Crimean Congo hemorrhagic fever virus (CCHFV) is one of the most severe viral zoonozes. It is prevalent throughout Africa, Asia and southern Europe. Limited availability of sequence data has hindered phylogeographic studies. The complete genomic sequence of all three segments of 14 Crimean Congo hemorrhagic fever virus strains isolated from 1958–2000 in Russia, Central Asia and Africa was identified. Each genomic segment was independently subjected to continuous Bayesian phylogeographic analysis. The origin of each genomic segment was traced to Africa about 1,000–5,000 years ago. The virus was first introduced to South and Central Asia in the Middle Ages, and then spread to China, India and Russia. Reverse transfers of genomic segments from Asia to Africa were also observed. The European CCHFV genotype V was introduced to Europe via the Astrakhan region in South Russia 280–400 years ago and subsequently gradually spread westward in Russia, to Turkey and the Balkans less than 150 years ago. Only a few recombination events could be suggested in S and L genomic segments, while segment reassortment was very common. The median height of a non-reassortant phylogenetic tree node was 68–156 years. There were reassortment events within the European CCHFV lineage, but not with viruses from other locations. Therefore, CCHFV in Europe is a recently emerged zoonosis that represents a spillover from the global gene pool. Nairovirus, family Bunyaviridae. The negative-sense RNA genome has three segments. The small (S) segment is about 1.7 Kb long and encodes a nucleoprotein. The medium (M) segment is approximately 5.3 Kb long and encodes a single ORF that is cleaved into two envelope glycoproteins and a hypervariable protein of ca. 250 amino acids known as a mucin-like domain. The large (L) segment is approximately 12.1 Kb long and encodes a single protein that contains a polymerase domain  for the S segment, 1.0 x 10−4 [0.65–1.4 x 10−4] for the M segment and 0.8 x 10−4 [0.6–1.1x10-4] for the L segment  have been previously reported for partial (ca. 300 or 500 nt) S segment sequences [−4 [1.76–5.51 x 10−4] instead of 1.3 x 10−4 [0.62–2.0 x 10−4] for the full S segment. Therefore, shorter S segment fragments indeed produce higher substitution rates than the complete S segment.Bayesian phylogenetic analysis implied substitution rates of 1.3x10 segment . These vthin GtV . Lower s−4 1.6–4. x 10−4 sPhylogenies of distinct segments were largely discordant due to reassortment, which is typical for CCHFV . There iThe tree root of all three segments mapped to Central or West Africa. However, 95% HPDs of tree roots were extremely wide: 4°S– 19°N and 20°W– 27°E for S segment; 1°S– 29°N and 21°W– 35°E for M segment; 10°S– 40°N and 11°E– 71°E for L segment; therefore, it is likely that CCHFV originated in Africa; however no more precise conclusions on viral origin could be made. The root age was 1902 [949–3023], 3915 [2588–5537] and 2253 [1572–3026] years for S, M and L segments, respectively. Interestingly, while the lowest substitution rate was predicted for the L segment, the M segment root had a much older age estimate. This may imply that some L and S segment lineages were extinct, or were not sampled yet. The root ages were comparable with those reported previously: 3,138, 3,560 and 7,358 years for the S, M and L segments, respectively  years in the M segment (node posterior probability 1) and 1,509  years in the L segment (node posterior probability 1). Analysis of the S segment phylogeny suggested two independent introductions to South Asia 847 [441–1377] years ago (node posterior probability 0.84) and to South Russia 248 [126–400] years ago (node posterior probability 1). However, this pattern may be an artifact and could be explained by a single ancient introduction and a reverse transfer of virus from Asia to Central Africa. Indeed, long-distance transfers of virus could be suggested for all genome segments according to the topology of the phylogenetic tree, and have been also discussed previously , 19.Complex virus networks were predicted in South Asia, mostly in Afghanistan and Pakistan  even among closely related viruses; therefore, we could analyze reassortment dynamics in detail. For this purpose, a dataset of 53 viruses with complete sequences in all three segments was used. Bayesian phylogenetic analysis revealed only eight tree nodes that were identical and reliably supported  in all three segments . The medRecombination within CCHFV genome segments was reported previously  and conf−2 ssy, and CCHFV was estimated to have rates of approximately 10−4 ssy. Polioviruses of the same type may contain up to 25% nucleotide sequence differences in the VP1 genomic region commonly used for dating [Phylogenetic analysis of all three genome segments suggested that CCHFV emerged in Africa only several thousand years ago. This value could be reproducibly obtained for different genome segments in this and other studies . Dating r dating . In the Virus introduction to Central and South Asia dated back to the Middle Ages. This was compatible with historical references describing a disease, which is now believed to have been CCHF, around 1100 in Middle Asia . Then, CSegment reassortment in CCHFV was first described over a decade ago  and has Orthobunyavirus and Hantavirus of the family Bunyaviridae, but not in the genus Nairovirus [Nairovirus.Due to reassortment, CCHFV genome segments generally had independent evolutionary patterns confined by ecological barriers to virus exchange , but not by molecular limitations of segment compatibility, as reassortment between very distantly related viruses was observed. Natural reassortment is an established species criterion in the genera irovirus . The freAbsence of segment reassortment between the European GtV and CCHFV from other regions contrasts with common gene transfer between Asian and African strains. It suggests that GtV could represent a spill-over from the main CCHFV gene pool, which then became reproductively isolated (not involved in gene transfer). This observation also supports the conclusion that CCHFV introduction to Europe was an exceptional event in the evolutionary history of the virus.It is believed that RNA viruses existed on the eve of the cellular world . The ecoS1 File(KML)Click here for additional data file.S2 File(KML)Click here for additional data file.S3 File(KML)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Tablehttps://developers.google.com/public-data/docs/canonical/countries_csv) or to a province/administrative region center for strains originating from Russia, China and Iran.Coordinates were approximated to a country according to the Google country list table ((XLSX)Click here for additional data file.


The viral replication cycle presents viruses with a series of logistical challenges: To be successful, the virus must synthesize new copies of its genetic material, induce the cell to translate its genome into protein, and coordinate the host and viral factors required to assemble new virus particles. Much of this occurs at intracellular membranes, and lipid bilayers are expected to both play a central role in organizing the molecular machinery required for each stage of virion biogenesis and provide a structural foundation for particle assembly. These functions are prominently exploited by viruses with single-strand, positive-sense RNA genomes, which dramatically remodel intracellular membranes to form specialized replication complexes (RC) in the cytoplasm of infected cells . While tAmong the major contributors to the positive-strand RNA virus global disease burden are the flaviviruses, a genus of enveloped viruses that includes Yellow fever virus (YFV), Zika virus (ZIKV), Dengue virus (DENV), and West Nile virus (WNV) and is closely related to Hepatitis C virus (HCV) . DespiteFlavivirus particles are internalized through receptor-mediated endocytosis, followed by pH-mediated fusion, uncoating, and release of the RNA genome from the endosome into the host cytoplasm. This strand of positive-sense RNA is translated by host ribosomes to a single polyprotein, which is cleaved by cellular and viral proteases into three structural  and seven nonstructural (NS) proteins that form the genomic replication machinery. A combination of membrane-associated NS proteins and co-opted host factors then collaborate to bend the surrounding endoplasmic reticulum (ER) membrane into clusters of invaginated vesicles (Ve) connected to the cytosol by a single pore –6 Fig 1Fig 1. A Inducing the negative membrane curvature required for vesicle formation is a thermodynamically unfavorable process . To loweSimilar results have been shown for phosphatidylcholine (PC), a second lipid class strongly enriched during flavivirus infection –22. RemaIn concert with changes in structure and lipid makeup, the ER undergoes significant expansion during flavivirus infection . This grFatty acid synthesis is regulated by sterol regulatory element-binding proteins (SREBPs), a family of membrane-bound transcription factors also responsible for controlling cholesterol biosynthesis . AlthougMounting evidence suggests that different flaviviruses have different cellular and tissue tropisms with distinctive disease pathology. Although the exact determinants of differential flavivirus tropism are unknown, the lipid microenvironment of the tissue may have a critical role in this process. For instance, it is now well established that ZIKV crosses the human fetal–placental barrier to infect the developing central nervous system to cause microcephaly . ZIKV inAn important process involved in the regulation of lipid metabolism that is affected by ZIKV and possibly by other flaviviruses is the Akt–mTOR signaling pathway . ZIKV reRecent advances in the fields of genomics and proteomics have transformed our understanding of health and diseases. Despite the latest improvements in analytical approaches—in particular, liquid chromatography and mass spectrometry—the field of lipids lacks a corresponding advancement of knowledge in host–pathogen interactions. Due to the complexity of lipids and the absence of robust tools, manipulation of lipid composition in a controlled manner remains an experimental challenge. A combination of recently developed lipid tools such as the bi- and trifunctional lipid probes would provide the means to monitor the spatial and temporal distribution of cellular lipids during infection . Combine


Cowpea aphid-borne mosaic virus (CABMV) isolated from cowpea in Uganda and compare it with five CABMV complete genome sequences from Brazil (2), India (2), and Zimbabwe (1). It most resembled the genomes of two Brazilian isolates (MG-Avr and BR1) and one Indian isolate (RR3).We present here the first complete genome sequence of  Cowpea aphid-borne mosaic virus (CABMV) is a single-stranded positive-sense RNA virus in the genus Potyvirus, family Potyviridae. Although it was first reported in Uganda in 1981 . The library made from eastern Ugandan samples yielded 1,761,488 reads, and after trimming, 1,329,444 reads remained. De novo assembly yielded 10,174 contigs.Viral diseases of cowpea are prevalent in the main cowpea-growing areas of Uganda . Cowpea  in 1981 , current in 1981 . Samples in 1981 , and de Potyvirus genus (A BLASTN search against a plant virus database of 57,417 sequences using NCBI-BLAST-2.2.29+ (us genus , 7. A paKT726938.The complete genome sequence of CABMV isolate Serere1 was deposited in DDBJ/ENA/GenBank under the accession number


Cryptococcus gattii is known to be an etiologic agent of human cryptococcosis, particularly in immunocompetent persons. C. gattii infection usually involves the central nervous system, the respiratory tract, or may be disseminated. Here we report an atypical manifestation of C. gattii infection in a patient who had C. gattii meningitis complicating the ventriculoperitoneal (VP) shunt infection and concurrent infected intraabdominal VP shunt pseudocyst.C. gattii was isolated from both the cerebrospinal fluid and the pseudocyst aspiration. C. gattii meningitis complicating the VP shunt infection and concurrent infected intraabdominal VP shunt pseudocyst was diagnosed. Prolonged antifungal therapy, removal of the infected VP shunt with subsequent implant of a new shunt provided a good outcome.A 66-year-old Thai female was initially diagnosed with normal pressure hydrocephalus (NPH) and underwent programmable VP shunt placement. However, she still suffered from recurrent communicating hydrocephalus with in-place VP shunt, and later developed recurrent gait impairment, chronic abdominal pain and abdominal mass. Radiological studies demonstrated recurrent hydrocephalus and a very large intraabdominal VP shunt pseudocyst. C. gattii meningitis should be aware in a patient presenting with normal pressure hydrocephalus. Under-diagnosed cryptococcal meningitis following VP shunt insertion for treating the hydrocephalus can render a complicated VP shunt infection including infected VP shunt pseudocyst.Chronic  Cryptococcus neoformans and Cryptococcus gattii. Cryptococcus is a spore forming, environmental encapsulated fungus [Cryptococcus had been grouped into 4 major serotypes based on antigenic differences of the polysaccharide capsules, namely serotypes A, B, C and D [C. neoformans was categorized into serotypes A and D whereas C. gattii was classified into the serotypes B and C. Inhalation of spores is a primary route of infection that mainly affects immunocompromised persons, such as HIV-infected patients, organ transplant recipients, and patients receiving corticosteroid or immunosuppressive agents. However, several published reports have demonstrated C. gattii to be frequently associated with cryptococcosis in patients with no known immunodeficiency [Cryptococcosis is one of the more common systemic fungal infections caused by two main species, d fungus , 2. Form C and D , 3. C. nficiency .C. gattii infection has increased since a major outbreak on Vancouver Island [C. neoformans, central nervous system (CNS) and respiratory tract infections are the most common presentations [C. gattii infection occurs more frequently in immunocompetent persons and more often results in CNS complications, such as hydrocephalus, and large cryptococcoma with delayed treatment response [C. gattii infection in Thailand have been published. A molecular typing study of 498 Cryptococcus spp. isolates from clinical, animal and environmental sources from Thailand found only 13 isolates (2.6%) were C. gattii, but clinical data have not been reported [C. gattii [C. gattii infection in a patient who had chronic C. gattii meningitis complicating a ventriculoperitoneal (VP) shunt infection and concurrent infected intraabdominal VP shunt pseudocyst.In recent years, the prevalence of r Island . Similarntations –4. Howevresponse , 2, 7. F. gattii . Here we3 with 96% lymphocytes, a protein 165 mg/dL, and a ratio of CSF/serum glucose of 25/126 mg/dL (0.2). The patient was diagnosed with normal pressure hydrocephalus (NPH) and underwent programmable VP shunt placement to relieve her symptoms. One year later, the gait difficulty and dizziness resumed. A brain CT showed recurrent communicating hydrocephalus. The neurological deficits were reduced after adjusting the VP shunt pressure. Six months following adjustment of the shunt pressure, the patient developed abdominal discomfort with a palpable mass at the right paraumbilical region. The patient did not feel feverish and did not have nausea or vomiting. Prior to this admission, gait difficulty and dizziness including diffuse headache recurred. Recurrent hydrocephalus was suspected and the patient was admitted for further investigation.A 66-year-old Thai female with underlying hypertension was admitted to Siriraj Hospital, Bangkok, Thailand on September 26, 2016 to evaluate the function of an implanted VP shunt and identify the cause of an abdominal mass. Two years earlier, she had progressive memory impairment, dizziness, and difficulty walking for 4 months. Computed tomography (CT) of the brain revealed communicating hydrocephalus without leptomeningeal enhancement or abnormal enhancing lesion. Initial cerebrospinal fluid (CSF) showed a white blood cell (WBC) count of 54 cells/mm. CSF was collected by lumbar puncture and returned a WBC count of 12 cells/mm3 with 94% lymphocytes, 20 mg/dL protein, and a ratio of CSF/serum glucose of 46/106 mg/dL (0.43). Ultrasonography-guided aspiration of the intraabdominal VP shunt pseudocyst was also performed. A total of 360 mL of clear yellow fluid was obtained and examined. The WBC count was 20 cell/mm3 with 97% lymphocytes, 472 mg/dL protein, and a ratio of CSF/serum glucose of < 4.32/120 mg/dL (< 0.03). An India ink preparation of the CSF taken from the pseudocyst and from the lumbar puncture revealed a few encapsulated budding yeasts. Cryptococcal antigen testing of the serum and the CSF was positive at the titers of 1:8 and of > 1:1024, respectively. The yeasts grew well on saboraud dextrose agar without cycloheximide. The isolate was identified as C. gattii by biochemical testing and a conversion to blue color on L-canavanine-glycine bromothymol blue (CGB) agar. A molecular typing using the restriction fragment length polymorphism (RFLP) of URA5 determined the isolate to be compatible with C. gattii molecular type VGI. Antifungal susceptibility testing by the broth microdilution method was performed and showed minimal inhibitory concentrations (MICs) of amphotericin B deoxycholate (ABD) of 0.5 μg/mL, 5-fluorocytosine (5-FC) of 0.5 μg/mL, fluconazole of 1 μg/mL, itraconazole of ≤ 0.015 μg/mL, voriconazole of 0.015 μg/mL, posaconazole of 0.03 μg/mL and all echinocandins  of > 8 μg/mL. Unfortunately, when NPH was initially diagnosed 2 yrs earlier the CSF culture had grown C. gattii, but the result was overlooked. Chest radiography found no pulmonary nodules or infiltrations. Complete blood count showed hemoglobin of 12.4 g/dL, hematocrit of 39.4%, WBC counts of 10,930/mm3  and platelet counts of 385,000/mm3. Blood urea nitrogen (BUN), creatinine, and liver function tests were within normal limits. Anti-HIV testing was negative. Immunological studies revealed CD4 T cell counts of 842 cells/mm3 (51.8%), CD8 T cell counts of 361 cells/mm3 (22.2%), IgG 1170 mg/dL, IgA 255 mg/dL and IgM 141 mg/dL. Anti-interferon gamma (anti-IFN-Ƴ) and anti-granulocyte-macrophage colony-stimulating factor (anti-GM-CSF) autoantibodies were both negative.The patient was afebrile and had a very large, ill-defined mass with cystic consistency and smooth surface at the right paraumbilical region. Neurological examination revealed a magnetic gait, no neck stiffness and otherwise normal findings. The brain CT revealed communicating hydrocephalus with appropriate in-place VP shunt as shown in Fig. During hospitalization, the patient received daily intravenous amphotericin B deoxycholate 40 mg (0.7 mg/kg/day) combined with oral fluconazole 800 mg/day. The VP shunt was removed after 14 days of antifungal treatment. After discharge to home, the patient received long-term oral fluconazole for consolidation and maintenance therapy according to the IDSA recommendation [C. gattii infection. Chronic, undiagnosed C. gattii meningitis following the VP shunt placement lead to multiple recurrent communicating hydrocephalus, and eventually resulted in a cryptococcal intraabdominal VP shunt pseudocyst. A summary of nine case reports of cryptococcal VP shunt infection including the present case is provided in Table C. gattii infection [Salmonella non-Typhi, Varizella-zoster virus and fungi [Cryptococcus infection might be the primary cause of neurological deficits in patients who presented with early onset infection. These patients received VP shunt placements to relieve their symptoms, but an occult Cryptococcus infection subsequently causes a shunt infection soon after. Cryptococcus can also cause a sporadic VP shunt infection in patients who had the shunt implant several years earlier. Of nine cases, three also had concurrent infected abdominal pseudocysts and one had a subcutaneous pseudocyst following the VP shunt infections. Clinical manifestations such as abdominal distention, abdominal pain, or abdominal mass were comparable among these patients. The infected pseudocysts were moderate to large in size, and cyst fluid tested for cryptococcal antigens from two patients showed high titers.This patient without apparent immunodeficiency presented with cryptococcal meningitis complicating communicating hydrocephalus similar to other reports , 4, 6, 8nfection . Howevernd fungi . In addind fungi , 20 and nd fungi  in HIV-nC. neoformans exhibited exopolymeric matrices including capsular polysaccharide promoting adherence to VP shunt [C. gattii was the only pathogen isolated from CSF and fluid from the pseudocyst in the present case while C. neoformans was the principle etiologic agent in the other cases. Third, intraabdominal VP shunt pseudocyst infection caused by C. gattii has not been previously reported. Molecular typing of the isolate identified C. gattii VGI strain, an uncommon type. According to the molecular typing studies, C. gattii VGII is the most common molecular type distributed in Thailand and other countries [Cryptococcus isolates from Thailand, 12 (3.1%) were C. gattii VGII and only 1 (0.3%) was a VGI strain [C. gattii VGII had an identical linkage to the genotype of Vancouver outbreak strains [C. gattii isolated from our patient. However, the standard breakpoint of antifungal susceptibility testing for Cryptococcus spp. is still not available. Fluconazole resistance is uncommon among C. gattii although increasing fluconazole MICs of C. gattii VGII has been reported [C. gattii infection.The present case is unique for several reasons. First, this patient was initially misdiagnosed as NPH for 2 years. The reasons of slow progression in this patient may be from the released CSF by VP shunt at the onset of NPH, an ability to produce biofilm formation and a variation of virulence based on strain typing. Previous experimental studies elucidated VP shunt  and plasVP shunt , and wasountries , 4. Of 3 strains –26. The reported –29. ThusC. neoformans infection [C. gattii at the onset. It is possible that placement of the VP shunt may have prevented a rapid deterioration in neurological status. Of the nine reported cases, eight had the VP shunt removal, and three of these including the present case subsequently had a shunt reimplant. All of these patients had neurological recovery and no recurrence following the reimplantation. Thus, in patients with cryptococcal meningitis complicating VP shunt infection, shunt removal followed by a reimplant is considered safe and provides a good outcome. Four patients who had concurrent infected abdominal pseudocysts required no surgical excision. Simple aspiration of the infected pseudocyst and prolonged antifungal therapy provided a favorable outcome.Most of the case reports received a combination of conventional or liposomal amphotericin with 5-FC, which is the first line antifungal therapy for treating severe cryptococcal infection . In the nfection . Althougnfection , severalnfection –33 Howevnfection . NeuroloC. gattii infection should be considered in patients who develop normal pressure hydrocephalus without apparent cause. VP shunt implant is usually performed to relieve neurological deficits. An undiagnosed cryptococcal infection can result in VP shunt infection and infected intraabdominal VP shunt pseudocyst. CSF examination, cryptococcal antigen testing and fungal culture are mandatory to diagnosis this condition. Long term antifungal treatment, removal of the infected shunt followed by reimplantation when appropriate results in a favorable outcome.


Marek’s disease virus (MDV) is a pathogen of chickens whose control has twice been undermined by pathogen evolution. Disease ecology is believed to be the main driver of this evolution, yet mathematical models of MDV disease ecology have never been confronted with data to test their reliability. Here, we develop a suite of MDV models that differ in the ecological mechanisms they include. We fit these models with maximum likelihood using iterated filtering in ‘pomp’ to data on MDV concentration in dust collected from two commercial broiler farms. We find that virus dynamics are influenced by between-flock variation in host susceptibility to virus, shedding rate from infectious birds, and cleanout efficiency. We also find evidence that virus is reintroduced to farms approximately once per month, but we do not find evidence that virus sanitization rates vary between flocks. Of the models that survive model selection, we find agreement between parameter estimates and previous experimental data, as well as agreement between field data and the predictions of these models. Using the set of surviving models, we explore how changes to farming practices are predicted to influence MDV-associated condemnation risk (production losses at slaughter). By quantitatively capturing the mechanisms of disease ecology, we have laid the groundwork to explore the future trajectory of virus evolution. Marek’s disease virus (MDV), the causative agent of Marek’s disease (MD), imposes a substantial economic burden on chicken meat and egg production, costing the worldwide poultry industry in excess of 1 billion USD per year . HistoriMathematical models of disease ecology can provide valuable insight into infectious disease dynamics. Such models quantitatively relate changes in ecology to changes in disease dynamics, which is particularly useful when experimental manipulation is unethical or, as with commercial-scale chicken rearing, financially costly. Models provide cheap and safe opportunities to explore the impact of system manipulation on pathogen control, and this approach has been applied to MDV , 2017. TPoultry intended for consumption are inspected and condemned for a condition called “leukosis” at the time of processing. This condition can be caused by various diseases, but in chickens reared for meat, leukosis is almost exclusively caused by MD . CurrentThe concentration of MDV in dust can vary several orders of magnitude between farms and within farms over time . The undWe model the transmission and persistence of MDV within and between flocks of broiler chickens on commercial poultry farms. Our models are constructed assuming standard rearing practices in Pennsylvania, United States. These practices are fairly standard for commercial poultry rearing across much of the developed world.ad libitum food and water. Temperature, humidity, and air quality are maintained by a combination of active ventilation through fans or wind tunnels and heating. Flocks are collected for processing when sufficient time has elapsed for birds to reach a particular target weight.Industrial-scale rearing of broiler chickens on farms tend to follow an “all-in, all-out policy,” meaning that all chickens within a house are reared as a single-aged cohort of birds. We refer to a cohort of birds that occupy a single house on a farm as a flock. Birds are placed on litter that consists of wood chips and sawdust at one-day-old, and the birds are reared in this environment until they are ready for processing. Birds in houses are provided While chickens are being reared, houses accumulate “chicken dust,” a by-product of farming that consists of bits of food, epithelial cells, dander, bacteria, and feces . The amoComing from extremely hygienic hatcheries, chickens are unexposed to MDV when first placed in a house. Shedding of virus from a bird can begin as early as one week post exposure to virus and tends to reach maximal levels two to three weeks post exposure . Once reTypical commercial broiler farms vaccinate against MD by using bivalent vaccination . AlthougS(t) is the number of birds susceptible to virus infection at time nt. E(t) is the number of exposed birds in class n at time t where there are 5 total exposed classes (see below), I(t) is the number of infectious birds at time t, D(t) is the total milligrams of dust in the house at time t, and V(t) is the total number of virus copies in the house at time ct. α is the log normally distributed transmission rate of the virus in flock c determined by mean αμ and adjusted scale parameter ασ, β is the transition rate between exposed classes, γ is the rate at which dust and virus are removed from the house through ventilation, and δ is the rate at which virus decays in a house. All birds in the house produce dust at a rate of d(τ), which is a function of flock age τ. Keeping in mind that bird mortality is assumed negligible, the total number of birds in the house at any time is equivalent to the initial number placed S0. Infectious birds from flock c also produce virus at a concentration of ca per mg of dust produced, which is log normally distributed with mean aμ and adjusted scale parameter aσ. Additionally, to allow for the possible reintroduction of virus on farms, a quantity of μM infectious virus copies are introduced at rate rM.In the above equations, N = 5 incubation classes with a transition rate β chosen to produce virus shed rates over time that were consistent with average viral copy number in feather tips of experimentally infected birds  and V(cl−1) the respective dust and virus at the end of the previous flock. cC is the fraction of dust and virus removed between flocks c −1 and c by the physical removal of dust, and cf is the fraction of persisting virus that is removed between flocks c −1 and c due to chemical disinfectants and other environmental decay factors. Note that cC and cf are beta distributed random numbers parameterized by their means  and sample sizes . The initial number of birds susceptible to virus infection at the start of flock c is equal to the total number of birds placed in the house, and the initial number of exposed and infectious birds are equal to 0. A list of all model parameters is provided in Here, t was distributed Binomial(S(t), 1 −eαc−V(t)Δt). Virus and dust continued to be treated as continuous variables. For example, the amount of virus that was removed through ventilation in a time step Δt was equal to γV(t)Δt.To account for the fact that bird population sizes are finite, and for computational convenience, the above differential equations were effectively replaced with their corresponding probabilistic transition equations , 2015a, ασ = 0 or aσ = 0. Variation in the cleanout efficiency of dust and virus or in the decay rate of virus during downtime can be respectively removed by assuming that Cν or fν are extremely large. Virus reintroduction can be removed by assuming that rM = 0. Note that this additionally removes the parameter μM from the model. To test for the importance of between-flock variation in transmission rate, virus shed rate, dust and virus cleanout efficiency, and virus degradation, and for the importance of virus reintroduction, we therefore generated every version of the model that includes or excludes each of these factors , and we compare the fit of each of these models to data. Our null model is the model that lacks all of these factors.The above model allows for variation between flocks of chickens in virus transmission rate, virus shed rate from infectious birds, dust and virus cleanout efficiency during downtime, virus decay rate during downtime, and virus reintroduction. These mechanisms for generating variation can however be removed from the model. Variation in virus transmission rate or in virus shed rate can be removed by respectively assuming that d(τ)  , the incd(τ) , which ivirus μα , 2013b. The data used in this study are qPCR data reflecting the virus copy number (VCN) per mg of dust. These data were originally collected as part of a surveillance study quantifying the spatial and temporal variation of MDV across farms in Pennsylvania . We use 10 VCN/mg of dust plus 1. Using a generalized linear model with binomial data and a probit link we found an intercept of −2.206 and a slope of 1.555 (10 plus 1 transformed), where the standard deviation was determined by a linear regression of the standard deviation with respect to the mean. We thus modeled this component of likelihood as a normal distribution with mean μ given by the model and standard deviation 1.127 – 0.151μ .Parameter estimation was performed using maximum likelihood approximated by iterated filtering. This was implemented using the ‘mif2’ function of the ‘pomp’ package in the R statistical computing language . FittingModels were compared using Akaike’s Information Criterion (AIC) . To aid MD takes four to twelve weeks post exposure to develop, and so the risk of leukosis condemnation is likely to positively correlate with the fraction of birds exposed to virus at least 30 days prior to load out. Hereafter, we refer to this fraction of birds exposed to virus 30 or more days before loadout as the “condemnation risk”. We present envelopes that encompass the fraction of birds exposed to MDV over time for the maximum likelihood estimated parameters from the set of reasonable models. We explore the impact of altering features of poultry rearing by exploring how two fold increases or decreases in model parameters relative to maximum likelihood estimates alter condemnation risk .To explore whether the parameter estimates that best explain the data from Farm A House 1 also provide a reasonable estimate for the data from Farm E House 4, and to explore the impact of rearing duration on MDV dynamics, we repeat these methods, applying the fitted models from Farm A House 1 to the rearing parameters (i.e. placement dates and load out dates) from Farm E House 4. We also do the reverse to see whether the best fit models and parameters from Farm E House 4 provide a reasonable explanation for the data from Farm A House 1.The structure of the above model comes from a qualitative understanding of MDV natural history. Relating this qualitative understanding to quantitative dynamics, however, at a minimum requires simulation of the model with parameter values within their respective plausible ranges. In ασ, in the shedding intensity of virus from infectious birds aσ, and in the cleanout efficiency of dust and virus between bird flocks Cν, suggesting that these features of the model are important to explaining virus dynamics. The decay of virus between flocks fν and the reintroduction rate of virus rM on the other hand are each excluded from at least one of the best three models, and therefore might not need to be included in models of virus dynamics on this farm.By fitting our suite of models to the data from Farm A House 1, we found that three models have ΔAIC < 2, and are thus essentially indistinguishable . Each ofμM and rM) has a better AIC score than every model that excludes this mechanism. This result suggests that virus reintroduction is important to explaining dynamics on this farm. In addition, we find that virus reintroduction alone is unable to explain the breadth of variation present on the farm, as noted by the fact that model M1 which includes only stochastic virus introduction is not in our set of models with weights greater than 0.01. Rather, we find that each of the models that provide reasonable explanations for the data also include variation between flocks in either virus transmission rate or virus shed rate from infectious birds.We find somewhat different results when we examine the Farm E House 4 data. While we again find that several models provide comparable fits to the data according to AIC , we findIn ασ, the mean virus shed rate aμ, the virus shed rate scale aσ, the mean between-flock cleanout efficiency Cμ, and the number of birds placed S0 have relatively large effects on condemnation risk for both farms. The ventilation rate γ, the sample size of cleanout efficiency Cν, and the mean decay rate of virus between flocks fμ have relatively large impacts on only one of the farms. The decay rate of virus during the rearing period δ, the sample size of virus decay between flocks fν, the rate of virus reintroduction rM, the quantity of virus reintroduced μM, and the initial virus population size V0 have relatively little effect on the condemnation risk for either farm.2 and 103 virus copies per mg of dust.Applying our maximum likelihood parameter estimates from Farm A House 1 to the rearing conditions of Farm E House 4, and vice versa, allows us to visualize the relative impacts of differences in model parameters and differences in rearing duration on virus dynamics. Here, we constructed mechanistic models to describe MDV dynamics across multiple flocks of chickens reared on a commercial chicken farm. We used data on virus concentration to identify the mechanisms that drive variation in virus dynamics between farms  and 3, aTwo previous sets of models have been developed to describe MDV dynamics. The first set by Our previous surveillance on MDV dynamics has revealed that virus concentrations in dust tend to form U-shaped trajectories . We attrασ, virus shedding rates aσ, and cleanout efficiency between flocks Cν, thus highlighting the importance of these mechanisms for explaining dynamics on this farm  is in close agreement with these previous studies. The decay rate of virus δ has never been measured, but two studies have looked at the infectiousness of dust after storage at room temperature. δ implies a half life of viral persistence of 66 days (range 20–425 days), consistent with those previous experiments, further suggesting that our model is reasonably capturing the biology of the system.The parameter estimates that arose from model fitting give further insight into model reliability, virus management, and perhaps even virus evolution. Regarding model reliability we can compare our parameter estimates to those measured in previous studies. To our knowledge, the only parameter in our model that was directly measured through lab experiments is the mean virus shed rate rM on Farm E House 4 imply a median reintroduction probability of 3.2% per day (range 1.7–5.1%). This equates to approximately one virus reintroduction event per month. Based on these estimates, a typical flock of chickens is likely to be exposed to virus even if the virus is not present at the time of placement. However, the timing of virus reintroduction is an important factor in determining whether virus amplification will take place. Introductions late in the rearing cycle, for example, are unlikely to amplify to high levels before birds are removed for processing, highlighting the importance of good biosecurity early in the rearing cycle. Nevertheless, the frequent reintroduction rate suggests that maintaining the absence of virus is likely to require rearing conditions that limit the long term amplification of virus.Our estimates of virus reintroduction rate Cμ and decay of virus fμ suggest that the cleanout and sanitization processes between flocks are fairly efficient, with the median estimate for cleanout efficiency at 95%, and the median estimate of virus decay at 69% (note that the median for Cμ includes only the estimates from Farm A House 1 because the estimates of this parameter from Farm E House 4 are highly uncertain across models). This relatively high efficiency implies that current practices are good at reducing dust and virus between flocks. This efficiency, however, may be a cause for concern given that Our estimates of the parameters dictating between-flock removal of virus and dust Cμ and flock size S0 when compared against the impact of the mean virus decay between flocks fμ, the ventilation rate γ, or the within flock virus decay rate δ. This suggests that, should vaccine efficacy be reduced by pathogen evolution, condemnation rates might best be managed by focusing efforts on blowing down dust, changing litter between flocks, and reducing flock sizes, as opposed to improving chemical treatments between flocks or increasing ventilation rates within flocks. Reducing the frequency rM or intensity μM of virus reintroduction from other farms also appears to have little influence on condemnation risk. Nevertheless, rM would become a key parameter should the goal change from reducing to eliminating MD related condemnation.We found that condemnation risk is highly sensitive to the mean cleanout efficiency ασ, and the virus shed rate scale parameter aσ. Because the scale parameters ασ and aσ are directly related to variability between flocks in transmission rate and virus shed rate, our analysis demonstrates that condemnation risk is highest when flocks have little variability between them in transmission rate and virus shed rate. This observation highlights the value of intervening when a farm is having problems, even if the intervention is economically unsustainable in the long term, because a reduction in the transmission and virus shedding rates within a single flock can have large impacts on condemnation risk in future flocks. Currently, such a strategy is practiced through remedial actions taken in response to MD breaks such as changing the vaccination program to include Rispens vaccine. Cycling flocks of chickens between standard broilers and MD-resistant broiler might have similar benefits .We note that the data used in this study were collected from farms in the United States, where universal bivalent vaccination of broiler chickens is standard. In other parts of the world, chickens are often given either different versions of the MD vaccine, or even no vaccine . These dGiven the history of MDV evolution undermining vaccine efficacy, any long term control strategy must carefully consider the ecology of disease transmission, and its impact on pathogen evolution. Our hope is that the data-tested models that we present here will lay the ground work for this approach. Future work might use these models to ask how altering rearing practices, approximated by changing model parameters, affects the profitability of poultry farming as well as how this might alter competition between virus strains and in turn the evolution of the virus.


Pleolipoviridae (termed pleolipoviruses) are pseudo-spherical and pleomorphic archaeal viruses. The enveloped virion is a simple membrane vesicle, which encloses different types of DNA genomes of approximately 7–16 kbp (or kilonucleotides). Typically, virions contain a single type of transmembrane (spike) protein at the envelope and a single type of membrane protein, which is embedded in the envelope and located in the internal side of the membrane. All viruses infect extremely halophilic archaea in the class Halobacteria (phylum Euryarchaeota). Pleolipoviruses have a narrow host range and a persistent, non-lytic life cycle. This is a summary of the International Committee on Taxonomy of Viruses (ICTV) Report on the taxonomy of the Pleolipoviridae which is available at www.ictv.global/report/pleolipoviridae.Members of the family  Virions are enveloped pleomorphic membrane vesicles of 40–70 nm diameter  with oneAlphapleolipovirus have circular ssDNA or dsDNA genomes, members of the genus Betapleolipovirus have circular dsDNA genomes with single-stranded discontinuities, and the only member of the genus Gammapleolipovirus has a linear dsDNA genome. A cluster of five genes/ORFs is conserved among the members of the family . The cluster includes genes encoding a spike and an internal membrane protein as well as an ORF encoding a putative NTPase. The only member of the genus Gammapleolipovirus is predicted to encode a putative type B DNA-dependent DNA polymerase. The genome ends bear terminal proteins.Virus genomes are circular ssDNA of 7.0–10.7 kilonucleotides , circulaPleolipoviruses are non-lytic and most likely enter cells by membrane fusion. Either rolling-circle or protein-primed replication may be used; transcription has not been studied. Progeny virions exit host cells continuously retarding host growth with concurrent unselective lipid acquisition implying that virions bud through the cell membrane.Haloarcula virus HHPV1, Haloarcula virus HHPV2, Halorubrum virus HRPV1, Halorubrum virus HRPV2 and Halorubrum virus HRPV6. The genomes of alphapleolipoviruses contain 8–15 ORFs, and besides the conserved gene/ORF cluster, an ORF encoding a putative rolling-circle replication protein and one ORF with unknown function [The genus includes the species function . VirionsHalogeometricum virus HGPV1 and Halorubrum virus HRPV3. The betapleolipovirus genomes contain 12 or 15 ORFs. In addition to the cluster of five genes/ORFs, genomes share two ORFs encoding proteins with unknown function [The genus includes the species function . Virionsfunction .Haloarcula virus His2 is the only species of this genus. The member has a genome with 35 ORFs [ 35 ORFs  and a vi 35 ORFs . One of www.ictv.global/report/pleolipoviridae.Full ICTV Online (10th) Report:


We present an empirical economic cost analysis of the April 2016 switch from trivalent (tOPV) to bivalent (bOPV) oral polio vaccine at the national-level and 3 provinces  for Indonesia’s Expanded Program on Immunization.Data on the quantity and prices of resources used in the 4 World Health Organization guideline phases of the switch were collected at the national-level and in each of the sampled provinces, cities/districts, and health facilities. Costs were calculated as the sum of the value of resources reportedly used in each sampled unit by switch phase.Estimated national-level costs were $46 791. Costs by health system level varied from $9062 to $34 256 at the province-level, from $4576 to $11 936 at the district-level , and from $3488 to $29 175 at the city-level. Estimated national costs ranged from $4 076 446  to $28 120 700 .Our findings suggest that the majority of tPOV to bOPV switch costs were borne at the subnational level. Considerable variation in reported costs among health system levels surveyed indicates a need for flexibility in budgeting for globally synchronized public health activities. Following the declaration of the global eradication of type 2 wild polio virus (WPV2) in 2015, the continued use of trivalent oral polio vaccine (tOPV), which contains attenuated types 1, 2, and 3 polioviruses, posed an unacceptable risk for creating new type 2 circulating vaccine-derived polio viruses (cVDPV2s) that could cause paralytic poliomyelitis . To addrThe gradual replacement of an existing vaccine with a new vaccine should normally have minimal cost implications because the existing vaccine would just be used up and then replaced as the new vaccine was supplied in its place. The globally synchronized switch from tOPV to bOPV was unusual because it required that all tOPV use stop during a window of a few weeks detailed in the WHO global switch implementation guidelines, that all remaining tOPV be recalled and promptly destroyed, and that bOPV be distributed and its use start during the same window of a few weeks . The GloNational immunization programs developed estimated budgets during the process of planning the switch with the help of coordinated multistakeholder regional and global technical assistance. These budgets informed the allocation of funding from the GPEI to countries to support the costs of the switch. Although the normative prospective budget projections provided an important basis for resource allocation, empirical data on the expenditures and value of in-kind resources used in implementing the switch provide an estimate of the actual costs of the switch. Such retrospective empirical cost estimates can provide valuable information for planning the resources required for potential future vaccine switches or withdrawals .Accordingly, this article presents results from a retrospective empirical economic cost analysis of tOPV–bOPV switch planning, implementation, and validation in Indonesia. Indonesia was chosen for this case study for several reasons. Its large population, complex geographic landscape, and middle-income status with a per capita income of $3346 made it an important test case for the switch ; resourcThis article addresses the following question: What were empirical economic costs of the tOPV to bOPV switch in Indonesia? We present costs by (1) health system level ; (2) switch phase as identified in the WHO global switch guidelines; and (3) estimated aggregate costs by sampled province. As a case study of a single country, our findings highlight the operational features of implementing a global initiative at national and subnational levels and suggest directions for estimating resource needs and allocating resources to support similar vaccine switches and withdrawals in the future.The evaluation protocol was determined to be not human subjects research and therefore exempt from institutional review board (IRB) review by the CDC Center for Global Health. The evaluation protocol was reviewed by the IRB of the National Institute of Health Research and Development, Indonesian Ministry of Health and determined to be exempt from further IRB review (no. LB.02.01/5.2/KE.267/2016).We estimated the incremental empirical economic cost of the switch from tOPV to bOPV at the national level and in 3 selected provinces for Indonesia’s Expanded Program on Immunization (EPI). We used the methodology as put forth by EPI Costing (EPIC) Common Approach to stratify the provinces , 15. A cWithin selected provinces, we stratified according to urban area (cities) and rural area (districts) and selected a convenience sample of 1 district and 1 city within each province. Districts are the next lowest level of health-system administration below the provincial level. Finally, within each city and district, we stratified (where possible) according to HF ownership (either publicly owned by the Indonesian government or privately owned for-profit or not-for-profit) and selected a convenience sample of 1 public and 1 private HF. Private HFs within the sampled rural settings were rare; in the absence of a private HF, we sampled 2 government HFs. In each province, we sampled 4 HFs, including 1 public and 1 private HF for each city; all rural district HFs were public except 1 private HF in Bali province.We collected costs for the 4 major phases of the switch defined by the WHO global switch guidelines—1) plan, 2) prepare, 3) implement, and (4) validate—using a standard activity-based costing method , 16, 17. plan,  were adequately stocked until the implementation of the switch and that sufficient bOPV was available for districts and cities. In turn, district and city offices provided an adequate supply OPV to HFs . All levStructured questionnaires were developed to collect data on the resource input types used to execute each phase of the switch, including personnel, supplies, equipment, training, travel (including vehicle use), and contracted services. The questionnaires were piloted at the provincial and district health offices in Jakarta and within a public clinic and revised based on the pilot results. The revised questionnaires were administered at the national EPI office, 3 provincial health offices, 3 district health offices, 3 city health offices, and 12 HFs. Respondents included personnel in the selected health facilities (n = 12), district or city health offices (n = 6), provincial health offices (n = 3), national EPI office (n = 1), and national Biofarma office . The questionnaire was administered to the same respondent for each phase of the switch. Data were collected on the quantity and price of resources reportedly used for each input category. We only examined incremental resources expended for the switch that were in addition to the resources reportedly used for regular immunization program activities. Questionnaires were administered in Bahasa Indonesia by a team of data collectors from the Ministry of Health’s Policy Unit with technical assistance from US Centers for Disease Control and Prevention staff during June–August 2016. Data were translated into English and entered and analyzed in Excel.The cost analysis was conducted from the perspective of the Indonesian government, including all sources of funding. We measured incremental economic costs, including all resources reportedly used for the 4 WHO switch phases, including financial outlays and use of existing or in-kind resources. The time frame and analytic horizon for the cost analysis were defined as the duration of the national switch in Indonesia, including the reported timeline for all 4 phases (July 2015 to June 2016). We calculated the costs for each sampled unit , switch phase, and resource input type. Vehicle and equipment costs were annuitized using a discount rate of 3%. All costs are presented in nominal US dollars (USD) using the average exchange rate with Indonesian rupiah (IDR) for the months of each switch-phase time period  . PersonnAlthough our convenience sample of provinces, cities/districts, and HFs does not permit statistically valid extrapolation of the cost-analysis results, as a hypothetical scenario analysis we explored what the aggregate switch costs for Indonesia might have been if the remaining 31 provinces had costs similar to those observed in our sample. We first estimated the total switch costs in each of our 3 sampled provinces for 3 scenarios based on applying the median, minimum, and maximum HF cost in each province to all HFs in that province, then applying the city health office costs to all cities in the province, the district health office costs to all districts in that province, and finally adding the reported provincial health office costs. Based on these median, minimum, and maximum scenarios for each of the 3 provinces, we then multiplied each scenario cost by the 31 remaining provinces and added the reported national-level costs for the EPI program to each to obtain an estimate of aggregate switch costs for Indonesia as a whole. The purpose of this subanalysis was to provide a hypothetical range of costs against which to compare the national switch budget that was developed to advocate for and secure funds for the switch in Indonesia . No training costs were reported at any level.The estimated national-level EPI program costs were $46 791, 45% of which was incurred during the switch-planning phase . EstimatMedian switch costs per HF varied by geography and ownership characteristics . Median andHypothetical scenarios for aggregate switch costs are presented based on the cost scenarios derived from the convenience sample of three provinces and6. ThiContrary to the conventional wisdom that vaccine withdrawal or recall does not entail additional costs, global partners thought the tOPV–bOPV switch would require additional resources, as evidenced by the global switch guidelines that include recommendations for resource mobilization as well as the financial assistance provided by GPEI to country governments to support the hypothesized extra costs of switch implementation . IndonesOur findings suggest that the majority of the switch costs were borne at the subnational levels. In part, this is expected, given the decentralized nature of Indonesia’s government and health system; however, it highlights the important contributions (many in-kind) by provincial, city, and district governments and by HF staff, as well as the importance of adequate budgeting for subnational coordination and implementation for future switches. In our study sample, the majority of switch costs were incurred during the planning and preparation phases, primarily reflecting personnel time spent on planning and coordination meetings. Costs during the implementation and validation phases were lower than might have been expected for the core activities of implementing and validating the switch. Validation-phase involvement was limited to some health-system levels , which may also explain the smaller share of costs for activities in this phase.Our findings suggest that in-kind contributions of countries to program costs of the switch were substantial, even though these are not traditionally reflected in financial costs presented in budgets. The variability in estimated switch costs across provinces, districts, and cities suggests a need for flexibility in budgeting for immunization activities involving multiple health-system levels. Attempting to force a rigid approach to budgeting upon all levels  would likely lead to suboptimal performance, particularly in a country as varied as Indonesia in which different levels of government may play different roles in planning, implementation, and monitoring across different areas.Supplementary Table 1). This may reflect prevailing perceptions around training as formal classroom delivery of new content at a dedicated time and place, for which transport costs and a per diem are paid to participants. Information about the switch that may have been conveyed through in-service training methods, such as at monthly meetings of HF staff at district or city health offices or during supervision visits, might not have been perceived as training by respondents. Training on the switch might also have been incorporated into trainings on other topics  such that respondents did not report any training on the switch per se. The reported limited investment in training may also reflect resource constraints because the external resources that Indonesia received from GPEI were less than requested. One implication for future vaccine switches and withdrawals may be that when resources are constrained, countries can find more efficient options than formal training  for transmitting information to subnational health authorities and HF staff.Although training was a major activity outlined in the global guidelines and Indonesian switch plan and budget, our respondents reported no training costs incurred at any level  are shared, the costs of the switch appear to have not been associated with vaccination coverage, perhaps reflecting the difference in core activities .Our findings should be interpreted in light of several limitations. We used convenience sampling for provinces, districts/cities, and HFs; therefore, the site sample may not be representative of all such sites in Indonesia. We did not collect data on the population served for the HFs in our sample, and thus we could not compare costs across HFs based on catchment population. Also, we did not include either the cost of bOPV distribution to all peripheral levels or Biofarma’s costs of destroying tOPV remaining after the switch due to missing information and quality of data received. Furthermore, our results may be subject to recall bias, as is possible with all retrospective cost data collection relying on respondents to recall time spent or resources used, possibly leading to personnel costs or other expenses being overestimated or underestimated. Respondents’ answers were solicited by self-report and not validated through document review  because the cost analysis was designed to be a program evaluation and not an audit. Moreover, respondents were unable to provide cost information for some activities when such information was deemed to be procurement-sensitive ; total costs are therefore underestimated. In addition, the switch in Indonesia was implemented simultaneously with several other immunization interventions, including IPV introduction and national immunization week 2016. It is therefore possible that some incremental costs of the switch are underreported by respondents because they were associated with or attributed to these parallel activities. Our analysis adopted a government perspective and therefore does not include contributions made by external partners that were not channeled through the government .Furthermore, we were unable to provide insight into potential shared costs with IPV introduction. Although IPV was scheduled to be introduced in Indonesia in June 2016, its introduction was delayed due to delivery constraints and did not overlap with the implementation of the tOPV-to-bOPV switch (which covers the period from July 2015 to June 2016); however, some preparatory activities for IPV introduction, such as training or planning meetings, may have overlapped with the switch. Although our questionnaire and data-collection team specifically asked about additional resources used only for the switch, it is possible that respondents may have attributed overlapping activities fully to IPV introduction or fully to the switch, leading to either an underestimate or overestimate of switch costs.This cost analysis of the tOPV-to-bOPV switch in Indonesia provides evidence of the reported resource contributions at all levels of the Indonesian health system to implement the switch. For all phases of the WHO global switch guidelines, we documented resources reportedly used for the phases of the switch, providing another lens on programmatic reports of the extent of switch planning and implementation. The initial INSP budget estimates for the switch were in the range of aggregate switch costs we present here based on a convenience sample of provinces, cities/districts, and HFs, although our cost estimates may be underreported due to recall bias and the integration of the switch with other concurrent vaccination activities. The empirical retrospective economic cost estimates presented here indicate that, although there was likely considerable variation in the resources used as reported to carry out the switch across provinces, cities, districts, and HFs, overall the switch’s resource needs were likely manageable. This suggests that the resource requirements for the future withdrawal of bOPV are likely to be similarly manageable at a country level.The Journal of Infectious Diseases online. Consisting of data provided by the authors to benefit the reader, the posted materials are not copyedited and are the sole responsibility of the authors, so questions or comments should be addressed to the corresponding author.Supplementary materials are available at Supplementary_Table1Click here for additional data file.


Although risks of H7N9 and H5N1 virus infections remain low in Beijing, continued preventive measures are warranted for poultry workers.Although several studies have reported seroprevalences of antibody against avian influenza A(H7N9) virus among poultry workers in southern China, results have varied and data in northern China are scarce. To understand risks of H7N9 and H5N1 virus infections in northern China, a serological cohort study was conducted. Poultry workers, swine workers and the general population in Beijing, China, were evaluated through three surveys in November 2013, April 2014 and April 2015. The highest seroprevalence to H7N9 virus among poultry workers was recorded in the April 2014 and April 2015 surveys (0.4%), while that to H5N1 clade 2.3.4 or clade 2.3.2.1 virus was noted in the April 2014 survey . The incidence of H7N9 virus infections among poultry workers (1.6/1000 person-months) was significantly lower than that of H5N1 clade 2.3.4 infections (3.8/1000 person-months) but higher than that of H5N1 clade 2.3.2.1 infections (0.3/1000 person-months). Compared with the general population, poultry workers were at higher risk of contracting H7N9 virus (IRR: 34.90;  In March 2013, human infections with a novel avian influenza AH7N9) virus were discovered in Southeast China1346N9 virus Along with severe and fatal cases of H7N9 virus infection reported thus far343101113141513141314In addition, given that pigs have the potential to be infected by avian influenza viruses1718192022Beijing is located in northern China. Although human infections with H7N9 and H5N1 viruses have been documented in Beijing10To understand the risk of human infections with H7N9 and H5N1 viruses in northern China, a serological cohort study was undertaken that surveyed poultry workers, swine workers and the general population in Beijing, China, between November 2013 and April 2015.In November 2013, a total of 3790 participants were included in the first survey: 1258 poultry workers (33.2%), 1332 swine workers (35.1%) and 1200 general individuals (31.7%).In April 2014, the 3790 participants in the first survey were followed up, 2563 of whom consented to participate in the second survey. Thus, the first cohort (2013–2014) included 2563 participants. To compensate for the number of participants lost to follow-up, an additional 935 participants were invited to participate in the second survey in April 2014, for a total sample size of 3498 participants.In April 2015, the 3498 participants in the second survey were followed up, 2012 of whom consented to participate in the third survey. Thus, the second cohort (2014–2015) included 2012 participants. Similar to the second survey, an additional 1244 participants were invited to participate in the third survey to compensate for participants lost to follow-up, and 3256 participants were finally analyzed.p < 0.001) and underlying disease status (p < 0.001) between the three independent surveys. Additionally, a statistically significant difference was noted in the participant category (p < 0.001) between 2013–2014 and 2014–2015 cohorts. However, in general, similar participant characteristics were recorded among the three surveys as well as between the two cohorts  of the general population tested positive for H7N9 virus, 0.1% (1/1332) and 0.2% (2/1332) of swine workers tested positive for H5N1 clade 2.3.4 virus and H5N1 clade 2.3.2.1 virus, respectively, and no other participants tested positive for any virus. No statistically significant differences were observed in the seroprevalence of antibody to each virus between participant categories, sexes, age groups or underlying disease statuses .p = 0.008) and H5N1 clade 2.3.4 virus (p < 0.001) between participant categories. In addition, a statistically significant difference in H5N1 clade 2.3.4 antibody seroprevalence existed between males and females (p = 0.007) , 1.6% (17/1056) and 0.2% (2/1056) of poultry workers tested positive for H7N9 virus, H5N1 clade 2.3.4 virus and H5N1 clade 2.3.2.1 virus, respectively. Only 0.1% (1/1254) of swine workers tested positive for H5N1 clade 2.3.4 virus. No participants from the general population tested positive for any virus. Statistically significant differences were observed in seroprevalences of antibodies to H7N9 virus (= 0.007) .p = 0.023) between participant categories. However, no statistically significant differences in the seroprevalence of antibody to each virus existed between sexes, age groups or underlying disease statuses , 0.2% (2/1123) and 0.1% (1/1123) of poultry workers tested positive for H7N9 virus, H5N1 clade 2.3.4 virus and H5N1 clade 2.3.2.1 virus, respectively. Among swine workers, 0.1% (1/998) and 0.2% (2/998) tested positive for H5N1 clade 2.3.4 virus and H5N1 clade 2.3.2.1 virus, respectively. No participants from the general population tested positive for any virus. A statistically significant difference was observed in the seroprevalence of antibody to H7N9 virus (statuses .p < 0.001), but not in that to H7N9 or H5N1 clade 2.3.2.1 virus. For either swine workers or the general population, no statistically significant difference in the seroprevalence of antibody to each virus among the three surveys was observed.For poultry workers, there was a statistically significant difference in the seroprevalence of antibody to H5N1 clade 2.3.4 virus among the three surveys  was significantly lower than that of H5N1 clade 2.3.4 virus infections , and similar to that of H5N1 clade 2.3.2.1 virus infections . Furthermore, among poultry workers, the incidence density rate of H7N9 virus infections (1.6/1000 person-months) was significantly lower than that of H5N1 clade 2.3.4 virus infections , but significantly higher than that of H5N1 clade 2.3.2.1 virus infections  (In the 2013= 0.008) .p < 0.001) or H5N1 clade 2.3.4 virus . However, neither poultry workers nor swine workers were at a higher risk of H5N1 clade 2.3.2.1 virus infection compared with the general population  was much lower than that in Shenzhen from May to December, 2013 (7 months)1314261314In this study, the seroprevalences of antibodies to H5N1 clade 2.3.4 or clade 2.3.2.1 virus in poultry workers were very low in the three cross-sectional surveys, and similar findings were also reported in studies conducted in southern China during the similar period1414In Beijing, an increased number of laboratory-confirmed patients of H7N9 virus infection has been reported in recent years1024In this study, we used a prospective cohort study design to establish that poultry workers were at a higher risk of H7N9 or H5N1 clade 2.3.4 virus infection compared with the general population, as described by previous cross-sectional studiesOur study has several limitations. First, the incidence of H7N9 and H5N1 virus infections relied on seroconversion of antibodies against respective viruses; however, we did not determine the incidence of symptomatic infections confirmed by virological assays. Second, although post-infection ferret antiserum against A/Anhui/1/2005 (H5N1) (clade 2.3.4) has very low cross-reactivity with H5N6 or H5N8 virusThe risks of H7N9 and H5N1 virus infection remain low in poultry workers, swine workers and the general populations in Beijing, China. Importantly, the risk posed by H5 viruses appears not lower than that of H7N9 viruses. Continued surveillance, using personal protective equipment and seasonal influenza vaccination are warranted for poultry workers, as they demonstrated a higher risk of H7N9 and H5N1 virus infection.This study was approved by the institutional review board and human research ethics committee of Beijing Center for Disease Prevention and Control, and all methods were carried out in accordance with the principles of the Declaration of Helsinki. Informed consent was obtained from all participants.This serological cohort study enrolled poultry workers, swine workers and individuals in the general population in Beijing, China and comprised three serological surveys implemented in November 2013, April 2014 and April 2015. For the first survey conducted in November 2013, multi-stage cluster sampling was used to enroll poultry and swine workers, and stratified multi-stage random sampling was used to recruit general population participants.Poultry and swine workers included workers from commercial breeding farms, abattoirs and private breeding sites. First, five districts that contain poultry and swine-related industries were selected. Second, for poultry and swine workers from commercial breeding farms and abattoirs, two poultry and two swine commercial breeding farms as well as the same number of poultry and swine abattoirs were selected in each of the five districts, and all poultry and swine workers from the selected commercial breeding farms and abattoirs were invited to participate in this study. For workers from private breeding sites, two towns with poultry industries and two towns with swine industries were selected in each of the five districts, and all workers from private poultry/swine-breeding sites in the selected towns were invited to participate in this study.The general population was classified as participants not involved in poultry and swine-related careers, and recruited from the same districts as poultry and swine workers. The sampling frame is briefly described as follows. First, one town and one street were randomly selected in each of the five districts. Second, two villages were randomly selected from the town chosen in the first stage, and two communities were selected from the street. Third, 60 people aged ≥18 years were randomly selected from each village or street.The participants enrolled in the first survey in November 2013 were invited to complete a questionnaire and provide serum samples for antibody detection. For the second survey conducted in April 2014, individuals who participated in the first survey were followed up to provide the second serum samples for antibody detection. However, as some participants were lost to follow-up, other individuals with similar occupations and residences as those lost to follow-up were invited to participate in the second survey in April 2014 to compensate for the lost sample size as much as possible,. For the third survey conducted in April 2015, individuals who participated in the second survey were followed up to provide serum samples for antibody detection. As in the second survey, individuals with similar characteristics as those lost to follow-up were invited to participate in the third survey.Individuals who participated in the first and second surveys were referred to as the 2013–2014 cohort, and those who participated in the second and third surveys were referred to as the 2014–2015 cohort.After informed consent was obtained from the participants, a questionnaire was administered via a face-to-face interview by trained staff. The questionnaire collected information on occupation, sex, age, underlying conditions and exposure to birds or pigs. Blood samples were collected from the participants for antibody testing against H7N9 and H5N1 viruses.Serum samples were pretreated and assayed by the hemagglutination-inhibition (HI) assay, as previously described® University Edition . We estimated the seroprevalence rates in three independent surveys and calculated the person-time incidence rates indicated by serologically confirmed infections (seroconversion) per 1000 person-months as follows: total number of infections in both cohorts divided by the number of person-months of follow-up. Percentages were calculated for categorical variables. Subgroup comparisons of participant characteristics were compared using Pearson’s χ2 test. Seroprevalence rates of H7N9 or H5N1 virus infection (HI titer ≥1:80) were compared between subgroups using Pearson’s χ2 test or Fisher’s exact test. Multivariate Poisson regression models were used to compare the person-month incidence rates of H7N9 and H5N1 virus infections between participant categories in the cohorts after adjusting for sex, age group and underlying disease status, assessed by the incidence rate ratio (IRR)p < 0.05.Data were entered in duplicate using EpiData Software and analyzed using SASHow to cite this article: Yang, P. et al. Avian influenza A(H7N9) and (H5N1) infections among poultry and swine workers and the general population in Beijing, China, 2013–2015. Sci. Rep. 6, 33877; doi: 10.1038/srep33877 (2016).


Aedes aegypti mosquitoes caught in Zhanjiang province of China. Phylogenetic analysis suggested that this virus belongs to a new genus, Goukovirus, in the family Bunyaviridae. This is the first reported genome sequence of a Phasi Charoen-like virus identified in China.Here, we report the genome sequence of a novel strain of a Phasi Charoen-like virus, isolated from  Bunyaviridae is one of the most diversified families of RNA viruses . Then, the RNA sample was sent to the Beijing Genome Institute (BGI) for deep sequencing. A BLASTn search against the NCBI nucleotide (nt) database determined that the most abundant sequences assembled were three genome segments of a Phasi Charoen-like virus; this was further confirmed by Sanger sequencing of both pooled female and male A. aegypti RNA samples.The ka virus . InvestiGoukovirus within the family Bunyaviridae. The detection of the same genome sequences in male adult mosquitoes suggests that this virus might be vertically transmitted.This virus was tentatively named Zhanjiang01 according to its isolated source. Similar to other Phasi Charoen-like viruses, the nearly complete genome sequence of the Zhanjiang01 virus consisted of three single-stranded RNA segments of 6,773 nt (segment L), 3,840 nt (segment M), and 1,333 nt (segment S), encoding a viral RNA-dependent RNA polymerase , a glycoprotein precursor , and a nucleocapsid protein (268 aa), respectively. Interestingly, the nonstructural (NS) protein-coding gene, which was absent in the genes of other insect-specific viruses, was not found in the M and S segments. Pairwise nucleotide identities among all the Phasi Charoen-like viruses ranged from 95% to 97%, while the aa identities ranged from 97% to 98%. Phylogeny based on both the nt and aa sequences indicated that the Zhanjiang01 virus was in the clade of the recently proposed novel genus A. aegypti in China. This discovery further enhances our understanding of the wide distribution and high diversity of insect-specific bunyaviruses and flaviviruses. In particular, the potential use of the Zhanjiang01 isolate as a vaccine is worthy of further study.To our knowledge, this is the first report of a Phasi Charoen-like virus identified from MF614132 (segment L), MF614133 (segment M), and MF614134 (segment S).Genome data for the Phasi Charoen-like virus isolate Zhanjiang01 have been deposited in GenBank under the accession numbers


Attempts to replace human or equine rabies immune globulins (HRIG or ERIG) have been made by several companies and institutes. We developed potent monoclonal antibodies to neutralize a broad spectrum of rabies viruses by screening hybridomas received from the U.S. Centers for Disease Control and Prevention (CDC). Two kinds of chimeric human antibodies (chimeric #7 and #17) were constructed by cloning the variable regions from selected hybridomas and the constant region of a human antibody. Two antibodies were bound to antigenic site III and I/IV, respectively, and were able to neutralize 51 field isolates of rabies virus that were isolated at different times and places such as Asia, Africa, North America, South America, and Australia. These two antibodies neutralize rabies viruses with high efficacy in an  Lyssavirus in the family Rhabdoviridae. Lyssavirus is one of seven genera in this family, comprising classical RABV (genotype 1), Lagos bat virus , Mokola virus , Duvenhage virus , European bat lyssavirus 1 , European bat lyssavirus 2 , and Australian bat lyssavirus . Recently, four additional viruses, isolated from insectivorous bats, have been proposed as new members of the Lyssavirus genus: Aravan virus (ARAV), Khujand virus (KHUV), Irkut virus (IRKV), and West Caucasian bat virus (WCBV) and other putative members have been characterized: Bokeloh bat lyssavirus(BBLV), Gannoruwa bat lyssavirus(GBLV), Shimoni bat virus(SHIBV), Lleida bat lyssavirus(LLEBV), and Ikoma lyssavirus(IKOV) -thymidine  and incubated for another 18 h before harvesting in filter mats using a TomTec Mach III cell harvester. Counts per minute (cpm) were determined by Meltilex (Perkin Elmer) scintillation counting on a 1450 Microbeta Wallac Trilux Liquid Scintillation Counter (Perkin Elmer) in paralux with low background counting. Keyhole Limpet Haemocyanin  was used as a positive control.The immunogenicity of MAb chimeras #7 and #17 was determined by Antitope in Cambridge, UK. Peripheral blood mononuclear cells (PBMCs) were isolated from healthy community donor buffy coat (from blood drawn within 24 hours) obtained from UK National Blood Transfer Service  and according to approval granted by Addenbrooke’s Hospital Local Research Ethics Committee. A cohort of 50 donors was selected to best represent the number and frequency of HLA-DR allotypes expressed in the world population. PMBSs obtained from selected 50 donors were plated on 24-well plates individually, and chimeras #7 and #17 were added to PBMCs for a final concentration of 50 μg/mL. Cultures were incubated for a total of 8 days at 37°C with 5% CO5) were plated, and chimeras #7 and #17 and a negative control  and positive control (Phytohaemagglutanin (PHA), Sigma, Poole, UK) were added. After 8 days of incubation, biotinylated detection antibody, streptavidin-AP, and BCIP/NBT substrate were used to develop the ELISpot plates. Dried plates were scanned on an Immunoscan Analyser, and spots per well (spw) were determined using Immunoscan Version 4 software.ELISpot plates  were pre-wetted and coated overnight with IL-2 capture antibody  in PBS. Non-specific binding was reduced by incubating with blocking buffer (1% BSA). PBMCs/well  was coated on ELISA plates and incubated with chimeric MAbs #7, #17 and HRIG. The ECectively .To demonstrate whether chimeric MAbs #7 and #17 bind to different epitopes, an SPR assay was conducted. Epitope mapping studies using Biacore on RABV G protein-immobilized C5 chips showed that chimeric MAbs #7 and #17 bound to different epitopes of the RABV Gprotein . Injectiin vitro escape virus generation experiment. Chimeric MAbs #7 and #17-resistant escape viruses were generated as described in Materials and Methods. The G protein-encoding genes of each escape virus were analyzed by direct sequencing of cDNA that was obtained from infected cells. DNA sequence analysis revealed several mutations in the G protein gene. Overall, two escape virus containing different amino acid at same amino acid site for chimeric MAb #7 and four unique escape viruses for chimeric MAb #17 were isolated based on G protein amino acid sequences  sequences in a previous report -thymine uptake) and IL-2 cytokine secretion (ELISpot) assays. Keyhole Limpet Haemocyanin (KLH) and Phytohaemagglutanin (PHA) were used as a positive control in T cell activation and ELISpot assays. The value of responses in test wells with antibodies relative to those measured in non-treated wells was the outcome criterion. The value is presented as a stimulation index (SI), with an SI equal to or greater than 2  was considered positive. Chimeric MAbs #7 and #17 induced a T cell proliferation response in five and four donors, respectively, among 50 donors, including one borderline response to each antibody, and the mean SIs were positive (SI ≥ 2.00). T cell responsive donors showed that both antibodies induced a low mean SI Click here for additional data file.S1 Table(PDF)Click here for additional data file.