms of understanding the major Cenozoic migration and dispersal events in the evolutionary history of Luminacea.

Multiple models for outbreak decision support in the face of uncertainty

Policymakers must make management decisions despite incomplete knowledge and conflicting model projections. Little guidance exists for the rapid, representative, and unbiased collection of policy-relevant scientific input from independent modeling teams. Integrating approaches from decision analysis, expert judgment, and model aggregation, we convened multiple modeling teams to evaluate COVID-19 reopening strategies for a mid-sized United States county early in the pandemic. Projections from seventeen distinct models were inconsistent in magnitude but highly consistent in ranking interventions. The 6-mo-ahead aggregate projections were well in line with observed outbreaks in mid-sized US counties. The aggregate results showed that up to half the population could be infected with full workplace reopening, while workplace restrictions reduced median cumulative infections by 82%. Rankings of interventions were consistent across public health objectives, but there was a strong trade-off between public health outcomes and duration of workplace closures, and no win-win intermediate reopening strategies were identified. Between-model variation was high; the aggregate results thus provide valuable risk quantification for decision making. This approach can be applied to the evaluation of management interventions in any setting where models are used to inform decision making. This case study demonstrated the utility of our approach and was one of several multimodel efforts that laid the groundwork for the COVID-19 Scenario Modeling Hub, which has provided multiple rounds of real-time scenario projections for situational awareness and decision making to the Centers for Disease Control and Prevention since December 2020.

Implementing landscape genetics in molecular epidemiology to determine drivers of vector-borne disease: A malaria case study

This study employs landscape genetics to investigate the environmental drivers of a deadly vector-borne disease, malaria caused by Plasmodium falciparum, in a more spatially comprehensive manner than any previous work. With 1804 samples from 44 sites collected in western Kenya in 2012 and 2013, we performed resistance surface analysis to show that Lake Victoria acts as a barrier to transmission between areas north and south of the Winam Gulf. In addition, Mantel correlograms clearly showed significant correlations between genetic and geographic distance over short distances (less than 70 km). In both cases, we used an identity-by-state measure of relatedness tailored to find highly related individual parasites in order to focus on recent gene flow that is more relevant to disease transmission. To supplement these results, we performed conventional population genetics analyses, including Bayesian clustering methods and spatial ordination techniques. These analyses revealed some differentiation on the basis of geography and elevation and a cluster of genetic similarity in the lowlands north of the Winam Gulf of Lake Victoria. Taken as a whole, these results indicate low overall genetic differentiation in the Lake Victoria region, but with some separation of parasite populations north and south of the Winam Gulf that is explained by the presence of the lake as a geographic barrier to gene flow. We recommend similar landscape genetics analyses in future molecular epidemiology studies of vector-borne diseases to extend and contextualize the results of traditional population genetics.

Content and sentiment surveillance (CSI): A critical component for modeling modern epidemics

Comprehensive surveillance systems are the key to provide accurate data for effective modeling. Traditional symptom-based case surveillance has been joined with recent genomic, serologic, and environment surveillance to provide more integrated disease surveillance systems. A major gap in comprehensive disease surveillance is to accurately monitor potential population behavioral changes in real-time. Population-wide behaviors such as compliance with various interventions and vaccination acceptance significantly influence and drive the overall epidemic dynamics in the society. Original infoveillance utilizes online query data (e.g., Google and Wikipedia search of a specific content topic such as an epidemic) and later focuses on large volumes of online discourse data about the from social media platforms and further augments epidemic modeling. It mainly uses number of posts to approximate public awareness of the disease, and further compares with observed epidemic dynamics for better projection. The current COVID-19 pandemic shows that there is an urgency to further harness the rich, detailed content and sentiment information, which can provide more accurate and granular information on public awareness and perceptions toward multiple aspects of the disease, especially various interventions. In this perspective paper, we describe a novel conceptual analytical framework of content and sentiment infoveillance (CSI) and integration with epidemic modeling. This CSI framework includes data retrieval and pre-processing; information extraction via natural language processing to identify and quantify detailed time, location, content, and sentiment information; and integrating infoveillance with common epidemic modeling techniques of both mechanistic and data-driven methods. CSI complements and significantly enhances current epidemic models for more informed decision by integrating behavioral aspects from detailed, instantaneous infoveillance from massive social media data.

Genetic variations of Plasmodium falciparum circumsporozoite protein and the impact on interactions with human immunoproteins and malaria vaccine efficacy

In October 2021, the world's first malaria vaccine RTS,S was endorsed by WHO for broad use in children, despite its low efficacy. This study examined polyclonal infections and the associations of parasite genetic variations with binding affinity to human leukocyte antigen (HLA). Multiplicity of infection was determined by amplicon deep sequencing of PfMSP1. Genetic variations in PfCSP were examined across 88 samples from Ghana and analyzed together with 1655 PfCSP sequences from other African and non-African isolates. Binding interactions of PfCSP peptide variants and HLA were predicted using NetChop and HADDOCK. High polyclonality was detected among infections, with each infection harboring multiple non-3D7 PfCSP variants. Twenty-seven PfCSP haplotypes were detected in the Ghanaian samples, and they broadly represented PfCSP diversity across Africa. The number of genetic differences between 3D7 and non-3D7 PfCSP variants does not influence binding to HLA. However, CSP peptide length after proteolytic degradation significantly affects its molecular weight and binding affinity to HLA. Despite the high diversity of HLA, the majority of the HLAI and II alleles interacted/bound with all Ghana CSP peptides. Multiple non-3D7 strains among P. falciparum infections could impact the effectiveness of RTS,S. Longer peptides of the Th2R/Th3R CSP regions should be considered in future versions of RTS,S.

Impact of SARS-CoV-2 vaccination of children ages 5-11 years on COVID-19 disease burden and resilience to new variants in the United States, November 2021-March 2022: A multi-model study

Background

The COVID-19 Scenario Modeling Hub convened nine modeling teams to project the impact of expanding SARS-CoV-2 vaccination to children aged 5-11 years on COVID-19 burden and resilience against variant strains.

Methods

Teams contributed state- and national-level weekly projections of cases, hospitalizations, and deaths in the United States from September 12, 2021 to March 12, 2022. Four scenarios covered all combinations of 1) vaccination (or not) of children aged 5-11 years (starting November 1, 2021), and 2) emergence (or not) of a variant more transmissible than the Delta variant (emerging November 15, 2021). Individual team projections were linearly pooled. The effect of childhood vaccination on overall and age-specific outcomes was estimated using meta-analyses.

Findings

Assuming that a new variant would not emerge, all-age COVID-19 outcomes were projected to decrease nationally through mid-March 2022. In this setting, vaccination of children 5-11 years old was associated with reductions in projections for all-age cumulative cases (7.2%, mean incidence ratio [IR] 0.928, 95% confidence interval [CI] 0.880-0.977), hospitalizations (8.7%, mean IR 0.913, 95% CI 0.834-0.992), and deaths (9.2%, mean IR 0.908, 95% CI 0.797-1.020) compared with scenarios without childhood vaccination. Vaccine benefits increased for scenarios including a hypothesized more transmissible variant, assuming similar vaccine effectiveness. Projected relative reductions in cumulative outcomes were larger for children than for the entire population. State-level variation was observed.

Interpretation

Given the scenario assumptions (defined before the emergence of Omicron), expanding vaccination to children 5-11 years old would provide measurable direct benefits, as well as indirect benefits to the all-age U.S. population, including resilience to more transmissible variants.

Funding

Various (see acknowledgments).

Projected resurgence of COVID-19 in the United States in July-December 2021 resulting from the increased transmissibility of the Delta variant and faltering vaccination

In Spring 2021, the highly transmissible SARS-CoV-2 Delta variant began to cause increases in cases, hospitalizations, and deaths in parts of the United States. At the time, with slowed vaccination uptake, this novel variant was expected to increase the risk of pandemic resurgence in the US in summer and fall 2021. As part of the COVID-19 Scenario Modeling Hub, an ensemble of nine mechanistic models produced 6-month scenario projections for July-December 2021 for the United States. These projections estimated substantial resurgences of COVID-19 across the US resulting from the more transmissible Delta variant, projected to occur across most of the US, coinciding with school and business reopening. The scenarios revealed that reaching higher vaccine coverage in July-December 2021 reduced the size and duration of the projected resurgence substantially, with the expected impacts was largely concentrated in a subset of states with lower vaccination coverage. Despite accurate projection of COVID-19 surges occurring and timing, the magnitude was substantially underestimated 2021 by the models compared with the of the reported cases, hospitalizations, and deaths occurring during July-December, highlighting the continued challenges to predict the evolving COVID-19 pandemic. Vaccination uptake remains critical to limiting transmission and disease, particularly in states with lower vaccination coverage. Higher vaccination goals at the onset of the surge of the new variant were estimated to avert over 1.5 million cases and 21,000 deaths, although may have had even greater impacts, considering the underestimated resurgence magnitude from the model.

Impact of SARS-CoV-2 vaccination of children ages 5-11 years on COVID-19 disease burden and resilience to new variants in the United States, November 2021-March 2022: a multi-model study

Background

SARS-CoV-2 vaccination of persons aged 12 years and older has reduced disease burden in the United States. The COVID-19 Scenario Modeling Hub convened multiple modeling teams in September 2021 to project the impact of expanding vaccine administration to children 5-11 years old on anticipated COVID-19 burden and resilience against variant strains.

Methods

Nine modeling teams contributed state- and national-level projections for weekly counts of cases, hospitalizations, and deaths in the United States for the period September 12, 2021 to March 12, 2022. Four scenarios covered all combinations of: 1) presence vs. absence of vaccination of children ages 5-11 years starting on November 1, 2021; and 2) continued dominance of the Delta variant vs. emergence of a hypothetical more transmissible variant on November 15, 2021. Individual team projections were combined using linear pooling. The effect of childhood vaccination on overall and age-specific outcomes was estimated by meta-analysis approaches.

Findings

Absent a new variant, COVID-19 cases, hospitalizations, and deaths among all ages were projected to decrease nationally through mid-March 2022. Under a set of specific assumptions, models projected that vaccination of children 5-11 years old was associated with reductions in all-age cumulative cases (7.2%, mean incidence ratio [IR] 0.928, 95% confidence interval [CI] 0.880-0.977), hospitalizations (8.7%, mean IR 0.913, 95% CI 0.834-0.992), and deaths (9.2%, mean IR 0.908, 95% CI 0.797-1.020) compared with scenarios where children were not vaccinated. This projected effect of vaccinating children 5-11 years old increased in the presence of a more transmissible variant, assuming no change in vaccine effectiveness by variant. Larger relative reductions in cumulative cases, hospitalizations, and deaths were observed for children than for the entire U.S. population. Substantial state-level variation was projected in epidemic trajectories, vaccine benefits, and variant impacts.

Conclusions

Results from this multi-model aggregation study suggest that, under a specific set of scenario assumptions, expanding vaccination to children 5-11 years old would provide measurable direct benefits to this age group and indirect benefits to the all-age U.S. population, including resilience to more transmissible variants.

Analysis of the Knockdown Resistance Locus (kdr) in Anopheles stephensi, An. arabiensis, and Culex pipiens s.l. for Insight Into the Evolution of Target-site Pyrethroid Resistance in Eastern Ethiopia

The malaria vector, Anopheles stephensi, which is typically restricted to South Asia and the Middle East, was recently detected in the Horn of Africa. Addressing the spread of this vector could involve integrated vector control that considers the status of insecticide resistance of multiple vector species in the region. Previous reports indicate that the knockdown resistance mutations (kdr) in the voltage-gated sodium channel (vgsc) are absent in both pyrethroid-resistant and pyrethroid-sensitive An. stephensi in eastern Ethiopia; however, similar information about other vector species in the same areas is limited. In this study, kdr and the neighboring intron were analyzed in An. stephensi, An. arabiensis, and Culex pipiens s.l. collected between 2016 and 2017 to determine the evolutionary history of kdr in eastern Ethiopia. A sequence analysis revealed that all of Cx. pipiens s.l. (N = 42) and 71.6% of the An. arabiensis (N = 67) carried kdr L1014F, which is known to confer target-site pyrethroid resistance. Intronic variation was only observed in An. stephensi (six segregating sites, three haplotypes), which was previously shown to have no kdr mutations. In addition, no evidence of non-neutral evolutionary processes was detected at the An. stephensi kdr intron, thereby further supporting the target-site mechanism not being a major resistance mechanism in this An. stephensi population. Overall, these results show key differences in the evolution of target-site pyrethroid/dichlorodiphenyltrichloroethane resistance mutations in populations of vector species from the same region. Variations in insecticide resistance mechanism profiles between eastern Ethiopian mosquito vectors may lead to different responses to insecticides used in integrated vector control.

Genetic diversity of Anopheles stephensi in Ethiopia provides insight into patterns of spread

BACKGROUND

The recent detection of the South Asian malaria vector Anopheles stephensi in the Horn of Africa (HOA) raises concerns about the impact of this mosquito on malaria transmission in the region. Analysis of An. stephensi genetic diversity and population structure can provide insight into the history of the mosquito in the HOA to improve predictions of future spread. We investigated the genetic diversity of An. stephensi in eastern Ethiopia, where detection suggests a range expansion into this region, in order to understand the history of this invasive population.

METHODS

We sequenced the cytochrome oxidase subunit I (COI) and cytochrome B gene (CytB) in 187 An. stephensi collected from 10 sites in Ethiopia in 2018. Population genetic, phylogenetic, and minimum spanning network analyses were conducted for Ethiopian sequences. Molecular identification of blood meal sources was also performed using universal vertebrate CytB sequencing.

RESULTS

Six An. stephensi COI-CytB haplotypes were observed, with the highest number of haplotypes in the northeastern sites (Semera, Bati, and Gewana towns) relative to the southeastern sites (Kebridehar, Godey, and Degehabur) in eastern Ethiopia. We observed population differentiation, with the highest differentiation between the northeastern sites compared to central sites (Erer Gota, Dire Dawa, and Awash Sebat Kilo) and the southeastern sites. Phylogenetic and network analysis revealed that the HOA An. stephensi are more genetically similar to An. stephensi from southern Asia than from the Arabian Peninsula. Finally, molecular blood meal analysis revealed evidence of feeding on cows, goats, dogs, and humans, as well as evidence of multiple (mixed) blood meals.

CONCLUSION

We show that An. stephensi is genetically diverse in Ethiopia and with evidence of geographical structure. Variation in the level of diversity supports the hypothesis for a more recent introduction of An. stephensi into southeastern Ethiopia relative to the northeastern region. We also find evidence that supports the hypothesis that HOA An. stephensi populations originate from South Asia rather than the Arabian Peninsula. The evidence of both zoophagic and anthropophagic feeding support the need for additional investigation into the potential for livestock movement to play a role in vector spread in this region.

Genetic variation of Plasmodium falciparum histidine-rich protein 2 and 3 in Assosa zone, Ethiopia: its impact on the performance of malaria rapid diagnostic tests

Background

Rapid diagnostic tests (RDT) are commonly used for the diagnosis of malaria caused by Plasmodium falciparum. However, false negative results of RDT caused by genetic variation of P. falciparum histidine-rich protein 2 and 3 genes (pfhrp2/3) threaten existing malaria case management and control efforts. The main objective of this study was to investigate the genetic variations of the pfhrp2/3 genes.

Methods

A cross-sectional study was conducted from malaria symptomatic individuals in 2018 in Assosa zone, Ethiopia. Finger-prick blood samples were collected for RDT and microscopic examination of thick and thin blood films. Dried blood spots (DBS) were used for genomic parasite DNA extraction and molecular detection. Amplification of parasite DNA was made by quantitative PCR. DNA amplicons of pfhrp2/3 were purified and sequenced.

Results

The PfHRP2 amino acid repeat type isolates were less conserved compared to the PfHRP3 repeat type. Eleven and eight previously characterized PfHRP2 and PfHRP3 amino acid repeat types were identified, respectively. Type 1, 4 and 7 repeats were shared by PfHRP2 and PfHRP3 proteins. Type 2 repeats were found only in PfHRP2, while types 16 and 17 were found only in PfHRP3 with a high frequency in all isolates. 18 novel repeat types were found in PfHRP2 and 13 novel repeat types were found in PfHRP3 in single or multiple copies per isolate. The positivity rate for PfHRP2 RDT was high, 82.9% in PfHRP2 and 84.3% in PfHRP3 sequence isolates at parasitaemia levels > 250 parasites/µl. Using the Baker model, 100% of the isolates in group A (If product of types 2 × type 7 repeats ≥ 100) and 73.7% of the isolates in group B (If product of types 2 × type 7 repeats 50–99) were predicted to be detected by PfHRP2 RDT at parasitaemia level > 250 parasite/μl.

Conclusion

The findings of this study indicate the presence of different PfHRP2 and PfHRP3 amino acid repeat including novel repeats in P. falciparum from Ethiopia. These results indicate that there is a need to closely monitor the performance of PfHRP2 RDT associated with the genetic variation of the pfhrp2 and pfhrp3 gene in P. falciparum isolates at the country-wide level.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12936-021-03928-3.

Modeling Plasmodium falciparum Diagnostic Test Sensitivity Using Machine Learning With Histidine-Rich Protein 2 Variants

Malaria, predominantly caused by Plasmodium falciparum, poses one of largest and most durable health threats in the world. Previously, simplistic regression-based models have been created to characterize malaria rapid diagnostic test performance, though these models often only include a couple genetic factors. Specifically, the Baker et al., 2005 model uses two types of particular repeats in histidine-rich protein 2 (PfHRP2) to describe a P. falciparum infection, though the efficacy of this model has waned over recent years due to genetic mutations in the parasite. In this work, we use a dataset of 100 P. falciparum PfHRP2 genetic sequences collected in Ethiopia and derived a larger set of motif repeat matches for use in generating a series of diagnostic machine learning models. Here we show that the usage of additional and different motif repeats in more sophisticated machine learning methods proves effective in characterizing PfHRP2 diversity. Furthermore, we use machine learning model explainability methods to highlight which of the repeat types are most important with regards to rapid diagnostic test sensitivity, thereby showcasing a novel methodology for identifying potential targets for future versions of rapid diagnostic tests.

Projected resurgence of COVID-19 in the United States in July-December 2021 resulting from the increased transmissibility of the Delta variant and faltering vaccination

WHAT IS ALREADY KNOWN ABOUT THIS TOPIC?

The highly transmissible SARS-CoV-2 Delta variant has begun to cause increases in cases, hospitalizations, and deaths in parts of the United States. With slowed vaccination uptake, this novel variant is expected to increase the risk of pandemic resurgence in the US in July-December 2021.

WHAT IS ADDED BY THIS REPORT?

Data from nine mechanistic models project substantial resurgences of COVID-19 across the US resulting from the more transmissible Delta variant. These resurgences, which have now been observed in most states, were projected to occur across most of the US, coinciding with school and business reopening. Reaching higher vaccine coverage in July-December 2021 reduces the size and duration of the projected resurgence substantially. The expected impact of the outbreak is largely concentrated in a subset of states with lower vaccination coverage.

WHAT ARE THE IMPLICATIONS FOR PUBLIC HEALTH PRACTICE?

Renewed efforts to increase vaccination uptake are critical to limiting transmission and disease, particularly in states with lower current vaccination coverage. Reaching higher vaccination goals in the coming months can potentially avert 1.5 million cases and 21,000 deaths and improve the ability to safely resume social contacts, and educational and business activities. Continued or renewed non-pharmaceutical interventions, including masking, can also help limit transmission, particularly as schools and businesses reopen.

Exploring Feasibility of Multivariate Deep Learning Models in Predicting COVID-19 Epidemic

Background: Mathematical models are powerful tools to study COVID-19. However, one fundamental challenge in current modeling approaches is the lack of accurate and comprehensive data. Complex epidemiological systems such as COVID-19 are especially challenging to the commonly used mechanistic model when our understanding of this pandemic rapidly refreshes. Objective: We aim to develop a data-driven workflow to extract, process, and develop deep learning (DL) methods to model the COVID-19 epidemic. We provide an alternative modeling approach to complement the current mechanistic modeling paradigm. Method: We extensively searched, extracted, and annotated relevant datasets from over 60 official press releases in Hubei, China, in 2020. Multivariate long short-term memory (LSTM) models were developed with different architectures to track and predict multivariate COVID-19 time series for 1, 2, and 3 days ahead. As a comparison, univariate LSTMs were also developed to track new cases, total cases, and new deaths. Results: A comprehensive dataset with 10 variables was retrieved and processed for 125 days in Hubei. Multivariate LSTM had reasonably good predictability on new deaths, hospitalization of both severe and critical patients, total discharges, and total monitored in hospital. Multivariate LSTM showed better results for new and total cases, and new deaths for 1-day-ahead prediction than univariate counterparts, but not for 2-day and 3-day-ahead predictions. Besides, more complex LSTM architecture seemed not to increase overall predictability in this study. Conclusion: This study demonstrates the feasibility of DL models to complement current mechanistic approaches when the exact epidemiological mechanisms are still under investigation.

Detection of high prevalence of Plasmodium falciparum histidine-rich protein 2/3 gene deletions in Assosa zone, Ethiopia: implication for malaria diagnosis

Background

Rapid diagnostic tests (RDTs) targeting histidine rich protein 2(HRP2) are widely used for diagnosis of Plasmodium falciparum infections. Besides PfHRP2, the PfHRP3 antigen contributes to the detection of P. falciparum infections in PfHRP2 RDTs. However, the performance HRP2-based RDT is affected by pfhrp2/3 gene deletions resulting in false-negative test results. The objective of this study was to determine the presence and prevalence of pfhrp2/3 gene deletions including the respective flanking regions among symptomatic patients in Assosa zone, Northwest Ethiopia.

Methods

A health-facility based cross-sectional study was conducted in febrile patients seeking a malaria diagnosis in 2018. Blood samples were collected by finger-prick for microscopic examination of blood smears, malaria RDT, and molecular analysis using dried blood spots (DBS) prepared on Whatman filter paper. A total of 218 P. falciparum positive samples confirmed by quantitative PCR were included for molecular assay of pfhrp2/3 target gene.

Results

Of 218 P. falciparum positive samples, exon 2 deletions were observed in 17.9% of pfhrp2 gene and in 9.2% of pfhrp3 gene. A high proportion of deletions in short segments of pfhrp2 exon1-2 (50%) was also detected while the deletions of the pfhrp3 exon1-2 gene were 4.1%. The deletions were extended to the downstream and upstream of the flanking regions in pfhrp2/3 gene (above 30%). Of eighty-six PfHRP2 RDT negative samples, thirty-six lacked pfhrp2 exon 2. Five PfHRP2 RDT negative samples had double deletions in pfhrp2 exon 2 and pfhrp3 exon2. Of these double deletions, only two of the samples with a parasite density above 2000 parasite/µl were positive by the microscopy. Three samples with intact pfhrp3 exon2 in the pfhrp2 exon2 deleted parasite isolates were found to be positive by PfHRP2 RDT and microscopy with a parasite density above 10,000/µl.

Conclusion

This study confirms the presence of deletions of pfhrp2/3 gene including the flanking regions. Pfhrp2/3 gene deletions results in false-negative results undoubtedly affect the current malaria control and elimination effort in the country. However, further countrywide investigations are required to determine the magnitude of pfhrp2/3 gene deletions and its consequences on routine malaria diagnosis.

A Novel Machine Learning Framework for Comparison of Viral COVID-19-Related Sina Weibo and Twitter Posts: Workflow Development and Content Analysis

Background

Social media plays a critical role in health communications, especially during global health emergencies such as the current COVID-19 pandemic. However, there is a lack of a universal analytical framework to extract, quantify, and compare content features in public discourse of emerging health issues on different social media platforms across a broad sociocultural spectrum.

Objective

We aimed to develop a novel and universal content feature extraction and analytical framework and contrast how content features differ with sociocultural background in discussions of the emerging COVID-19 global health crisis on major social media platforms.

Methods

We sampled the 1000 most shared viral Twitter and Sina Weibo posts regarding COVID-19, developed a comprehensive coding scheme to identify 77 potential features across six major categories (eg, clinical and epidemiological, countermeasures, politics and policy, responses), quantified feature values (0 or 1, indicating whether or not the content feature is mentioned in the post) in each viral post across social media platforms, and performed subsequent comparative analyses. Machine learning dimension reduction and clustering analysis were then applied to harness the power of social media data and provide more unbiased characterization of web-based health communications.

Results

There were substantially different distributions, prevalence, and associations of content features in public discourse about the COVID-19 pandemic on the two social media platforms. Weibo users were more likely to focus on the disease itself and health aspects, while Twitter users engaged more about policy, politics, and other societal issues.

Conclusions

We extracted a rich set of content features from social media data to accurately characterize public discourse related to COVID-19 in different sociocultural backgrounds. In addition, this universal framework can be adopted to analyze social media discussions of other emerging health issues beyond the COVID-19 pandemic.

COVID-19 reopening strategies at the county level in the face of uncertainty: Multiple Models for Outbreak Decision Support

Policymakers make decisions about COVID-19 management in the face of considerable uncertainty. We convened multiple modeling teams to evaluate reopening strategies for a mid-sized county in the United States, in a novel process designed to fully express scientific uncertainty while reducing linguistic uncertainty and cognitive biases. For the scenarios considered, the consensus from 17 distinct models was that a second outbreak will occur within 6 months of reopening, unless schools and non-essential workplaces remain closed. Up to half the population could be infected with full workplace reopening; non-essential business closures reduced median cumulative infections by 82%. Intermediate reopening interventions identified no win-win situations; there was a trade-off between public health outcomes and duration of workplace closures. Aggregate results captured twice the uncertainty of individual models, providing a more complete expression of risk for decision-making purposes.

Patch dynamics modeling framework from pathogens' perspective: Unified and standardized approach for complicated epidemic systems

Mathematical models are powerful tools to investigate, simulate, and evaluate potential interventions for infectious diseases dynamics. Much effort has focused on the Susceptible-Infected-Recovered (SIR)-type compartment models. These models consider host populations and measure change of each compartment. In this study, we propose an alternative patch dynamic modeling framework from pathogens' perspective. Each patch, the basic module of this modeling framework, has four standard mechanisms of pathogen population size change: birth (replication), death, inflow, and outflow. This framework naturally distinguishes between-host transmission process (inflow and outflow) and within-host infection process (replication) during the entire transmission-infection cycle. We demonstrate that the SIR-type model is actually a special cross-sectional and discretized case of our patch dynamics model in pathogens' viewpoint. In addition, this patch dynamics modeling framework is also an agent-based model from hosts' perspective by incorporating individual host's specific traits. We provide an operational standard to formulate this modular-designed patch dynamics model. Model parameterization is feasible with a wide range of sources, including genomics data, surveillance data, electronic health record, and from other emerging technologies such as multiomics. We then provide two proof-of-concept case studies to tackle some of the existing challenges of SIR-type models: sexually transmitted disease and healthcare acquired infections. This patch dynamics modeling framework not only provides theoretical explanations to known phenomena, but also generates novel insights of disease dynamics from a more holistic viewpoint. It is also able to simulate and handle more complicated scenarios across biological scales such as the current COVID-19 pandemic.

Evaluation of PfHRP2 and PfLDH Malaria Rapid Diagnostic Test Performance in Assosa Zone, Ethiopia

In malaria-endemic countries, rapid diagnostic tests (RDTs) targeting Plasmodium falciparum histidine-rich protein 2 (PfHRP2) and lactate dehydrogenase (PfLDH) have been widely used. However, little is known regarding the diagnostic performances of these RDTs in the Assosa zone of northwest Ethiopia. The objective of this study was to determine the diagnostic performances of PfHRP2 and PfLDH RDTs using microscopy and quantitative PCR (qPCR) as a reference test. A health facility-based cross-sectional study design was conducted from malaria-suspected study participants at selected health centers from November to December 2018. Finger-prick blood samples were collected for microscopy, RDTs, and qPCR method. The prevalence of P. falciparum was 26.4%, 30.3%, and 24.1% as determined by microscopy, PfHRP2 RDT, and PfLDH RDT, respectively. Compared with microscopy, the sensitivity and specificity of the PfHRP2 RDT were 96% and 93%, respectively, and those of the PfLDH RDT were 89% and 99%, respectively. Compared with qPCR, the specificity of the PfHRP2 RDT (93%) and PfLDH RDT (98%) was high, but the sensitivity of the PfHRP2 RDT (77%) and PfLDH RDT (70%) was relatively low. These malaria RDTs and reference microscopy methods showed reasonable agreement with a kappa value above 0.85 and provided accurate diagnosis of P. falciparum malaria. Thus, the current malaria RDT in the Ministry of Health program can be used in the Assosa zone of Ethiopia. However, continuous monitoring of the performance of PfHRP2 RDT is important to support control and elimination of malaria in Ethiopia.

Four Challenges Associated With Current Mathematical Modeling Paradigm of Infectious Diseases and Call for a Shift

Mathematical models are critical tools to characterize COVID-19 dynamics and take action accordingly. We identified 4 major challenges associated with the current modeling paradigm (SEIR) that hinder the efforts to accurately characterize the emerging COVID-19 and future epidemics. These challenges included (1) lack of consistent definition of “case”; (2) discrepancy between patient-level clinical insights and population-level modeling efforts; (3) lack of adequate inclusion of individual behavioral and social influence; and (4) allowing little flexibility of including new evidence and insights when our knowledge evolved rapidly during the pandemic. Therefore, these challenges made the current SEIR modeling paradigm less practical to handle the complex COVID-19 and future pandemics. Novel and more reliable data sources and alternative modeling paradigms are needed to address these issues.

Genetic capitalism and stabilizing selection of antimicrobial resistance genotypes in Escherichia coli

Antimicrobial resistance (AMR) in pathogenic strains of bacteria, such as Escherichia coli (E. coli), adversely impacts personal and public health. In this study, we examine competing hypotheses for the evolution of AMR including (i) 'genetic capitalism' in which genotypes that confer antibiotic resistance are gained and not often lost in lineages, and (ii) 'stabilizing selection' in which genotypes that confer antibiotic resistance are gained and lost often. To test these hypotheses, we assembled a dataset that includes annotations for 409 AMR genotypes and a phylogenetic tree based on genome-wide single nucleotide polymorphisms from 29 255 isolates of E. coli collected over the past 134 years. We used phylogenetic methods to count the times each AMR genotype was gained and lost across the tree and used model-based clustering of the genotypes with respect to their gain and loss rates. We demonstrate that many genotypes cluster to support the hypothesis for genetic capitalism while a few genotypes cluster to support the hypothesis for stabilizing selection. Comparing the sets of genotypes that fall under each of the hypotheses, we found a statistically significant difference in the breakdown of resistance mechanisms through which the AMR genotypes function. The result that many AMR genotypes cluster under genetic capitalism reflects that strong positive selective forces, primarily induced by human industrialization of antibiotics, outweigh the potential fitness costs to the bacterial lineages for carrying the AMR genotypes. We expect genetic capitalism to further drive bacterial lineages to resist antibiotics. We find that antibiotics that function via replacement and efflux tend to behave under stabilizing selection and thus may be valuable in an antibiotic cycling strategy.

Ensemble machine learning modeling for the prediction of artemisinin resistance in malaria

Resistance in malaria is a growing concern affecting many areas of Sub-Saharan Africa and Southeast Asia. Since the emergence of artemisinin resistance in the late 2000s in Cambodia, research into the underlying mechanisms has been underway. The 2019 Malaria Challenge posited the task of developing computational models that address important problems in advancing the fight against malaria. The first goal was to accurately predict artemisinin drug resistance levels of Plasmodium falciparum isolates, as quantified by the IC50. The second goal was to predict the parasite clearance rate of malaria parasite isolates based on in vitro transcriptional profiles. In this work, we develop machine learning models using novel methods for transforming isolate data and handling the tens of thousands of variables that result from these data transformation exercises. This is demonstrated by using massively parallel processing of the data vectorization for use in scalable machine learning. In addition, we show the utility of ensemble machine learning modeling for highly effective predictions of both goals of this challenge. This is demonstrated by the use of multiple machine learning algorithms combined with various scaling and normalization preprocessing steps. Then, using a voting ensemble, multiple models are combined to generate a final model prediction.

A parallelized strategy for epistasis analysis based on Empirical Bayesian Elastic Net models

MOTIVATION

Epistasis reflects the distortion on a particular trait or phenotype resulting from the combinatorial effect of two or more genes or genetic variants. Epistasis is an important genetic foundation underlying quantitative traits in many organisms as well as in complex human diseases. However, there are two major barriers in identifying epistasis using large genomic datasets. One is that epistasis analysis will induce over-fitting of an over-saturated model with the high-dimensionality of a genomic dataset. Therefore, the problem of identifying epistasis demands efficient statistical methods. The second barrier comes from the intensive computing time for epistasis analysis, even when the appropriate model and data are specified.

RESULTS

In this study, we combine statistical techniques and computational techniques to scale up epistasis analysis using Empirical Bayesian Elastic Net (EBEN) models. Specifically, we first apply a matrix manipulation strategy for pre-computing the correlation matrix and pre-filter to narrow down the search space for epistasis analysis. We then develop a parallelized approach to further accelerate the modeling process. Our experiments on synthetic and empirical genomic data demonstrate that our parallelized methods offer tens of fold speed up in comparison with the classical EBEN method which runs in a sequential manner. We applied our parallelized approach to a yeast dataset, and we were able to identify both main and epistatic effects of genetic variants associated with traits such as fitness.

AVAILABILITY AND IMPLEMENTATION

The software is available at github.com/shilab/parEBEN.

Active Notch signaling is required for arm regeneration in a brittle star

Cell signaling pathways play key roles in coordinating cellular events in development. The Notch signaling pathway is highly conserved across all multicellular animals and is known to coordinate a multitude of diverse cellular events, including proliferation, differentiation, fate specification, and cell death. Specific functions of the pathway are, however, highly context-dependent and are not well characterized in post-traumatic regeneration. Here, we use a small-molecule inhibitor of the pathway (DAPT) to demonstrate that Notch signaling is required for proper arm regeneration in the brittle star Ophioderma brevispina, a highly regenerative member of the phylum Echinodermata. We also employ a transcriptome-wide gene expression analysis (RNA-seq) to characterize the downstream genes controlled by the Notch pathway in the brittle star regeneration. We demonstrate that arm regeneration involves an extensive cross-talk between the Notch pathway and other cell signaling pathways. In the regrowing arm, Notch regulates the composition of the extracellular matrix, cell migration, proliferation, and apoptosis, as well as components of the innate immune response. We also show for the first time that Notch signaling regulates the activity of several transposable elements. Our data also suggests that one of the possible mechanisms through which Notch sustains its activity in the regenerating tissues is via suppression of Neuralized1.

Insecticide resistance in Anopheles stephensi in Somali Region, eastern Ethiopia

Background

The movement of malaria vectors into new areas is a growing concern in the efforts to control malaria. The recent report of Anopheles stephensi in eastern Ethiopia has raised the necessity to understand the insecticide resistance status of the vector in the region to better inform vector-based interventions. The aim of this study was to evaluate insecticide resistance in An. stephensi in eastern Ethiopia using two approaches: (1) World Health Organization (WHO) bioassay tests in An. stephensi; and (2) genetic analysis of insecticide resistance genes in An. stephensi in eastern Ethiopia.

Methods

Mosquito larvae and pupae were collected from Kebri Dehar. Insecticide susceptibility of An. stephensi was tested with malathion 5%, bendiocarb 0.1%, propoxur 0.1%, deltamethrin 0.05%, permethrin 0.75%, pirimiphos-methyl 0.25% and DDT 4%, according to WHO standard protocols. In this study, the knockdown resistance locus (kdr) in the voltage gated sodium channel (vgsc) and ace1R locus in the acetylcholinesterase gene (ace-1) were analysed in An. stephensi.

Results

All An. stephensi samples were resistant to carbamates, with mortality rates of 23% and 21% for bendiocarb and propoxur, respectively. Adult An. stephensi was also resistant to pyrethroid insecticides with mortality rates 67% for deltamethrin and 53% for permethrin. Resistance to DDT and malathion was detected in An. stephensi with mortality rates of 32% as well as An. stephensi was resistance to pirimiphos-methyl with mortality rates 14%. Analysis of the insecticide resistance loci revealed the absence of kdr L1014F and L1014S mutations and the ace1R G119S mutation.

Conclusion

Overall, these findings support that An. stephensi is resistant to several classes of insecticides, most notably pyrethroids. However, the absence of the kdr L1014 gene may suggest non-target site resistance mechanisms. Continuous insecticide resistance monitoring should be carried out in the region to confirm the documented resistance and exploring mechanisms conferring resistance in An. stephensi in Ethiopia.

Evolution of endemic and sylvatic lineages of dengue virus

Recent disease outbreaks have raised awareness of tropical pathogens, especially mosquito-borne viruses. Dengue virus (DENV) is a widely studied mammalian pathogen transmitted by various species of mosquito in the genus Aedes, especially Aedes aegypti and Aedes albopictus. The prevailing view of the research community is that endemic viral lineages that cause epidemics of DENV in humans have emerged over time from sylvatic viral lineages, which persist in wild, non-human primates. These notions have been examined by researchers through phylogenetic analyses of the envelope gene (E) from the four serotypes of DENV (serotypes DENV-1 to DENV-4). In these previous reports, researchers used visual inspection of a phylogeny in order to assert that sylvatic lineages lead to endemic clades. In making this assertion, these researchers also reasserted the model of periodic sylvatic to endemic disease outbreaks. Since that study, there has been a significant increase in data both in terms of metadata (e.g., place and host of isolation) and genetic sequences of DENV. Here, we re-examine the model of sylvatic to endemic shifts in viral lineages through a phylogenetic tree search and character evolution study of metadata on the tree. We built a dataset of nucleotide sequences for 188 isolates of DENV that have metadata on sylvatic or endemic sampling along with three orthologous sequences from West Nile virus as the outgroup for the phylogenetic analysis. In contrast to previous research, we find that there are several shifts from endemic to sylvatic lineages as well as sylvatic to endemic lineages, indicating a much more dynamic model of evolution. We propose that a model that allows oscillation between sylvatic and endemic hosts better captures the dynamics of DENV transmission.

The first complete mitochondrial genome of the sand dollar Sinaechinocyamus mai (Echinoidea: Clypeasteroida)

Morphologic and molecular data often lead to different hypotheses of phylogenetic relationships. Such incongruence has been found in the echinoderm class Echinoidea. In particular, the phylogenetic status of the order Clypeasteroida is not well resolved. Complete mitochondrial genomes are currently available for 29 echinoid species, but no clypeasteroid had been sequenced to date. DNA extracted from a single live individual of Sinaechinocyamus mai was sequenced with 10× Genomics technology. This first complete mitochondrial genome (mitogenome) for the order Clypeasteroida is 15,756 base pairs in length. Phylogenomic analysis based on 34 ingroup taxa belonging to nine orders of the class Echinoidea show congruence between our new genetic inference and published trees based on morphologic characters, but also includes some intriguing differences that imply the need for additional investigation.

Geographical distribution of Anopheles stephensi in eastern Ethiopia

Background

The recent detection of the South Asian malaria vector Anopheles stephensi in Ethiopia and other regions in the Horn of Africa has raised concerns about its potential impact on malaria transmission. We report here the findings of a survey for this species in eastern Ethiopia using both morphological and molecular methods for species identification.

Methods

Adult and larval/pupal collections were conducted at ten sites in eastern Ethiopia and Anopheles specimens were identified using standard morphological keys and genetic analysis.

Results

In total, 2231 morphologically identified An. stephensi were collected. A molecular approach incorporating both PCR endpoint assay and sequencing of portions of the internal transcribed spacer 2 (ITS2) and cytochrome c oxidase subunit 1 (cox1) loci confirmed the identity of the An. stephensi in most cases (119/124 of the morphologically identified An. stephensi confirmed molecularly). Additionally, we observed Aedes aegypti larvae and pupae at many of the An. stephensi larval habitats.

Conclusions

Our findings show that An. stephensi is widely distributed in eastern Ethiopia and highlight the need for further surveillance in the southern, western and northern parts of the country and throughout the Horn of Africa.

StrainHub: a phylogenetic tool to construct pathogen transmission networks

SUMMARY

In exploring the epidemiology of infectious diseases, networks have been used to reconstruct contacts among individuals and/or populations. Summarizing networks using pathogen metadata (e.g. host species and place of isolation) and a phylogenetic tree is a nascent, alternative approach. In this paper, we introduce a tool for reconstructing transmission networks in arbitrary space from phylogenetic information and metadata. Our goals are to provide a means of deriving new insights and infection control strategies based on the dynamics of the pathogen lineages derived from networks and centrality metrics. We created a web-based application, called StrainHub, in which a user can input a phylogenetic tree based on genetic or other data along with characters derived from metadata using their preferred tree search method. StrainHub generates a transmission network based on character state changes in metadata, such as place or source of isolation, mapped on the phylogenetic tree. The user has the option to calculate centrality metrics on the nodes including betweenness, closeness, degree and a new metric, the source/hub ratio. The outputs include the network with values for metrics on its nodes and the tree with characters reconstructed. All of these results can be exported for further analysis.

AVAILABILITY AND IMPLEMENTATION

strainhub.io and https://github.com/abschneider/StrainHub.

Discovery of Adults Linked to Cloning Oceanic Starfish Larvae (Oreaster, Asteroidea: Echinodermata)

Two juvenile specimens of a new species of Oreaster were collected at Parque Nacional Arrecife Alacranes and Triángulos Oeste in the southern Gulf of Mexico. DNA of mitochondrial loci identifies them as members of the same clade as cloning larvae of Oreaster found abundantly in waters of the Florida Current-Gulf Stream system, and distinct from Oreaster clavatus and Oreaster reticulatus, the two known Oreasteridae species in the North Atlantic. Larvae from the new species of Oreaster persist as clones but also metamorphose and settle to the benthos with typical asteroid morphology.

A new species of Astropecten (Echinodermata: Asteroidea: Paxillosida: Astropectinidae) and a comparison of the Astropecten species from the Gulf of Mexico and the East Florida Shelf

A new species of Astropecten is described, supported by morphological and molecular evidence, from the Gulf of Mexico and the East Florida Shelf with most specimens from 30-60 m in depth. The new species, A. mcedwardi n. sp., is small, with a maximum major radius 30 mm. Specimens of A. mcedwardi n. sp. have been found in five museums as an undescribed species or misidentified under several names. The spination of the oral surface most closely resembles that of Astropecten antillensis Lütken, 1859 from the Caribbean, but the body form is similar to that of Astropecten duplicatus Gray, 1840, which is found in the same geographic range. Examination of specimens from different collections indicates that the new species may overlap in distribution with A. antillensis along the East Florida Shelf.  Sequencing and phylogenetic analysis of two mitochondrial genes reveal that A. mcedwardi is closely related to A. antillensis but that its phylogenetic lineage is distinct from that of A. antillensis.

Sequence-based identification of Anopheles species in eastern Ethiopia

BACKGROUND

The recent finding of a typically non-African Anopheles species in eastern Ethiopia emphasizes the need for detailed species identification and characterization for effective malaria vector surveillance. Molecular approaches increase the accuracy and interoperability of vector surveillance data. To develop effective molecular assays for Anopheles identification, it is important to evaluate different genetic loci for the ability to characterize species and population level variation. Here the utility of the internal transcribed spacer 2 (ITS2) and cytochrome oxidase I (COI) loci for detection of Anopheles species from understudied regions of eastern Ethiopia was investigated.

METHODS

Adult mosquitoes were collected from the Harewe locality (east) and Meki (east central) Ethiopia. PCR and Sanger sequencing were performed for portions of the ITS2 and COI loci. Both NCBI's Basic Local Alignment Search tool (BLAST) and phylogenetic analysis using a maximum-likelihood approach were performed to identify species of Anopheles specimens.

RESULTS

Two species from the east Ethiopian collection, Anopheles arabiensis and Anopheles pretoriensis were identified. Analyses of ITS2 locus resulted in delineation of both species. In contrast, analysis of COI locus could not be used to delineate An. arabiensis from other taxa in Anopheles gambiae complex, but could distinguish An. pretoriensis sequences from sister taxa.

CONCLUSION

The lack of clarity from COI sequence analysis highlights potential challenges of species identification within species complexes. These results provide supporting data for the development of molecular assays for delineation of Anopheles in east Ethiopia.

Venomix: a simple bioinformatic pipeline for identifying and characterizing toxin gene candidates from transcriptomic data

The advent of next-generation sequencing has resulted in transcriptome-based approaches to investigate functionally significant biological components in a variety of non-model organism. This has resulted in the area of “venomics”: a rapidly growing field using combined transcriptomic and proteomic datasets to characterize toxin diversity in a variety of venomous taxa. Ultimately, the transcriptomic portion of these analyses follows very similar pathways after transcriptome assembly often including candidate toxin identification using BLAST, expression level screening, protein sequence alignment, gene tree reconstruction, and characterization of potential toxin function. Here we describe the Python package Venomix, which streamlines these processes using common bioinformatic tools along with ToxProt, a publicly available annotated database comprised of characterized venom proteins. In this study, we use the Venomix pipeline to characterize candidate venom diversity in four phylogenetically distinct organisms, a cone snail (Conidae; Conus sponsalis), a snake (Viperidae; Echis coloratus), an ant (Formicidae; Tetramorium bicarinatum), and a scorpion (Scorpionidae; Urodacus yaschenkoi). Data on these organisms were sampled from public databases, with each original analysis using different approaches for transcriptome assembly, toxin identification, or gene expression quantification. Venomix recovered numerically more candidate toxin transcripts for three of the four transcriptomes than the original analyses and identified new toxin candidates. In summary, we show that the Venomix package is a useful tool to identify and characterize the diversity of toxin-like transcripts derived from transcriptomic datasets. Venomix is available at: https://bitbucket.org/JasonMacrander/Venomix/.

A new strategy to infer circularity applied to four new complete frog mitogenomes

We applied a novel strategy to infer sequence circularity and complete assembly of four mitochondrial genomes (mitogenomes) of the frog families Bufonidae (Melanophryniscus moreirae), Dendrobatidae (Hyloxalus subpunctatus and Phyllobates terribilis), and Scaphiopodidae (Scaphiopus holbrookii). These are the first complete mitogenomes of these four genera and Scaphiopodidae. We assembled mitogenomes from short genomic sequence reads using a baiting and iterative mapping strategy followed by a new ad hoc mapping strategy developed to test for assembly circularization. To assess the quality of the inferred circularization, we used Bowtie2 alignment scores and a new per-position sequence coverage value (which we named "connectivity"). Permutation tests with 400 iterations per specimen and 1% or 5% chance of mutation at the ends of the putative circular sequences showed that the proposed method is highly sensitive, with a single nucleotide insertion or deletion being sufficient for circularity to be rejected. False positives comprised only 2% of all observations and possessed significantly lower alignment scores. The size, gene content, and gene arrangement of each mitogenome differed among the species but matched the expectations for their clades. We argue that basic studies on circular sequences can benefit from the results and bioinformatics procedures introduced here, especially when closely related references are lacking.

Regeneration in bipinnaria larvae of the bat star Patiria miniata induces rapid and broad new gene expression

BACKGROUND

Some metazoa have the capacity to regenerate lost body parts. This phenomenon in adults has been classically described in echinoderms, especially in sea stars (Asteroidea). Sea star bipinnaria larvae can also rapidly and effectively regenerate a complete larva after surgical bisection. Understanding the capacity to reverse cell fates in the larva is important from both a developmental and biomedical perspective; yet, the mechanisms underlying regeneration in echinoderms are poorly understood.

RESULTS

Here, we describe the process of bipinnaria regeneration after bisection in the bat star Patiria miniata. We tested transcriptional, translational, and cell proliferation activity after bisection in anterior and posterior bipinnaria halves as well as expression of SRAP, reported as a sea star regeneration associated protease (Vickery et al., 2001b). Moreover, we found several genes whose transcripts increased in abundance following bisection, including: Vasa, dysferlin, vitellogenin 1 and vitellogenin 2.

CONCLUSION

These results show a transformation following bisection, especially in the anterior halves, of cell fate reassignment in all three germ layers, with clear and predictable changes. These results define molecular events that accompany the cell fate changes coincident to the regenerative response in echinoderm larvae.

Zika Virus: Medical Countermeasure Development Challenges

INTRODUCTION

Reports of high rates of primary microcephaly and Guillain-Barré syndrome associated with Zika virus infection in French Polynesia and Brazil have raised concerns that the virus circulating in these regions is a rapidly developing neuropathic, teratogenic, emerging infectious public health threat. There are no licensed medical countermeasures (vaccines, therapies or preventive drugs) available for Zika virus infection and disease. The Pan American Health Organization (PAHO) predicts that Zika virus will continue to spread and eventually reach all countries and territories in the Americas with endemic Aedes mosquitoes. This paper reviews the status of the Zika virus outbreak, including medical countermeasure options, with a focus on how the epidemiology, insect vectors, neuropathology, virology and immunology inform options and strategies available for medical countermeasure development and deployment.

METHODS

Multiple information sources were employed to support the review. These included publically available literature, patents, official communications, English and Lusophone lay press. Online surveys were distributed to physicians in the US, Mexico and Argentina and responses analyzed. Computational epitope analysis as well as infectious disease outbreak modeling and forecasting were implemented. Field observations in Brazil were compiled and interviews conducted with public health officials.

Phylogeny of Echinoderm Hemoglobins

BACKGROUND

Recent genomic information has revealed that neuroglobin and cytoglobin are the two principal lineages of vertebrate hemoglobins, with the latter encompassing the familiar myoglobin and α-globin/β-globin tetramer hemoglobin, and several minor groups. In contrast, very little is known about hemoglobins in echinoderms, a phylum of exclusively marine organisms closely related to vertebrates, beyond the presence of coelomic hemoglobins in sea cucumbers and brittle stars. We identified about 50 hemoglobins in sea urchin, starfish and sea cucumber genomes and transcriptomes, and used Bayesian inference to carry out a molecular phylogenetic analysis of their relationship to vertebrate sequences, specifically, to assess the hypothesis that the neuroglobin and cytoglobin lineages are also present in echinoderms.

RESULTS

The genome of the sea urchin Strongylocentrotus purpuratus encodes several hemoglobins, including a unique chimeric 14-domain globin, 2 androglobin isoforms and a unique single androglobin domain protein. Other strongylocentrotid genomes appear to have similar repertoires of globin genes. We carried out molecular phylogenetic analyses of 52 hemoglobins identified in sea urchin, brittle star and sea cucumber genomes and transcriptomes, using different multiple sequence alignment methods coupled with Bayesian and maximum likelihood approaches. The results demonstrate that there are two major globin lineages in echinoderms, which are related to the vertebrate neuroglobin and cytoglobin lineages. Furthermore, the brittle star and sea cucumber coelomic hemoglobins appear to have evolved independently from the cytoglobin lineage, similar to the evolution of erythroid oxygen binding globins in cyclostomes and vertebrates.

CONCLUSION

The presence of echinoderm globins related to the vertebrate neuroglobin and cytoglobin lineages suggests that the split between neuroglobins and cytoglobins occurred in the deuterostome ancestor shared by echinoderms and vertebrates.

Tracing Origins of the Salmonella Bareilly Strain Causing a Food-borne Outbreak in the United States

BACKGROUND

Using a novel combination of whole-genome sequencing (WGS) analysis and geographic metadata, we traced the origins of Salmonella Bareilly isolates collected in 2012 during a widespread food-borne outbreak in the United States associated with scraped tuna imported from India.

METHODS

Using next-generation sequencing, we sequenced the complete genome of 100 Salmonella Bareilly isolates obtained from patients who consumed contaminated product, from natural sources, and from unrelated historically and geographically disparate foods. Pathogen genomes were linked to geography by projecting the phylogeny on a virtual globe and produced a transmission network.

RESULTS

Phylogenetic analysis of WGS data revealed a common origin for outbreak strains, indicating that patients in Maryland and New York were infected from sources originating at a facility in India.

CONCLUSIONS

These data represent the first report fully integrating WGS analysis with geographic mapping and a novel use of transmission networks. Results showed that WGS vastly improves our ability to delimit the scope and source of bacterial food-borne contamination events. Furthermore, these findings reinforce the extraordinary utility that WGS brings to global outbreak investigation as a greatly enhanced approach to protecting the human food supply chain as well as public health in general.

Syndromic Surveillance of Infectious Diseases meets Molecular Epidemiology in a Workflow and Phylogeographic Application

Traditionally, epidemiologists have counted cases and groups of symptoms. Modeling on these data consists of predicting expansion or contraction in the number of cases over time in epidemic curves or compartment models. Geography is considered a variable when these data are presented in choropleth maps. These approaches have significant drawbacks if the cases counted are not accurately diagnosed. For example, most regional public health authorities count influenza like illnesses (ILI). Cases of these diseases are designated as ILI if the patient exhibits fever, respiratory symptoms, and perhaps gastrointestinal symptoms. Several molecular epidemiological studies have shown that there are many pathogens that cause these symptoms and the relative proportions of these pathogens change over time and space. One way to bridge the gap between syndromic and genetic surveillance of infectious diseases is to compare signals of symptoms to pathogens recorded in molecular databases. We present a web-based workflow application that uses chief complaints found in the public Twitter feed as a syndromic surveillance tool and connects outbreak signals in these data to pathogens historically known to circulate in the same area. For the pathogen(s) of interest, we provide Genbank links to metadata and sequences in a workflow for phylogeographic analysis and visualization. The visualizations provide information on the geographic traffic of the spread of the pathogens and places that are hubs for their transport.

Genotypic and phenotypic heterogeneity in the U3R region of HIV type 1 subtype C

Approximately 20% of all HIV-1 mother-to-child transmission (MTCT) occurs in utero (IU). In a chronic HIV infection, HIV-1 exists as a complex swarm of genetic variants, and following IU MTCT, viral genomic diversity is restricted through a mechanism that remains to be described. The 5' U3R region of the HIV-1 long terminal repeat (LTR) contains multiple transcription factor (TF) binding sites and regulates viral transcription. In this study, we tested the hypothesis that sequence polymorphisms in the U3R region of LTR are associated with IU MTCT. To this end, we used single template amplification to isolate 517 U3R sequences from maternal, placental, and infant plasma derived from 17 HIV-infected Malawian women: eight whose infants remained HIV uninfected (NT) and nine whose infants became HIV infected IU. U3R sequences show pairwise diversities ranging from 0.2% to 2.3%. U3R sequences from one participant contained two, three, or four putative NF-κB binding sites. Phylogenetic reconstructions indicated that U3R sequences from eight of nine IU participants were consistent with placental compartmentalization of HIV-1 while only one of eight NT cases was consistent with such compartmentalization. Specific TF sequence polymorphisms were not significantly associated with IU MTCT. To determine if replication efficiency of the U3R sequences was associated with IU MTCT, we cloned 90 U3R sequences and assayed promoter activity in multiple cell lines. Although we observed significant, yet highly variable promoter activity and TAT induction of promoter activity in the cell lines tested, there was no association between measured promoter activity and MTCT status. Thus, we were unable to detect a promoter genotype or phenotype associated with IU MTCT.

Reconstructing k-Reticulated Phylogenetic Network from a Set of Gene Trees

The time complexity of existing algorithms for reconstructing a level-x phylogenetic network increases exponentially in x. In this paper, we propose a new classification of phylogenetic networks called k-reticulated network. A k-reticulated network can model all level-k networks and some level-x networks with x > k. We design algorithms for reconstructing k-reticulated network (k = 1 or 2) with minimum number of hybrid nodes from a set of m binary trees, each with n leaves in O(mn²) time. The implication is that some level-x networks with x > k can now be reconstructed in a faster way. We implemented our algorithm (ARTNET) and compared it with CMPT. We show that ARTNET outperforms CMPT in terms of running time and accuracy. We also consider the case when there does not exist a 2-reticulated network for the input trees. We present an algorithm computing a maximum subset of the species set so that a new set of subtrees can be combined into a 2-reticulated network.

Eco-virological approach for assessing the role of wild birds in the spread of avian influenza H5N1 along the Central Asian Flyway

A unique pattern of highly pathogenic avian influenza (HPAI) H5N1 outbreaks has emerged along the Central Asia Flyway, where infection of wild birds has been reported with steady frequency since 2005. We assessed the potential for two hosts of HPAI H5N1, the bar-headed goose (Anser indicus) and ruddy shelduck (Tadorna tadorna), to act as agents for virus dispersal along this 'thoroughfare'. We used an eco-virological approach to compare the migration of 141 birds marked with GPS satellite transmitters during 2005-2010 with: 1) the spatio-temporal patterns of poultry and wild bird outbreaks of HPAI H5N1, and 2) the trajectory of the virus in the outbreak region based on phylogeographic mapping. We found that biweekly utilization distributions (UDs) for 19.2% of bar-headed geese and 46.2% of ruddy shelduck were significantly associated with outbreaks. Ruddy shelduck showed highest correlation with poultry outbreaks owing to their wintering distribution in South Asia, where there is considerable opportunity for HPAI H5N1 spillover from poultry. Both species showed correlation with wild bird outbreaks during the spring migration, suggesting they may be involved in the northward movement of the virus. However, phylogeographic mapping of HPAI H5N1 clades 2.2 and 2.3 did not support dissemination of the virus in a northern direction along the migration corridor. In particular, two subclades (2.2.1 and 2.3.2) moved in a strictly southern direction in contrast to our spatio-temporal analysis of bird migration. Our attempt to reconcile the disciplines of wild bird ecology and HPAI H5N1 virology highlights prospects offered by both approaches as well as their limitations.