Seasonal Outbreaks Research Articles

1162. A data-driven approach to predict plasma leakage using explainable machine learning

Abstract Background Dengue could cause complications with an estimated 10,000 deaths per annum. It mostly affects low- and middle-income countries such as Sri Lanka with limited healthcare resources to handle seasonal outbreaks. A third of dengue patients usually have a critical phase characterized by plasma leakage with increased risk of life-threatening complications. A data-driven approach was required to find early predictors of plasma leakage that are usually available in routine care from a resource limited setting, as means of triaging patients for hospital admission. Methods We utilized a prospective cohort (The Colombo dengue study in Sri Lanka) that recruits patients meeting the clinical case definition of dengue fever. The cohort includes 4,781 instances of clinical signs, symptoms, and in-hospital laboratory tests from N=877 patients (60.3% patients infected by Dengue) recorded in first four days of fever. By excluding incomplete patient instances, the data was randomly split to a development set (N=378) and a test set (N=144). From the development set, five most informative features were identified using the minimum description length (MDL) algorithm. Logistic regression was used to create a prediction model using the development set. Shapley analysis was used to explain the model on the test set extracting the extent by which each predictor contributed to the predictions. Results The MDL algorithm revealed that hemoglobin (HGB), hematocrit (HCT), aspartate aminotransferase (AST), age, and gender to be the most informative predictors of plasma leakage. The logistic regression model predicted plasma leakage with (AUC = 0.76) on the test set (Fig 1). The HGB appeared to contribute the most to the predictions with the higher values associated with higher predicted risk of plasma leakage and vice versa (Fig 2). Figure 2 SHAP decision plot for the logistic regression model on the test set. The features are sorted from top to bottom by their mean absolute SHAP values (higher interpreted as more contributing). Feature values are normalised to [0 1] by the min-max normalisation method and colour-coded (grey points are missing values), outliers were squished to the range using Hampel filter. For gender, male is indicated by red and female by blue. The colour of each line is the same as the value of the feature connected to in downwards direction. Conclusion Our results give support to the predictability of plasma leakage in patients suspected of Dengue fever in their first four days of fever onset. The study also underlines the relevance of the machine learning approach to identify the predictors and the practicality of the prediction model as reflected by the prediction performance to triage patients for hospital admission in resource limited settings. Disclosures All Authors: No reported disclosures.

Modelling the Transmission Dynamics of Meningitis among High and Low-Risk People in Ghana with Cost-Effectiveness Analysis

Meningitis is an inflammation of the meninges, which covers the brain and spinal cord. Every year, most individuals within sub-Saharan Africa suffer from meningococcal meningitis. Moreover, tens of thousands of these cases result in death, especially during major epidemics. The transmission dynamics of the disease keep changing, according to health practitioners. The goal of this study is to exploit robust mechanisms to manage and prevent the disease at a minimal cost due to its public health implications. A significant concern found to aid in the transmission of meningitis disease is the movement and interaction of individuals from low-risk to high-risk zones during the outbreak season. Thus, this article develops a mathematical model that ascertains the dynamics involved in meningitis transmissions by partitioning individuals into low- and high-risk susceptible groups. After computing the basic reproduction number, the model is shown to exhibit a unique local asymptotically stability at the meningitis-free equilibrium E † , when the effective reproduction number R 0 < 1 , and the existence of two endemic equilibria for which R 0 † < R 0 < 1 and exhibits the phenomenon of backward bifurcation, which shows the difficulty of relying only on the reproduction number to control the disease. The effective reproductive number estimated in real time using the exponential growth method affirmed that the number of secondary meningitis infections will continue to increase without any intervention or policies. To find the best strategy for minimizing the number of carriers and infected individuals, we reformulated the model into an optimal control model using Pontryagin’s maximum principles with intervention measures such as vaccination, treatment, and personal protection. Although Ghana’s most preferred meningitis intervention method is via treatment, the model’s simulations demonstrated that the best strategy to control meningitis is to combine vaccination with treatment. But the cost-effectiveness analysis results show that vaccination and treatment are among the most expensive measures to implement. For that reason, personal protection which is the most cost-effective measure needs to be encouraged, especially among individuals migrating from low- to high-risk meningitis belts.

Model Forecasting Development for Dengue Fever Incidence in Surabaya City Using Time Series Analysis

Dengue hemorrhagic fever (DHF) is one of the most widespread and deadly diseases in several parts of Indonesia. An accurate forecast-based model is required to reduce the incidence rate of this disease. Time-series methods such as autoregressive integrated moving average (ARIMA) models are used in epidemiology as statistical tools to study and forecast DHF and other infectious diseases. The present study attempted to forecast the monthly confirmed DHF cases via a time-series approach. The ARIMA, seasonal ARIMA (SARIMA), and long short-term memory (LSTM) models were compared to select the most accurate forecasting method for the deadly disease. The data were obtained from the Surabaya Health Office covering January 2014 to December 2016. The data were partitioned into the training and testing sets. The best forecasting model was selected based on the lowest values of accuracy metrics such as the root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). The findings demonstrated that the SARIMA (2,1,1) (1,0,0) model was able to forecast the DHF outbreaks in Surabaya City compared to the ARIMA (2,1,1) and LSTM models. We further forecasted the DHF cases for 12 month horizons starting from January 2017 to December 2017 using the SARIMA (2,1,1) (1,0,0), ARIMA (2,1,1), and LSTM models. The results revealed that the SARIMA (2,1,1) (1,0,0) model outperformed the ARIMA (2,1,1) and LSTM models based on the goodness-of-fit measure. The results showed significant seasonal outbreaks of DHF, particularly from March to September. The highest cases observed in May suggested a significant seasonal correlation between DHF and air temperature. This research is the first attempt to analyze the time-series model for DHF cases in Surabaya City and forecast future outbreaks. The findings could help policymakers and public health specialists develop efficient public health strategies to detect and control the disease, especially in the early phases of outbreaks.

Prevention of seasonal influenza outbreak via healthcare insurance

The outbreak of seasonal flu costs billions of dollars in health care utilization and lost productivity. Despite the effectiveness of vaccination and antiviral medications to prevent serious flu-related complications and slow down the spread of an influenza epidemic, only 52% of the U.S. population aged 6 months and older received flu vaccines in the 2019-20 flu season. In addition, a costly out-of-pocket expense results in fewer patients seeking treatment, leading to potential hospitalizations and even flu-related deaths. In this study, we develop an integrated healthcare insurance mechanism that optimizes two incentive policies, vaccination reward and cost-sharing, to alleviate the medical cost and disease burden while preventing the outbreak of seasonal influenza. We model the dynamic interaction between a single insurer and multiple insureds as a Stackelberg vaccination game; we then embed the game into an agent-based simulation to model the spread of flu in a population under different policies. Finally, we apply machine learning and simulation optimization to optimize healthcare incentive policies in a large-scale flu transmission simulation. Simulation results indicate that the proposed methodology efficiently identifies a set of good incentive policies under different scenarios of flu vaccine efficacy and reproduction numbers.

All-Serotype Dengue Virus Detection through Multilayered Origami-Based Paper/Polymer Microfluidics.

The dengue virus (DENV) infection commonly triggers threatening seasonal outbreaks all around the globe (estimated yearly infections are in the order of 100 million, combining all the viral serotypes), testifying the need for early detection to facilitate disease management and patient recovery. The laboratory-based testing procedures for detecting DENV infection early enough are challenged by the need of resourced settings that result in inevitable cost penalty and unwarranted delay in obtaining the test results due to distance-related factors with respect to the patient's location. Recognizing that the introduction of alternative extreme point-of-care technologies for early detection may potentially mitigate this challenge largely, we develop here a multiplex paper/polymer-based detection strip that interfaces with an all-in-one simple portable device, synchronizing the pipeline of nucleic acid isolation, isothermal amplification, and colorimetric analytics as well as readout for detecting all the four serotypes of dengue viruses in around 30 min from about 50 μL of human blood serum with high specificity and sensitivity. Aligned with the mandatory guidelines of the World Health Organization, the ultralow-cost test is ideal for dissemination at different community centers via a user-friendly device interface, not only as a critical surveillance measure in recognizing the potential cocirculation of the infection across regions that are hyperendemic for all four DENV serotypes but also for facilitating effective monitoring of patients infected by any one of the particular viral serotypes as well as timely administration of life-saving measures on need.

Essential Oils and Their Compounds as Potential Anti-Influenza Agents.

Essential oils (EOs) are chemical substances, mostly produced by aromatic plants in response to stress, that have a history of medicinal use for many diseases. In the last few decades, EOs have continued to gain more attention because of their proven therapeutic applications against the flu and other infectious diseases. Influenza (flu) is an infectious zoonotic disease that affects the lungs and their associated organs. It is a public health problem with a huge health burden, causing a seasonal outbreak every year. Occasionally, it comes as a disease pandemic with unprecedentedly high hospitalization and mortality. Currently, influenza is managed by vaccination and antiviral drugs such as Amantadine, Rimantadine, Oseltamivir, Peramivir, Zanamivir, and Baloxavir. However, the adverse side effects of these drugs, the rapid and unlimited variabilities of influenza viruses, and the emerging resistance of new virus strains to the currently used vaccines and drugs have necessitated the need to obtain more effective anti-influenza agents. In this review, essential oils are discussed in terms of their chemistry, ethnomedicinal values against flu-related illnesses, biological potential as anti-influenza agents, and mechanisms of action. In addition, the structure-activity relationships of lead anti-influenza EO compounds are also examined. This is all to identify leading agents that can be optimized as drug candidates for the management of influenza. Eucalyptol, germacrone, caryophyllene derivatives, eugenol, terpin-4-ol, bisabolene derivatives, and camphecene are among the promising EO compounds identified, based on their reported anti-influenza activities and plausible molecular actions, while nanotechnology may be a new strategy to achieve the efficient delivery of these therapeutically active EOs to the active virus site.

Distinct immunological and molecular signatures underpinning influenza vaccine responsiveness in the elderly

Seasonal influenza outbreaks, especially in high-risk groups such as the elderly, represent an important public health problem. Prevailing inadequate efficacy of seasonal vaccines is a crucial bottleneck. Understanding the immunological and molecular mechanisms underpinning differential influenza vaccine responsiveness is essential to improve vaccination strategies. Here we show comprehensive characterization of the immune response of randomly selected elderly participants (≥ 65 years), immunized with the adjuvanted influenza vaccine Fluad. In-depth analyses by serology, multi-parametric flow cytometry, multiplex and transcriptome analysis, coupled to bioinformatics and mathematical modelling, reveal distinguishing immunological and molecular features between responders and non-responders defined by vaccine-induced seroconversion. Non-responders are specifically characterized by multiple suppressive immune mechanisms. The generated comprehensive high dimensional dataset enables the identification of putative mechanisms and nodes responsible for vaccine non-responsiveness independently of confounding age-related effects, with the potential to facilitate development of tailored vaccination strategies for the elderly.

Association between multiple meteorological variables and seasonal influenza A and B virus transmission in Macau

Few studies have evaluated the influence of meteorological variables on different influenza types in subtropical regions. This study aimed to explore the association between meteorological variables and the onset of influenza A (Flu-A) and B (Flu-B) in Macau. Daily influenza case data in Macau were collected from Kiang Wu Hospital from 1 January, 2014 to 31 December, 2018. Daily meteorological data were obtained from the Macau Meteorological Service. The distributed lag non-linear model (DLNM) was used to estimate the effects of meteorological variables on seasonal influenza outbreaks. Regarding mean air temperature (temp), the peaks of the cumulative relative risks (RRs) of Flu-A and Flu-B were both at 4.0 °C and 28.0 °C. Regarding the diurnal temperature range (DTR), the peaks of the cumulative RR of Flu-A were at 1.0 °C and 5.0 °C, while the cumulative RR of Flu-B increased as the DTR decreased. The association between influenza risks and relative humidity (RH) showed a U-shape curve. The risk of influenza increased when the RH was below 50% or above 90%. The risk of both types of influenza increased significantly when the sunshine duration (SD) was below 3.5 h. Taking the median value as the reference, a significant cold effect was observed over 16–24 days lag for Flu-A. Lag effects were found for both types of influenza in low-DTR, and humid and short SD conditions. This study revealed complex non-linear association between meteorological variables and the different influenza types in Macau.

Seasonality of SARS-CoV-2 Infections in G20 Countries: Ecological Analysis of Publicly Available Data.

Previous laboratory studies of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) revealed that the stability of the virus in the air or on surfaces is sensitive to seasonally relevant environmental conditions. However, the seasonality of the virus in the real world remains unclear because each country adopted various infection control policies. Therefore, we investigated peak dates with regard to new confirmed cases of coronavirus disease 2019 (COVID-19) and an association of these dates with the timing of the lockdown among G20 countries that have four seasons from 1 June 2020 to 18 February 2021. As a result, countries in both hemispheres experienced seasonal peaks in the number of COVID-19 cases both in the middle of warm and cold seasons. In addition, there were no apparent relationships between the peak date and periods with stringent measures. Our study demonstrated that SARS-CoV-2 causes seasonal outbreaks in the winter and possibly summer and thus, countries might need to consider measures to prepare for resurgence of the virus in the middle of 2021.

Efficient algorithms for real-time syndromic surveillance

ObjectiveOutbreaks of influenza-like diseases often cause spikes in the demand for hospital beds. Early detection of these outbreaks can enable improved management of hospital resources. The objective of this study was to test whether surveillance algorithms designed to be responsive to a wide range of anomalous decreases in the time between emergency department (ED) presentations with influenza-like illnesses provide efficient early detection of these outbreaks. MethodsOur study used data on ED presentations to major public hospitals in Queensland, Australia across 2017–2020. We developed surveillance algorithms for each hospital that flag potential outbreaks when the average time between successive ED presentations with influenza-like illnesses becomes anomalously small. We designed one set of algorithms to be responsive to a wide range of anomalous decreases in the time between presentations. These algorithms concurrently monitor three exponentially weighted moving averages (EWMAs) of the time between presentations and flag an outbreak when at least one EWMA falls below its control limit. We designed another set of algorithms to be highly responsive to narrower ranges of anomalous decreases in the time between presentations. These algorithms monitor one EWMA of the time between presentations and flag an outbreak when the EWMA falls below its control limit. Our algorithms use dynamic control limits to reflect that the average time between presentations depends on the time of year, time of day, and day of the week. ResultsWe compared the performance of the algorithms in detecting the start of two epidemic events at the hospital-level: the 2019 seasonal influenza outbreak and the early-2020 COVID-19 outbreak. The algorithm that concurrently monitors three EWMAs provided significantly earlier detection of these outbreaks than the algorithms that monitor one EWMA. ConclusionSurveillance algorithms designed to be responsive to a wide range of anomalous decreases in the time between ED presentations are highly efficient at detecting outbreaks of influenza-like diseases at the hospital level.

Current status of influenza: What do we know so far?

Over the last years much research has been carried out, especially after the influenza pandemic in 2009. It is known that many strains of influenza virus are widely spread in the global population causing seasonal outbreaks every year. Influenza leads to flu-like symptoms, which present favorable prognosis, since most patients completely recover from it within two to five days. Many new antiviral drugs have been developed in the last few years, but since the infection generally presents a mild course, symptom-based care is often enough. Influenza virus can undergo mutations through some mechanisms, like antigenic drift and antigenic shift. Alongside with its high infectivity such mechanisms probably have worked together to keep influenza infection viable through the years, despite all new drugs and preventive measures. This review aimed to bring the most relevant data about influenza obtained in the past years, including some historical aspects, pathophysiology, clinical findings, and diagnostic and therapeutic approach.

Influenza During the 2010-2020 Decade in the United States: Seasonal Outbreaks and Vaccine Interventions.

The 10 years between the last influenza pandemic and start of the severe acute respiratory syndrome coronavirus 2 pandemic have been marked by great advances in our ability to follow influenza occurrence and determine vaccine effectiveness (VE), largely based on widespread use of the polymerase chain reaction assay. We examine the results, focusing mainly on data from the United States and inactivated vaccines. Surveillance has expanded, resulting in increased ability to characterize circulating viruses and their impact. The surveillance has often confirmed previous observations on timing of outbreaks and age groups affected, which can now be examined in greater detail. Selection of strains for vaccines is now based on enhanced viral characterization using immunologic, virologic, and computational techniques not previously available. Vaccine coverage has been largely stable, but VE has remained modest and, in some years, very low. We discuss ways to improve VE based on existing technology while we work toward supraseasonal vaccines.

Deep sequencing analysis of clinical samples from patients with acute infectious conjunctivitis during the COVID-19 delta surge in Madurai, India.

BackgroundSeasonal outbreaks of infectious conjunctivitis remain a public health issue. Determination of outbreak etiologies in the context of a worldwide pandemic may provide useful information to guide public health strategies. The aim of this study was to identify pathogens associated with outpatient infectious conjunctivitis during the COVID-19 Delta surge.MethodsThis prospective study was conducted from April 2021 to September 2021. All outpatients presenting to the Aravind Eye Center (Madurai, India) with signs and symptoms consistent with acute infectious conjunctivitis were eligible. Three swabs were obtained from each participant: one from each conjunctiva and one from the anterior nares. Samples were processed for metagenomic RNA deep sequencing (RNA-seq).ResultsSamples from 106 study participants were sequenced. The most common presenting symptoms were tearing (86%) and itching (71%). Preauricular lymphadenopathy was present in 38% of participants. 20% of participants had close contacts with similar symptoms. Systemic symptoms such as coughing, runny nose, vomiting or diarrhea were uncommonly reported. 60% of all participants used some medicated eye drops upon enrollment. 75% of study participants demonstrated infection with human adenovirus D (HAdV-D). 11% of conjunctivitis was associated with SARS-CoV-2. 15% had no definitive pathogen detected. 8% of all participants had codetection of more than one pathogen on RNA-seq.ConclusionsDuring the COVID-19 Delta surge in India, HAdV-D was the most common pathogen associated with infectious conjunctivitis. SARS-CoV-2 was the second most common etiology. Seasonal surveillance may be necessary for the determination of emerging and reemerging pathogens responsible for infectious conjunctivitis.

Функционально-технологические свойства и пищевая ценность медузы Rhizostoma pulmo (Macri, 1778)

Representatives of jelly plankton (in particular, scyphoid) are the underutilized resources in the Azov-Black Sea Basin. The wave nature of their reproduction has a powerful impact on the ecosystems of the two seas. Seasonal outbreaks of abundance cause an increasing pressure on the food supply of commercial aquatic organisms, a change in the structure and quality of fish catches, and a deterioration in recreational conditions in the resort areas of the Azov-Black Sea Basin. The importance of the search for a rational solution to the problem of processing Rhizostoma pulmo jellyfish can be explained by the specific features of their biochemical composition, presence of biologically active substances in the tissues of aquatic organisms, their low liquidity as raw stuff and seasonal resource availability. There are presented the data on the size-weight composition of Rh. pulmo caught in the Sea of Azov, the influence of various types of heat treatment (freezing, boiling, microwave treatment, enzymatic hydrolysis) on the change in the chemical composition and functional and technological properties of jellyfish is considered. The biological and energy value 
 of raw and boiled jellyfish is calculated. Taking into account the active syneresis of jellyfish tissues (up to 60% of water is released during the first 3 hours) and the inexpediency of transporting fresh jellyfish, the preliminary heat treatment is recommended, followed by the use of the object of study in culinary products and semi-finished products, as well as enrichment as a prerequisite for obtaining original products both from the point of view of organoleptics and enhancing the nutritional value of food products. Studies with the use of freeze drying, as well as with the use of minced jellyfish to obtain a wide range of combined products are particularly noteworthy.

Estimation of introduction time window of highly pathogenic avian influenza virus into broiler chicken farms during the 2020 – 2021 winter season outbreak in Japan

When an infectious disease occurs in an area, early detection of infected farms is important to respond quickly and contain the outbreak on a small scale. Estimating the time window for the introduction of the infection is important for its prevention and control. The aim of this study was to estimate the farm-specific time window from the introduction of the highly pathogenic avian influenza (HPAI) virus into poultry farms using field data from the HPAI H5N8 outbreak in the 2020–2021 winter season in Japan. Daily mortality data from 12 broiler chicken farms during the outbreak were used for the analysis. A mathematical model (Susceptible-Exposed-Infectious-Removed, SEIR model) was applied to generate the within-flock transmission of HPAI. The model-predicted mortality was fitted to the observed excess mortality data induced by HPAI to estimate the farm-specific transmission rate and the time of virus introduction. The estimated value of the transmission rate in each farm was 1.449 day−1 in median (min: 0.661 day−1, max: 3.387 day−1). The time window from the introduction of the virus to notification in each farm was estimated at 14.0 days in median (min: 8.6 days, max: 24.1 days) in the deterministic model. In addition, in the stochastic model considering the randomness of transmission in the early phase of the outbreak, the upper value of 95 % credible interval of the time window ranged from 12 to 34 days, with a median of 21 days. The results suggest that although one to three weeks had elapsed on most farms until notification after the virus introduction, the time window could exceed three weeks considering the stochasticity of disease transmission. As for the potential farm characteristics affecting within-flock transmission, the transmission rate was smaller (p-value=0.02) and the estimated time window from introduction to notification was longer (p-value=0.02) when birds were older. This study provides reliable information for setting up a tracing period for a potential source farm and enhancing the efforts for early detection.

West Nile virus transfusion-transmission risk in Australia associated with a seasonal outbreak in the United States.

West Nile virus (WNV) is a potentially transfusion-transmissible virus endemic in the US. The aim of this study was to estimate the monthly WNV transfusion transmission (TT) risk in Australia associated with donors returning from the US in 2018 and consider the implications for mitigation strategies. We used a probabilistic risk model to estimate the monthly WNV TT risks for each outbreak state/district in the US for the 2018 transmission season and the cumulative monthly risk for all US states/districts. The highest monthly cumulative transfusion risk in Australia occurred in August 2018 when 746 West Nile neuroinvasive disease cases were reported in the US and the estimated mean WNV TT risk in Australia was 1 in 1.0× 108 donations (95% confidence interval [CI]: 1.6× 108 -7.0× 107 ). The highest risk during August was associated with California, with a mean risk of 1 in 4.1× 108 donations (95% CI: 2.9× 108 -6.6× 108 ), representing 24% of the total risk in Australia. The cumulative TT risk in Australia for the other 11 months varied from 1 in 1.5× 108 donations (95% CI: 2.3× 108 -1.0× 108 ) in September to 1 in 3.9× 1010 donations (95% CI: 6.1× 1010 -2.7× 1010 ) in February. Our modeling indicates that the WNV TT risk in Australia associated with seasonal outbreaks in the US is extremely small and may not warrant donation restrictions for donors returning from the US.

A retrospective analysis of respiratory virus transmission before and during the COVID-19 pandemic in Pune the western region of India.

BackgroundSARS-CoV-2 was first reported in China in December 2019 and quickly spread across the world. Non-pharmaceutical interventions (NPIs) are the key to control the transmission of respiratory viruses. To stop the spread, NPI is widely recommended and is still followed by most countries.MethodsAt the National Influenza Center of the Indian Council of Medical Research-National Institute of Virology (ICMR-NIV), the surveillance of severe acute respiratory illness and acute respiratory illness cases for influenza and other respiratory viruses is in place. In this study, we analyzed surveillance data on respiratory viruses and/or SARS-CoV-2 testing from January 2017 to December 2021. Multiplex real-time PCR was used to detect the respiratory viruses.ResultsOur findings indicate that during the pandemic, the positivity for influenza A and B, metapneumovirus, parainfluenza virus, respiratory syncytial virus, and human coronavirus declined significantly.ConclusionThe annual distinct seasonal outbreaks of influenza, RSV, and other respiratory viruses as observed during the pre-COVID-19 period were not observed during the COVID-19 pandemic in years 2020 and 21. Social distancing, lock-downs, and non-pharmaceutical interventions may play an important role in the reduction of respiratory viruses. Understanding the seasonal respiratory virus decline could help public health experts prepare for future respiratory virus pandemics.

Origins of high latitude introductions of Aedes aegypti to Nebraska and Utah during 2019

Aedes aegypti (L.), the yellow fever mosquito, is also an important vector of dengue and Zika viruses, and an invasive species in North America. Aedes aegypti inhabits tropical and sub-tropical areas of the world and in North America is primarily distributed throughout the southern US states and Mexico. The northern range of Ae. aegypti is limited by cold winter months and establishment in these areas has been mostly unsuccessful. However, frequent introductions of Ae. aegypti to temperate, non-endemic areas during the warmer months can lead to seasonal activity and disease outbreaks. Two Ae. aegypti incursions were reported in the late summer of 2019 into York, Nebraska and Moab, Utah. These states had no history of established populations of this mosquito and no evidence of previous seasonal activity. We genotyped a subset of individuals from each location at 12 microsatellite loci and~14,000 single nucleotide polymorphic markers to determine their genetic affinities to other populations worldwide and investigate their potential source of introduction. Our results support a single origin for each of the introductions from different sources. Aedes aegypti from Utah likely derived from Tucson, Arizona, or a nearby location. Nebraska specimen results were not as conclusive, but point to an origin from southcentral or southeastern US. In addition to an effective, efficient, and sustainable control of invasive mosquitoes, such as Ae. aegypti, identifying the potential routes of introduction will be key to prevent future incursions and assess their potential health threat based on the ability of the source population to transmit a particular virus and its insecticide resistance profile, which may complicate vector control.

Comparative genomic analysis of five freshwater cyanophages and reference-guided metagenomic data mining

BackgroundAs important producers using photosynthesis on Earth, cyanobacteria contribute to the oxygenation of atmosphere and the primary production of biosphere. However, due to the eutrophication of urban waterbodies and global warming, uncontrollable growth of cyanobacteria usually leads to the seasonal outbreak of cyanobacterial blooms. Cyanophages, a group of viruses that specifically infect and lyse cyanobacteria, are considered as potential environment-friendly agents to control the harmful blooms. Compared to the marine counterparts, only a few freshwater cyanophages have been isolated and genome sequenced to date, largely limiting their characterizations and applications.ResultsHere, we isolated five freshwater cyanophages varying in tail morphology, termed Pam1~Pam5, all of which infect the cyanobacterium Pseudanabaena mucicola Chao 1806 that was isolated from the bloom-suffering Lake Chaohu in Anhui, China. The whole-genome sequencing showed that cyanophages Pam1~Pam5 all contain a dsDNA genome, varying in size from 36 to 142 Kb. Phylogenetic analyses suggested that Pam1~Pam5 possess different DNA packaging mechanisms and are evolutionarily distinct from each other. Notably, Pam1 and Pam5 have lysogeny-associated gene clusters, whereas Pam2 possesses 9 punctuated DNA segments identical to the CRISPR spacers in the host genome. Metagenomic data-based calculation of the relative abundance of Pam1~Pam5 at the Nanfei estuary towards the Lake Chaohu revealed that the short-tailed Pam1 and Pam5 account for the majority of the five cyanophages. Moreover, comparative analyses of the reference genomes of Pam1~Pam5 and previously reported cyanophages enabled us to identify three circular and seven linear contigs of virtual freshwater cyanophages from the metagenomic data of the Lake Chaohu.ConclusionsWe propose a high-throughput strategy to systematically identify cyanophages based on the currently available metagenomic data and the very limited reference genomes of experimentally isolated cyanophages. This strategy could be applied to mine the complete or partial genomes of unculturable bacteriophages and viruses. Transformation of the synthesized whole genomes of these virtual phages/viruses to proper hosts will enable the rescue of bona fide viral particles and eventually enrich the library of microorganisms that exist on Earth.4WYBiJEHr_CXoYqKn7ZoJMVideo abstract

Pediatric burden and seasonality of human metapneumovirus over 5 years in Managua, Nicaragua.

Human metapneumovirus (hMPV) is an important cause of pediatric respiratory infection. We leveraged the Nicaraguan Pediatric Influenza Cohort Study (NPICS) to assess the burden and seasonality of symptomatic hMPV infection in children. NPICS is an ongoing prospective study of children in Managua, Nicaragua. We assessed children for hMPV infection via real-time reverse-transcription polymerase chain reaction (RT-PCR). We used classical additive decomposition analysis to assess the temporal trends, and generalized growth models (GGMs) were used to estimate effective reproduction numbers. From 2011 to 2016, there were 564 hMPV symptomatic infections, yielding an incidence rate of 5.74 cases per 100 person-years (95% CI 5.3, 6.2). Children experienced 3509 acute lower respiratory infections (ALRIs), of which 160 (4.6%) were associated with hMPV infection. Children under the age of one had 55% of all symptomatic hMPV infections (62/112) develop into hMPV-associated ALRIs and were five times as likely as children over one to have an hMPV-associated ALRI (rate ratio 5.5 95% CI 4.1, 7.4 p < 0.001). Additionally, symptomatic reinfection with hMPV was common. In total, 87 (15%) of all observed symptomatic infections were detected reinfections. The seasonality of symptomatic hMPV outbreaks varied considerably. From 2011 to 2016, four epidemic periods were observed, following a biennial seasonal pattern. The mean ascending phase of the epidemic periods were 7.7weeks, with an overall mean estimated reproductive number of 1.2 (95% CI 1.1, 1.4). Symptomatic hMPV infection was associated with substantial burden among children in the first year of life. Timing and frequency of symptomatic hMPV incidence followed biennial patterns.