Genetic analysis of complete viral genomes generated in real-time reveals a probable chain of transmission events, predicting undisclosed infected premises, and connecting the second cluster of outbreaks in September to those in August. Complete genome sequence analysis of FMD viruses conducted in real-time have identified the initial and intermediate sources of these outbreaks and demonstrate the value of such techniques in providing information useful to contemporary disease control programmes.Foot-and-mouth disease (FMD) virus causes an acute vesicular disease of domesticated and wild ruminants and pigs. Identifying sources of FMD outbreaks is often confounded by incomplete epidemiological evidence and the numerous routes by which virus can spread . Here, we show that the outbreaks of FMD in the United Kingdom in August 2007 were caused by a derivative of FMDV O Foot-and-mouth disease (FMD) outbreaks in the United Kingdom during August and September 2007 have caused severe disruption to the farming sector and cost hundreds of millions of pounds. Investigating and determining the source of these outbreaks is imperative for their effective management and future prevention. Foot-and-mouth disease virus (FMDV) has a high mutation rate, resulting in rapid evolution. We show how complete genome sequences (acquired within 24–48 h of sample receipt) can be used to track FMDV movement from farm to farm in real time. This helped to determine the most likely source of the outbreak, assisted ongoing epidemiological investigations as to whether these field cases were linked to single or multiple releases from the source, and predicted the existence of undetected intermediate infected premises. Aphthovirus in the family Picornaviridae. Its genome consists of a single strand of positive-sense RNA approximately 8.3 kb in length Foot-and-mouth disease (FMD) is an economically devastating vesicular disease of domesticated and wild cloven-hoofed animals. FMD is caused by a 30 nm un-enveloped virus belonging to the genus The molecular epidemiology of FMDV has been extensively studied rd August 2007 from animals exhibiting suspect clinical signs were submitted to the World Reference Laboratory for FMD located at the Institute for Animal Health (IAH), Pirbright, Surrey. Within 24 hours, FMDV sequence data obtained from the first IP (holding IP1b) revealed a VP1 gene-identity of 99.84% to FMDV O1 British Field Sample 1860 (O1 BFS 1860); intratypic identities between type O VP1 sequences may be as low as 80% 1BFS 1860 is a widely used reference and vaccine strain, originally derived from bovine tongue epithelium received at the World Reference Laboratory for FMD at Pirbright in 1967 from a farm near Wrexham, England. The Pirbright site, comprising the laboratories of the IAH and Merial Animal Health Limited , is situated 4.4 km from the first IP. Both laboratories were working with the O1 BFS 1860 virus strain, making this site a likely source of the outbreak. Three days after the case at IP1b, a second infected premises (IP2b) was identified at Willey Green, approximately 1.5 Km from IP1b. Cattle at a further holding (IP2c) near to and under the same ownership as IP2b were found to be incubating disease at the time of slaughter. Animals on both the affected farms were destroyed and the premises were disinfected. Subsequent clinical and serological surveillance within a 10 km control zone found no evidence of further dissemination of FMD. However, on 12th September 2007, five weeks after the IP1 and IP2 cattle had been culled, FMD was confirmed on the holding of a new IP (IP3b) situated outside the 10 km control zone surrounding IP1 and IP2  have been identified , two in  and IP2 . FMD out and IP2 .These outbreaks of FMDV in the UK during August and September 2007 have caused severe disruption to the farming sector and cost more than one hundred million pounds. Investigating and determining the source of these outbreaks has been imperative for their effective management and is vital for future prevention. The aim of this study was to trace FMDV movement from farm-to-farm by comparing complete genome sequences acquired during the course of the epidemic. These “real-time” analyses helped to determine the most likely source of the outbreak, assisted ongoing epidemiological investigations as to whether these field cases were linked to single or multiple releases from the source, and predicted the existence of undetected intermediate infected premises that were subsequently identified.1 BFS 1860 (AY593815) are illustrated in 134 and/or VP35660 to a neutral residue often occurs but may not be essential for HS binding 1 BFS 1860 have lysine at VP2134, histidine at VP356 and aspartic acid at VP360, none of the residues associated with HS binding, whereas substitutions at VP356 (arginine) and VP360 (glycine) are present in MAH and IAH2, consistent with their history of extensive culture passage (60 (aspartic acid to glycine) in all but one of the field viruses provides evidence that a cell culture adapted virus is an ancestor of the outbreak. Since this residue is not critical for HS binding it is less likely to undergo reversion 56 in all of the outbreak viruses and at VP360 in the IP5 virus most likely reflect reversions that have been selected upon replication within the animal host. It is known that there is a strong selection pressure for the reversion at VP356 when FMDV replicates in cattle The UK 2007 FMD outbreaks were characterised by the emergence of two temporally and spatially distinct clusters. The genetic relationships of FMDV present in eleven field samples from the 2007 outbreak, three cell culture derived laboratory viruses see  used at  passage . The preThe viruses from the outbreaks differ by at least five unique synonymous substitutions from the laboratory viruses examined . In termin-vitro experiments; Sequence analysis of virus from the first affected holding identified in the second cluster of outbreaks (IP3b) demonstrated that it had evolved from virus from the first cluster of outbreaks . The seqDuring the second phase of the epidemic, analysis of the data  could account for the transmission within each geographic and temporal cluster. Although the epidemiological link between the August and September clusters is not known, the genetic data provide strong evidence to link FMDV transmission between these and the other infected farms. The consensus sequences from individual farms were found to differ by 1–5 nucleotide substitutions. It is probable that the variation in number of changes observed (between premises) have resulted from a number of factors including variation in the degree of bottleneck on the transmitted virus population by different transmission routes and number of virus replication cycles that have occurred in the host post-transmission. The genetic relationships between viruses from individual animals shown in Total RNA was extracted directly from a 10% epithelial suspension using the RNeasy Mini Kit , or from blood or oesophageal/pharyngeal scrapings using TRIzol . Reverse transcription of the RNA was performed using Superscript III reverse transcriptase (Invitrogen) and an oligo-dT primer see . Twenty π) value of 7×10−4The raw data was assembled using the Lasergene® 7 Software package  and all further sequence manipulations were performed using BioEdit (version 7.0.1 The FMDV genome sequences have been submitted to the GenBank/EMBL/DDBJ and assigned the accession numbers EU448368 to EU448381.Table S1Passage histories of the reference viruses studied.(0.03 MB DOC)Click here for additional data file.Table S2Oligonucleotide primers used for the amplification and sequencing of the FMDV genomes studied.(0.11 MB PDF)Click here for additional data file.Figure S1A Bayesian majority rule consensus tree of all sequences included in this study.(0.03 MB PDF)Click here for additional data file.


We report the isolation of chikungunya virus from a patient during an outbreak of a denguelike syndrome in Cameroon in 2006. The virus was phylogenetically grouped in the Democratic Republic of the Congo cluster, indicating a continuous circulation of a genetically similar chikungunya virus population during 6 years in Central Africa. Chikungunya virus (CHIKV), formerly only an anecdotally described arbovirus, is now a worldwide public health problem .P. falciparum according to rapid test  and thick smear examination.In Douala, Cameroon, 2 sporadic cases of a denguelike syndrome were recorded in French soldiers (patients 1 and 2) on April 3 and May 22, 2006, respectively . From thSerum specimens were tested for immunoglobulin M (IgM) and IgG antibodies specific for DENV, West Nile virus (WNV), Wesselsbron virus, Rift Valley fever virus, Bunyamwera virus, and CHIKV by IgM-antibody capture (MAC-ELISA) and IgG sandwich ELISA, respectively  had only IgM antibodies specific to CHIKV, while patients 18 and 28 had both IgM and IgG antibodies specific to CHIKV . One patAedes albopictus tends to replace indigenous Ae. aegypti in rural and urban Cameroonian environments (In Cameroon, as in DRC (Since its first isolation in 1953 (


The vast sequence divergence among different virus groups has presented a great challenge to alignment-based analysis of virus phylogeny. Due to the problems caused by the uncertainty in alignment, existing tools for phylogenetic analysis based on multiple alignment could not be directly applied to the whole-genome comparison and phylogenomic studies of viruses. There has been a growing interest in alignment-free methods for phylogenetic analysis using complete genome data. Among the alignment-free methods, a dynamical language (DL) method proposed by our group has successfully been applied to the phylogenetic analysis of bacteria and chloroplast genomes.In this paper, the DL method is used to analyze the whole-proteome phylogeny of 124 large dsDNA viruses and 30 parvoviruses, two data sets with large difference in genome size. The trees from our analyses are in good agreement to the latest classification of large dsDNA viruses and parvoviruses by the International Committee on Taxonomy of Viruses (ICTV).The present method provides a new way for recovering the phylogeny of large dsDNA viruses and parvoviruses, and also some insights on the affiliation of a number of unclassified viruses. In comparison, some alignment-free methods such as the CV Tree method can be used for recovering the phylogeny of large dsDNA viruses, but they are not suitable for resolving the phylogeny of parvoviruses with a much smaller genome size. Viruses were traditionally characterized by morphological features  and physicochemical and antigenic properties . At the The phylogeny based on single genes or gene families contains ambiguity because horizontal gene transfer (HGT), along with gene duplication and gene capture from hosts, appear to be frequent in large DNA viruses -10. WhetThe DNA of DNA viruses is usually double-stranded (dsDNA), but may also be single-stranded (ssDNA). According to the VIIIth Report of the International Committee on Taxonomy of Viruses (ICTV) , the dsDK-word (KW) or K-mer distance , Average method  by replae method . Testinge method ,34,40, we method , while te method  to analyhttp://www.ncbi.nlm.nih.gov/genbank/genomes.In order to explore the feasibility of our method, the whole DNA sequences (including protein-coding and non-coding regions), all protein-coding DNA sequences and all protein sequences from the complete genomes of the following two data sets were obtained from the NCBI genome database Bovine adenovirus D , Ovine adenovirus D , Duck adenovirus A , Fowl adenovirus A  and Fowl adenovirus D  in the genus Atadenovirus; Bovine adenovirus B , Canine adenovirus , Human adenovirus A , Human adenovirus B , Human adenovirus C , Human adenovirus D , Human adenovirus E , Murine adenovirus A , Ovine adenovirus A , Porcine adenovirus C , Simian adenovirus A , Bovine adenovirus A , Human adenovirus F , Porcine adenovirus A , Tree shrew adenovirus  and Simian adenovirus 1  in the genus Mastadenovirus; Frog adenovirus  and Turkey adenovirus A  in the genus Siadenovirus. In the family Asfarviridae, we only selected the African swine fever virus  in the genus Asfivirus. The viruses in the family Baculoviridae are: Adoxophyes orana granulovirus , Agrotis segetum granulovirus , Cryptophlebia leucotreta granulovirus , Cydia pomonella granulovirus , Phthorimaea operculella granulovirus , Plutella xylostella granulovirus  and Xestia c-nigrum granulovirus  in genus Granulovirus; Autographa californica nucleopolyhedrovirus , Bombyx mori nucleopolyhedrovirus , Choristoneura fumiferana defective nucleopolyhedrovirus , Choristoneura fumiferana MNPV , Epiphyas postvittana nucleopolyhedrovirus , Helicoverpa armigera nuclear polyhedrosis virus , Helicoverpa armigera nucleopolyhedrovirus G4 , Helicoverpa zea single nucleocapsid nucleopolyhedrovirus , Lymantria dispar nucleopolyhedrovirus , Mamestra configurata nucleopolyhedrovirus A , Mamestra configurata nucleopolyhedrovirus B , Neodiprion sertifer nucleopolyhedrovirus , Orgyia pseudotsugata multicapsid nucleopolyhedrovirus , Rachiplusia ou multiple nucleopolyhedrovirus , Spodoptera exigua nucleopolyhedrovirus  and Spodoptera litura nucleopolyhedrovirus  in genus Nucleopolyhedrovirus; and two unclassified viruses Culex nigripalpus baculovirus , Neodiprion lecontei nucleopolyhedrovirus . The species in the family Herpesviridae are: Gallid herpesvirus 1  in genus Iltovirus; Gallid herpesvirus 2 , Gallid herpesvirus 3  and Meleagrid herpesvirus 1  in genus Mardivirus; Meleagrid herpesvirus 1 , Cercopithecine herpesvirus 1 , Human herpesvirus 1 , Human herpesvirus 2  and Cercopithecine herpesvirus 2  in genus Simplexvirus; Bovine herpesvirus 1 , Bovine herpesvirus 5 , Cercopithecine herpesvirus 9 , Equid herpesvirus 1 , Equid herpesvirus 4 , Suid herpesvirus 1  and Human herpesvirus 3 (strain Dumas)  in genus Varicellovirus; Human herpesvirus 5 strain AD169 , Human herpesvirus 5 strain Merlin , Pongine herpesvirus 4  and Cercopithecine herpesvirus 8  in genus Cytomegalovirus; Murid herpesvirus 1  and Murid herpesvirus 2  in genus Muromegalovirus; Human herpesvirus 6 , Human herpesvirus 6B  and Human herpesvirus 7  in genus Roseolovirus; Callitrichine herpesvirus 3 , Human herpesvirus 4  and Cercopithecine herpesvirus 15  in genus Lymphocryptovirus; Cercopithecine herpesvirus 17 , Alcelaphine herpesvirus 1 , Bovine herpesvirus 4 , Equid herpesvirus 2 , Human herpesvirus 8 , Murid herpesvirus 4  and Saimiriine herpesvirus 2  in genus Rhadinovirus; Ictalurid herpesvirus 1  in genus Ictalurivirus; and 4 unassigned species Tupaiid herpesvirus 1 , Ostreid herpesvirus 1 , Psittacid herpesvirus 1  and Ateline herpesvirus 3 . The species in the family Iridoviridae are: Invertebrate iridescent virus 6  in genus Iridovirus; Lymphocystis disease virus - isolate China  and Lymphocystis disease virus 1  in genus Lymphocystivirus; Infectious spleen and kidney necrosis virus  in genus Megalocytivirus; Frog virus 3 , Regina ranavirus  and Singapore grouper iridovirus  in genus Ranavirus. In the family Nimaviridae, we only selected Shrimp white spot syndrome virus  in genus Whispovirus. The two species in the family Phycodnaviridae are Paramecium bursaria Chlorella virus 1  in genus Chlorovirus and Ectocarpus siliculosus virus  in genus Phaeovirus. The two species in the family Polydnaviridae are Cotesia congregata virus  and Microplitis demolitor bracovirus  in genus Bracovirus. The species in family Poxviridae are: Canarypox virus  and Fowlpox virus  in genus Avipoxvirus; Lumpy skin disease virus  and Sheeppox virus  in genus Capripoxvirus; Myxoma virus  and Rabbit fibroma virus  in genus Leporipoxvirus; Molluscum contagiosum virus  in genus Molluscipoxvirus; Camelpox virus , Cowpox virus , Ectromelia virus , Monkeypox virus , Vaccinia virus  and Variola virus  in genus Orthopoxvirus; Bovine papular stomatitis virus  and Orf virus  in genus Parapoxvirus; Swinepox virus  in genus Suipoxvirus; Yaba monkey tumor virus  and Yaba-like disease virus  in genus Yatapoxvirus; Amsacta moorei entomopoxvirus  and Melanoplus sanguinipes entomopoxvirus  in genus Betaentomopoxvirus; and unclassified Mule deer poxvirus . There are another two viruses Acanthamoeba polyphaga mimivirus  in genus Mimivirus (unassigned to a family) and Heliothis zea virus 1  (unclassified).We selected 124 large dsDNA viruses. The species in the family Adenoviridae are: Aleutian mink disease virus  in the genus Amdovirus; Minute virus of canines  in the genus Bocavirus; Adeno-associated virus 1 , Adeno-associated virus 2 , Adeno-associated virus 3 , Adeno-associated virus 4 , Adeno-associated virus 5 , Adeno-associated virus 7 , Adeno-associated virus 8 , Avian adeno-associated virus ATCC VR-865 , Avian adeno-associated virus strain DA-1 , Bovine adeno-associated virus , Bovine parvovirus-2 , Goose parvovirus  and Muscovy duck parvovirus  in the genus Dependovirus; B19 virus  in the genus Erythrovirus; Canine parvovirus , LuIII parvovirus , Mouse parvovirus 3  and Porcine parvovirus  in the genus Parvovirus. The species in the subfamily Densovirinae are: Aedes albopictus densovirus  in the genus Brevidensovirus; Acheta domesticus densovirus , Diatraea saccharalis densovirus , Galleria mellonella densovirus , Junonia coenia densovirus  and Mythimna loreyi densovirus  in the genus Densovirus; Bombyx mori densovirus 1 , Bombyx mori densovirus 5  and Casphalia extranea densovirus  in the genus Iteravirus; and Periplaneta fuliginosa densovirus  in the genus Pefudensovirus. The genera Amdovirus and Bocavirus, and the genus Pefudensovirus are newly defined genera in the subfamilies Parvoririnae and Densovirinae respectively in the VIIIth Report of ICTV Atadenovisults in , our tre viruses , the optParvovirus, Erythrovirus, Dependovirus, Amdovirus and Bocavirus group together as subgroups respectively. In the Densovirinae group, the parvoviruses in the genera Densovirus, Iteravirus and Brevidensovirus cluster together as subgroups correctly. All the groups and subgroups shown in Figure Brevidensovirus in the VIIth Report of the ICTV [Pefudensovirus in the VIIIth Report of ICTV [Densovirus rather than Brevidensovirus. Thus the grouping of PfDNV with Densoviru in our tree  probably have different evolutionary history as compared to large DNA viruses ,49. Our e method . Yet no Our approach is faster than the traditional approaches of phylogenetic analysis, particularly when dealing with a large number of genomes. Moreover, since multiple sequence alignment is not necessary, the intrinsic problems associated with this complex procedure can be avoided. Our method may provide a quick reference on virus phylogeny and a fast analysis of co-evolution of viruses and their hosts when their proteomes are available ,50.Using the DL method, we have studied the molecular phylogeny between families of large dsDNA viruses and parvoviruses. The present method provides a new way for recovering the phylogeny of large dsDNA viruses and parvoviruses, and also insights on the affiliation of some unclassified viruses and relationships among some families. It appears that some alignment-free methods such as the CV Tree method  can be uIn this paper, three kinds of data from the complete genomes of large dsDNA viruses and parvoviruses are analysed using the DL method proposed by our group . They arN = 4K(for DNA sequences) or 20K(for protein sequences) possible types of K-mers (the strings with fixed length K). We denote the length of a DNA or protein sequence as L. Then a window of length K is used to slide through the sequences by shifting one position at a time to determine the frequencies of each of the N kinds of K-mers in this sequence. We define p(α1α2...αK) = n(α1α2...αK)/(L - K + 1) as the observed frequency of a K-mer α1α2...αK, where n(α1α2...αK) is the number of times that α1α2...αK appears in this sequence. For the DNA or amino acid sequences of the protein-coding genes, denoting by m the number of protein-coding genes from each complete genome, we define K-mer α1α2...αK; here nj(α1α2...αK) means the number of times that α1α2...αK appears in the jth protein-coding DNA sequence or protein sequence, and Lj the length of the jth sequence in this complete genome. Then we can form a composition vector for a genome using p(α1α2...αK) as components for all possible K-mers α1α2...αK. We use pi to denote the i-th component corresponding to the mer type i, i = 1,..., N . In this way we construct a composition vector p =  for a genome.There are a total of K-mer s1s2...sK is possibly constructed by adding a letter sK to the end of the (K-1) -mer s1s2...sK-1or a letter s1 to the beginning of the (K-1)-mer s2s3...sK. After counting the observed frequencies for all (K-1)-mers and the four or 20 kinds of letters, the expected frequency of appearance of K-mers is predicted byYu et al  considerp(s1) and p(sK)are frequencies of nucleotides or amino acids s1and sK appearing in this genome. Then q(s1s2...K) of all 4K or 20KsK-mers is viewed as the noise background. We then subtract the noise background by definingwhere X = (p/q)-1 has the desired effect of subtraction of random background in p and rendering it a stationary time series suitable for subsequent cross-correlation analysis. X can also be regarded as the relative difference between p and q.The transformation X(s1s2...sK) for all possible K-mers s1s2...sK as components and arrange them according to a fixed alphabetical order to form a composition vector X =  for genome X, and likewise Y =  for genome Y.Then we use N components in the vectors X and Y as samples of these two random variables respectively. The sample correlation C between any two genomes X and Yis defined in the usual way. The dissimilarity D between the two genomes is then defined by D = )/2. A dissimilarity matrix for all the genomes under study is then generated for the construction of phylogenetic trees. This method to construct phylogenetic tree is referred to as the dynamical language (DL) method [Then we view the ) method .SplitsTree4 [Finally, for convenience to compare the results with those of the previous works, based on the distance matrices, we construct all trees for data sets 1 and 2 using the neighbour-joining (NJ) method  in the sitsTree4  (versionK-mers. The method of Qi et al. [K-mer in different genomes. The bootstrap is applied to the columns of the table except those are redrawn [K-mer maintains the same frequency as in the original table. Because it is allowed that some positions could be redrawn more than once in the traditional bootstrap analyses with sequence alignment [In order to estimate the robustness of tree topology, Qi et al  proposedi et al.  is not ai et al.  which wo redrawn . Thus, tlignment , we beliZGY conceived the study, downloaded the data, analyzed the results, and was involved in programming, drafting and revising the manuscript. KHC and CPL participated in designing the study, analyzed the results, and were involved in drafting and revising the manuscript. VVA, LQZ and WRW participated in the discussion of the results, and were involved in revising the manuscript. All authors read and approved the final manuscript.


Negotiations on a future climate policy framework addressing Reduced Emissions from Deforestation and Degradation (REDD) are ongoing. Regardless of how such a framework will be designed, many technical solutions of estimating forest cover and forest carbon stock change exist to support policy in monitoring and accounting. These technologies typically combine remotely sensed data with ground-based inventories. In this article we assess the costs of monitoring REDD based on available technologies and requirements associated with key elements of REDD policy.We find that the design of a REDD policy framework  can have a significant impact on monitoring costs. Costs may vary from 0.5 to 550 US$ per square kilometre depending on the required precision of carbon stock and area change detection. Moreover, they follow economies of scale, i.e. single country or project solutions will face relatively higher monitoring costs.Although monitoring costs are relatively small compared to other cost items within a REDD system, they should be shared not only among countries but also among sectors, because an integrated monitoring system would have multiple benefits for non-REDD management. Overcoming initialization costs and unequal access to monitoring technologies is crucial for implementation of an integrated monitoring system, and demands for international cooperation. Globally, by far, the biggest greenhouse gas mitigation potential in forestry is reducing emissions from deforestation. The negotiations on a future REDD (Reduced Emissions from Deforestation and Degradation) policy framework are ongoing and many options exist for its implementation . REDD acA key element of the REDD discussion are monitoring costs. Their estimation and extent will have an impact on the success of REDD mechanisms. Monitoring costs, however, will depend also on the scope and implementation of REDD mechanisms. Elements that will have an influence on the costs include the payment scheme for REDD, whether it is market or non-market based or a combination of such. The scope of the system, either national or sub-national, will have an impact on the costs as well as the type and level of verifications that will be applied. In this article we identify key elements of REDD policy, evaluate requirements for monitoring efforts and assess their costs.REDD policies and REDD monitoring systems will co-evolve. A REDD monitoring system needs to be designed to serve known current and future REDD policy requirements conditional on technical capabilities and costs. Likewise, future REDD policy designs will need to be based on comprehensive, international consistent and accurate spatially explicit data on global forest change and carbon stocks, emissions and trends. Herold and Johns,  define aUnder the UNFCCC and Kyoto Protocol, no climate policies exist to reduce emissions from deforestation or forest degradation in developing countries. In December 2005, COP-11 established a two-year process to review relevant scientific, technical, and methodological issues and to consider possible policy approaches and positive incentives for reducing emissions from deforestation in developing countries ,9. RecenMost approaches suggest voluntary participation and require an assessment of historic and future deforestation rates based on detectable change in forest area using remote sensing imagery. The use of positive incentives as a source of finance for activities and policies is the ultimate basis of all proposals. The scope and design of the REDD approach  has implications for monitoring and verification efforts. From the proposals analyzed in this study we identified several aspects relevant for monitoring  and for costs of remote sensing, change detection and verification (in particular) (see Table Different monitoring options are available to detect change of forest cover and of carbon stocks within forests (see Section "Assessment of monitoring technologies and costs" below). It has to be considered that the decision of which monitoring system will be applied in the final REDD system will not depend on availability of technologies alone. Policy makers will base their decision on the REDD design considering other factors including: definitions, scale and scope of activities, financing mechanisms, trading of credits and their own country context. In the following text we provide an outline of the role of monitoring within a REDD system.According to the Food and Agricultural Organization (FAO), the definition of deforestation refers to a change in land cover with depletion of tree crown cover to less than 10 percent . The UNFChanges within the forest class (e.g. from closed to open forest) which negatively affect the stand or site and, in particular, lower the production capacity, are termed forest degradation.The Good Practice Guidance for Land Use, Land-Use Change and Forestry (GPG LULUCF) of the Intergovernmental Panel on Climate Change (IPCC) as well as the 2006 guidelines for Agriculture, Forestry and Other Land Uses (AFOLU) include definitions that might be used as a basis for definitions in a potential REDD mechanism. The options for definitions of forests and deforestation within future policy frameworks might range from the application of existing national definitions to globally harmonized definitions such as the FAO. They might be based on technically detailed descriptions like those prescribed by the Marrakech Accords  and used by Annex I countries for Kyoto reporting and CDM projects. Existing inventories can provide such technical features of forests in developed countries.Technical requirements and associated costs between a RED (without degradation) and a REDD (including degradation) monitoring system would turn out to be starkly different. Measuring forest degradation through remote sensing is technically more challenging. In the transition from intact to degraded forest the canopy may still be closed (or closed again), whilst the carbon stocks may be reduced by up to 75% . In addiA more general approach discussed for tropical forests and proposed from a monitoring perspective by the Joint Research Centre  forms thRecently, the UNFCCC negotiation enhanced the REDD discussion with the inclusion of sustainable forest management and the conservation and enhancement of forest carbon stocks . MonitorAt the current stage it remains unclear if the REDD mechanism will be applied at the national level or sub-national level, or a combination of both. Likewise, it is unclear whether monitoring will in the end be done on the project, country or even global scale. Following a project approach , simplifies quantification and monitoring efforts because of the clearly defined boundaries for project activities, the relative ease of stratification of the project area, and the choice of carbon pools to measure . At the The Minimum Mapping Unit (MMU) required for effective monitoring directly influences the costs of monitoring. Remote sensing data analysis becomes more difficult and more expensive with smaller MMUs, i.e. more detailed MMUs increase mapping efforts and usually decrease change mapping accuracy . For exaGHG emissions displacement might occur when interventions to reduce emissions in one geographical area  cause an increase in emissions in another area through the relocation of activities. Monitoring of REDD also needs to address this leakage of emissions which will have direct implications for monitoring costs.Wall-to-wall coverage , with high resolution satellite imagery or even with airborne imagery will provide a high level of certainty to estimate land-use change . A globaHowever, to be effective, an assessment of displacement of REDD would require a "land based" reporting approach. Clearly, a global forest carbon observatory would probably also yield the lowest cost solution per MRV-REDD unit by reaping the most of economies of scale. Economies of scope relate to the fact that in a "system of systems" approach, such as GEOSS , one observing system creates benefits to another. For example, an Earth Observation (EO) satellite system dedicated to yield and acreage estimation in agriculture could at the same time be used for deforestation monitoring. Thus, the cost per unit carbon from REDD will be decreased by its complementary use for agricultural monitoring.REDD policies have to address the difficulty in determining additionality compared to a baseline. Historic baselines use national historical averages as reference and compare them to current rates. However, reliable estimates of historical carbon emissions from deforestation and degradation are de facto not available. Considerable (re-)analysis of recently opened remote sensing archives would be necessary. Similarly, global average deforestation rates are discussed that would require less historic deforestation data at the national level. Models for baseline estimations range from relatively simple extrapolations of past trends in land use to more complex extrapolations of past trends using spatially explicit models of land-use change driven by biophysical and socioeconomic factors . SophistThere is a great diversity of methods for estimating carbon stocks in forests. Therefore, it is extremely important for the planning of forest carbon observation systems to agree internationally on common methods and standards. Three approaches (tiers) for estimating carbon are proposed by the IPCC LULUCF ,22. TierThe GOFC-GOLD sourcebook reviews in detail the question of which tier should be used. The choice is relevant not only for costs but also for the level of total uncertainty. The error in applying a relatively coarse IPCC Tier 1 approach (as compared to carbon stocks estimated from ground plot measurements from six sites around the world) can range between an overestimation of 33% to an underestimation of 44% . The souIt is currently still unclear, which level of minimum precision and accuracy is required under which REDD implementation scenario. Moreover, no decision is made on the system boundaries, i.e. which carbon pools are going to be included in REDD. While including soil carbon pools will increase uncertainty and costs, an integrated forest sector view including harvested wood products might actually decrease relative uncertainty of carbon stock change estimates   due to sREDD aims to encourage permanent forest management to ensure that carbon emissions are not occurring. Permanence is therefore the core element of the whole REDD approach and points to the requirement of temporal consistency of monitoring. At the moment, different financing options are discussed including fund based and market based approaches . While aIn a market based approach, carbon credits are traded on the carbon market and paid by private or sovereign clients. Different trading mechanisms have been discussed including different types of credits (e.g. temporary versus permanent credits). Under the CDM projects relating to afforestation and reforestation (A/R), a system of temporary credits has been implemented which differentiates between short-term credits (tCERs) and long-term credits (lCERs) .Short-term credits are given for existing carbon stocks at the time of verification, which expire after 5 years. Verification takes place again every 5 years until the end of the project period, taking changes in carbon stocks into account by adjusting the number of tCERs issued every 5 years . To deteA different approach to issue permanent carbon credits is currently applied on the voluntary carbon market by VCS (Voluntary Carbon Standard) and Carbon Fix standards . This apEach of these elements discussed above offers various options for implementation. An international agreement might also leave the final implementation to the member states, prescribing only the range within which countries have to choose their definition (i.e. the forest definition in the Kyoto Protocol implementation as defined in the Marrakech Accords).The more flexibility such an agreement leaves to each country for implementation, the more options a country has for a cost efficient and locally adapted design of its monitoring systems. Thus, it appears that a clear choice for the right monitoring system for the ultimate REDD carbon trading is not yet possible. However, the exchange rate between a REDD unit and an Assigned Amount Unit will surely also depend on the level of measurement uncertainty of individual REDD units. The less MRV a REDD unit will appear, the more it will be discounted.Remote sensing will be an essential method to establish baselines and monitor progress in reducing emissions from deforestation and there will be considerable need to build capacity in this regard in many non-Annex I countries ,27. The The assessment of emissions from deforestation and degradation requires data on both change in forest cover and estimates of carbon stock changes associated with transition between land use types . It is aSatellite sensors can be generally grouped into optical and radar systems. Both systems collect data routinely and at least at moderate resolution, data are often freely available . The quality of global products derived from those sensors depends upon many factors , the need for time series, and the availability of ancillary data for validation. A wide spectrum of bands and radiometric resolution offer high information content. However, there is still a limited ability to develop accurate biomass estimation models for tropical forests based on remotely sensed optical data . Early sRandom errors in the methods applying optical remote sensing for deforestation detection typically range from a few per cent up to 20%, depending on sampling frequency, sample size and deforestation rates, e.g., . SystemaTechnologies based on Synthetic Aperture Radar (SAR) backscatter depend on the number of scattering elements seen by the radar wave, as well as on their geometric and dielectric properties. These features are directly related to parameters expressing forest density such as the forest growing stock volume . A methoThere are a number of new and innovative technologies which have recently approached operational feasibility, such as light detection and ranging . LiDAR A 'hierarchical nested approach' combines high and coarse resolution optical, SAR, and/or LiDAR data -41. CoarTraditional  forest inventories (NFI) provide data of the growing stock timber volume per unit area by tree diameter or age classes and species composition. To estimate changes in growing stocks, repeated measurements at permanent sample plots are carried out. There are a few developing countries like India and China that are conducting a national forest inventory on a regular basis . The bioIn many developing countries there are obvious limitations in the availability of appropriate reference data especially for the period 1990–2000. If no robust reference data are available, at a minimum, a consistency assessment should allow some estimation of the forest change quality, i.e. reinterpretation of small samples in an independent manner by regional experts. When completeness or accuracy of estimates cannot be achieved, high uncertainties in input data can be overcome by applying the conservativeness principle  proposedTable Financial resources for remote sensing assessments of deforestation and degradation are required to acquire suitable satellite data, for processing hardware and software, training and capacity building, data processing and analysis, field work and travel, and for accuracy assessment. The costs for EO are determined by the data quality, resolution, cloud cover, order size, imaging window, and provider. Data processing costs occur for: hardware, software and data analysis. Overall costs depend on factors like existing capabilities and capacities and the comprehensiveness of the monitoring systems. However the costs for data analysis requires special REDD adaptations that depend on the degree of automated processes, effort, and accuracy. Costs that occur in acquiring ground-based data are for field work and travel. Whichever REDD system is applied, it is likely that training and capacity building will be needed in all areas which are part of a monitoring process and there will be costs associated with it.As stated above, the costs of monitoring REDD are a function of the desired level of precision – which may vary by the size of the project, terrain and heterogeneity of the landscape, location of areas and degree of coherence, and natural variation of carbon pools under observation , and als[A transparent form of validation could be achieved by publishing interpreted maps on the internet, or even by allowing public validation of land cover interpretation and land cover change (e.g. ).Building on and enhancing traditional and local level forest governance capacities and establishing community-based forest management systems can be an essential step to efficiently prevent and/or monitor deforestation and degradation . This kiRemote sensing has been identified as a key technology to successfully implement and monitor a future REDD mechanism . As descStern  identifiIt is clear at this stage that an important cost component is related to making the participating countries ready for REDD. The vast majority of forest nations participating in REDD will face capacity building costs, i.e. costs for establishing research capacity, technology transfer and legal support. Further costs might include those for land tenure reform and governance reform where these are required to facilitate a REDD financing regime. These so called costs for "readiness" are related to capacity building and policy development to create a framework in which the REDD system can be applied. As these types of costs depend on the national context and do not apply in all cases, they should be regarded as a separate cost type. The Eliasch Review  estimateImplementation costs of projects in a REDD system depend on the final structure of the REDD mechanism, whether it is market or non market based, implemented at the national or sub national level, etc. They comprise a variety of costs which include: monitoring, planning, verification, certification, enforcement, administration, insurance, brokerage and governance. There will also be costs involved in addressing the risk of leakage in the implementation of a REDD program. In certain cases, this cost could be high .2, e.g. [As it is unclear what structure a future REDD system will have, cost estimates for the implementation are difficult. However experience has been obtained in specific carbon related projects. The literature values from single projects can only be compared if they indicate what cost items are included and what the framing scheme looks like. Implementation costs typically range from 400 to 1500 US$/km2, e.g. ,55.When considering these cases for implementation costs for REDD, it has to be taken into account that only specific elements of a future REDD system in local contexts were part of these cases. It is therefore difficult to use these cases for an estimation of implementation costs of a future REDD system. However, it can be seen that each element of the implementation costs has to be considered separately and maybe even looked at in the national/local context to arrive at meaningful estimates.A key variable for deforestation is how attractive land conversion for individual land owners is, based on the balance between forest value and the value of alternative land-use. Opportunity costs represent the highest alternative land-use of the area under deforestation threat, including net revenue from the conversion itself .2  and a higher (10 US$/t CO2) value for a global carbon price.Compared to other cost items, monitoring costs of REDD projects are rather low on a per square kilometre basis. Future monitoring costs are likely to decrease because different sampling intensities will be used building on existing data. Additionally, project implementers will be able to build on previous experience. This is especially true for the more cost intensive monitoring efforts like estimating biomass expansion factors or establishing the first inventory. In particular, opportunity costs rule out expenses that even relatively intensive monitoring would require. As Kindermann et al.  estimateThere are two types of integration effects with respect to REDD monitoring. The first integration relates to cost savings due to integration of different observation systems/components. When observations are stratified (i.e. higher accuracy and precision in hot spot areas) and different observation systems are integrated to form an observation portfolio , monitoring costs per MRV REDD unit are minimized. The other integration effect refers to economies of scope when one observing system can yield multiple benefits. In this paper the costs of monitoring for REDD included only the carbon sector. In fact, monitoring of forests has the potential to benefit the development of the forestry sector in general. Those forests monitored for carbon stocks can more easily be assessed for timber supply, the provision of non-timber forest products or other forest functions. Moreover, an integrated monitoring system could result in optimized general land management, including fire management, implementation of land use policies, etc. From an integrated monitoring perspective the currently ongoing discussion on a REDD plus mechanism, that includes measures to lower emissions from deforestation and degradation, also rewards forest management activities that increase carbon stocks in forests. This would help to increase incentives for integrated forest monitoring with associated benefits for the forestry sector.), which is used for many different applications.Other co-benefits exist in the field of ecosystem services with an improved monitoring system, e.g. biodiversity and ecosystem services can be better measured and identified. For example, specific areas of both high carbon value and high ecosystem values can be identified. Additionally, general cadastral mapping services could be supplied by an integrated monitoring system as co-benefits. Disaster management could also be integrated e.g. the Disaster Management Constellation , claim that better international cooperation in the collection, interpretation, and sharing of EO information is an important and cost-effective mechanism for improving information available to decision makers. Fritz et al.  asked hoIn this paper we assess costs of REDD monitoring. These include information that are available from implemented or planned projects and monitoring companies . In the case that information from REDD projects could be fed into GEO and contribute to the Global Earth Observation System of Systems (GEOSS), benefits for society could be manifold. Therefore, some of the REDD costs could be offset by benefits to the forestry sector and potentially to many other of the societal benefit areas such as water, health, energy, disaster, biodiversity, ecosystems, climate and weather.Applied to the case of REDD policy and monitoring requirements, the benefit chain in REDD policy with respect to a better observing system could be described as follows: improved accuracy of monitoring of forest carbon stock changes leads to a better constraint on potential emissions from these forests and more realistic baselines, therefore giving the REDD process much more credibility overall, leading in the end to lower insurance costs.One resulting question from the GEOSS perspective is how costs are going to be distributed globally in a REDD framework. An appropriate answer would probably require at least a general assessment of the potential distribution of the benefits. This will remain a major challenge.The design of a REDD policy framework , can have a substantial effect on monitoring costs. Nevertheless, many of the technical challenges of monitoring emissions from deforestation (and to a lesser extent degradation) are feasible. Moreover, costs of REDD monitoring are affordable and relatively low compared to other cost items that occur in REDD .Future monitoring costs are likely to decrease because different sampling intensities will be used, project implementers can build on previous experience and existing data, and advances in technology will be available. If the advantages of co-benefits in other sectors  are included in a cost benefit analysis, costs of REDD monitoring will further decrease. Considering REDD as part of a Global Earth Observation System of Systems (GEOSS) will help to realize these benefits.International cooperation is, however, needed to overcome initialization costs, unequal access to monitoring technologies and know-how.The authors declare that they have no competing interests.HB coordinated the work on this paper, designed structure and concept and is the major contributor to the text. KE provided data and substantially contributed to several paragraphs of the manuscript. GK helped with critical revision and literature input. SF and FK incorporated the links to the GEO process. IM provided literature and data and helped drafting paragraphs of this paper. MO had the idea, contributed to the design of the study and critically reviewed the manuscript. All authors read and approved the final version of the manuscript.


Borna disease virus, a newly classified nonsegmented negative-strand RNA virus with international distribution, infects a broad range of warm-blooded animals from birds to primates. Infection causes movement and behavioral disturbances reminiscent of some neuropsychiatric syndromes. The virus has not been clearly linked to any human disease; however, an association between infection with the virus and selected neuropsychiatric disorders has been suggested. We reviewed recent advances in Borna disease virus research, focusing on evidence of infection in humans.


To the Editor: Knowledge of avian influenza (AI) virus and its host epidemiology and ecology is essential for effective monitoring and mitigation  and 770 Glaucous-winged Gulls (Larus glaucescens) were sampled during peak spring migration at Hartney Bay, Cordova, Alaska . Fresh fecal samples were obtained from tidal flats within <1 to 90 min after identified flocks were dispersed, and samples were placed in sterile medium  and either kept cool (<1 week) before transport to Fairbanks (2006) or placed into liquid nitrogen within 2 h of collection (2007). Samples were stored at –70° C; shipped frozen overnight to Athens, Georgia; and maintained frozen until analyzed.In 2006 and 2007, 1,050 shorebirds . RNA was tested for AI virus matrix (M) gene. A positive test result for this gene indicates the presence of any influenza viruses  for influenza A virus, and virus isolation was performed on samples that were positive. RNA was extracted by adding 250 μL of sample to 750 μL Trizol LS reagent . Samples were mixed and incubated at room temperature for 10 min. A total of 200 μL of chloroform was then added, incubation was continued for 5 min, and samples were centrifuged for 15 min at 12,000 × Screening for AI virus was conducted on 1,820 samples . Among t>6% in 2006 and >1%–2% in 2007) was 95%.Results of power analysis , representing 25% and 33% of those studies, respectively. Our study covered only 2 years, but it would detect AI virus infections in shorebirds at rates >1%–2% within each year with 95% probability and at rates >0.9% across years with 99% probability. Thus, the prevalence rate among Copper River Delta shorebirds in our study is lower than that found in the 16-year Delaware Bay study  that the role of shorebirds in AI virus ecology and epidemiology is heterogeneous within North America and within a genus . These findings confirm that knowledge of how AI viruses cycle in wild bird hosts remains incomplete at continental and family-level taxonomic scales. Only further surveillance can fill these knowledge gaps.Our results corroborate other recent results (


Cryptococcus gattii recently emerged as a significant cause of cryptococcal disease in human and animals in the Pacific Northwest of North America. Although genetic studies indicated its possible presence in the Pacific Northwest for more than 30 years, C. gattii as an etiological agent was largely unknown in this region prior to 1999. The recent emergence may have been encouraged by changing conditions of climate or land use and/or host susceptibility, and predictive ecological niche modeling indicates a potentially wider spread. C. gattii can survive wide climatic variations and colonize the environment in tropical, subtropical, temperate, and dry climates. Long-term climate changes, such as the significantly elevated global temperature in the last 100 years, influence patterns of disease among plants and animals and create niche microclimates habitable by emerging pathogens. C. gattii may have exploited such a hitherto unrecognized but clement environment in the Pacific Northwest to provide a wider exposure and risk of infection to human and animal populations.The relatively uncommon fungal pathogen  Cryptococcus gattii  °C; and the linear warmingtrend over the last 50 years  is nearly twice that forthe last 100 years . The lasSpeculative studies on how awidespread climate change, specifically the elevation of global temperature,might affect the distribution of infectious diseases began about two decadesago. Health outcomes of climate change are diverse, and depend upon manydifferent factors with respect to biodiversity, nutrient cycle, physicalrelocation, internal defense systems, and transmission dynamics within microbes. The iniIn 2004, the World HealthOrganization (WHO) published a study of the global disease burden attributableto human-induced climate change up to the year 2000, and made quantitativemodel-based predictions of climate change-associated health risks up to 2030 . DespiteCryptococcus species to produce melanin; this has beenhypothesized to contribute to their environmental survival [C. gattii with Eucalyptus has been sporadically reported from India,Brazil, Italy, and the United States [C. gattii from non-Eucalyptus trees and tree materials [C. gattii has consistently been recovered in high concentrationfrom native trees, soil, air, freshwater, and seawater [In Australia, in the early 1990s,Ellis and Pfeiffer observed a correlation between the distributions of humancryptococcal disease and of Eucalyptus trees , 46, whosurvival , 48. Assd States –52. Howd States ,Papua Nd States , Centrald States , Brazil,aterials –60, indiseawater , 19.C. gattii epidemic on Vancouver Island and adjoining areas, epidemiological studiesshowed that all humans and animals with cryptococcal infection either livedwithin or traveled to areas identified by unique weather and vegetation zones. The British Columbia Ministry of Forests categorizes areas with similarecologies within the province by biogeoclimatic zone designations. Abiogeoclimatic zone is a geographical area with an ecosystem comprised of arelatively uniform macroclimate, defined vegetation, soils, and animal life inhabitingthat climate, and may contain smaller ecosystems (subzones) that reflectdifferences in regional climate, soil moisture, soil nutrient status, andenvironmental disturbance [C. gattii has been repeatedly and consistently isolated from theenvironment in these biogeoclimatic zones [C. gattii, are present in Western NorthAmerica [C. gattii serotype B  [Does the Vancouver Island outbreak,then, point to a changing ecological niche for this organism in Western NorthAmerica? From the beginning of the turbance . Theuniic zones , 19, 22.America . Indeed,wo VGIs) .C. gattii in the soil, indicating a potential source of exposure [C. gattii is well adapted for survival in a dry, nutrient-deprived soil and is morelikely to spread as airborne propagules during dry summer weather [C. gattii was consistently isolated from localized areas, it wasnot found, or was below the limit of detection in other areas, such as the SanJuan Islands [that is, zones ofhigh concentration, of C. gattii withinthe same broad ecological niches. There are also areas with transientcolonization, with sites intermittently positive over time, and some siteswhich initially tested negative which subsequently either became colonized, orthe concentration rose above the limit of detection [C. gattii now inhabits a stable ecologic niche in the environmentof the Pacific Northwest in concentrations high enough to pose a risk ofinfection through environmental exposures. Prior to the outbreak of C. gattii disease, there was no reasonto seek its presence in the environment, as there were no cases of locallyacquired infection, and C. gattii isnot a phytopathogen. The relationships between an emerging pathogen and theenvironment are not often studied, and it is even rarer that researchers havebeen able to document the emergence with as much detail as has been possible inthis BC outbreak. The unexpectedly rich dataset obtained from BC environmentaland epidemiological ecologies can be used to inform future eco-healthinvestigations. In this case, the existenceof libraries of cryptococcal strains from global clinical and environmental origins provided theresources for swift characterization and molecular epidemiology of thecausative organism; this information changed forever the simplistic idea of unvaryinggeographic boundaries on infectious organisms.Environmental sampling studies onVancouver Island revealed high concentrations of exposure , and dat weather . However Islands , despiteetection , 19. WheC. gattii to spread to other areas ofthe Pacific Northwest [C. gattii recovered fromthe predicted zone which was not on Vancouver Island [C. gattii in the foreseeable future due to the extremes oftemperature, snow cover, or lack of suitable habitat [A promising tool which can be usedby climatologists working with epidemiologists to predict public health impactsis ecological niche modeling –65. Thesorthwest . Usingeorthwest , 21, 24,r Island , 17, 20. habitat .C. gattii, similar to the congenericpathogen C. neoformans, has evolved mechanisms whichprotect it from the environment. For example, the melanin production by theseorganisms contributes to the protection from the ultraviolet radiation insunlight. The predominant genotype of the Vancouver Island outbreak thrives indry, nutrient poor soil at high concentrations. It appears to be well adaptedfor this marginal microecologic zone [C. gattii is greater during periods of high humidity or rain [C. gattii was restricted to a symbiotic relationship with Eucalyptustrees has since been shown not to be the case, as C. gattii regularly colonizes native trees in countries outsideAustralia [gic zone . InColo or rain . In Aust or rain . Neitherustralia , 58, 69.Cyclical climate change patterns are driven by forces such as solar cycles and the oceancurrents characterized by the El Niño and La Niña years, whereas more long-term changes, suchas an elevation of global temperatures, must be measured over decades orcenturies. Therefore, the problem of association of climate changes with theepidemiology of various infectious diseases is twofold. Climate changes may be long term, withthe potential to cause significant epidemiological changes over long-timehorizons, or the climate may suddenly shift, changing patterns and spread ofexposure in a given climatic zone. Both of these possibilities pose challengesto public health studies.C. gattii emerged as a human and animal pathogen in the late 1990s,in a new habitat, is indisputable. Whether or not the climatic conditions (bothshort- and long-term) in the new habitat made it conducive for the organism todo so is under debate. The oscillatory climate change patterns preceding theemergence were similar to the patterns seen in this area over the last 30years. The years 1992–1994 and 1998–2003 were dryerthan the 30-year average during the summer, followed by the years 1997, 2004,and 2005, which had higher-than-average summer rainfall; the winter rains roughly followed the samepattern. Importantly, however, the amount of snow coverage on southernVancouver Island and the CDF biogeoclimatic zone decreased over the same timeperiod. This constantly repeating dry/wet pattern in conjunction with theelevation of temperature may well be a driving factor in the prolongation of C. gattii as a pathogen in thisgeographic location. However, given the paucity of long-term data on C. gattii emergence in relation to climate changes, it is difficult to attribute theemergence entirely to climate change. A proof-of-concept example supporting thenotion is Coccidioides immitis, an environmental organism whichproliferates in wet years and is widely dispersed in dry years, causinglocalized spikes in hospitalization for areas adjacent to endemic locations [C. gattii, some factor(s) must have become permissive to thepermanent colonization of the organism in high enough concentration to causedisease with an incidence rate of around 28 cases/million population  [That ocations . For C. d alone) . IdentiC. gattii diseasehas escalated to an “off island” problem as well, involving the Northwestern US(Washington and Oregon). The extent of disease and thedistribution of the cryptococcal isolates within the United Statesare unknown, and will remain underappreciated untillaboratory methods to identify and speciate the isolates, standardizedreporting of disease, and environmental studies are established.It is also clear that C. gattii already had the ability to survive in a wide range of environmentalvariations, but the Western North America outbreak teaches us that it mayexploit hitherto unrecognized but clement environments and provide a widerexposure, and thereby, risk of infection to the human and animal populations. The challenge for public health is to coordinate efforts toward earlyrecognition of the emergence of new or reemergence of previously encounteredinfectious diseases to alert primary health care providers as to the diagnosisand appropriate treatment in order to prevent excess morbidity and mortality. However, understanding theconsequences of ecological change is a group effort whichincludes health researchers, climatologists, and ultimatelyglobal citizens whose health may depend on the capacity toadapt to a rapidly changing environment.


Amyotrophic lateral sclerosis (ALS) is a fatal, degenerative neuromuscular disease characterized by a progressive loss of voluntary motor activity. About 95% of ALS patients are in "sporadic form"-meaning their disease is not associated with a family history of the disease. To date, the genetic factors of the sporadic form of ALS are poorly understood.We proposed a two-stage approach based on seventeen biological plausible models to search for two-locus combinations that have significant joint effects to the disease in a genome-wide association study (GWAS). We used a two-stage strategy to reduce the computational burden associated with performing an exhaustive two-locus search across the genome. In the first stage, all SNPs were screened using a single-marker test. In the second stage, all pairs made from the 1000 SNPs with the lowest p-values from the first stage were evaluated under each of the 17 two-locus models.http://ccr.coriell.org/ninds/. Our two-locus analysis showed that two two-locus combinations--rs4363506 (SNP1) and rs3733242 (SNP2), and rs4363506 and rs16984239 (SNP3) -- were significantly associated with sporadic ALS. After adjusting for multiple tests and multiple models, the combination of SNP1 and SNP2 had a p-value of 0.032 under the Dom∩Dom epistatic model; SNP1 and SNP3 had a p-value of 0.042 under the Dom × Dom multiplicative model.we performed the two-stage approach on a GWAS data set of sporadic ALS from the SNP Database at the NINDS Human Genetics Resource Center DNA and Cell Line Repository The proposed two-stage analytical method can be used to search for joint effects of genes in GWAS. The two-stage strategy decreased the computational time and the multiple testing burdens associated with GWAS. We have also observed that the loci identified by our two-stage strategy can not be detected by single-locus tests. Amyotrophic lateral sclerosis (ALS) is a fatal progressive neurodegenerative disease that attacks nerve cells in the brain and spinal cord resulting in muscle weakness and atrophy. Although ALS is listed as a rare disease with a prevalence of approximately 1 per 10,000, it is the most common adult onset form of motor neuron diseases . EpidemiAPOE-e4 allele does in Alzheimer disease (AD) and the H1 MAPT haplotype does in parkinsonian syndromes [The identification of susceptibility genes of sporadic ALS has been slow in arriving. The search for sporadic ALS genes has generated a large number of candidate-gene association studies -19. To dyndromes . There iyndromes . The lac-7. After adjusted by permutation procedure, none of these SNPs reached the significance level of 0.05. This finding suggests that the ALS phenotype is not driven by a single powerful locus. By testing one marker at a time, the first stage analysis made the implicit assumption that susceptibility loci can be identified through their independent, marginal contributions to the trait variability. More recently, other GWAS in ALS have been conducted by different research groups [Recently, Schymick et al. made the first attempt to identify genetic factors that might be relevant in the pathogenesis of sporadic ALS by using a well-designed GWAS . The firh groups -24. Howeh groups -32. ThusIn this article, we used seventeen two-locus models to analyze the previously published genome-wide association data for ALS. We found that three SNPs were significantly associated with sporadic ALS. After we observed the significant two-locus combinations, we further estimated the impact (relative risk and odds ratio) of each of the two-locus combinations on sporadic ALS. It has been recognized that the traditional method will over estimate the odds ratio or relative risk in GWAS ,33. ReceIn this section, we will give details of the data set and describe a new analytical method to analyze this data set.http://ccr.coriell.org/ninds[Schymick et al. have made their data set publicly available through the website of the National Institute of Neurological Disorders and Stroke (NINDS) Human Genetics Resource Center at the Coriell Institute org/ninds. The datIn this article, we used seventeen two-locus models to analyze the genome-wide association data. For each SNP, we called one allele a high-risk allele if its frequency in cases was larger than the frequency in controls. For SNP A with alleles A, a and SNP B with alleles B, b, Figure GH = {aAbB, AAbB, aABB, AABB} and the low-risk group was GL = {aabb, aAbb, AAbb, aaBB} For the eight epistatic models, we used one degree of freedom (df) χ2 test statistic given byUnder each of the epistatic models, the nine two-locus genotypes were divided into two groups: high-risk genotype group and low-risk genotype group. For example, under the model Dom∩Dom, the high-risk group was to test for association of two-locus joint effects, where Disease|g) denote the penetrance of two-locus genotype combination g = , where g1 and g2 are the genotypes in the first and second markers, respectively. Let β0 denote the logarithm of the penetrance of genotypes with a relative risk of 1 in the models  = β0 + β1X, where X = x1 + x2, x1 is the numerical code of g1 and is given byFor the nine multiplicative models, we constructed a two-locus association test as follows. Let P = β0 + β1X, β1 = 0 means that all the genotypes have the same penetrance which implies that θ = 1. So a test of the association between the disease and the two loci under the nine multiplicative models is equivalent to a test of the null hypothesis H0: β1 = 0. For the ith individual, let yi denote the trait value  and Xi denote the numerical code of the genotype (X in the log linear model). The score test statistic is given byfor a dominant, recessive or multiplicative model, respectively; X1,..., XN, and y1,..., yN. Under the null hypothesis, Tscore follows a χ2 distribution with 1 df. Note that under each of the two-locus epistatic models, if we code X = 1 for a high-risk genotype group and X = 0 for a low-risk genotype group, then Tepi = Tscore.where N is the sample size, The method to search for significant two-locus combinations for each of the seventeen models has the following two steps:Step 1: For each SNP, let n and m denote the number of individuals in cases and controls (different SNPs may have a different number of cases and controls due to missing genotypes). Let n1, n2, n3 and m1, m2, m3 denote the number of three genotypes in cases and controls, respectively. The 2 df genotypic test statistic is given byM SNPs with the smallest p-values .where Step 2: Under each of the seventeen two-locus models, we applied a two-locus association test to each of the L two-locus combinations among the M retained SNPs, where L = M(M-1)/2. For a two-locus epistatic model given in figure epiT. For a multiplicative model given in figure scoreT. In this step, we got a p-value  for each of the L two-locus combinations and each of the seventeen two-locus models.e L = MM-/2. For aB times . For the ith model and lth two-locus combination , let pil and bth permutated data, respectively. LetA permutation procedure was used to adjust for multiple tests and multiple models. In each permutation, we randomly shuffled the cases and controls and repeated step 1 and step 2 based on the permuted data. We performed the permutation procedure ith model and lth two-locus combination, Pil, the p-value adjusted for multiple tests and multiple models, was given by Then, for the When a study identifies a locus or locus-combination that shows evidence of association with a disease, it is common to estimate the impact of this locus or locus-combination on the phenotype of interest. This impact is often expressed as an odds ratio. Estimation of the odds ratio is also helpful for planning successful replication studies.It is recognized that the traditional estimate of odds ratio is up-biased because it is typically estimated for the locus which was significant for association ,33. ReceWe use the following notation:n, m: the number of cases and controlsD = {n1,..., n9; m1,..., m9}: the counts of nine two-locus genotypes in cases and controls that constitute the significant signal for associationthe data q1,..., q9): the population frequencies of the genotypes/m is known in the following discussion. In the Dom∩Dom model, the 5th, 6th, 8th and 9th genotype combination {, , , } is the high-risk genotype combination, and the combination of the other genotypes is the low-risk genotype combination. Let qH = q5 + q6 + q8 + q9 denote the population frequency of the high-risk genotype combination. Then, the penetrance α and β  using the equationThus, we have only one unknown parameter D = {n1,..., n9; m1,..., m9}. Since the data D constitutes, by definition, a significant result, so D implies S; hence Pr = 1. If the value of L(R) can be calculated for each given R, we can obtain the MLE of R by using a numerical optimization method (grid search was used in this article). For each R, the numerator can be calculated by the product of two multinomial distributionswhere the data kth genotype is a low-risk genotype; D|R), the numerator in the likelihood function L(R), without considering the fact that the loci were significant for association. There is no simple method to calculate the denominator Pr(S|R), the power of our two-step test. We propose to use a simulation method as described below. For a given R, the values of α and β can be calculated by equation (1). When α, β, and qi are known, we can generate the two-locus genotypes for n cases and m controls. Next, we will perform the single-marker test and the two-locus test on the data set. If the p-values of the two single-marker tests are less than α1 and the p-value of the two-locus test is less than α2, the data set is said to be significant for association. We repeat the process to generate the data sets many times (1 million was used in this article). The proportion of significant data sets is the estimate of Pr(S|R).where R has been estimated, the corresponding estimates of α and β can be obtained from equation (1). The estimate of odds ratio of the high-risk genotype group is given by When the relative risk GL = {aabb}, middle risk genotype group GM = {aabB, aaBB, aAbb, AAbb}, and high risk genotype group GH = {aAbB, aABB, AAbB, AABB}. The odd ratio of the high risk group ORH is the odds of GH divided by the odds of GM ∪ GL = {aabb, aabB, aaBB, aAbb, AAbb}. The odd ratio of the low risk genotype group ORL is the odds of GL divided by the odds of GM ∪ GH = {aabB, aaBB, aAbb, AAbb, aAbB, aABB, AAbB, AABB}. The odds ratio estimation method will be the same as the case of two genotype groups.Following Zollner and Prichard, when there are more than two genotype groups in the models such as these in Figure We used this new proposed method to estimate the odds ratio for each of the two-locus combinations that showed significant association with ALS in our two-locus analysis. Based on the estimated penetrance, we used a simulation method to estimate the sample size required to replicate the findings with 80% power.α1 = 0.0023. Then we tested all of the L = 499,500 two-locus combinations under each of the seventeen models and used 1,000 permutations to evaluate the adjusted p-value for each of the two-locus combinations. After adjusting for multiple tests and multiple SNPs, we found two two-locus combinations with p-values less than 0.05. There were three SNPs involved in the two two-locus combinations. The details of the three SNPs are given in Table st with a p-value of 6.8 × 10-7, SNP 2 was ranked 10th with a p-value of 2.2 × 10-5, and SNP 3 was ranked 2nd with a p-value of 1.7 × 10-6.We applied the two-locus analysis with two steps to the genome-wide association data set for sporadic ALS. The analysis was done for all genotypes with a call rate greater than or equal to 95% . SNPs on the sex chromosome were excluded in the analysis. In the first step, we returned 1,000 SNPs with the smallest p-values which corresponded to use a p-value cut-off χ2 distribution with 1 df. The estimations using both the proposed method (adjusted estimates) and the traditional method (unadjusted estimates) are summarized in Table To estimate the impact of the two two-locus combinations on sporadic ALS, we first estimated the penetrance of the two-locus genotypes for each of the two two-locus combinations under the corresponding model. Based on the estimated penetrance, we estimated the relative risk, odds ratio and sample size required to replicate the significant findings with 80% power. We followed what is in Zollner and Pritchard to obtain the 95% CI of the estimates , that isIn this study we proposed a new analytical method that considered joint effects of genes to analyze a data set from the GWAS in sporadic ALS previously performed by Schymick et al. . Our anaPopulation stratification may lead to false-positive results. We had also checked the population stratification problem in this data set using the following method. We randomly chose 5,000 SNPs and got their p-values by a single marker test. If population stratification did exist in this data set, among the 5,000 p-values, there should be more small p-values than expected under the uniform distribution. We used the one-side Kolmorgorov test statistic to test if the 5,000 p-values followed a uniform distribution. We repeated the procedure 10 times. The Kolmorgorov test results showed that the p-values followed a uniform distribution for all 10 replications, which indicated that there was no population stratification in this data set. The lack of population stratification in the data set was consistent with the results of Schymick et al. . The anaSignificant associations claimed by association studies often fail to be replicated. One possible reason is the overestimation of the effect in terms of the odds ratio or relative risk of the claimed variants. The overestimation of the effect leads to the underestimation of the sample size required to replicate the finding. In this article, we proposed a new method to estimate the effect of claimed variants. Based on the study of Zollner and Pritchard , we expeH1H2 ≥ penetrance of H1h2 ≥ penetrance of h1h2, where H1(h1) and H2(h2) are the high-risk (low-risk) genotypes in the first and second marker, respectively. The CSM and MDR ignore the order of genotypes and therefore can group any two genotypes together-in essence searching for the "best" one among 21,146 different partitions of the two-locus genotypes. By searching for irrelevant two-locus genotype combinations, the CSM and MDR did not gain more information but increased the noise level, and thus lost power.Currently, several methods are available to test associations by taking joint effects of genes into account, such as combinatorial searching method (CSM) and the multifactor dimensionality reduction (MDR) method ,40. We uThe proposed two-stage analytical method can be used to search for two-locus joint effects of genes in GWAS. The two-stage strategy significantly decreased the computational time and the multiple testing burdens associated with GWAS. We have also observed that the three SNPs identified by our two-stage strategy can not be detected by single-locus tests.The authors declare that they have no competing interests.QS and SZ designed the study. ZZ contributed the two-locus data analysis under the direction of SZ. SZ performed the penetrance estimation. JCS & BJT assisted in data interpretation and approved the final manuscript. QS and SZ contributed to the writing of the manuscript. All authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2350/10/86/prepub


Aedes albopictus and the geographical proximity of coastal Karnataka with the adjoining Kerala state. This is the first report regarding the appearance of this mutation in Karnataka state of India. The present study identified the presence and association of A226V mutant virus with Chikungunya outbreak in India during 2008.Chikungunya has resurged in the form of unprecedented explosive epidemic in 2006 after a long gap in India affecting 1.39 million of persons. The disease continued for the next two consecutive years affecting 59,535 and 64,548 persons during 2007 and 2008 respectively. The 2008 outbreak being the second largest among these three years the information regarding the etiology and the mutations involved are useful for further control measures. Among the 2008 outbreaks the Coastal Karnataka accounts for the 46,510 persons. An in-depth investigation of Chikungunya epidemic of Coastal Karnataka, India, 2008 by serology, virus isolation, RT-PCR and genome sequencing revealed the presence and continued circulation of A226V mutant Chikungunya virus. The appearance of this mutant virus was found to be associated with higher prevalence of vector  Alphavirus of the family Togaviridae and is primarily transmitted by the Aedes aegypti mosquito [Chikungunya fever is an acute arthropod borne viral illness reported from many parts of Africa and south east Asia. The causative agent is Chikungunya virus (CHIKV), a member of the genus mosquito -3. CHIKVmosquito -6. At prAedes albopictus mosquito vector with progression of epidemic from 2006 to 2007 [An outbreak of Chikungunya virus infection occurred during 2006 in 15 states or union territories in India affecting more than 1.39 million of persons -10. The  to 2007 ,12.Aedes albopictus created an apprehension regarding involvement of similar etiology. To rule out any confusion, an in depth virological, serological and molecular investigation was carried out to identify the etiology of this unprecedented outbreak, which is considered to be the second highest in terms of number of persons affected since its resurgence in 2006.In 2008, an outbreak of fever with severe arthralgia occurred in costal Karnataka, in India adjoining the state of Kerala, affecting 46,510 persons [A total of 100 blood samples from patients suspected of having Chikungunya fever were brought from District Surveillance Unit, Mangalore which were collected from Mangalore, Puttur, Sulya and other parts of the Dakshina Kannada district for this study. Two sets of blood samples were collected with and without anticoagulant for virus isolation and serology respectively. All these samples were investigated for the presence of Chikungunya specific RNA by RT-PCR and for the presence of CHIKV specific IgM antibodies by using recombinant E1 and E2 protein based IgM ELISA.AF490259[The presence of CHIKV specific RNA in clinical samples was detected using the Access quick one-step RT-PCR kit  employing a primers pair targeting the E1 gene (CHIK13: TTACATCACGTGCGAATAC genome position 10128-10146 and CHIK14: CTTTGCTCTCAGGCGTGCGACTTT genome position 10604-10627); designed from the nucleotide sequence of the reference S27 strain, GenBank Acc No. AF490259. The 8 rFor the construction of phylogenetic trees, the neighbour-joining algorithm and the Kimura two-parameter distance modelwere utilized. The reliability of the analysiswas evaluated by a bootstrap test with 10,000 replications6/36 cells from selected RT-PCR positive samples following the virus adsorption technique [The isolation of virus was also attempted in Cechnique . The ser6/36 cells, which yielded 8 CHIKV isolates. The isolation of the virus was further confirmed by RT-PCR and nucleotide sequencing.The serological analysis of the samples indicated overall 28% seropositivity for IgM antibodies. A total of 40 (40%) serum samples were found positive for the presence of CHIKV specific RNA, through demonstration of CHIKV specific 500 bp amplicon by RT-PCR. A representative of 20 RT-PCR positive samples were subjected to virus isolation in CNucleotide sequencing of the partial E1 gene of 8 representative CHIKV strains was determined and were compared with 27 other globally diverse CHIKV isolates including Chikungunya isolates from 2006, 2007 outbreaks of India and Reunion islands (Table Since early 2005, a major epidemic of CHIK started in many Indian Ocean island nations; and towards end of 2005, it reemerged in several parts of India -9,16. Thin vitro passaging. During this outbreak, patients also reported non classical symptoms including hemorrhage, lymphadenitis, ictures and liver involvement, etc. similar to the cases in Reunion 2005-06 and Kerala India-2007 [Aedes albopictus leads to the speculation for the involvement of Kerala 2007 strain for the current outbreak. The sequencing of CHIKV strains from the current 2008 outbreak led to identification of A226V shift in these isolates. The E1:A226V mutation was earlier correlated with vector specificity as well as epidemic potential [Aedes albopictus vector in Coastal Karnataka in 2008. However, the complete genome sequencing is required to see other mutations other than A226V which are unique for the current 2008 outbreakThe present study revealed less seropositivity in comparison to RT-PCR which may be attributed to the collection of samples at very early or acute stage of the illness. The demonstration of CHIKV RNA in 40% samples by RT-PCR and detection of IgM antibodies in 28% of sample confirmed the causative agent of this epidemic to be CHIKV. The isolation of CHIKV from clinical samples further confirmed this etiology. The sequence of CHIKV were directly determined from clinical samples without risk of altering the genome by dia-2007 ,16. Thesotential . This shThe phylogenetic analysis revealed that all isolates from the current outbreak were very closely related to analogous strains from Kerala 2007 outbreak. All these isolates harbor valine at E1-226 position compared to alanine in the 2006 Indian isolatesAedes albopictus vector, supporting the higher epidemic potential of A226V mutant virus. However, a continuous surveillance is warranted to monitor its spread and track possible evolution of the virus during the epidemic.In summary, to the best of our knowledge, this is the first report regarding the appearance of this mutation in Karnataka state of India. The involvement of A226V mutant virus was attributed to the continued circulation of the 2007 Kerala strain in the current outbreak due its geographical proximity coupled with higher prevalence of The authors declare that they have no competing interests.SRS defined the study and carried out the laboratory experiments, interpreted the results and wrote the manuscript. PKD designed the primers, analyzed the data. MMP co-interpreted the results and co-wrote the manuscript. PVLR and MK contributed their ideas to the design of the study and the manuscript. All authors read and approved the final manuscript.


Aedes mosquitoes. CHIK virus is a member of the Alphavirus genus of the family Togaviridae. Previous reports have indicated that infection with CHIK virus produces an acute arthritis in human hosts by large area of necrosis and collagenosis or fibrosis.Chikungunya (CHIK) fever is a viral disease transmitted to humans by the bite of Chikungunya virus (CHIK virus) infected We carried out the present study to determine the effect of chikungunya on the collagen and connective tissue metabolism in 75 chikungunya-affected people. First, we screened for mucopolysaccharides in urine by Cetyl Trimethyl Ammonium Bromide (CTAB) test. Appearance of heavy precipitate indicates the presence of higher levels of mucopolysaccharides and later quantified by DMB dye method. The urinary mucopolysaccharide in CHIK patients was 342 ± 45 mg/l compared to healthy controls (45 ± 5.6 mg/l). The collagen building blocks, proline and hydroxyproline were also measured in CHIK patients and observed higher excretion compared to healthy controls. Urinary excretions hydroxyproline was greater than the proline levels.These results indicate that CHIK virus infection affects and damage the cartilage and connective metabolism and releases the degraded products from the tissue and responsible for increasing the levels of proline, hydroxyproline and mucopolysaccharides in CHIK affected patients. Aedes mosquitoes. CHIK virus is a member of the Alphavirus genus of the family Togaviridae. CHIK virus was first isolated from the serum of a febrile patient during a dengue epidemic that occurred in the Newala District, Tanzania in 1953 [Alphaviruses are enveloped particles and their genome consists of a single-stranded, positive-sense RNA molecule of approximately 12000 nucleotides. CHIK virus is an important human pathogen that causes a disease syndrome characterized by fever, headache, rash, nausea, vomiting, myalgia and arthralgia [Aedes species mosquitoes [Arboviruses, such as dengue.Chikungunya (CHIK) fever is a viral disease transmitted to humans by the bite of Chikungunya virus (CHIK virus) infected thralgia . Its asssquitoes -12. Recesquitoes ,13. Reemsquitoes  and veryPrevious reports have indicated that infection with CHIK virus produces an acute arthritis in human hosts ,15,16 byCHIK patients have reported incapacitating joint pain, or arthritis, which may last for weeks or months. The general biochemical parameters of CHIK patients and healthy controls were depicted in Table Proteoglycan, a mucopolysaccharide building block of cartilage within the joint space, is used by chondrocytes (cartilage-building cells) to create more cartilage. Following pathogenic infection, joints typically exhibit a natural inflammatory response that mainly affects the synovium (synovitis). This process is necessary for the innate repair of damaged tissues, allowing the joint to recoup normal function. Inflammatory mediators released into the joint from such sources as nerves, immunocytes, synoviocytes, and vascular endothelium help to orchestrate these healing responses. These same inflammatory mediators also act on joint sensory nerves, leading to either excitation or sensitization . When peMeasurement of urinary hydroxyproline excretion is used to estimate collagen catabolism. Degradation of bone collagen releases free 4-hydroxyproline and peptides containing 4-hydroxyproline into the plasma. Urinary hydroxyproline measurement is particularly useful in monitoring treatment and progress in metabolic bone diseases such as osteomalacia and osteoporosis and Paget's disease. Hydroxyprolme appears in urine after degradation of collagen (collagenosis), which may originate from various tissues . MeasuriIn CHIK patients the collagen and connective tissue metabolism was greatly affected and increased metabolism leads to increased excretion of proline, hydroxyproline and mucopolysaccharides in urine compared to healthy controls. It indicates that the CHIK virus infection, connective tissue typically exhibits inflammatory response and leads to the damage of cartilage and connective tissue; increases metabolism and releases degraded products in to blood and ultimately excreted through urine.During last year July – October 2006, CHIK out break in Andhra Pradesh, India, a total of 75  patients were selected based on disease symptoms for this study. We also taken the 20 age-matched healthy persons were chosen from the community and used as control subjects. Blood and urine samples were collected from each subject with their prior written consent for further studies. From all subjects, 5 ml of blood was collected in heparinized tubes and centrifuged at 1500 ×g for 15 min at 4 C. Plasma and pelleted RBC were separated stored in eppendorf tubes and kept at -80 C until analysis. Commercially available kits measured Blood analysis and other general parameters.The urine samples were collected from the patients and used for estimation of mucopolysaccharides, proline and hydroxyproline. Total urinary hydroxyproline determinations were performed employing the method described by Cleary and Saunders (1974) . BrieflyStatistical analysis was performed by GraphPad InStat software. Subjects with CHIK were compared with healthy controls. Means and standard error of means were calculated and differences between means were student's t-test.SL designed and conducted the experiments with patient's blood, urine samples. SL also performed the final statistical analysis of the data and contributed to writing the paper. SV  guide to us for selecting the chikungunya patients for research during time of blood and urine collection. RV supervised the overall project, designed experiments, analyzed the data and wrote the paper. All authors read and approved the final manuscript.


The aim of this study was to estimate the costs of treatment of children who present with the signs and symptoms of invasive bacterial diseases in Khanh Hoa province, Viet Nam. The study was an incidence-based cost-of-illness analysis from the health system perspective. The hospital costs included labour, materials and capital costs, both direct and indirect. Costs were determined for 980 children, with an average age of 12.67 months (standard deviation±11.38), who were enrolled in a prospective surveillance at the Khanh Hoa General Hospital during 2005-2006. Of them, 57% were male. By disease-category, 80% were suspected of having pneumonia, 8% meningitis, 3% very severe disease consistent with pneumococcal sepsis, and 9% other diseases. Treatment costs for suspected pneumonia, meningitis, very severe disease, and other diseases were US$ 31, US$ 57, US$ 73, and US$ 24 respectively. Costs ranged from US$ 24 to US$ 164 across different case-categories. Both type of disease and age of patient had statistically significant effects on treatment costs. The results showed that treatment costs for bacterial diseases in children were considerable and might differ by as much as seven times among invasive pneumococcal diseases. Changes in costs were sensitive to both age of patient and case-category. These cost-of-illness data will be an important component in the overall evidence base to guide the development of vaccine policy in Viet Nam. Streptococcus pneumoniae causes half of all deaths due to pneumonia in children, and 70% of these deaths occur in Africa and Asia  . Besidesand Asia . A largeand Asia ,5. Howevand Asia –8. To adWe conducted an incidence-based cost-of-illness analysis from the healthcare system perspective, a technique used for measuring the economic burden of patients from the onset to the end of illness ,10. We sFor our analysis, cost-of-illness was defined as the sum of direct medical, direct non-medical, and indirect costs . Direct To compute costs of each medical service, we used the ratio of costs to charges (RCCs) method ,16. In tDemographic information was described by frequency and proportion. We calculated means with standard deviations (SDs) for continuous variables and used multiple regression analysis to explore the factors associated with variations in treatment costs. Covariates considered in this modelling included age (months), duration of stay (days), and diagnosis. In addition, since prices of drugs may vary substantially among hospitals, we performed a sensitivity analysis to explore the effect of variation in prices of antibiotics across a range of prices of antibiotics available from the KHGH databases.Patients’ demographics are presented in To convert the payment to economic cost, the cost-to-charge ratio method was applied. The KHGH had total costs of VND 172,482 million with revenue of VND 151,219 million or a hospital cost-to-charge ratio of 1.14. Of 48 antibiotics used for treating the study patients, ceftriaxone, cefotaxime, and cefuroxime were the most expensive drugs . These t2=0.172). Estimated costs regarding age and diagnosis were constructed. The costs of all disease-groups were inversely related to age—as the age of patients increased, costs decreased , labour (28%), and capital (18%) were all similar to those of Thailand –29. For 2=0.172), other variables not included in our modelling procedures may explain variations in cost. In addition, the relationship of the independent variables may be confounded by age. For example, younger children may be more vulnerable to pneumococcal diseases than older children. In addition, drug-prescribing behaviours may affect both use of certain drugs and costs of drugs provided (In general, consumption of drugs was positively associated with both body-weight and age. Due to the relatively low explanatory power of the fitted cost model (adjusted Rprovided . We wereWe believe that data used in our analysis are both reliable and internally valid. The KHGH has a well-organized computerized database, from which all costing data were drawn. Our findings regarding age- and disease-specific treatment costs can be used together with age-specific incidence rates for such economic evaluations as cost-effectiveness, cost-utility, and cost-benefit analyses . Age-speTreatment cost of pneumococcal diseases was considerable in Viet Nam. Costs of suspected/probable and confirmed pneumococcal disease were statistically different as were costs of pneumococcal pneumonia and meningitis, which can differ by as much as seven times. Age of patient and type of disease affect costs of treating pneumococcal diseases in Viet Nam. These data will be important in the overall evidence to guide the development of vaccine policy in Viet Nam.The study was supported by the PneumoADIP and by the Governments of Kuwait, Sweden, and South Korea.The authors thank Dr. Le Thi Phuong, Mr. Nguyen Thanh Hien, Mr. Nguyen Vu Cuong, and Mr. Van Hoi for superb work in data collection and assistance for data management and Kathy Murray for editorial assistance.


After an outbreak of Rift Valley fever in Southern Mauritania in 1987, entomologic studies were conducted in a bordering region in Sénégal from 1991 to 1996 to identify the sylvatic vectors of Rift Valley fever virus. The virus was isolated from the floodwater mosquitoes Aedes vexans and Ae. ochraceus. In 1974 and 1983, the virus had been isolated from Ae. dalzieli. Although these vectors differ from the main vectors in East and South Africa, they use the same type of breeding sites and also feed on cattle and sheep. Although enzootic vectors have now been identified in West Africa, the factors causing outbreaks remain unclear.