Jamestown Canyon virus (JCV) is a historically understudied mosquito-borne virus of increasing concern in North America. We generated 658 whole-genome JCV sequences from northeast United States, including 84% (500/597) of all JCV-positive mosquitoes detected in Connecticut from 1997 to 2022. Then, we applied phylodynamic methods to demonstrate how mosquito phenology structures the maintenance and evolution of JCV. Our phylogenetic analyses estimate that JCV was introduced in the Northeast by at least the early 1700s, and the primary introductions of lineages A and B into Connecticut occurred during the mid-1800s to mid-1900s. Further, we estimate that JCV evolves at a rate of ∼3 × 10-5 substitutions per site per year (s/s/y), making it one of the slowest-evolving known RNA viruses, because the virus spends ∼10 months per year in evolutionary stasis while overwintering in mosquito eggs. To investigate ecological drivers of JCV spread in Connecticut, we paired discrete trait and continuous phylogeographic reconstructions with mosquito surveillance data. We estimate that JCV has a low diffusion rate of ∼30-60 km2/year, which is more similar to slow-moving tick-borne viruses than to other mosquito-borne viruses. We found that univoltine Aedes mosquitoes were likely to maintain the virus across years through overwintering in eggs, accounting for its slow evolution and dispersal, while multivoltine mosquitoes contributed to periodic bursts of spatial diffusion and amplification within seasons. We demonstrate the utility of dense sequencing and phylodynamics to disentangle complex transmission cycles, offering a framework for rapidly advancing our evolutionary and ecological knowledge of understudied viruses.

SARS-CoV-2 infection rates displayed strikingly organized patterns of temporal and spatial spread as new variants were introduced and subsequently transmitted within the United States. While these spatio-temporal "waves" of infection have been described previously, attempts to quantify the speed and extent of these waves have been limited. Here, we estimate and compare the wavefront speed and spatial expansion of the first two major infection waves in the United States, illustrating these dynamics through detailed visualizations. Our findings reveal that the origins of these waves coincide with large gatherings and the relaxation of masking mandates. Notably, we found that the second wave spread more rapidly than the first, possibly driven by multiple introduction events. These analyses highlight regional heterogeneity in epidemic dynamics and underscore the importance of localized public health measures in mitigating ongoing outbreaks.

BACKGROUND:Effective immune protection against SARS-CoV-2 infection and severe COVID-19 disease continues to change due to viral evolution and waning immunity. We estimated population-level immunity to SARS-CoV-2 for each of the 50 United States (U.S.) and the District of Columbia from January 2020 through December 2023. METHODS:We updated a model of SARS-CoV-2 infections to align with the latest evidence on SARS-CoV-2 natural history and waning of immunity, and to integrate various data sources available throughout the pandemic. We used this model to produce population estimates of effective protection against SARS-CoV-2 infection and severe COVID-19 disease. RESULTS:On 30 December 2023, 98.6% of the U.S. population had experienced immunological exposure to SARS-CoV-2 through infection and/or vaccination, with 88.3% (95% credible interval: 78.4%, 95.5%) having had at least one SARS-CoV-2 infection. Despite this high exposure, the average population-level protection against infection was 31.6% (25.1%, 41.2%). Population-level protection against severe disease was 66.1% (59.2%, 74.3%). CONCLUSION:A new wave of SARS-CoV-2 infections and COVID-19-associated hospitalizations began near the end of 2023, with the introduction of the JN.1 variant. This upturn suggests that the U.S. population remains at risk of SARS-CoV-2 infection and severe COVID-19 disease despite the high level of cumulative exposure in the United States. This decline in effective protection is likely due to both waning and continued viral evolution.

Jamestown Canyon virus (JCV) is a re-emerging mosquito-borne virus of increasing concern in North America. It has been historically understudied, leading to significant gaps in our understanding of its evolutionary history, ecological maintenance, and transmission dynamics. Here, we generated 658 whole-genome JCV sequences from the Northeast United States, including 84% (500/597) of all JCV-positive mosquitoes detected in Connecticut from 1997-2022. Then we applied phylodynamic methods to demonstrate how mosquito phenology and host interaction structure the persistence and spread of JCV. Our phylogenetic analyses estimate that JCV was introduced in the Northeast by at least the early 1700s and the primary introductions of lineages A and B into Connecticut occurred during the mid-1800s to mid-1900s. Further, we estimate that JCV evolves at a rate of ~3 × 10-5 s/s/y, making it one of the slowest evolving known RNA viruses, because the virus spends ~10 months per year in evolutionary stasis while over-wintering in mosquito eggs. To investigate ecological drivers of JCV spread in Connecticut, we paired discrete trait and continuous phylogeographic reconstructions with mosquito surveillance data. We estimate that JCV has a low diffusion rate of ~30-60 km2/year, which is more similar to slow-moving tick-borne viruses than other mosquito-borne viruses. We found that univoltine Aedes mosquitoes were likely to maintain the virus across years through overwintering in eggs, accounting for its slow evolution and dispersal, while multivoltine mosquitoes contribute to periodic bursts of spatial diffusion and amplification within seasons. By characterizing seasonal dynamics of JCV, we demonstrate the utility of dense sequencing and phylodynamics to disentangle complex transmission cycles, offering a framework to rapidly advance our evolutionary and ecological knowledge of understudied viruses.

In recent years, detection of local dengue cases in Florida have increased in both frequency and geographical extent. From 2022 to 2024, consecutive outbreaks in Miami-Dade County were mainly caused by a single lineage of dengue virus (DENV) serotype 3, prompting questions about changing epidemiology and a transition towards endemicity. In this study, we used mathematical modeling and genomic epidemiology to reveal the spatiotemporal dynamics and drivers of local dengue cases in Florida. We found that annual clusters and outbreaks were caused by frequent short-lived DENV introductions, primarily from the Caribbean, and did not find evidence for local trans-seasonal DENV lineage persistence. Further, we show that the climate-driven increases in local suitability for Aedes aegypti transmission and travel-associated cases were the greatest risk factors for outbreaks in Miami-Dade and the geographic expansion of dengue in Florida. Overall, while we do not yet find evidence for endemicity, we demonstrate how climatic trends are enhancing the local public health risk caused by dengue in Florida.

The COVID-19 pandemic has been marked by continuous emergence of novel SARS-CoV-2 variants. Questions remain about the mechanisms with which those variants establish themselves in new geographic areas. We performed a discrete phylogeographic analysis on 18,529 sequences of the SARS-CoV-2 Omicron BA.5 sublineage sampled during February-June 2022 to elucidate emergence of that sublineage in different regions of the United States. The earliest BA.5 sublineage introductions came from Africa, the putative variant origin, but most were from Europe, matching a high volume of air travelers. In addition, we discovered extensive domestic transmission between different US regions, driven by population size and cross-country transmission between key hotspots. We found most BA.5 virus transmission within the United States occurred between 3 regions in the southwestern, southeastern, and northeastern parts of the country. Our results form a framework for analyzing emergence of novel SARS-CoV-2 variants and other pathogens in the United States.

The global incidence of dengue has been rising during the past several decades as a result of increased risk as well as enhanced surveillance. This positively-sloped long-term trend in dengue cases has made it difficult to identify anomalously high intensity years. To address this issue, constructed a hierarchical Bayesian Poisson regression model to extract the long-term trend of annual incidence across 57 countries from 1990-2023 and quantify the difference between reported cases and baseline, which we call the Relative Intensity Score (RISc). To accommodate the peak transmission that often extends through December and January in the Southern Hemisphere, we used an annual time frame from July to June to determine RISc in this region (e.g., for these counties, the 2023-24 transmission season is listed as 2023). RISc provides a standardized measure of incidence intensity that adjusts for location- and time-specific contexts, thereby allowing intensities to be compared across geographies and timeframes. We found that globally, 1995, 1998, 2019, and 2023 represented the highest RISc years and that high RISc tends to follow multi-year cycles. Finally, we identified that temperature anomalies are most strongly associated with elevated RISc. This study provides the first standardized global analysis of dengue intensity, and provides a window into how spatial and temporal trends of dengue intensity may continue to evolve into the future.

In 2022, consecutive sweeps of highly transmissible SARS-CoV-2 Omicron-derived lineages (B.1.1.529*) maintained viral transmission despite extensive antigen exposure from both vaccinations and infections. To better understand Omicron variant emergence in the context of the dynamic fitness landscape of 2022, we aimed to explore putative drivers behind SARS-CoV-2 lineage replacements. Variant fitness is determined through its ability to either outrun previously dominant lineages or more efficiently circumvent host immune responses to previous infections and vaccinations. By analyzing data collected through our local genomic surveillance program from Connecticut, USA, we compared emerging Omicron lineages' growth rates, estimated infections, effective reproductive rates, average viral copy numbers, and likelihood for causing infections in recently vaccinated individuals. We find that newly emerging Omicron lineages outcompeted dominant lineages through a combination of enhanced viral shedding or advanced immune escape depending on the population-level exposure state. This analysis integrates individual-level sequencing data with demographic, vaccination, laboratory, and epidemiological data and provides further insights into host-pathogen dynamics beyond public aggregate data.

Omicron surged as a variant of concern in late 2021. Several distinct Omicron variants appeared and overtook each other. We combined variant frequencies and infection estimates from a nowcasting model for each US state to estimate variant-specific infections, attack rates, and effective reproduction numbers (Rt). BA.1 rapidly emerged, and we estimate that it infected 47.7% of the US population before it was replaced by BA.2. We estimate that BA.5 infected 35.7% of the US population, persisting in circulation for nearly 6 months. Other variants-BA.2, BA.4, and XBB-together infected 30.7% of the US population. We found a positive correlation between the state-level BA.1 attack rate and social vulnerability and a negative correlation between the BA.1 and BA.2 attack rates. Our findings illustrate the complex interplay between viral evolution, population susceptibility, and social factors during the Omicron emergence in the US.

In 2022, consecutive sweeps of the highly transmissible SARS-CoV-2 Omicron-family maintained high viral transmission levels despite extensive antigen exposure on the population level resulting from both vaccinations and infections. To better understand variant fitness in the context of the highly dynamic immunity landscape of 2022, we aimed to dissect the interplay between immunity and fitness advantages of emerging SARS-CoV-2 Omicron lineages on the population-level. We evaluated the relative contribution of higher intrinsic transmissibility or immune escape on the fitness of emerging lineages by analyzing data collected through our local genomic surveillance program from Connecticut, USA. We compared growth rates, estimated infections, effective reproductive rates, average viral copy numbers, and likelihood for causing vaccine break-through infections. Using these population-level data, we find that newly emerging Omicron lineages reach dominance through a specific combination of enhanced intrinsic transmissibility and immune escape that varies over time depending on the state of the host-population. Using similar frameworks that integrate whole genome sequencing together with clinical, laboratory, and epidemiological data can advance our knowledge on host-pathogen dynamics in the post-emergence phase that can be applied to other communicable diseases beyond SARS-CoV-2.### Competing Interest StatementNDG is a paid consultant for BioNTech, DMW has received consulting fees from Pfizer, Merck, and GSK, unrelated to this manuscript, and has been PI on research grants from Pfizer and Merck to Yale, unrelated to this manuscript. JLW has received consulting fees from Pfizer and Revelar Biotherapeutics Inc unrelated to this manuscript.### Funding StatementThis project is supported by the CDC Broad Agency Announcement Contracts 75D30122C14697 and 75D30121C10273, the Connecticut Department of Public Health (CDPH) contract 21PSX0049, and the Council of State and Territorial Epidemiologists contract NU38OT000297. This work does not necessarily represent the views of the CDC or CDPH.### Author DeclarationsI confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.YesThe details of the IRB/oversight body that provided approval or exemption for the research described are given below:The Institutional Review Board from the Yale University Human Research Protection Program determined that the RT-qPCR testing and sequencing of de-identified remnant COVID-19 clinical samples obtained from clinical partners conducted in this study is not research involving human subjects (IRB Protocol ID: 2000028599).I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).YesI have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.YesAll data produced in the present study are available upon reasonable request to the authors.

AbstractObjective:To determine risk factors for the development of long coronavirus disease 2019 (COVID-19) in healthcare personnel (HCP).Methods:We conducted a case–control study among HCP who had confirmed symptomatic COVID-19 working in a Brazilian healthcare system between March 1, 2020, and July 15, 2022. Cases were defined as those having long COVID according to the Centers for Disease Control and Prevention definition. Controls were defined as HCP who had documented COVID-19 but did not develop long COVID. Multiple logistic regression was used to assess the association between exposure variables and long COVID during 180 days of follow-up.Results:Of 7,051 HCP diagnosed with COVID-19, 1,933 (27.4%) who developed long COVID were compared to 5,118 (72.6%) who did not. The majority of those with long COVID (51.8%) had 3 or more symptoms. Factors associated with the development of long COVID were female sex (OR, 1.21; 95% CI, 1.05–1.39), age (OR, 1.01; 95% CI, 1.00–1.02), and 2 or more SARS-CoV-2 infections (OR, 1.27; 95% CI, 1.07–1.50). Those infected with the SARS-CoV-2 δ (delta) variant (OR, 0.30; 95% CI, 0.17–0.50) or the SARS-CoV-2 o (omicron) variant (OR, 0.49; 95% CI, 0.30–0.78), and those receiving 4 COVID-19 vaccine doses prior to infection (OR, 0.05; 95% CI, 0.01–0.19) were significantly less likely to develop long COVID.Conclusions:Long COVID can be prevalent among HCP. Acquiring >1 SARS-CoV-2 infection was a major risk factor for long COVID, while maintenance of immunity via vaccination was highly protective.

The rise of advanced chatbots, such as ChatGPT, has sparked curiosity in the scientific community. ChatGPT is a general-purpose chatbot powered by large language models (LLMs) GPT-3.5 and GPT-4, with the potential to impact numerous fields, including computational biology. In this article, we offer ten tips based on our experience with ChatGPT to assist computational biologists in optimizing their workflows. We have collected relevant prompts and reviewed the nascent literature in the field, compiling tips we project to remain pertinent for future ChatGPT and LLM iterations, ranging from code refactoring to scientific writing to prompt engineering. We hope our work will help bioinformaticians to complement their workflows while staying aware of the various implications of using this technology. Additionally, to track new and creative applications for bioinformatics tools such as ChatGPT, we have established a GitHub repository at https://github.com/csbl-br/awesome-compbio-chatgpt. Our belief is that ethical adherence to ChatGPT and other LLMs will increase the efficiency of computational biologists, ultimately advancing the pace of scientific discovery in the life sciences.

Despite the considerable advances in the last years, the health information systems for health surveillance still need to overcome some critical issues so that epidemic detection can be performed in real time. For instance, despite the efforts of the Brazilian Ministry of Health (MoH) to make COVID-19 data available during the pandemic, delays due to data entry and data availability posed an additional threat to disease monitoring. Here, we propose a complementary approach by using electronic medical records (EMRs) data collected in real time to generate a system to enable insights from the local health surveillance system personnel. As a proof of concept, we assessed data from São Caetano do Sul City (SCS), São Paulo, Brazil. We used the “fever” term as a sentinel event. Regular expression techniques were applied to detect febrile diseases. Other specific terms such as “malaria,” “dengue,” “Zika,” or any infectious disease were included in the dictionary and mapped to “fever.” Additionally, after “tokenizing,” we assessed the frequencies of most mentioned terms when fever was also mentioned in the patient complaint. The findings allowed us to detect the overlapping outbreaks of both COVID-19 Omicron BA.1 subvariant and Influenza A virus, which were confirmed by our team by analyzing data from private laboratories and another COVID-19 public monitoring system. Timely information generated from EMRs will be a very important tool to the decision-making process as well as research in epidemiology. Quality and security on the data produced is of paramount importance to allow the use by health surveillance systems.

Due to the rapid growth of traffic supported by the 5th generation of mobile communication systems (5G) networks on the Internet backbone, concepts such as space-division mul-tiplexing elastic optical networks have gained prominence as a possible solution to the future scarcity of resources. Considering the growing volume of data that the core of the network can transport simultaneously, the resilience of these networks becomes increasingly indispensable. This paper proposes a routing and resource allocation algorithm for space-division multiplexing elastic optical networks (for 5G scenarios), which has a traffic priority-aware protection mechanism and uses preemption to save protection resources, enabling a wide variety of 5G services, new verticals, and a rich set of use cases. The results demonstrate the effectiveness of the proposed algorithm in establishing connections with high priority concerning similar algorithms in the literature, reducing the probability of bandwidth blocking by up to 17 % for this type of traffic.

A crescente popularização de aplicações e dispositivos conectados à Internet evidenciou que as tecnologias atuais de backbone não suportarão a demanda prevista para as próximas décadas. Nesse contexto, as redes ópticas elásticas com multiplexação por divisão espacial (SDM-EON) tem tomado grande aceitação pela comunidade científica como possível solução. No entanto, um dos principais desafios deste tipo de conexão está em garantir resiliência à rede, mas com baixa sobrecarga, dada a sua enorme capacidade de tráfego. Este artigo apresenta quatro algoritmos de roteamento e alocação de recursos para SDM-EON, com ciência da prioridade de tráfego, que proporcionam resiliência com maior eficiência espectral à rede.

Background: Previous studies have shown that COVID-19 In-Hospital Fatality Rate (IHFR) varies between regions and has been diminishing over time. It is believed that the continuous improvement in the treatment of patients, age group of hospitalized, and the availability of hospital resources might be affecting the temporal and regional variation of IHFR. In this study, we explored how the IHFR varied along time and among age groups and federative states in Brazil. In addition, we also assessed the relationship between hospital structure availability and peaks of IHFR. Methods: A retrospective analysis of all COVID-19 hospitalizations with confirmed outcomes in 21 states between March 01 and September 22, 2020 (N=345,281) was done. We fit GLM binomial models with additive and interaction effects between age groups, epidemiological weeks, and states. We also evaluated the association between the modeled peak of IHFR in each state and the variables of hospital structure using the Spearman rank correlation test. Results: We found that the temporal variation of the IHFR was heterogeneous among the states, and in general it followed the temporal trends in hospitalizations. In addition, the peak of IHFR was higher in states with a smaller number of doctors and intensivists, and in states in which a higher percentage of people relied on the Public Health System (SUS) for medical care. Conclusions: Our results suggest that the pressure over the healthcare system is affecting the temporal trends of IHFR in Brazil.

Resilience is a critical issue to the Space-Division Multiplexing Elastic Optical Networks technology due to the enormous amount of data these networks carry. This paper proposes a routing, modulation, spectrum, and core allocation algorithm supporting differentiated resilience services. The proposed mechanism uses classes of service to provide transport services to requests for lightpath establishment. It reduces wastage of spectrum in the provisioning of protected services and proposes a spectrum release mechanism. The results obtained demonstrate the high efficiency of the proposed algorithm in the provisioning of resilience to high priority requests compared to similar algorithms in the literature, reaching a lower blocking probability of up to 60%.

A introdução de novas tecnologias e aplicações conectadas à Internet tem demonstrado a incapacidade física das redes ópticas atuais no aprovisionamento de recursos em um futuro próximo. Neste sentido, as redes ópticas elásticas com multiplexação por divisão espacial tem se mostrado uma solução promissora para lidar com este problema de capacidade. No entanto, considerando a enorme quantidade de dados que essas redes podem transportar, a garantia de resiliência dessas redes ainda é um problema em aberto. Este artigo propõe um algoritmo de roteamento com mecanismo de proteção para redes ópticas elásticas com multiplexação por divisão espacial, ciente da prioridade de tráfego. Os resultados obtidos demonstram a alta eficiência do algoritmo proposto no estabelecimento e resiliência de conexões de alta prioridade em comparação com algoritmos semelhantes na literatura.