Novel Synthesis and Phenotypic Analysis of Mutant Clouds for Hepatitis E Virus Genotype 1.

Many RNA viruses exist as an ensemble of genetically diverse, replicating populations known as a mutant cloud. The genetic diversity (cloud size) and composition of this mutant cloud may influence several important phenotypic features of the virus, including its replication capacity. We applied a straightforward, bacterium-free approach using error-prone PCR coupled with reverse genetics to generate infectious mutant RNA clouds with various levels of genetic diversity from a genotype 1 strain of hepatitis E virus (HEV). Cloning and sequencing of a genomic fragment encompassing 70% of open reading frame 1 (ORF1) or of the full genome from variants in the resultant clouds showed the occurrence of nucleotide mutations at a frequency on the order of 10-3 per nucleotide copied and the existence of marked genetic diversity, with a high normalized Shannon entropy value. The mutant clouds showed transient replication in cell culture, while wild-type HEV did not. Cross-sectional data from these cell cultures supported the existence of differential effects of clouds of various sizes and compositions on phenotypic characteristics, such as the replication level of (+)-RNA progeny, the amounts of double-stranded RNA (a surrogate for the rate of viral replication) and ORF1 protein, and the expression of interferon-stimulated genes. Since mutant cloud size and composition influenced the viral phenotypic properties, a better understanding of this relationship may help to provide further insights into virus evolution and prediction of emerging viral diseases.IMPORTANCE Several biological or practical limitations currently prevent the study of phenotypic behavior of a mutant cloud in vitro We developed a simple and rapid method for synthesizing mutant clouds of hepatitis E virus (HEV), a single-stranded (+)-RNA [ss(+) RNA] virus, with various and controllable levels of genetic diversity, which could then be used in a cell culture system to study the effects of cloud size and composition on viral phenotype. In a cross-sectional analysis, we demonstrated that a particular mutant cloud which had an extremely high genetic diversity had a replication rate exceeding that of wild-type HEV. This method should thus provide a useful model for understanding the phenotypic behavior of ss(+) RNA viruses.

Melioidosis diagnostic workshop, 2013.

Melioidosis is a severe disease that can be difficult to diagnose because of its diverse clinical manifestations and a lack of adequate diagnostic capabilities for suspected cases. There is broad interest in improving detection and diagnosis of this disease not only in melioidosis-endemic regions but also outside these regions because melioidosis may be underreported and poses a potential bioterrorism challenge for public health authorities. Therefore, a workshop of academic, government, and private sector personnel from around the world was convened to discuss the current state of melioidosis diagnostics, diagnostic needs, and future directions.

Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations.

Background: Short-term forecasts of infectious disease burden can contribute to situational awareness and aid capacity planning. Based on best practice in other fields and recent insights in infectious disease epidemiology, one can maximise the predictive performance of such forecasts if multiple models are combined into an ensemble. Here, we report on the performance of ensembles in predicting COVID-19 cases and deaths across Europe between 08 March 2021 and 07 March 2022. Methods: We used open-source tools to develop a public European COVID-19 Forecast Hub. We invited groups globally to contribute weekly forecasts for COVID-19 cases and deaths reported by a standardised source for 32 countries over the next 1-4 weeks. Teams submitted forecasts from March 2021 using standardised quantiles of the predictive distribution. Each week we created an ensemble forecast, where each predictive quantile was calculated as the equally-weighted average (initially the mean and then from 26th July the median) of all individual models' predictive quantiles. We measured the performance of each model using the relative Weighted Interval Score (WIS), comparing models' forecast accuracy relative to all other models. We retrospectively explored alternative methods for ensemble forecasts, including weighted averages based on models' past predictive performance. Results: Over 52 weeks, we collected forecasts from 48 unique models. We evaluated 29 models' forecast scores in comparison to the ensemble model. We found a weekly ensemble had a consistently strong performance across countries over time. Across all horizons and locations, the ensemble performed better on relative WIS than 83% of participating models' forecasts of incident cases (with a total N=886 predictions from 23 unique models), and 91% of participating models' forecasts of deaths (N=763 predictions from 20 models). Across a 1-4 week time horizon, ensemble performance declined with longer forecast periods when forecasting cases, but remained stable over 4 weeks for incident death forecasts. In every forecast across 32 countries, the ensemble outperformed most contributing models when forecasting either cases or deaths, frequently outperforming all of its individual component models. Among several choices of ensemble methods we found that the most influential and best choice was to use a median average of models instead of using the mean, regardless of methods of weighting component forecast models. Conclusions: Our results support the use of combining forecasts from individual models into an ensemble in order to improve predictive performance across epidemiological targets and populations during infectious disease epidemics. Our findings further suggest that median ensemble methods yield better predictive performance more than ones based on means. Our findings also highlight that forecast consumers should place more weight on incident death forecasts than incident case forecasts at forecast horizons greater than 2 weeks. Funding: AA, BH, BL, LWa, MMa, PP, SV funded by National Institutes of Health (NIH) Grant 1R01GM109718, NSF BIG DATA Grant IIS-1633028, NSF Grant No.: OAC-1916805, NSF Expeditions in Computing Grant CCF-1918656, CCF-1917819, NSF RAPID CNS-2028004, NSF RAPID OAC-2027541, US Centers for Disease Control and Prevention 75D30119C05935, a grant from Google, University of Virginia Strategic Investment Fund award number SIF160, Defense Threat Reduction Agency (DTRA) under Contract No. HDTRA1-19-D-0007, and respectively Virginia Dept of Health Grant VDH-21-501-0141, VDH-21-501-0143, VDH-21-501-0147, VDH-21-501-0145, VDH-21-501-0146, VDH-21-501-0142, VDH-21-501-0148. AF, AMa, GL funded by SMIGE - Modelli statistici inferenziali per governare l'epidemia, FISR 2020-Covid-19 I Fase, FISR2020IP-00156, Codice Progetto: PRJ-0695. AM, BK, FD, FR, JK, JN, JZ, KN, MG, MR, MS, RB funded by Ministry of Science and Higher Education of Poland with grant 28/WFSN/2021 to the University of Warsaw. BRe, CPe, JLAz funded by Ministerio de Sanidad/ISCIII. BT, PG funded by PERISCOPE European H2020 project, contract number 101016233. CP, DL, EA, MC, SA funded by European Commission - Directorate-General for Communications Networks, Content and Technology through the contract LC-01485746, and Ministerio de Ciencia, Innovacion y Universidades and FEDER, with the project PGC2018-095456-B-I00. DE., MGu funded by Spanish Ministry of Health / REACT-UE (FEDER). DO, GF, IMi, LC funded by Laboratory Directed Research and Development program of Los Alamos National Laboratory (LANL) under project number 20200700ER. DS, ELR, GG, NGR, NW, YW funded by National Institutes of General Medical Sciences (R35GM119582; the content is solely the responsibility of the authors and does not necessarily represent the official views of NIGMS or the National Institutes of Health). FB, FP funded by InPresa, Lombardy Region, Italy. HG, KS funded by European Centre for Disease Prevention and Control. IV funded by Agencia de Qualitat i Avaluacio Sanitaries de Catalunya (AQuAS) through contract 2021-021OE. JDe, SMo, VP funded by Netzwerk Universitatsmedizin (NUM) project egePan (01KX2021). JPB, SH, TH funded by Federal Ministry of Education and Research (BMBF; grant 05M18SIA). KH, MSc, YKh funded by Project SaxoCOV, funded by the German Free State of Saxony. Presentation of data, model results and simulations also funded by the NFDI4Health Task Force COVID-19 (https://www.nfdi4health.de/task-force-covid-19-2) within the framework of a DFG-project (LO-342/17-1). LP, VE funded by Mathematical and Statistical modelling project (MUNI/A/1615/2020), Online platform for real-time monitoring, analysis and management of epidemic situations (MUNI/11/02202001/2020); VE also supported by RECETOX research infrastructure (Ministry of Education, Youth and Sports of the Czech Republic: LM2018121), the CETOCOEN EXCELLENCE (CZ.02.1.01/0.0/0.0/17-043/0009632), RECETOX RI project (CZ.02.1.01/0.0/0.0/16-013/0001761). NIB funded by Health Protection Research Unit (grant code NIHR200908). SAb, SF funded by Wellcome Trust (210758/Z/18/Z).

Evaluation of FluSight influenza forecasting in the 2021-22 and 2022-23 seasons with a new target laboratory-confirmed influenza hospitalizations.

Accurate forecasts can enable more effective public health responses during seasonal influenza epidemics. Forecasting teams were asked to provide national and jurisdiction-specific probabilistic predictions of weekly confirmed influenza hospital admissions for one through four weeks ahead for the 2021-22 and 2022-23 influenza seasons. Across both seasons, 26 teams submitted forecasts, with the submitting teams varying between seasons. Forecast skill was evaluated using the Weighted Interval Score (WIS), relative WIS, and coverage. Six out of 23 models outperformed the baseline model across forecast weeks and locations in 2021-22 and 12 out of 18 models in 2022-23. Averaging across all forecast targets, the FluSight ensemble was the 2nd most accurate model measured by WIS in 2021-22 and the 5th most accurate in the 2022-23 season. Forecast skill and 95% coverage for the FluSight ensemble and most component models degraded over longer forecast horizons and during periods of rapid change. Current influenza forecasting efforts help inform situational awareness, but research is needed to address limitations, including decreased performance during periods of changing epidemic dynamics.

Workshop on treatment of and postexposure prophylaxis for Burkholderia pseudomallei and B. mallei Infection, 2010.

The US Public Health Emergency Medical Countermeasures Enterprise convened subject matter experts at the 2010 HHS Burkholderia Workshop to develop consensus recommendations for postexposure prophylaxis against and treatment for Burkholderia pseudomallei and B. mallei infections, which cause melioidosis and glanders, respectively. Drugs recommended by consensus of the participants are ceftazidime or meropenem for initial intensive therapy, and trimethoprim/sulfamethoxazole or amoxicillin/clavulanic acid for eradication therapy. For postexposure prophylaxis, recommended drugs are trimethoprim/sulfamethoxazole or co-amoxiclav. To improve the timely diagnosis of melioidosis and glanders, further development and wide distribution of rapid diagnostic assays were also recommended. Standardized animal models and B. pseudomallei strains are needed for further development of therapeutic options. Training for laboratory technicians and physicians would facilitate better diagnosis and treatment options.

Public health response to an anthrax attack: an evaluation of vaccination policy options.

A discrete-time, deterministic, compartmental model was developed and analyzed to provide insight into how the use of anthrax vaccine before or after a large-scale attack can reduce casualties. The model accounts for important response and protection factors such as antibiotic and vaccine efficacy, the protective effects of buildings, the timing of emergency response, and antibiotic adherence and vaccine coverage in the population prior to the attack. The relative benefit of pre- versus post-exposure vaccination is influenced by the timing of the post-exposure antibiotic distribution campaign as well as assumptions of antibiotic adherence. The results indicate that, regardless of which vaccination policy is adopted, a rapid and effective post-attack medical response has a large impact on the number of lives that can be saved by a post-exposure prophylaxis (PEP) campaign. A sensitivity analysis of the model indicates that uncertainty in medical efficacy and the time to initiate a PEP campaign are the model parameters that have the greatest impact on the number of predicted deaths. It is shown that for each day that a mass prophylaxis campaign is delayed, more casualties and deaths result than for each day that the completion of the campaign is delayed.

Assessing the Capacity of the US Health Care System to Use Additional Mechanical Ventilators During a Large-Scale Public Health Emergency.

OBJECTIVE: A large-scale public health emergency, such as a severe influenza pandemic, can generate large numbers of critically ill patients in a short time. We modeled the number of mechanical ventilators that could be used in addition to the number of hospital-based ventilators currently in use. METHODS: We identified key components of the health care system needed to deliver ventilation therapy, quantified the maximum number of additional ventilators that each key component could support at various capacity levels (ie, conventional, contingency, and crisis), and determined the constraining key component at each capacity level. RESULTS: Our study results showed that US hospitals could absorb between 26,200 and 56,300 additional ventilators at the peak of a national influenza pandemic outbreak with robust pre-pandemic planning. CONCLUSIONS: The current US health care system may have limited capacity to use additional mechanical ventilators during a large-scale public health emergency. Emergency planners need to understand their health care systems' capability to absorb additional resources and expand care. This methodology could be adapted by emergency planners to determine stockpiling goals for critical resources or to identify alternatives to manage overwhelming critical care need.

Mass prophylaxis dispensing concerns: traffic and public access to PODs.

The ability to quickly dispense postexposure prophylaxis (PEP) using multiple points of dispensing (PODs) following a bioterrorism event could potentially save a large proportion of those who were exposed, while failure in PEP dispensing could have dire public health consequences. A Monte Carlo simulation was developed to explore the traffic flow and parking around PODs under different arrival rates and how these factors might affect the utilization rate of POD workers. The results demonstrate that the public can reasonably access the PODs under ideal conditions assuming a stationary (uniform) arrival rate. For the 5 nonstationary arrival rates tested, however, the available parking spaces quickly become filled, causing long traffic queues and resulting in total processing times that range from 1 hour to over 6 hours. Basic planning considerations should include the use of physical barriers, signage, and traffic control officers to help direct vehicular and pedestrian access to the PODs. Furthermore, the parking and traffic surrounding PODs creates long queues of people waiting to access the PODs. Thus, POD staff are fully used approximately 90% of the time, which can lead to worker fatigue and burn out.

Kinetics of influenza A virus infection in humans.

Currently, little is known about the viral kinetics of influenza A during infection within an individual. We utilize a series of mathematical models of increasing complexity, which incorporate target cell limitation and the innate interferon response, to examine influenza A virus kinetics in the upper respiratory tracts of experimentally infected adults. The models were fit to data from an experimental H1N1 influenza A/Hong Kong/123/77 infection and suggest that it is important to include the eclipse phase of the viral life cycle in viral dynamic models. Doing so, we estimate that after a delay of approximately 6 h, infected cells begin producing influenza virus and continue to do so for approximately 5 h. The average lifetime of infected cells is approximately 11 h, and the half-life of free infectious virus is approximately 3 h. We calculated the basic reproductive number, R(0), which indicated that a single infected cell could produce approximately 22 new productive infections. This suggests that antiviral treatments have a large hurdle to overcome in moderating symptoms and limiting infectiousness and that treatment has to be initiated as early as possible. For about 50% of patients, the curve of viral titer versus time has two peaks. This bimodal behavior can be explained by incorporating the antiviral effects of interferon into the model. Our model also compared well to an additional data set on viral titer after experimental infection and treatment with the neuraminidase inhibitor zanamivir, which suggests that such models may prove useful in estimating the efficacies of different antiviral therapies for influenza A infection.

Evolution of the equine infectious anemia virus long terminal repeat during the alteration of cell tropism.

Equine infectious anemia virus (EIAV) is a lentivirus with in vivo cell tropism primarily for tissue macrophages; however, in vitro the virus can be adapted to fibroblasts and other cell types. Tropism adaptation is associated with both envelope and long terminal repeat (LTR) changes, and findings strongly suggest that these regions of the genome influence cell tropism and virulence. Furthermore, high levels of genetic variation have been well documented in both of these genomic regions. However, specific EIAV nucleotide or amino acid changes that are responsible for cell tropism changes have not been identified. A study was undertaken with the highly virulent, macrophage-tropic strain of virus EIAV(wyo) to identify LTR changes associated with alterations in cell tropism. We found the stepwise generation of a new transcription factor binding motif within the enhancer that was associated with adaptation of EIAV to endothelial cells and fibroblasts. An LTR that contained the new motif had enhanced transcriptional activity in fibroblasts, whereas the new site did not alter LTR activity in a macrophage cell line. This finding supports a previous prediction that selection for new LTR genetic variants may be a consequence of cell-specific selective pressures. Additional investigations of the EIAV(wyo) LTR were performed in vivo to determine if LTR evolution could be detected over the course of a 3-year infection. Consistent with previous in vivo findings, we observed no changes in the enhancer region of the LTR over that time period, indicating that the EIAV(wyo) LTR was evolutionarily stable in vivo.

Isolation and characterization of RNase LTR sequences of Ty1-copia retrotransposons in common bean (Phaseolus vulgaris L).

Retroelements have proved useful for molecular marker studies and play an important role in genome evolution. Ty1-copia retrotransposons are ubiquitous and heterogeneous in plant genomes, and although many elements have been isolated and characterized, almost no information about them is available in the literature for Phaseolus vulgaris L. We report here the isolation and characterization of new RNase long terminal repeat (LTR) sections of the Ty1-copia group for this crop plant. RNAse sections showed conserved amino acids with the downstream sections corresponding to the polypurine-tract and 5' sections of 3' LTRs. The RNase sections were aligned using ClustalX to find potential relationships between sequences. A comparison with this analysis was made using the partition analysis of quasispecies package (PAQ), which is specific for quasispecies-like populations. The analysis revealed eight distinct groups. To uncover LTR variability and potential conserved promoter motifs, we also designed new primers from the presumed polypurine-tract regions. A similarity search found short stretches similar to upstream and downstream regions of some genes. Conserved motifs, corresponding to transcription factor binding sites, were discovered through MatInspector software and two sequences characterized. From a putative LTR fragment, we then designed a new primer, which, through sequence-specific amplification polymorphism (SSAP), showed numerous polymorphic bands between two distinct P. vulgaris accessions.

Subpopulations of equine infectious anemia virus Rev coexist in vivo and differ in phenotype.

Lentiviruses exist in vivo as a population of related, nonidentical genotypes, commonly referred to as quasispecies. The quasispecies structure is characteristic of complex adaptive systems and contributes to the high rate of evolution in lentiviruses that confounds efforts to develop effective vaccines and antiviral therapies. Here, we describe analyses of genetic data from longitudinal studies of genetic variation in a lentivirus regulatory protein, Rev, over the course of disease in ponies experimentally infected with equine infectious anemia virus. As observed with other lentivirus data, the Rev variants exhibited a quasispecies character. Phylogenetic and partition analyses suggested that the Rev quasispecies comprised two distinct subpopulations that coexisted during infection. One subpopulation appeared to accumulate changes in a linear, time-dependent manner, while the other evolved radially from a common variant. Over time, the two subpopulations cycled in predominance coincident with changes in the disease state, suggesting that the two groups differed in selective advantage. Transient expression assays indicated the two populations differed significantly in Rev nuclear export activity. Chimeric proviral clones containing Rev genotypes representative of each population differed in rate and overall level of virus replication in vitro. The coexistence of genetically distinct viral subpopulations that differ in phenotype provides great adaptability to environmental changes within the infected host. A quasispecies model with multiple subpopulations may provide additional insight into the nature of lentivirus reservoirs and the evolution of antigenic and drug-resistant variants.