In this work, we develop a theoretical model that explains the survival data in West Nile Virus infection.We build a model based on three cell populations in an infected host; the collateral damage cells, the infected dividing cell, and the infected non-dividing cells. T cell-mediated lysis of each of these populations is dependent on the level of MHC-1 upregulation, which is different in the two infected cell populations, interferon-gamma and free virus levels.The model allows us to plot a measure of host health versus time for a range of initial viral doses and from that infer the dependence of minimal health versus viral dose. This inferred functional relationship between the minimal host health and viral dose is very similar to the data that has been collected for WNV survival curves under experimental conditions. Viruses in the family Flaviviridae are single-stranded, plus-sense RNA viruses, principally transmitted by mosquitoes and ticks. They are either viscerotropic, causing diseases such as dengue and yellow fever, or neurotropic, causing central nervous system disease, like West Nile virus encephalitis, Japanese encephalitis or Saint Louis encephalitis, all of which may be fatal. These viruses are found worldwide and the diseases they cause pose a significant public health burden. A careful discussion of the many facets of flavivirus infections can be found in , and we Virus-infected cells process virus proteins into peptide fragments in the proteasome and these are bound to class I major histocompatibility complex molecules (MHC-I) in the endoplasmic reticulum. With the transport of MHC molecules to the cell surface, viral peptides displayed in the context of MHC on the infected cell can be recognized by virus-specific cytotoxic T lymphocytes (CTL). Such CTL kill infected cells by a variety of lytic effector molecules prior to the release of mature virus progeny from infected cells, thereby progressively reducing virus numbers and ultimately eradicating virus from surviving hosts (reviewed in ).hi, infected cells by CTL clones of low MHC-virus peptide affinity, i.e., clones previously below the recognition threshold. Some of these low-affinity CTL clones are likely to be self-reactive ),reactive . Thus, treactive . Adhesioreactive . In addiet cells . In thisortality . We haveortality . We haveortality . The preT which consists of cells which are dividing and are infected, D, cells which are not dividing but are infected, N, non infected cells, H and non infected cells which will be removed due to auto immune action which we call C, for collateral damage. We assume all of these variables depend on the three parameters, I, the IFN- γ signal; the MHCI upregulation factor U and the free virus level A. The approach we take here is a common one in such a nonlinear interaction environment. An example of how this is used to develop a model of diabetes detection can be found in [In a survival experiment, a population of animals such as mice are infected with a common amount of virus and the number of animals that die are counted. This experiment is repeated for a range of virus. With most viruses, the survival experiment gives a classical dose-response curve which progressively and smoothly decays down to a survival of 0 animals at high virus. However, WNV has a peculiar survival curve as shown in  but we cfound in .Fig. 1SWe assume the dynamics here are F1, F2 and F3 because we know C, D and N depend on each other’s levels in very complicated ways. Usually, we assume the initial dose S0 gives rise to some fraction of infected cells which will differ in both the dividing and nondividing cells. In our previous WNV simulations of collateral damage [p0=1.0e−3 of the initial dose gives rise to infected cells. This gives the number of infected cells to be p0S0 which is split into p1p0S0 infected non dividing cells - i.e. in N, and p2p0S0 dividing infected cells, i.e. in D, where p1+p2=1. Typically, we let p1=0.99 and p2=0.01 but other choices could be made. Thus, the total amount of virus that goes into infected cells is p0S0 and the amount of free virus is therefore (1−p0) S0. Thus, we could expect C0=0, D0=p2p0S0 and N0=D0=p1p0S0. However, we will explicitly assume we are starting from a point of equilibrium prior to the administration of the viral dose S0. We could assume there is always some level of collateral damage, C0 in a host, but we will not do that. We will therefore assume C, D and N have achieved these values C0=0, D0=0 and N0=0 right before the moment of infection. Hence, we don’t expect there to be initial contribution to C′(0), D′(0) and N′(0); i.e. F1=0, F2=0 and F3=0. We are interested in the deviation of C, D and N from their optimal values C0, D0 and N0, so let c=C−C0, d=D−D0 and n=N−N0. We can then write C=C0+c, D=D0+d and N=N0+n The model can then be rewritten as There are then three nonlinear interaction functions l damage , we assuor F1, F2 and F3 to derive approximation dynamics.Next, we do a standard tangent plane approximation on the nonlinear dynamics functions CDN dynamics, we use an appropriate tangent plane approximation on the nonlinear dynamics functions F1, F2 and F3. The mathematics behind this approximation come from multivariate calculus and can easily be reviewed if required. The standard tangent plane expansions is as follows. To develop a useful approximation to the F1=0, F2=0 and F3=0. so we can simplify to But the terms o denote that we are evaluating the partial derivatives at the point . Thus our model approximation is It seems reasonable to assume that since we are so close to ordinary operating conditions, the errors and our corresponding nonlinear dynamics approximation is We can write this approximation In matrix - vector form also: γ, upregulation and virus level.where we now use a standard subscript scheme to indicate the partials. Now let’s add IFN- C, D and N as depending on the interferon level I, the upregulation level U and the free virus level A. Thus, we have We can think each variable We assume the dynamics here are then C, D and C have achieved the same optimal values C0=0, D0=0 and N0=0 prior to the moment of infection with virus dose S0. These correspond to the starting values of prior to infection for base values I0, U0 and A0. Initially, we don’t expect IFN- γ signals so I0=0. Eventually, we do expect some level of upregulation due to this initial dose and from experimental data, we expect the upregulation to be proportional to the level of the dose S0; we will assume this is a simple scaling factor, i.e. U0=q1S0 for some suitable parameter q1. Also, once the viral dose is administered, we would expect some fraction of it to remain as free virus which as discussed in [A0=(1−p0)S0. We will deal with these initial values after infection later in the Section “I0=0, U0=0 and A0=0. We still don’t expect to have any contribution to C′(0), D′(0) and N′(0); i.e. H1=0, H2=0 and H3=0. We are interested in the deviation of C, D and N from their optimal values C0, D0 and N0 due to the changes i, u and a from the base IFN- γ, upregulation and virus values. So let c=C−C0, d=D−D0 and n=N−N0. We can then write C=C0+c, D=D0+d and N=N0+n The model can then be rewritten as As before assume ussed in  is modelwhich, as usual, implies H1, H2 and H3 just as we did in Section “F1, F2 and F3 we use for the CDN model.Next, we again perform a tangent plane approximation on the nonlinear dynamics functions o denote that we are evaluating the partial derivatives at the point , we find Once again, it seems reasonable to assume that since we are so close to ordinary operating conditions, the tangent plane errors are negligible. Letting The corresponding nonlinear dynamics approximation in matrix - vector form is then where we now use a standard subscript scheme to indicate the partials. We therefore find the nonlinear dynamics approximation is where we now use a standard subscript scheme to indicate the partials.i which we increase to i+δi, what happens? Increasing the IFN- γ level should increase collateral damage. Hence, Ψ so far looks like If we hold everything constant except u and increase u to u+δu. What happens? Increasing the upregulation level should increase collateral damage. Hence, γ upregulation hits a certain level and the increase in upregulation that makes the infected cells more visible certainly effects this as it will take fewer additional IFN- γ signals to trigger lysis. In general, there is a lag of say δu, we expect D and N decrease and we have Now hold everything constant except the upregulation a and increase a to a+δa. What happens? Let’s think of the virus increase δa as giving rise to an increase in the amount of virus stored inside a dividing cell or a non dividing cell. Now if the amount of virus in the cell goes up, that means when the cell is lysed, there is more virus available to infect cells which means more cells will be infected in later times. An increase in virus means an increase in collateral damage in general, so A is the virus level. We can write A=AF+AD+AN where AF is the free virus, AD is the virus inside the dividing cells and AN is the virus inside the nondividing cells. So if AD goes up, we expect the amount of A and AF to remain constant. Hence, if AD goes up, AN goes down.Next hold everything constant except the virus level AD would mean more virus is released on lysis and hence an increase in D. However, that increase in AD is a concomitant decrease in N. So we should have The increase in Or letting Thus, we have the changes in collateral damage and infection levels i, u and a.Now we need to estimate I, the upregulation level U and the free virus level A depend on the initial amount of virus applied when in the equilibrium state; i.e. this is the amount that causes the initial infection. This is So. We assume the dynamics here are The amount of interferon level C, D and N depended on the perturbations of I, U and A from a zero state. Now, we want to model the I, U and A deviations from a base state I0, U0 and A0 which is not zero. As previously discussed, we expect A0=(1−p0) S0, the initial IFN- γ level I0=0 and the initial upregulation level U0=q1S0. Let the deviations from these equilibrium values be given by i=I−I0, u=U−U0 and a=A−A0. The model can then be rewritten as In the model of “or i, u and a model is handled in a way that is very similar to the previous two expansions which were explained in some detail in “The approximation to the G1, G2 and G3. It seems reasonable to assume that since we are so close to ordinary operating conditions, the tangent plane errors as usual will be negligible. Thus, using The linearization again involves a tangent plane approximation to nonlinear dynamics functions, which are now the functions Thus our dynamics approximation in matrix - vector form is The dynamics approximation is then given by i which we increase to i+δi, what happens? Increasing the IFN- γ level should increase IFN- γ. Hence, Φ so far looks like The analysis of the signs of these partials is next. This is similar to what we did for the previous model. If we hold everything constant except u and increase u to u+δu. What happens? Increasing the upregulation level should increase the IFN- γ level. Hence, u′ goes up, hence γ signals to trigger lysis. In general, there is a lag of say δu, we expect Now hold everything constant except the upregulation a and increase a to a+δa. What happens? Let’s think of the virus increase δa as giving rise to an increase in the amount of virus stored inside a dividing cell or a non dividing cell. Now if the amount of virus in the cell goes up, that means when the cell is lysed, there is more virus available to infect cells which means more cells will be infected in later times. An increase in virus should not effect IFN- γ levels, so A is the free virus level. We can write A=AF+AD+AN where AF is the free virus, AD is the virus inside the dividing cells and AN is the virus inside the nondividing cells. So if AF goes up and the total amount of virus at this time is constant, we expect AD and AN to go down. If the amount of virus inside the cells decreases, we expect the upregulation to decrease. So Now hold everything constant except the virus level Or letting λI−Φ)=0. Thus, for the coefficient matrix Φ, we have We now use standard results from the theory of linear differential equation systems. This analysis relies on the eigenvalues of our model systems. The eigenvalues of this linearized system are found by solving the det2−4bc<0. In particular, if a=d=α and b=β and c=β, then we have −4β2<0 and the roots are complex. We can have complex roots for other choices of a, b, c and D which implies conditions on the partials which is something we can explore, but we will not do that here. For our purposes, we will examine closely what happens when α±βj where we have complex roots if (u and a to find We can then solve for Letting δ is defined as tan−1(b/a). The full solution is then where A, R, β and δ determine a given model.where u0=q1S0 and a0=(1−p0)S0. Hence, we roughly know at the time of the infection Here, we have Taking a ratio, we find Hence, Finally, recall we have δ should be a constant; i.e. independent of S0. After all, the reasoning above is approximate and we should not think of this as actually fixed. So we will assume that all critical parameters here are proportional to S0. Our rough calculation showed us R is proportional to S0 in general. Therefore, we now assume It seems unreasonable to us that the phase shift r1, r2, r3 and r4. This leads to our estimate of the dependence of the upregulation and free virus on the initial dose S0: we have for a new parameters T, the number of healthy cells can be approximated by Roughly speaking, if the total number of cells is We know I0=0, U0=q1S0 and A0=(1−p0)S0. It follows we have and so we are looking at deviations from the base values C0=0, D0=p2p0S0 and N0=p2p0S0. So we have As discussed earlier, we have initially, Thus, we have S0 and set that coefficient to Λ for convenience. Making this replacement, we have Now collect all the terms involving This leads to the simplification IT for the transient i integration; UT for the transient u integration; and AT for the transient a integration. We then have Now we have to compute these integrated transient values. We label them as i integration is straightforward The UT integration are more complicated.however, the UT for the transient u integration; and AT for the transient a integration. We then have To evaluate this term, we use integration by parts. Now we have to compute the integrated transient values required to find the health estimate. We have labeled the integrations as UT. To evaluate this term, we use integration by parts. We find First, let’s calculate UT as follows: and so we can find We can rewrite this is a much better form using our assumptions. First, rewrite as AT. Another standard integration by parts gives Now we find AT also: and so we can find α, β, δ and R are really dependent of S0. For convenience of exposition, we drop the superscript S0 in our calculations below We know θ1 and θ2 as UT(t) as Finally, let’s define two new parameters, θ2, we see UT(t) again as Using a standard reference triangle for the phase angle and using standard trigonometric identities, we then have Recall, as showing in as shown in “AT. We note the same comment on the dependence of R, α, β and δ on S0 holds still. Now using these values and the terms Q1 and Q2, we we can rewrite AT(t) as follows: Another standard integration by parts similar to what was done in “α and β in terms of r2 and r3, we can rewrite this complicated expression as Now using the simplifications we obtained for θ2, we have Next, using the phase shift This then leads to our final form Recall the health model is Let Now plug what we have found for our integrations. We have Then we can rewrite as Now put the Let’s simplify some more using another phase shift. Define the phase angle This can be recast as Next, we can combine the ratio Finally, let S0. We would model this by implying We could also assume the terms S0 and it is very difficult to tease out the details. The only data we have to help fit this model is the survival data, so the next step of seeing how this model of health gives rise to the experimental survival data requires additional analysis.These parameters depend in complex ways on the initial virus dose We are going to graph Eq. Then, we define some auxiliary functions in Fig. S0, here represented by S0 as shown in Fig. Min. We can then plot Min versus initial viral dose S0 and see if the plot looks like a traditional WNV survival curve. We then place all of this code into a MatLab function and use it in the usual way to generate the plots. For these choice of parameters, we can plot the minimal health curve which is shown in Fig. Then, we generate a plot of health versus time for a range of choices of decay and push - pull terms as follows: can be written in terms of H(t) always decreases unless the push - pull terms counteract that decay. Hence, what is important is the term Thus, we have Δ(t) to be out of phase. Hence, roughly speaking cos(r3S0t−r4S0−θ2+θ3) must be sometimes negative when cos(r4S0−θ2−θ3) is positive. This allows for an increase in health of approximately ξs1 where ξ is the difference between the two terms. This is possible when the two cos arguments are out of phase by about π radians. Note it is also important that the exponential term can oscillate as viral load increases. To do this, it is important for the two terms in and we have argued that the appropriate algebraic signs for this coefficient matrix We can have complex eigenvalues and hence oscillating behavior if the signs were but then the real part of the eigenvalues would be negative and we would have to model the exponential term as We should also see oscillations in the collateral damage. Recall the collateral damage population is given by IT(t), UT(t) and AT(t) and obtain We can then substitute for Now, collect terms as we did in our earlier simplifications. We rewrite as ϕ, as follows. It will be different from the phase shift We can also introduce an additional phase shift, H1 partials: as here we only use the We can then use the the usual cos laws of addition and subtraction of angles to repackage this as Now define Since collateral damage is initially zero, we have as our final form Previously, we used the simplification This needs to be modified to S2, Λ1 and ϕ are different. Our final collateral damage function is then Hence, although we can use some of parameter choices from our health simulations, it is difficult to compare completely as Collateral for this which is listed in Fig. s2, Λ1 and ϕ are necessarily new choices. We ran the simulation with these parameter values and then plotted both the maximum and minimum collateral values versus the viral dose in Fig. u, and the free virus, a.We can easily run a quick simulation to see if our prediction that the collateral damage will have oscillations is true or not. We use the function i, u and a model: The presence of a form of self damage in our WNV infection model therefore appears to be a consequence of the nonlinear interactions in the D and C coincide, one signal is unnecessary - say u - and this model reduces to a two dimensional model Note, if the two populations and the chance of oscillation between the cellular population groups is lost. Hence, we can note some consequences and predictions due to our model.D and N, respectively. We can envision other infectious agents or triggers that give rise to such a split response which then in principle could engender a similar collateral damage response which we interpret as an autoimmune reaction. So there is hope that this approach could perhaps give us insight into more general autoimmune responses. Note that Fig. The crucial assumption here is that the viral infections effect on the host divides into two parts. For a WNV infection, these two cell populations are the dividing and nondividing infected cells, Specific to the WNV model, we assume that specific relations that give rise to clearcut oscillations. We have assumed This algebraic sign pattern itself can give rise to complex eigenvalues for the linearized nonlinear interaction model and we have not explored this more general problem. We have noted in our discussion in Section “The assumptions above then give rise to a general model of how the minimal health changes with varying initial virus dose, which appears to allow us to approximate the data we can measure.The exact replication of the data found in the biological situation is unlikely to occur. Indeed, a standard dose response curve of survival does not usually repeat exactly from experiment to experiment, except in the form of it, even when using genetically identical animals. The ragged dose response of mortality seen in WNV infection is similarly subject to biological variability. Clearly, with no virus there will be 100% survival, and with a large amount of virus there will be 100% death, as expected in a standard dose response curve. In between these two doses, however, the response to infection is subject to probability, which affects the outcome . Thus, if the experiment were undertaken several times it would show the ragged form on each occasion, but not exactly the same percentage survival at each dose used. This implies that small biological differences at the starting point of infection, albeit in genetically identical mice, may subtend a large range of endpoints. It is of interest to note that bypassing the early initiation of the adaptive immune response, by inoculating virus intracranially, that the standard graded dose response occurs with WNV. This is because the replication of the virus overtakes the animal before an effective immune response can be generated, emphasising the role of the immune system in generating this ragged survival curve .We have shown that we can build a reasonable model of how WNV infects a host’s cell in such a way that damage to the host can decrease, even though the inoculating viral dose increases.Our model is a macro one and we believe it provides insight as to how we can model more general auto-immune reactions. We propose that for an infectious agent or trigger to cause oscillations in health it is required that the trigger causes alterations in two distinct cell populations. Then, if the nonlinear interactions between these two populations satisfies the conditions for damped oscillatory response we have mentioned here, we should see oscillations in the host health. We consider this work essentially a theoretical model and as we explore what we can do to extend the results to more general auto-immune settings, we hope that we can generate greater mechanistic insight into auto-immune disease.


RNA viruses are a major threat to human health. Here, based on extensive literature searches carried out over a period of 18 years, we provide a catalogue of all 214 known human-infective RNA virus species. We link these viruses to metadata for a number of traits that influence their epidemiology, including the date of the first report of human infection, transmissibility in human populations, transmission route(s) and host range. This database can be used in comparative studies of human-infective RNA viruses to identify the characteristics of viruses most likely to pose the greatest public health threat, both now and in the future. In addition, RNA viruses are very prominent among the emerging infectious diseases: SARS coronavirus, Ebola virus and MERS coronavirus are recent, high profile examples2.Infections due to RNA viruses such as influenza virus, measles virus, HIV-1 and rotavirus impose a huge public health burden, particularly in lower and middle income countries3. Since then we have regularly refined our methodology and updated the catalogue as new human-infective RNA viruses are recognised and reported in the scientific literature5. In addition to identifying human-infective viruses, we record a number of traits including the date of the first report of human infection, transmissibility in human populations, transmission route(s) and host range .Our research group first published a catalogue of human-infective RNA viruses (and other infectious agents) in 20014 and an ecological diversity measure6. Second, they have been used to identify traits associated with newly emerging viruses5. Third, they have been used to identify RNA viruses with future epidemic potential in human populations2. Chikungunya virus, Ebola virus and Zika virus all met the criteria prior to their actual emergence in 2005, 2014 and 2015 respectively. It has also contributed to the World Health Organisation’s Blueprint for R&D preparedness and response to public health emergencies due to highly infectious pathogens7.This catalogue and metadata have been used in a number of ways. First, to estimate the diversity of human-infective RNA viruses using two techniques, extrapolation of the virus discovery curve8), and link this to metadata on seven different virus traits. We describe the distribution of virus species by discovery date, transmissibility in human populations, non-human host range, and transmission route below.The catalogue and metadata are subject to continual change, not only as new, human-infective RNA viruses are recognised or discovered but also as new information about their transmission routes, host range and other traits is reported in the scientific literature. The catalogue can also change retrospectively as taxonomies are revised. Here, we present a snapshot of information available as of 21st July 2017, insofar as we have been able to ascertain it using the methodology described below. We provide a list of human-infective RNA viruses, cataloguing species currently recognised by the International Committee for the Taxonomy of Viruses .The database lists 214 ICTV-recognised, human-infective RNA virus species (Data Citation 1). This is a substantial increase on previously published countsYellow fever virus–was reported. The number of species increases slowly up to the mid 1950s and somewhat faster thereafter. Methods are available for extrapolating from this curve to predict future rates of discovery and the total diversity of human-infective RNA virus species4. We note that there is often a lag of several years between a novel virus being reported to infect humans and that virus being recognised as a new species, or not, by ICTV. As of July 2017 there were more than 20 putative RNA virus species in that category. For this reason we extend The accumulation of (currently) ICTV-recognised, human-infective RNA virus species up to 2015 is depicted in 9, transmissibility is assigned to one of four levels. Level 2 indicates human infective but not transmissible (between humans). Level 3 indicates transmissible from one human to another , but so far restricted to self-limiting outbreaks. Level 4 indicates viruses that are endemic in human populations or have the potential to cause major epidemics. Level 4 viruses are divided into 4a and 4b. Level 4a viruses are known to naturally infect non-human hosts; Level 4b viruses are known only to infect humans. Where virus species include subtypes known to have different levels of transmissibility, e.g. influenza A, the highest level is assigned. The distribution of species by these different levels of transmissibility is given in Each virus is classified according to its known level of transmissibility in human populations. Transmission may be via a natural route, including by arthropod vectors, or as unintentional iatrogenic transmission, but deliberate laboratory exposures are excluded. In keeping with previous usageFor each virus, we record the known range of non-human hosts, categorised as follows: non-human primates; other mammals (apart from primates); birds; reptiles; and fish. The distribution of virus species by host category is shown in For each virus, we record all known transmission routes, categorised as follows and fully defined in the Methods section: inhalation; ingestion; sexual contact; any form of direct physical contact; fomites; bites/broken skin; iatrogenic; vector (biting arthropod); maternal. The distribution of virus species by transmission route is shown in We hope that this database will be of value to researchers undertaking comparative studies of human-infective RNA viruses, as well as being a useful reference for those interested in pathogen diversity. We also hope that the database will continue to be a valuable tool for identifying characteristics of viruses likely to pose the greatest emerging public health threat. We note that the database could be extended to include further information on human-infective RNA viruses. We welcome and encourage suggestions from the scientific community for updating the information contained in the database, with the proviso that all information included must be supported by a published literature reference.Literature searches were performed on multiple occasions  over a period of 18 years up to July 2017. Any inconsistencies or ambiguities in the interpretation of the literature were resolved by reaching consensus following independent assessments by at least two individuals . Throughout, a positive data entry implies positive evidence from the scientific literature; a negative data entry may reflect a lack of information or partial information and it is to be expected that some negative data entries will be revised as further studies are published.No new code was used in compiling this database.Influenza A virus: contrast the human influenza viruses such as H1N1 and H3N2 with avian influenza viruses such as H5N1 and H7N9 that only occasionally infect humans. Although Influenza A virus is exceptionally variable in this respect, there are other virus species with distinct subtypes that differ in traits such as antigenicity (e.g. Dengue virus) or host preference (e.g. Rabies virus). Here, we combine the traits of all identified subtypes to make up the traits of that virus species as a whole.Viruses were eligible for inclusion in our catalogue only if they were classified as species by the ICTV as of July 21st 2017. The main ICTV criteria for defining RNA virus species are: relatedness based on sequence data; serological cross-reactivity; host range; and transmission route. We note that our focus on species ignores pertinent within-species variation. For example, there is huge variation in the epidemiology of subtypes of www.promedmail.org/] and one comprehensive review article10. We also followed up primary references linked to RNA virus genome sequence data in NCBI [www.ncbi.nlm.nih.gov/nucleotide/], where this suggested human infection. Structured searches typically included the following key words: human and virus and (case* or patient* or infection* or disease* or outbreak* or zoono*). Searches for newly reported viruses added the key word term: (new or novel). The critical criteria for inclusion in the database are: i) RNA virus species recognised by the International Committee for the Taxonomy of Viruses (ICTV) as of July 2017); ii) peer-reviewed primary report providing robust evidence of human infection.Our list of human-infective RNA viruses results from a series of systematic literature reviews carried out over the past 18 years. Literature reviews covered multiple reference databases , supplemented by secondary sources such as the WHO and CDC web sites, moderated web resources such as ProMed -like” or “potential [virus name]” infections were excluded. Both natural infections and unintentional iatrogenic or otherwise artificially-acquired infections  were considered as evidence supporting human infection, though intentional infections  and in vitro infections (e.g. cell cultures) were excluded. Acceptance of viruses as human-infective was primarily based on expert review, where the following were systematically taken into consideration: diagnostic method(s) used to identify human infection, number of reported infections, number of authors independently reporting infection, author confidence in identity and infectivity of virus. Pathogenicity was not considered a criterion for human infectivity. Where a virus was only known to infect humans from serological methods, we attempted to minimise false positives from serological cross-reactions with related viruses by only accepting evidence from studies where cross-reactions did not demonstrably occur. However, we have indicated where a virus is known only from serological evidence; there are 36 viruses of this kind, of which 28 have not been reported in humans for the past 10 years.In summary, it is important to note that the information reported here reflects the published literature rather than direct testing of human infectivity for all virus species listed.Dates of first reported human infection are supported by a peer-reviewed primary publication. We treated the date of publication as the date of discovery of human infection and we used structured searches (as above) to confirm the absence of earlier reports. The linked publication provides available details of detection and identification methods used.2; full details of the search strategy are provided in the Technical Appendix to that publication.Data on transmissibility in humans are reproduced from a previous study11 and one primary publication12. Our data were supplemented by reference to an extensive data set on virus-mammal associations provided prior to publication by Kevin Olival of EcoHealth Alliance in New York; these data have since been published13.Literature reviews for host range data were conducted within Web of Science, Google Scholar, and Scopus. Structured search key words were:  and . Data on host range is supported by peer-reviewed primary publications where available (PubMed IDs and hyperlinks provided), supplemented by information from the Global Mammal Parasite DatabaseLiterature reviews for transmission route data were conducted within Web of Science, Google Scholar, and Scopus. Structured search key words were:  and (transm* or *borne or vector). Information on transmission routes is similarly supported by peer-reviewed primary publications where available (PubMed IDs and hyperlinks provided).The database is available via Edinburgh DataShare in. xslx format (Data Citation 1) and contains the following records for all 214 currently recognised species of RNA virus for which there is published evidence of human infectivity.Column A: Current ICTV species name. Human-infective RNA viruses identified as described above.https://talk.ictvonline.org/).Column B: Current ICTV genus name. From the ICTV website .Column C: Current ICTV family name. From the ICTV website .Column D: Whether or not the virus is enveloped . From the ICTV website .Column F: ICTV taxonomic history URL (ICTV taxon ID and hyperlink to corresponding webpage).Column G: Discovery date as calendar year of first published report of virus infecting humans. Obtained from literature review as described above.Column H: Discovery reference (PubMed ID where available and hyperlink to reference).Column I: Whether or not virus is known only from serological evidence .Columns J to R: Whether or not transmission to humans is via any of nine possible routes . NB. Vector=arthropod vector; Broken skin includes wounds and bites; Maternal=mother to offspring transmission in utero or via breast milk; Direct contact=any form of close physical contact. Obtained from literature review as described above.Columns S, T: Transmission route references (PubMed ID and hyperlink to reference(s)).Column U: Transmission level as 2 (=human infective but not transmissible between humans); 3 ; 4a ; or 4b ). Obtained from literature review as described above.Column V: Whether or not there is evidence of person-to-person transmission . Obtained from Column U .Column W: Host range summary as broad (=humans and at least one other non-primate host category), narrow (=humans only or humans and other primates only) or unknown.Columns X to AC: Whether or not the virus is known to be capable of infecting any of six host categories : only humans; or humans plus non-human primates, other mammals, birds, reptiles or fish. Obtained from literature review as described above.Columns AD, AE: Host range references (PubMed ID and hyperlink to reference(s)).Information on discovery date, transmissibility level, host range and transmission route is all supported by a reference from the scientific literature. Links to the supporting literature references are provided as the PubMed ID if available or other reference database if not . Over 90% of discovery dates are supported by PubMed linked reference.The database contains no information requiring restrictions on use. The database is licenced as Creative Commons Attribution 4.0.How to cite this article: Woolhouse, M. E. J. & Brierley, L.Epidemiological characteristics of human-infective RNA viruses Sci. Data 5:180017 doi: 10.1038/sdata.2018.17 (2018).Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


With the dawn of the new sustainable development goals, we face not only a world that has seen great successes in alleviating poverty but also a world that has left some groups, such as persons with disabilities, behind. Middle-income countries (MICs) are home to a growing number of persons with disabilities. As these countries strive to achieve the new goals, we have ample opportunity to include persons with disabilities in the emerging poverty alleviation strategies. However, a lack of data and research on the linkages between economic vulnerability and disability in MICs hampers our understanding of the factors increasing economic vulnerability in people with disabilities.This article aims to present data related to elements of this vulnerability in one MIC, South Africa. Focusing on out-of-pocket costs, it uses focus group discussions with 73 persons with disabilities and conventional content analysis to describe these costs.A complex and nuanced picture of disability-driven costs evolved on three different areas: care and support for survival and safety, accessibility of services and participation in community. Costs varied depending on care and support needs, accessibility , availability, and knowledge of services and assistive devices.The development of poverty alleviation and social protection mechanisms in MICs like South Africa needs to better consider diverse disability-related care and support needs not only to improve access to services such as education and health  but also to increase the effect of disability-specific benefits and employment equity policies. The UN 2015 report on the Millennium Development Goals (MDGs) states that the MDGs have driven ‘the most successful anti-poverty movement in history’  and safe water, risky environments and work conditions and food insecurity (Banks & Polack Economic vulnerability can be driven by the costs (or resource changes) incurred by the individual or household as a result of disability. These costs can be divided into direct  and indirect costs (opportunity costs) . Opportunity cost on a household level can also relate to a caregiver and will be equal to the income that this person could have earned if their family did not include a member with disability that required additional caregiving. Literature on disability has described these disability-associated opportunity costs in LMICs including South Africa, usually in the form of educational outcomes, employment or days out of role Eide . These sMuch less is known about the disability-driven out-of-pocket costs that persons with disabilities and their households experience in MICs . It is therefore currently impossible to estimate what costs need to be covered by social protection mechanisms to mitigate the economic vulnerability of this population in countries like South Africa. Hence, it is essential to better describe these disability-related out-of-pocket costs from the perspective of persons with disabilities and their household members.Understanding these costs is also essential to enable countries like South Africa to implement obligations arising through the Convention on the Rights of Persons with Disabilities , tax rebates, a housing subsidy and the Employment Equity Act (1998/2016). In addition, South Africa is currently developing a National Health Insurance (NHI) scheme as part of its plans to achieve Universal Health Coverage  to promote the rights of persons with disabilities. South Africa focused in this process on the economic vulnerability of persons with disabilities and their households and needed ‘further understanding of the economic vulnerabilities’  from the perspective of persons with disabilities and their caregivers. Within this investigation, we took cognisance of the diverse experiences of varied groups of persons with disabilities. In order to capture these varied experiences, we applied a participatory study design using focus group discussions (FGDs) to develop a consensus of the typical costs that may be borne by households with persons with disabilities, for different types of disability. The study was conducted in close cooperation with the South African Department of Social Development (DSD) and disability sector  through the leadership of the representative DPOs. Participants had to belong to one of the identified groups and included persons with disabilities see  who wereEach FGD, with the exception of the deaf-blind, included 3–14 people with disabilities. FGDs were conducted in the participants’ first language (including sign language) by the researchers themselves and trained research assistants. Persons who are deaf-blind were interviewed on a one-on-one basis to enable effective participation, which because of the nature of the impairment was difficult to enable in a discussion group.Participation was voluntary and informed consent achieved in two stages. Firstly, potential participants were approached through their respective DPO, and a written information sheet  explaining the purpose and nature of the study was provided. Secondly, on the day of the FGD, verbal  information was provided before informed consent was signed and FGD was conducted. Participants were reimbursed for their time and transport.Overall, 73 adults with disabilities participated in the qualitative part of the study see . Each grPrior to the discussion group, participants had also completed the cross-sectional survey prompting their personal disability-related costs in the domains of education, work, housing, care and support, health care, transport and employment .The study revealed a diverse set of needs and costs that varied not only by degree and type of disability but also depending on the available infrastructure and accessibility of services. What people spent primarily depended on their household income level. Considering the participants’ emphasis on differences between survival and participation, we structure costs in those (1) arising through increased care and support needs , (2) accessing essential services (access) and (3) participating in home and community (participation and dignity). These themes do overlap. However, for the purpose of this article, this structure helps to highlight the participant’s perceptions of the difference between addressing disability-related out-of-pocket costs impacting on survival or access to essential services from those enabling participation on an equal basis to others as defined in the CRPD. It also helped to highlight that some services, such as access to Internet and mobile devices, are for some subgroups (e.g. deaf-blind) not a matter of luxury, but a matter of survival (instead of participation).thousand rand] and probably more skilled person would be five or six .’ ‘I suppose it will depend on the hours, probably let’s say unskilled person would be three  you have to use private transport up to R500… It would be R20 for the person and R20 for the assistant with Dial-a-ride …’ ‘People will need an assistant to get to the clinics, which can cost R200 a day …. if you don’t get Dial-a-ride , relax you will be fine. So afterwards I rang to find out the price of when they came to pick me up and it was about R3000 or something.’ ‘I was very sick at home and everyone was still putting me in an ambulance, close to my house on the road and I was still getting seizures, number one, number two and the guy sitting with me inside just said  is not reliable, so we as a company have been given three vehicles [a donation]… but the company has to provide by employing the driver and cover the petrol and insurance and it is a big cost … and who’s going to cover the cost of running the service?’ ‘It  need me to support them they were like shouting at me … because I support the Blue Bulls [a Rugby team] … they were shouting ‘why are you supporting the Blue Bulls’. We can communicate as the blind-deaf people… you don’t have to feel you are so lonely. That is just an example of what technology can do … So it opens the world it makes the world smaller and brings us together. Unlike the deaf and blind people who are sitting at home no technology at all, they live there and do nothing until they die...’ ‘You see that is how deaf-blind works. … On Facebook they were telling me that they  and the deaf person does not hear. Only later do you realize that the route has changed and then you have to get off and catch another taxi and it takes long and you pay double fares.’ ‘The problem is when the routes change. The driver asks all the passengers: if he can change his route on that day [The main additional out-of-pocket costs that were reported by participants related to travel to work. Many persons with more severe disabilities travel with an assistant, need specialised transport or make use of private transport. The use of an assistant is closely linked to the availability of accessible transport to places of work. Where accessible transport or subsidised, specialised services such as Dial-A-Ride or Sukuma were available, individuals experienced transport costs that were similar to the general population. In addition, participants reported cases of additional cost being incurred through inaccessibility of information about change in transport routes.In order to enable meaningful participation in home, community and the workplace, we need innovations for universal design and reasonable accommodation, which target major cost drivers related to communication, housing, road infrastructure and transport.Through using a participatory design, this article highlights the voices of persons with disabilities and their perception of disability-related out-of-pocket costs in South Africa. The study design has a number of limitations. Firstly, it was conducted in a very short period of time (three months), which allowed little time for data collection and restricted the researchers to interview persons only in three different areas of South Africa  and those disability groups that were identified in the inception workshop. This allowed us to narrow down the investigation but also leaves out experiences of persons from other disability groups . Secondly, this article reports only on the qualitative part of the study and therefore only describes possible costs and is not representative for South Africa as a whole. Furthermore, current spending on disability-related costs for poorer socio economic groups is strongly curtailed by available income as well as limited knowledge about available goods and services. Hence, it is very likely that the cost of fulfilling actual needs is far greater than currently reported in this study.The study reaffirms previous conclusions  are often prioritised before interventions that are perceived to improve quality of life only. Needs of disability (e.g. the provision of appropriate assistive devices and rehabilitation) are often seen as affecting quality of life and not survival. Yet, the voices of persons with disabilities highlight this as a misconception. As our examples highlighted, the absence of assistance and support for some conditions may be as life-threatening as the absence of medication (for conditions such as for TB or HIV). For instance, a quadriplegic person can die of pressure sore-related complications if they do not receive the necessary care and support. Similarly, a deaf-blind person without a translator is not able to communicate about his or her health and therefore cannot seek health care, which can lead to life-threatening situations. Hence, prioritising the needs of people with disabilities in national programmes is therefore a necessity to address economic vulnerability as well as to reinforce the right to live.Secondly, the need to improve access to essential services fits within current developments of the South African National Health Insurance (NHI), which embraces the SDG idea of Universal Health Care access  for some groups but leaves out groups with less visible disabilities (Jelsma et al.


A considerable economic burden has been repeatedly associated with alcohol dependence (AD) – mostly calculated using aggregate data and alcohol-attributable fractions (top-down approach). However, this approach is limited by a number of assumptions, which are hard to test. Thus, cost estimates should ideally be validated with studies using individual data to estimate the same costs (bottom-up approach). However, bottom-up studies on the economic burden associated with AD are lacking. Our study aimed to fill this gap using the bottom-up approach to examine costs for AD, and also stratified the results by the following subgroups: sex, age, diagnostic approach and severity of AD, as relevant variations could be expected by these factors.Sample: 1356 primary health care patients, representative for two German regions. AD was diagnosed by a standardized instrument and treating physicians. Individual costs were calculated by combining resource use and productivity data representing a period of six months prior to the time of interview, with unit costs derived from the literature or official statistics. The economic burden associated with AD was determined via excess costs by comparing utilization of various health care resources and impaired productivity between people with and without AD, controlling for relevant confounders. Additional analyses for several AD characteristics were performed.Mean costs among alcohol dependent patients were 50 % higher compared to the remaining patients, resulting in 1836 € excess costs per alcohol dependent patient in 6 months. More than half of these excess costs incurred through increased productivity loss among alcohol dependent patients. Treatment for alcohol problems represents only 6 % of these costs. The economic burden associated with AD incurred mainly among males and among 30 to 49 year old patients. Both diagnostic approaches were significantly related to the economic burden, while costs increased with alcohol use disorder severity but not with other AD severity indicators.Our study confirms previous studies using top-down approaches to estimate the economic burden associated with AD. Further, we highlight the need for efforts aimed at preventing adverse outcomes for health and occupational situation associated with alcohol dependence based on factors associated with particularly high economic burden.The online version of this article (doi:10.1186/s12889-016-3578-8) contains supplementary material, which is available to authorized users. Alcohol dependence (AD) is a prevalent disorder in high-income countries, with 3.4 % of the adult population in the European Union being alcohol dependent . AD reprWhile a number of other studies have estimated the economic burden of alcohol in the EU in general, and Germany in particular e.g. , 14–16, Cost-of-illness studies usually lack empirical assessments of health care utilization and therefore combine aggregated data with alcohol-attributable fractions (top-down method) to determine the share of alcohol consumption or alcohol dependence in cause-specific mortality and hospitalizations . HoweverCost-of-illness studies that collect individual data (bottom-up method) do not rely on these assumptions and have thus the advantage of directly calculating the economic burden, making them a benchmark against which other studies using the top-down method can be compared with. Additionally, unlike studies relying on aggregate data, studies employing the bottom-up method generally allow for a more detailed calculation and separation of costs for specific subgroups for which population-attributable fractions are not available. The economic burden of a given disease is estimated by comparing mean costs of diseased and healthy individuals, which results in excess costs. While this approach suits well to determine costs due to morbidity and disability, it can hardly assess costs related to mortality, which should be considered  as they To date, previous studies have used bottom-up approaches to estimate the economic burden for some diseases e.g. , 20; howPrevious estimates also indicated that 70 % of the economic burden related to alcohol consumption in Germany were caused by males , which iFor the purpose of estimating the economic burden associated with AD, we analysed a sample of German primary care patients from the ‘Alcohol Dependence in Primary and Specialist Care in Europe’ (APC) study , 24. We In conclusion, the present studies’ primary objective was to estimate the cost-of-illness of AD patients, from a societal German primary care perspective using a bottom-up approach. The results were compared to previous estimates of German cost-of-illness studies of alcohol that used a top-down approach. In addition, this study stratified the results by sex and age, and performed additional analyses to examine the impact of AD characteristics (diagnosis and severity).The APC study was designed to gain knowledge about the distribution, recognition, and treatment of AD in European primary health care settings. Previous results of the study indicate that AD among primary care patients was characterized by low socioeconomic status, unemployment, co-morbidities, mental distress, and disability . For theGPs assessed patients aged 18–64 on one day or more than one consecutive days using a brief questionnaire, which focused on the patients’ health and included alcohol-related questions as well as present, and lifetime AD diagnoses. A probability sample of all assessed patients was then drawn while oversampling patients perceived to have alcohol problems or AD by their GP. Sampled patients were further interviewed using a range of standardized instruments including the Composite International Diagnostic Interview CIDI;  which isFurthermore, the patient interview also included the World Health Organization Disability Assessment Schedule (WHODAS) 2.0  to assesThe German population sample of the APC study was identified by primary health care GPs from the ‘National Association of Statutory Health Insurance Physicians’ (“Kassenärztliche Bundesvereinigung”) (response rate at GP level: 36.7 %) who were practicing in two German regions: Berlin-Brandenburg  and eastern Saxony . Interviews with patients were conducted mainly via telephone (response rate at patient level: 75.7 %) between March 2013 and January 2014. All patients gave informed consent prior to being interviewed. A more detailed description of the APC study, its sampling design and instruments used can be found elsewhere .In order to determine the costs for each patient, we combined individual resource use and productivity data with respective unit costs. For example, in order to determine the costs of GP visits, we multiplied the number of visits with the costs of a single visit. All resource use data was collected via the patient interview and included hospital attendances, general practice visits, home care by healthcare and support professionals, medication use, and alcohol services use. If respondents felt that their type of contact was not part of the provided categories, they could specify the number of ‘other’ contacts. Most of these responses could be classified into existing or new categories based on their specification. If the specification was missing, the contacts were not included in determining respective costs.Additionally, productivity data was also assessed through the interview and encompassed questions on the patients’ employment status and their absenteeism in the workplace. Most of these data were assessed using the UKATT questionnaire . All queThese resource use and productivity data were then combined with German unit costs following a bottom-up approach, in compliance with the guidelines of the ‘Working Group Methods in Health Economic Evaluation’, which lists standard unit costs for health economy evaluations in Germany . The majFor hospital attendance rates, we assessed up to three (limit given by UKATT questionnaire) department-specific inpatient admissions, outpatient visits, day case surgery attendances, and accident and emergency attendances. Unit costs were derived from the guidelines for health economic evaluation in Germany . For inpWe assessed the number of surgery and home visits, as well as practice nurse contacts (at the surgery) for each patient, while GP and nurse contacts were collapsed into a single category. Unit costs for personal patient contacts and home visits were taken from the standard evaluation criteria (Einheitlicher Bewertungsmaßstab)  – a cataIn this section, we asked patients for the number of home contacts by health professionals, including occupational therapists, support workers, social workers, community psychiatric nurses, district nurses, and other professionals, where patients specified using the service of physiotherapists and alternative practitioners. Unit costs for these services were drawn from different sources: For services provided by occupational therapists, we referred to the mean cost of various services at the patients’ home remunerated by different statutory health insurances in Berlin , after bFor prescribed medication, we assessed brand names, size of each dose (in mg), frequency of doses per day, and period of intake of up to five medications per patient . The cost of each medication was determined by multiplying costs per day with period of intake. While period of intake was directly assessed, costs per day were based on costs per dose , for which the mean period was imputed among the same category of drugs. One case reported using a very costly drug . In order to make sure that this outlier would not overly inflate the medication costs, we decided to adjust the costs to 150 % of the second highest single medication cost (adjusted costs: 7328€).Taken together, two core variables were needed to calculate medication costs: a) medication costs per day multiplied with b) period of medication intake. This data was only available in association with 37 % of all reported medications, mainly because the exact brand names were not given for 61 % of all reported medications, but instead their prescribed purpose or another reference category . Because assessment of costs per package without a valid trade name were not feasible, the respective costs per day were unavailable. Therefore, we imputed the costs per day by using the Alcohol specific individual or group contacts with GPs or their nurses, with alcohol agencies, at residential rehabilitation institutions, at hospitals, with self-help groups, and other contacts were assessed. All reported contacts were grouped into GP contacts, inpatient admissions , outpatient visits , counselling  and group therapies. Alcohol-related GP contacts were valued as any other GP visit with unit costs taken from the standard evaluation criteria , and adjWe considered absenteeism, unemployment, and disability or early retirement as indirect costs. First, respondents were classified as employed (paid work or self-employed), unemployed (but job seeking), or disabled/in early retirement  based on responses given in the WHODAS 2.0 sociodemographic section. Patients pursuing non-paid work, studying, or homemaking in their own home were not assigned any productivity-related costs, representing 17.2 % of all studied cases. The costs related to absenteeism among gainfully employed patients were estimated using the friction cost approach  and a frIn addition to calculating productivity-related costs, we also present descriptive statistics on presenteeism, i.e. reduced capacity while being at work. We did not include a standardized measure to directly determine the respective costs but collected information approximating this concept: number of days with reduced or cut-back work due to any health condition (WHODAS 2.0); number of days with at most half of the usual capacity due to mental distress (K10); number of days with affected productivity at work due to alcohol use (UKATT service use questionnaire). These measures are presented descriptively in order to grasp the magnitude of lost productivity while being at work.2-Tests (for the remaining binary variables). The sampling design implied patients with alcohol problems or AD to be overrepresented in the interviewed sample. This distortion was accounted for by weighting all analyses with the inverse sampling probability.As contextual information, descriptive statistics for sociodemographic variables and the WHODAS sum score as indicator for disability were presented for patients with AD diagnoses and for those without. We tested for statistical differences by diagnostic status via negative binomial regression (for the WHODAS sum score) and via ChiThe economic burden associated with AD was determined via excess costs, which were calculated as mean difference between patients with and without AD diagnosis by sex in the following sectors of direct and indirect costs: inpatient admission, outpatient visit, any GP visit, prescribed medication, home care, alcohol treatment, absenteeism, unemployment, disability/early retirement. Given the right skewed distribution of cost variables, significant differences were tested using age-adjusted negative binomial regressions for all count variables (most cost variables). Decisions for or against utilization of zero-inflated models for variables with excess zeros, i.e. resources being utilized only by a minority of patients, were based on results from Vuong tests and actual distribution. Zero-inflated models are nested models predicting both the occurrence of any costs (logit model) and the amount of costs (count model) . Thus, tFurther, descriptive measures on presenteeism were presented separately, as they could not be included in the cost estimations. Sex-stratified differences between patients with and without AD were tested by using age-adjusted zero-inflated negative binomial regressions for count variables (as above) and logistic regressions for binary variables (at least one such day).To test for the robustness of our results, we ran one-way sensitivity analyses and varied the following assumptions: human capital approach instead of frictional cost to assess costs due to absenteeism ; only complete responses compared to complete & imputed responses ; and uniform annual inflation rates (0 % vs. 5 %)  comparedTo examine the impact of AD characteristics on the economic burden, we performed additional analyses. For both CIDI and GP diagnoses, costs were estimated separately and compared to the remaining sample without the respective diagnosis using negative binomial regressions adjusting for age and sex. Direct comparisons were not immediately feasible as GP and CIDI diagnoses did not result in two entirely distinct but in two slightly overlapping groups . As indiN = 1,356), Table A detailed description of the entire APC sample can be found in previous publications . In comparison to direct costs, productivity loss made up the larger share of this burden . The three largest components of the economic burden were disability/early retirement (27 %), inpatient treatment (26 %) and unemployment (20 %), which represented almost 75 % of the entire economic burden associated with AD. Alcohol treatment constituted only about 6 % of this burden.All presented costs represent the results from combining collected resource use data from the German APC study Table  with Gerp = .558; other results not shown). Fig. p = .099; 18–29 year old), 4211.53€ , 3850.06€ , 775.86€ , and 715.26€ .Sex-stratified analyses for both male and female AD patients found significant excess costs, too. Adding sex as further covariate to the age-adjusted main model , followed by GP treatment (120.34€) and alcohol-specific treatment (104.87€).According to the results from the zero-inflated model, excess costs in inpatient admissions were likely to be due to a greater proportion of male AD cases reporting any inpatient admission (logit model), while differences in the costs of admissions were not found (count model). For home care related costs , the results were the other way round, with male AD patients being just as likely to report any home care (logit model), but if they received any home care, their costs were higher (count model) than those of other male patients.Results of direct mean cost differences among female patients with or without an AD diagnosis were quite mixed and not significant in most sectors, except for reception of home care services. Only one female AD case reported receiving some kind of home care, thus resulting in significant lower costs compared to other female patients . Alcohol treatment was found to make up the largest share of excess costs among female AD cases (261.08€ per case), but this was mainly due to a single case reporting 40 inpatient nights which resulted in a large mean and standard deviation. Overall, the excess direct costs associated with AD equalled 230.78€ per female patient but were not significantly different from zero.Indirect costs due to absenteeism, unemployment and disability or early retirement were found to be concentrated among AD cases, which were responsible for 14 % of all indirect costs. On average, these costs were 1,050.97€ higher among AD cases as compared to other patients. This finding was also reproduced in sex-stratified analyses but not for all components of indirect costs. In total, indirect costs account for more than half of all observed excess costs, which is also demonstrated by Fig. We found that presenteeism was also more prevalent among AD cases  and total costs (−1.6 %). Compared to sector- and year-specific inflation rates , estimating the economic burden assuming 0 % or 5 % inflation led to a 4.1 % decrease and a 5.5 % increase, respectively.Results from additional analyses examining the impact of the diagnostic approach (GP vs. CIDI) and of different AD severity indicators are presented in Table Results of the association of various AD severity indicators and incurred costs were mixed. For AD severity I, costs were not significantly different between treated and untreated AD cases. Similarly, no significant association between daily drinking levels (AD severity III) and individual costs was observed for any of the examined cost domains. However, the only significant association was established for the number of AUD criteria (AD severity II) and indirect as well as total costs: The more AUD criteria were met, the higher costs incurred.This study sought to estimate the economic burden associated with AD using a bottom-up approach with a sample of 1356 German primary care patients. Our estimations considered direct and indirect costs separately and additional analyses examined the impact of several AD characteristics on respective costs.In the sample studied, the economic burden of AD was determined using excess costs which amounted to about 1867€ per case. In other words, the direct and indirect costs among AD patients were about 50 % higher compared to primary care patients without AD. Age- and sex-stratified analyses showed that these costs were concentrated among males and patients aged 30 to 50. Excess costs were largely attributable to indirect costs (57 %) in addition to costs related to inpatient treatment (25 %). Costs reported for different kinds of alcohol treatment made up a relatively small share, amounting to 6 % of the excess costs. Sensitivity analysis suggests that the estimates are robust under various assumptions.Additional analyses indicate that increased costs were independent of the approach to diagnose AD, as overall costs were significantly higher among both GP and CIDI diagnosed primary care patients, as compared to non-AD cases. Furthermore, our results suggest that costs may increase with AD severity but this finding was limited to the number of AUD criteria and could not be reproduced with treatment of AD and drinking levels.In comparison with previous top-down estimations, the costs per patient and the breakup of direct and indirect costs were very similar , 12, 21,Our results suggest that the overwhelming majority of the economic burden was not due to the direct treatment of AD but more than 80 % was attributable to paid absence from work, disability or early retirement, and inpatient treatment of comorbid health problems. Given the low treatment rates for AD  and a nApart from costs for alcohol treatment, inpatient treatment and GP visits made up most of the excess direct costs. While alcohol is a component cause of over 200 health conditions , 62, AD Half of the excess costs were due to increased rates of unemployment, early retirement, and disability. Further, absenteeism was also identified as important contributor to excess costs. While presenteeism was not part of cost calculations in this study, it was very common among AD cases. Some guidelines suggested the inclusion of presenteeism in the calculation of indirect costs  while otA major implication of this study concerns the observation that impaired productivity represents the largest share of the economic burden associated with AD, confirming previous top-down estimations from Germany , 65 and Sex-stratified cost estimations in our study suggest that excess costs mainly incurred among male AD cases in our study. This is not only due to males representing about two thirds of all AD cases, but rather to generally larger cost differences in most sectors between both sexes: Total direct costs were significantly increased in male but not in female AD cases, and excess costs were almost doubled among male compared to female AD cases. While we corroborate that female AD patients are less likely to receive treatment for their alcohol problems than male AD patients are , the greFurthermore, our results indicate that most of the excess costs incurred among individuals aged 30 to 49, while no substantial cost differences was observed in younger or older cohorts. This effect is interesting and could likely be linked to mortality, which has been shown to be constantly high among young and middle aged individuals with alcohol use disorders, as opposed to the general population, for which mortality increases steadily with age .Lastly, results from additional analyses show that the economic burden is not only associated with DSM-IV AD diagnoses but also with GP AD diagnoses, which can be viewed as a validation for their diagnostic quality. In addition, DSM-5 AUD severity was also found to be associated with increased economic burden. Associations with direct costs were only found for GP AD diagnoses and future research should examine why associations with other AD characteristics, including treatment and drinking levels, remained insignificant. In this study, small sample sizes cannot be excluded as potential cause.For a variety of reasons, the estimates in this study are not directly comparable to a general population cost-of-illness study. The primary reason is that this study referred to a representative sample of primary care patients, which differs considerably in its distribution of sex and health measures from the general population. Further, the reported estimations do not consider intangible costs and costs associated with premature disability and presenteeism – measures that should be considered when estimating the economic burden of AD to the whole society. In addition, further wider social costs are evident as a result of alcohol related crime, both to the criminal justice system and also victim costs and further injury-related productivity costs through absenteeism. Although these go beyond the remit of the current study, crime costs form a substantial proportion of alcohol related societal costs –74. TakeWith regard to valuating productivity losses, we used the friction cost approach for absenteeism and considered transfer payments  for unemployment, disability and early retirement. This was a conservative decision avoiding the potential overestimation of the true cost of productivity losses as if using the human capital approach, and was believed to better represent the real costs of productivity losses than with the human capital approach, which shows the potential consequences of disease for an economy in a state of full employment equilibrium. The friction approach is also believed to prevent adverse equity implications , 56.Despite the mentioned limitations, the present study corroborates the importance of the economic burden associated with AD as determined by previous general population top-down estimates by using a bottom-up approach: Total costs were 50 % higher among patients with AD compared to patients without this diagnosis (excess costs: 1836€ per case), with excess costs mainly attributable to reduced productivity. We found high concentrations of AD-associated costs among males and patients aged 30 to 50. Furthermore, DSM-5 AUD severity was also significantly linked to the economic burden. While we generally confirm previous estimations of AD-associated economic burden, we have also identified several discrepancies. Future cost-of-illness studies using a bottom-up approach in a general population sample may be able to examine possible reasons for these gaps, and they should consider presenteeism in their estimations.


Citrus leprosis virus C (CiLV-C), belonging to a new genus Cilevirus. It is transmitted by Brevipalpus yothersi Baker , and is characterized by the localized symptoms it induces on the leaves, fruits and stems. It occurs in the American continents from Mexico to Argentina. The virus was until recently considered restricted to Citrus spp. However, it was found naturally infecting other plants species as Swinglea glutinosa Merrill and Commelina benghalensis L., and has been experimentally transmitted by B. yothersi to a large number of plant species. Despite these advances little is known about the virus-vector relationship that is a key to understanding the epidemiology of the disease. Some components of the CiLV-C/B. yothersi relationship were determined using the common bean (Phaseolus vulgaris L. cv. ‘IAC Una’) as a test plant. They included: (a) the virus acquisition access period was 4 h; (b) the virus inoculation access period was 2 h; (c) the latent period between acquisition and inoculation was 7 h; (d) the period of retention of the virus by a single viruliferous mite was at least 12 days; (d) the percentage of viruliferous individuals from mite colonies on infected tissues ranged from 25 to 60%. The experiments confirmed previous data that all developmental stages of B. yothersi  were able to transmit CiLV-C and that transovarial transmission of the virus did not occur. CiLV-C can be acquired from lesions on leaves, fruits and stems by B. yothersi. Based on the distribution of lesions produced by single viruliferous B. yothersi on bean leaves, it is concluded that they tend to feed in restricted areas, usually near the veins. The short latent and transmission periods during the larval stage suggest that the CiLV-C/B. yothersi relationship is of the persistent circulative type.Citrus leprosis has been one of the most destructive diseases of citrus in the Americas. In the last decade important progress has been achieved such as the complete genome sequencing of its main causal agent,  Citrus sinensis Osbeck) and currently restricted to the American continents between Mexico and Argentina. The disease is characterized by the appearance of localized lesions on leaves, fruits and stems. In severe cases, it causes significant fruit drop and may lead to plant death. There are at least four different viruses that induce leprosis-like symptoms and are associated with Brevipalpus mites   and the rare nuclear type that is apparently restricted to more moderate temperature conditions  . This virus causes essentially the same symptoms as CiLV-C and is vectored by B. yothersi Baker  is a destructive viral disease of citrus, especially sweet orange , CiLV-C has received more attention because of its prevalence and importance. Its entire genome was sequenced  Merrill, in Colombia , Brevipalpus obovatus Donnadieu and Brevipalpus phoenicis Geijskes have been considered the vectors of BTV  are able to transmit CiLV-C and no vertical transmission (through eggs) occurs (Chiavegato Phaseolus vulgaris L.) cv. ‘IAC Una’ was found to be an excellent test plant for CiLV-C. Necrotic lesions are produced within 5 days or less after mite inoculation B. yotherd et al. .Fig. 1MoP. vulgaris used as the indicator plant were planted in 10 cm diameter plastic pots and filled with sterilized soil under greenhouse conditions. About 10 days later, plants emerged and exhibited two expanded unifoliar leaves, which were used for the experiments. Instead of using the entire plant, leaves were detached and placed in a 10-cm plastic Petri dish, on moistened filter paper. The detached leaves lasted for 10–14 days.Seeds of B. yothersi from the non-viruliferous stock colony were transferred onto sweet orange leaves with numerous leprotic lesions from field leprosis-infected plants, to acquire the CiLV-C. This was achieved using a stereoscopic microscope and a fine metal needle. Based on previous literature  were used. In the exploratory attempts, intervals of 1, 2 and 3 days were assessed after which the mites were removed and transferred onto detached bean leaves. Daily inspections followed to observe the appearance of lesions. After 1 day of feeding, mites were able to transmit the virus, so the intervals were reduced to 1, 2, 4 and 6 h. In this phase, each experiment had 5 replicates and was repeated 8 times.Groups of adult mites were provided a virus acquisition access period (AAP) of 4 h on sweet orange leaves with leprosis lesions and then transferred onto detached bean leaves in groups of 10 mites. One group was left feeding for 24 h as a positive control and then removed to another bean leaf to assure that they were viruliferous. Other groups were left feeding for 2, 3, 4, 5, 6, 7, 8 and 9 h, respectively. This experiment was repeated 5 times.2 , mounted on aluminum stubs with adhesive copper tape, sputter coated with gold , and examined either in a LEO 435 VP or Phenom scanning electron microscope. The images were digitally recorded.Individual mites, each at a different developmental stage  Fig.  reared oB. yothersi on infested host plants. This occurs because populations of some Brevipalpus species  are formed by haploid individuals who are females due to infection by the symbiotic bacterium Cardinium  were transferred onto individual detached bean leaves and observed daily for virus lesions over 2 weeks.To assess possible transovarial transmission of the CiLV-C, eggs were removed from a colony of 2) of the density of the lesions should be equal to the mean (m), following Taylor’s Power law s2 = amb (a and b are known variables). If this does not occur, then lesions had a tendency to be aggregated. For the analysis, data were converted to logarithms and a regression curve was generated by the log value of the mean and variance.In one of the above experiments, we analyzed the pattern of lesion distribution on bean leaves with a single mite transmission assay. In this experiment, 37 of 110 single viruliferous mites transferred onto isolated leaves were able to induce lesions. The lesions varied in number and distribution on the leaf surface. Each of the leaves was scanned, magnified 1.25× and then printed. A plastic transparent sheet, marked with rectangles of 2.5 × 1 cm (quadrants), was overlaid on the image and the number of quadrants per leaf and the number of lesion per quadrant were recorded. Thus, a correlation of the size of the leaf and the number of lesions per quadrant was obtained. The analysis followed the statistical procedure proposed by Taylor . The basPrevious studies  gene was performed following the RT-PCR protocol described by Locali et al.  was determined as 4 h Table . In assaus Table . All deves Table a–e.TableB. yothersi was exposed to the virus source for acquisition  and inoculate (IAP) CiLV-C resulting in lesions on bean leaves were estimated to be at least 4 and 2 h, respectively. Both values obtained for AAP and IAP agree with preliminary data reported by Freitas-Astúa et al.  that infected leaves, fruits and stems serve as virus sources; (4) that not all B. yothersi mites colonizing an infected source transmit CiLV-C, and the rate of transmission is usually below 50%; (5) that under the described conditions, CiLV-C was retained, and available for inoculation, for at least 12 days after acquisition by a viruliferous mite; (6) that feeding behavior of B. yothersi on bean leaves tends to be localized in small areas; and (7) that the common bean, P. vulgaris cv. ‘IAC Una’, represents an excellent indicator plant for this type of evaluation because results are obtained in a much shorter period of 5 days versus 3 weeks using sweet orange as indicator plants and at much lower cost and use of space. These parameters should be considered in organizing management strategies for CiLV-C in citrus orchards.In conclusion, our data point out: (1) relatively short AAP, IAP and latent periods, favoring the hypothesis of the persistent-circulative type of virus-vector relationship; (2) that all developmental stages of