Background: Reliable data on health care costs in Ireland are essential to support planning and evaluation of services. New unit costs and high-quality utilisation data offer the opportunity to estimate individual-level costs for research and policy.Methods: Our main dataset was The Irish Longitudinal Study on Ageing (TILDA). We used participant interviews with those aged 55+ years in Wave 5 (2018) and all end-of-life interviews (EOLI) to February 2020. We weighted observations by age, sex and last year of life at the population level. We estimated total formal health care costs by combining reported usage in TILDA with unit costs (non-acute care) and public payer reimbursement data . All costs were adjusted for inflation to 2022, the year of analysis. We examined distribution of estimates across the population, and the composition of costs across categories of care, using descriptive statistics. We identified factors associated with total costs using generalised linear models.Results: There were 5,105 Wave 5 observations, equivalent at the population level to 1,207,660 people aged 55+ years and not in the last year of life, and 763 EOLI observations, equivalent to 28,466 people aged 55+ years in the last year of life. Mean formal health care costs in the weighted sample were EUR 8,053; EUR 6,624 not in the last year of life and EUR 68,654 in the last year of life. Overall, 90% of health care costs were accounted for by 20% of users. Multiple functional limitations and proximity to death were the largest predictors of costs. Other factors that were associated with outcome included educational attainment, entitlements to subsidised care and serious chronic diseases. Conclusions: Understanding the patterns of costs, and the factors associated with very high costs for some individuals, can inform efforts to improve patient experiences and optimise resource allocation. Resource allocation decisions in health care therefore have substantial impacts at the macroeconomic level, but also at the microeconomic level, where funding and availability of services may affect individual health, wealth and productivity.Health care spending accounted for approximately 9% of gross domestic product in Organisation for Economic Co-Operation and Development (OECD) countries in 2019, the last complete year before the COVID-19 pandemic. This challenge, which economists frame in terms of ‘scarcity’, will be increasingly complex through the 21st century as populations age. Research has repeatedly shown that at the individual level the most important drivers of rising costs are not ageper se, but instead, on the demand side, proximity to death, and, on the supply side, technology and staffing. At the population level, costs will increase due to the rising total number of people living and dying with serious medical illness, the demand for health care workers growing more quickly than supply, and the number of medical technologies increasing persistently.Health-related demands will always exceed available resources, placing a moral and practical imperative on decision-makers to fund those services that provide the best value, but faces the same structural challenges and the same need to reform health care services for the population health needs and resource constraints of the 21st century. A relatively young population today translates to faster-growing future needs: Ireland has the fastest ageing population in the European Union, with an expected three-fold increase in those aged 80+ years and near doubling of annual deaths in the next 20 years. Compared to other western European nations, the Irish health care system has long been distinguished by a combination of relatively high per-capita spending and a relatively limited basket of entitlements under universal coverage. Notable system characteristics include a reliance on acute inpatient hospital admissions given weak primary care capacity, high medications spending, non-compulsory insurance that improves access to some services for policyholders, and fast-growing population health needs. Partly in recognition of these issues, Ireland has engaged in a wide-ranging health policy update since 2017, known as the ‘Sláintecare’ reforms, with mixed progress.Ireland is early in the demographic ageing process compared to other high-income countries. In 2021 researchers published the first standardised set of unit costs for non-acute health care in Ireland, a long-awaited development and a critical step for health economics research in the state.High-quality data on individual-level health care costs in Ireland are essential to support monitoring, planning and evaluation of services, and the allocation of scarce resources to maximise public welfare. The lack of a unique patient identifier prevents researchers from using routine administrative data to estimate individual-level costs. Utilisation data are collected via frequency questions on categories of health, social and residential care in the preceding year,e.g., ‘how many times did you visit the GP in the last 12 months?’ While previous papers have estimated costs in TILDA, these have been constrained by incomplete availability of unit costs in non-acute care and crude casemix estimates for acute care.The Irish Longitudinal Study on Ageing (TILDA) is a biennial study of people aged 50+ years in the Republic of Ireland that started in 2009–2011. Among many individual-level variables in a rich dataset, TILDA collects data on participants’ demographic, socioeconomic, early life, physical and mental health, and household characteristics, delineating patterns and trajectories of health care use, surveying end-of-life needs, and projecting future needs and costs. They can also contribute to future studies both within TILDA, for example prediction exercises to identify high-cost users; and in wider modelling frameworks, for example cost-effectiveness analyses that have to characterise disease trajectories and costs in different clinical populations.Combining the new unit cost database with TILDA offers the opportunity to estimate in the greatest detail yet health, social and residential care costs  among a population-representative sample of older people in Ireland. We supplement this new non-acute unit cost database with our own analyses of hospital inpatient admissions, adjusting for age, sex, diagnoses and discharge status, and a costing exercise of medications reported by TILDA respondents. The arising estimates can inform ongoing research studies, including those evaluating specific policies and models of deliveryOur aims in this paper are to first document the methods by which health care costs are estimated in TILDA, and then to address the following research questions:1. What are the health care costs for older people in Ireland? How are costs distributed across the population?2. What is the underlying composition of these costs between primary and community care, hospital care, home care, residential care, and medications?3. What individual-level predictors are associated health care costs?. Full details of the study design, recruitment, consent and data collection are available elsewhere. Briefly, computer-assisted personal interview (CAPI) and a self-completion questionnaire (SCQ) are used to collect data on demographic and socioeconomic characteristics such as early life, household composition, employment history, income and asset levels, as well as detailed information on health status  and healthcare utilisation. When a participant dies, a family member or close friend is approached to conduct a voluntary end-of-life interview (EOLI) on the decedent’s experiences in the last 12 months of life. This process, including the ethical guidelines and procedures, has been detailed elsewhere. The EOLI represents a shortened version of the CAPI, asking the respondent questions on the decedent’s living situation, health, health care use and other factors. This is a costing study using secondary data sources. Our main data source was TILDA, which recruited a population-representative sample of more than 8,000 community-dwelling people aged 50+ years at Wave 1 (2009–2011)The baseline sample were invited to participate in CAPI and SCQ follow-up at Wave 2 (2012), Wave 3 (2014), Wave 4 (2016), Wave 5 (2018) and Wave 6 . Ethical approval for each wave is obtained from the Faculty of Health Sciences Research Ethics Committee in Trinity College Dublin. Participants make an informed decision about their participation, receiving advanced notice and information booklets; they may refuse to take part in any study section or withdraw at any time without justification; for each CAPI and EOLI question, available answers include “Refuse to answer” and “Don’t know”., and the Hospital Inpatient Enquiry (HIPE) database of admissions to public acute hospitals. We also draw on Census data from the Central Statistics Office (CSO), for the purposes of population weighting, and the General Register Office (GRO) to identify deaths. All deaths in the Republic of Ireland must be recorded with the GRO, and TILDA is linked to the GRO in a process described previously.Secondary data sources were two unit cost databases for non-acute costsIn terms of perspective, we estimate the cost associated with providing the formal health care that TILDA participants and EOLI respondents report. We do not analyse or report out-of-pocket spending, which has implications for how costs are distributed between the system and households, and we do not analyse or report unpaid care provided by family and friends.Dependent variableThe primary dependent variable is formal health care costs, which combines the estimated costs associated with acute and non-acute care, and medications, reported by participants.f) of use for a number (n) of categories (h), wheren varies slightly between CAPI and EOLI, because hospice inpatient stays are not asked in the CAPI (implicitly assumed as zero). A unit cost (c) was identified for each category. Therefore, for each individual CAPI or EOLI (i), a specific acute or non-acute category (h) has associated costs  given by:For acute and non-acute care, TILDA collects data on the frequency  activity-based funding (ABF) guidance, which determines reimbursement rates according to primary diagnosis and length of stay. We categorised each of these admissions by age, sex and diagnostic profile, and then calculated acute unit costs for each age/sex/diagnostic group as the mean reimbursement for an overnight stay in that group. We linked these acute unit costs to each CAPI and EOLI by age/sex/diagnostic profile, incorporating for EOLIs the additional cost associated with a death in hospital.Unit costs for non-acute care have been calculated in two prior costing exercises by Brick. In data processing we created sub-groups for ease of interpretation: primary and community care, hospital care, home care, residential care, and medications. Each category of care, its’ variable name in the most recent publicly available CAPI and EOLI, the unit cost source, the unit cost after adjusting to 2022 prices, and the sub-group to which it was allocated are presented inIn all cases we chose the most recently available unit costs available. These most recent unit costs were calculated in different years. We standardised all unit costs to 2022, the year that the analyses were conducted, using the Consumer Price Index (CPI) for healthi.e., certain vitamins and over-the-counter products). For each included medication (m) we identified the associated cost (c), assuming the respondent was prescribed the WHO Defined Daily Dosage corresponding to the ATC code for one year, in the 2020 Health Service Executive reimbursement list. Therefore, for each individual CAPI (i), reported regular usage ofn medications has associated annual costs  given by:For medications, CAPI respondents detail the medications that they take “on a regular basis”, which includes prescribed medications, as well as those purchased over-the-counter, vitamins and supplements, and herbal products. Medication names are recorded as they are reported (either branded/generic product name or drug name), however strength and dosage are not captured. Each medication is assigned a WHO Anatomical Therapeutic Chemical (ATC) code where available relating to the drug they contain. We excluded reported products that do not have an ATC code, and any non-prescription items not reimbursed on Ireland’s community drug schemes , expressed in euro  adjusted to 2022, is then calculated by summingi,hy forn categories of acute and non-acute care and adding the medications costs:The primary outcome, an individual CAPI or EOLI’s total formal health care costs , the baseline sample (aged 50+ years in 2009–2011) are nearly all aged 55+ years (the only exceptions are those who enrolled aged <50 years old while participating as the spouse of a participant aged >50 years old). Therefore, we excluded those aged <55 years from all analyses; the numbers are presented with the population-level weights in ‘Appendix 2’, found as.We include EOLIs from all waves prior to March 2020, since wave-by-wave samples are relatively small, these observations heavily influence cost estimates (see Results for full details), and we consider it a reasonable assumption that pre-pandemic deaths in all TILDA years are substantively comparable. The sample eligibility was therefore defined as all Wave 5 CAPI participants aged 55+ years, and all EOLIs aged 55+ years at any wave, except (i) deaths occurring after 29/2/20, and (ii) the deaths for participants at Wave 5 and so individuals already in our sample. We summarise how this sample is reached in the Results and present the characteristics of those excluded in ‘Appendix 2’As such our reported estimates reflect our best understanding of health care costs among older people in Ireland in 2019, updated for inflation to 2022.e.g., at baseline this was less than 1% for predisposing, enabling and need characteristics , although there have been small increases in such missingness wave-on-wave. For the dependent variable, prior analyses of TILDA have suggested that of all categories in. Any sample-eligible CAPI or EOLI that was missing two or more of these four categories was flagged and removed from primary analysis as having insufficient outcome data. For those interviews missing one or fewer of these categories, and or missing any other categories of health care frequency, we imputed age- and sex-adjusted medians. For independent variables, any sample-eligible CAPI or EOLI that was missing three or more baseline predictors was removed from primary analysis as having insufficient baseline data. For those interviews missing two or fewer baseline variables, we imputed the same individual’s data from the most recently available prior wave.Prior studies have found that missing data in both CAPIs and EOLIs is relatively rare;.TILDA in Wave 1 aimed to recruit a population-representative sample of community-dwelling adults aged 50+ years but the sample inevitably differs from the population, and this variation will have increased if those who die or drop out or have missing data differ systematically from those who continue to take part. We addressed this sampling uncertainty, and the concomitant risk of bias, through sampling weights that used the CSO population data to calculate the probability of any given participant having been included in the sample. For this paper we weighted by age (five-year bands), sex , and last year of life . Weights were calculated using the CSO population data for 2019, the most recent pre-pandemic year full data were available. See ‘Appendix 2’Stata version 15 (RRID:SCR_012763); an open access alternative that can perform equivalent tasks is R (RRID:SCR_001905). For research questions 1 and 2, we report descriptive and distributional statistics after applying the population weights. For research question 3, we run multivariate regressions in the eligible Wave 5 CAPIs, in the eligible EOLIs, and in the CAPIs and the EOLIs pooled. In all cases we modelled outcomes using a generalised linear model with a power link, selected using information criteria before inspecting or interpreting results. Prior to estimating results we assessed collinearity of predictors using the –collin- command. For each association between predictor and outcome, we report dy/dx using the –margins- command; this reflects the estimated mean association with outcome of increasing the value of the predictor by one point while holding all other values in the model constant.All analyses were performed inAdditional data and sensitivity analysesFor reader information, we present the following data before and after weighting in supplementary materials:• Characteristics of the sample, and those excluded per• Extended data)Research Question 1 • Summary statistics and distributions for total health care costs in CAPI waves 1–4 (Appendix 5)We performed sensitivity analyses on our regressions, presented in Appendices:I. Research Question 3 for CAPI and EOLI without weights (Appendix 6)II. Research Question 3 for CAPI and EOLI with alternative acute inpatient costs as outlined in Appendix 1 (Appendix 6)III. Research Question 3 for CAPI and EOLI using those with complete outcome data only (Appendix 6). Additional information on the medications costing exercise are presented in ‘Appendix 8’.Diagnostic checks for model choice and collinearity are presented in ‘Appendix 7’The analytic samples are provided inth century and lower widowhood effects. Those in the last year of life were much more likely to have a medical card, reflecting wider entitlement from the age of 70 years, but less likely to have private insurance. EOLI observations had much higher prevalence of serious illness, functional impairment, and ED attendance; but much lower prevalence of regular physical exercise. In the pooled sample, patterns of characteristics substantively reflect the CAPI sample as these are 87% of observations before weighting and 98% after weighting.There were large differences between the EOLI (last year of life) and CAPI (not last year of life) samples on all baseline predictors except region. More than two thirds of those in the last year of life were aged over 75 years, and more than three quarters not in the last year of life were aged under 75 years. Males were slightly more represented among the EOLI than CAPI samples, reflecting higher male mortality rates. The younger CAPI interviewees had higher average educational achievement, reflecting cohort effects in access, and higher prevalence of marriage, reflecting rising marriage rates in the middle of the 20What are the health care costs for older people in Ireland? How are costs distributed across the population?Total formal care costs, in the CAPI and EOLI samples and pooled together, are presented inThe distribution of formal costs across deciles are presented inThe corresponding population-level costs are presented in. For equivalent data in the CAPI and EOLI samples separately, see Appendix 3What is the underlying composition of these costs between primary and community care, hospital care, home care, residential care, and medications?The composition of costs for CAPI, EOLI and pooled samples are presented inThe composition of costs across deciles of total health care costs in the pooled sample is presented inth quantile, hospital costs account for a consistently increasing proportion. Home care costs are close to zero until the top five quantiles, thereafter, accounting for 2–12%. Residential care costs are close to zero until the top two quantiles, accounting for over a quarter of costs in the highest-cost group.The composition of costs among non-zero users, across 20 quantiles of total health care costs in the pooled sample, is presented in. For equivalent data in the CAPI and EOLI samples separately, see Appendix 4What individual-level predictors are associated health care costs?The results of the multivariate regressions are presented inIn the CAPI sample, the largest associations were 2+ IADLs, which was associated with EUR 21,376 higher costs compared to none  and being in the last two years of life . Other variables positively associated with outcome were: older age; medical card entitlement; living in a town or city other than Dublin; diagnoses of cancer, heart disease or multimorbidity; and prior ED attendance. Negative associations were college education and engaging in regular physical exercise, which in this context is a proxy for unobserved general health. In the EOLI sample, the largest association was again 2+ IADLs . Other positive associations were cancer diagnosis and multimorbidity. Negative associations were male sex, living outside Dublin, regular exercise and multiple prior ED attendances.In the pooled sample, results were substantively consistent with the CAPI, reflecting the fact that these are 98% of observations in the weighted sample. The largest associations were again multiple IADLs , and proximity to death variables in the last two years of life  and in the last year of life ..This paper presents the most comprehensive picture to date on individual-level health care costs for older people in Ireland. We found that, adjusted to end 2022, mean costs among people aged 55+ years were EUR 8,053, with a large differential between those in the last year of life , and not in the last year of life . Hospital costs accounted for over half of costs in all three sampling frames. There was a very large skew in the data: the top 20% of users accounted for 90% of all costs at the population level, and the top 10% accounted for 74%. In multivariate regressions, multiple IADLs and proximity to death had the largest associations with outcome. In sensitivity analyses, our results are substantively similar for the IADL and proximity to death associations, and >95% of all associations in. Optimising care and supports for this group, in particular to support ageing in place and to minimise avoidable acute hospital admissions and residential care costs, remains an urgent priority.The large association between multiple IADLs and costs is not surprising, and in part reflects the high level of health care worker time that are required to support this population. Nevertheless, the magnitude of association, and in particular that this variable is more strongly predictive of costs than proximity to death, is somewhat unexpected and has important policy implications. As the population ages, and in particular as dementia becomes more prevalent, the number of people living with multiple IADLs will grow. The population-level costs of end-of-life care are therefore approximately EUR 2 billion annually. Total health spending in Ireland in 2019 was EUR 25.3 billion. This suggests that the <1% of people who die each year in Ireland account for around 8% of health care spending, although the true ratio is sensitive to recent rapid increases in both inflation and public health spending, and the fact that our estimates likely undervalue total spend by using public service unit costs only. This ratio is consistent with what has been reported in other high-income countries. The large projected growth in the number of older people dying annually as the population ages emphasises the urgent need to plan and fund palliative and end-of-life care services.The significant differential between those in the last two years of life and not approaching end of life reflects a long-standing evidence base that proximity to death is a key determinant of health care costs. There were 31,184 deaths recorded in Ireland in 2019, of which over 28,000 (91%) occurred in people aged over 55 years. Men have lower costs in the last-year-of-life cohort due to the higher prevalence of sudden death, but there is no apparent association in the whole population. Socioeconomic disparities in health are reflected in lower costs among those who stayed longer in education. Medical card entitlements lead to higher health care use in the general population, but in the end-of-life cohort where entitlements are more universal, they have no relationship. This relationship between entitlements and costs is complicated by potential confounding by socioeconomic and health status; those entitled to a medical card are more likely to have care needs that are not captured in the model. Diagnoses of serious disease necessitate higher health care costs and regular physical exercise protects against episodes of ill-health, although in our data this is likely not causal but reflects the better health of regular exercisers. Prior patterns of hospital admittance strongly predict costs. One surprising result was the association between region and outcome: those living outside Dublin in other urban settings had higher costs in the general population, but both urban and rural dwellers had substantially lower costs than those in the capital in the end-of-life cohort. Complex patterns of use by geographical region are commonplace among older populations, and this warrants further investigation to pick apart issues of need, access and value.Some of the other associations were predictable in the context of prior literature. Older age is associated with somewhat higher costs compared to younger people While skew in health care costs is a long-established phenomenon, the distribution in our data is still unusual. The historic interest has been in an ‘80–20 ratio’, where 20% of people account for 80% of spending, but we find that 20% of people account for 90%. Taking into account that our calculations are in the population aged 55+ years, and older people account disproportionately for health care spending, the ratio at the population level must be still more imbalanced. Ex ante identification of people who account heavily for health care costs, identifying and addressing low-value care, and reforming provision for an age of multimorbidity and ongoing supportive care are all strategies with enormous potential fiscal pay-offs, as well as improved outcomes for patients.. This is potentially associated with Ireland’s historic reliance on acute care and weak primary care capacity, and implies opportunity to reduce hospital costs through more cost-effective models of community care delivery. Such aims are consistent with the ongoing Sláintecare reforms.The estimation that hospital costs account for nearly 60% of total costs is higher than comparable data in England (50%)TILDA collects all CAPI data by self-report, and all EOLI by interview with a family member or friend, which may result in omissions and recall bias among both predictors and outcome. Absent a unique identifier in routine data, TILDA is nevertheless among the most powerful sources of individual-level data for understanding health care costs among older people in Ireland.. This means that our total reported costs are likely underestimates, and that interpreting associations between outcome and variables strongly associated with use of private care  must be done with caution. TILDA collects frequency data on private hospital and home care use, meaning that future work can address this gap if credible unit costs of private care can be identified. Proportion of use that is private among different allied and social care categories varies widely, and as such so does the public/private distinction. We are unable to quantify the distribution of costs between payers . Many hospital engagements in Ireland are ‘day cases’; engagements not requiring an overnight stay. While TILDA captures ED attendances and same-day discharge following emergency admissions are included in our reported costs, we don’t include diagnosis-specific costs for outpatient engagements or procedures but instead estimate resources at a flat unit cost rate. Healthcare Pricing Office costs for inpatient stays do not include superannuation, while unit costs for other types of care do.Our unit cost estimates do not take account of differential costs in private settings, which by volume account for approximately 20% of care in Ireland’s mixed system, and age/sex/diagnostic profiles capture a significant proportion of proximity to death among those discharged alive, hospital costs are still highly heterogeneous within each age/sex/diagnostic category, reflecting myriad factors including physician and patient preferences, access, specific hospital setting, and discharge location options. Future work might address this to some extent;e.g., HIPE records whether a person was discharged home or to a hospice or a nursing home. However, arising estimated costs would be contingent on additional unverifiable assumptions and so come with increased risk of new biases. The promised implementation of a unique identifier would provide ‘true’ individual-level costs in HIPE against which different cost mix methods in TILDA could then be benchmarked.Hospital costs account for a majority of overall costs, but unit costs for acute care are age/sex/diagnosis-adjusted national averages only. While we have adjusted for discharge status The COVID-19 pandemic complicated choice of an appropriate timeframe. For CAPI observations, we used Wave 5 as the most recent pre-pandemic wave (2018), and for EOLI observations, we used all pre-pandemic observations to maximise sample size in a group that has disproportionate influence on estimates. Our reported estimates reflect best understanding of health care costs among older people in Ireland in 2019, updated for inflation to 2022. The information provided in this manuscript and in the appendices equips readers to revise group averages using the CPI for health, and/or to weight at the population level for other years using CSO data, should they choose to do so. Parsing the effects of COVID-19 on general health care use, both during the heights of the pandemic 2020–2021, and into the future, have been examined to some extent in other TILDA analyses and are an important topic for ongoing study.. While these data are availablevia the census for the CAPI, the last matching exercise by the CSO to the GRO was after the 2016 Census. Our strategy therefore reflects the best approach with publicly available data for all CAPI and EOLI observations. Future work may seek to improve the precision of weighting, for example by getting additional ‘enabling’ variables on decedents from the CSO’s data controllers.TILDA recruited a population-representative sample in 2009–2011, but attrition to Wave 5 (2018) may have undermined this representativeness. We weighted using age, sex and last year of life since these data are theoretically associated with outcome and easily available from the CSO. Prior weighting exercises in the TILDA CAPI have also incorporated education, marital status and geographical location to maximise generalisabilityHigh-quality data on health care costs are essential to support monitoring, planning and evaluation of services, and the allocation of scarce resources to maximise public welfare. By combining newly available unit cost data in non-acute care, our own estimates of acute costs, and the rich data in TILDA, we present the most comprehensive picture to date on individual-level costs among older people in Ireland. We quantify more precisely some well-known relationships, particularly the high costs associated with end-of-life care, and also identify some potentially underestimated dynamics, in particular that multiple functional impairments appear a more significant driver of costs than age, diagnosis, multimorbidity or proximity to death. The derived estimates can inform multiple ongoing research studies and policy activities, as well as providing a foundation for future work, which should include consideration of private provider costs in Ireland’s unusual mixed system.Ethical approval for each wave of the TILDA study is obtained from the Faculty of Health Sciences Research Ethics Committee in Trinity College Dublin. Participants are provided with sufficient information to make an informed decision about their participation including advance notice of the study. Written consent is obtained for separate components of the study ; participants may refuse to take part in or withdraw at any time without justification. Ethical approval for the secondary analysis of TILDA data used in this study was part of this overall approval. Review of ”Formal health care costs among older people in Ireland: methods and estimates using the Irish Longitudinal Study on Ageing (TILDA)” I would like to start by congratulating the authors on a well-performed and well-presented study. The work is of importance for policymakers as well as for future research. Overall, the study design seems well-constructed and outlined. It is easy to follow the authors' considerations behind the study approach and limitations are also well described. Additional material in the appendix (not reviewed) can make the result transparent and offer the reader additional insight. I believe that the manuscript is close to publishable as it is.The study is based on a sample enrolled in interviews. It is well known that this might create a selection bias. The authors also adjust for this by applying a weighting approach. They also refer to appendices/other articles for more information. As a reader it would be informative to state/discuss in the manuscript (1) what characteristics the enrolled sample differs most from the general population, and (2) whether is there a risk that those enrolled could differ in cost due to unobservable characteristics and what would that imply?Predictors of costs are analyzed using multivariate regression. In these kinds of studies, it is often difficult to identify the causes of costs as there are often confounding factors. The authors use careful language but draw some conclusions that indicate that associations are interpreted as causes. The manuscript could perhaps benefit from making it more clear to the reader that although there may be significant associations, this might not mean this should be interpreted as a cause of higher costs.Finally, some notes on the terminology. Health care is often differentiated from caregiving  as they usually serve different purposes. However, sometimes they are difficult to separate. Maybe a brief note or discussion of this could be added to the manuscript. Also, formal care differs from informal care provided by relatives/friends. Maybe I missed a clarification on these concepts, but if not, it would be beneficial to include them in the manuscript. Maybe it should also be highlighted in the discussion that informal caregiving could be substantial and that the true cost to society is therefore expected to be even higher. However, below are some suggestions for clarification:Is the rationale for developing the new method (or application) clearly explained?YesIs the description of the method technically sound?YesAre the conclusions about the method and its performance adequately supported by the findings presented in the article?YesIf any results are presented, are all the source data underlying the results available to ensure full reproducibility?YesAre sufficient details provided to allow replication of the method development and its use by others?YesReviewer Expertise:Health economicsI confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Thank you for the opportunity to review this manuscript, which clearly reports this excellent piece of work. I find it makes a significant contribution to the literature in terms of healthcare costs among older people in Ireland, but also provides interesting findings making it of interest more broadly internationally. I find the data acquisition, preparation, and analysis and interpretation have all been undertaken with care and clearly a detailed understanding of the Irish health system and the strengths and limitations of the data available, which are acknowledged and at times potential recommendations to address these in the future are presented in the discussion . I have some minor comments which it would be good to address for clarity. Firstly, I find the use of the term multiple IADLs in the results strange - as I think you mean multiple impairments in IADLs as compared to having multiple IADLs. Secondly, I was unclear on the time to death variable less than 1 year - it seems this is solely based on whether they were part of the EOLI or CAPI interviews. But was there a chance that people in the EOLI interview lived for more than 1 year or than some people in the CAPI group died within 1 year? It seems that for the death within 2 years variable this was done by datalinkage - is there the option to do this for the 1 year variable and if so why was this not done? I think in this I am thinking about whether this variable represents the 'actual' costs associated with death, as compared to whether it is a cost associated with 'expecting death' in the next year? I would also like to see potentially trials of different models from the GLM family compared with the GLM with (these could be mentioned in the text and presented as an appendix) to check for the robustness of the results across different models. I find the discussion provides really clear and tangible implications for policy, and excellent suggestions for future research. I do hope that the authors go on and undertake some of this further research.Is the rationale for developing the new method (or application) clearly explained?YesIs the description of the method technically sound?YesAre the conclusions about the method and its performance adequately supported by the findings presented in the article?YesIf any results are presented, are all the source data underlying the results available to ensure full reproducibility?YesAre sufficient details provided to allow replication of the method development and its use by others?YesReviewer Expertise:Dementia care, health economics, health services researchI confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. This is an exceptionally careful and well-executed paper studying questions that are hugely challenging yet important to answer. The authors have written a forthright manuscript that details lucidly what they have done—or, in some instances, have not done—and why. Many assumptions and decisions must be made in such a micro-costing exercise and the authors have gone to great pains to be clear about the nature of these assumptions and decisions. Micro-costing exercises will generally be an amalgam of art and science and the authors have succeeded in making clear where the science ends and the art (i.e. assumptions) begins. Given existing data it is difficult to imagine gleaning better insights into the nature and composition of healthcare costs among older people in Ireland than by learning from the results offered in this paper. The authors have evidently been excruciatingly careful in assembling their sample and have deployed what appear to be suitable empirical methods in their analyses.The data from the EOLI are intriguing and permit insights into healthcare resource use in older populations that are often not available in other surveys. In light of the magnitudes of the costs in the EOLI subsample I might hope that the authors could shed some further light on its nature in the Methods section. It would be interesting to know, for instance, how well or poorly the demographics of the decedents in EOLI sample correspond to those in the main sample before weighting (the weighted results appearing in table 4), with concerns about possible non-random selection into the EOLI sample being prominent.Do the authors have access to any supplemental data that would help readers estimate the extent to which the costs associated with the omitted categories (table 2) are large or small or trivial as compared with costs in those categories that are included?On page 6 appears: "Any sample-eligible CAPI or EOLI that was missing two or more of these four categories was flagged and removed from primary analysis as having insufficient outcome data. For those interviews missing one or fewer of these categories, and or missing any other categories of health care frequency, we imputed age- and sex-adjusted medians." In general I am not a fan of imputation methods and in this instance would be keen to know how the results presented in the paper would differ from those obtained if subjects having missing data for any category were deleted from the analytical sample.I am curious to know why the authors (p. 7) elected to use a power-link in their GLM analysis rather than the more-standard log-link. They allude to "information criteria" but a bit more detail might be helpful.On page 11 appears: "The composition of costs among non-zero users, across 20 quantiles of total health care costs in the pooled sample, is presented in Figure 5." Is this the only analysis in the paper that drops the zero-users or are there others? Either way the rationales for dropping zero-users in figure 5 might be made clearer.Finally it is both obvious and defensible why the authors have undertaken a micro-costing analysis: Assigning currency values to different forms of healthcare utilization permits aggregation of the different categories of healthcare utilization. That being said I often suggest—and indeed am suggesting here—that in addition to their spending outcomes the authors also consider the merits of studying utilization data themselves as outcomes of potential interest. Such analyses would obviously need to be conducted category-by-category. But what such analyses can reveal is the extent to which variations in healthcare resource utilization are due to prices or quantities. This is perhaps a paper for another day but one whose merits might be given some thought. I would like to pose several questions, more in the spirit of clarification than criticism.Is the rationale for developing the new method (or application) clearly explained?YesIs the description of the method technically sound?YesAre the conclusions about the method and its performance adequately supported by the findings presented in the article?YesIf any results are presented, are all the source data underlying the results available to ensure full reproducibility?No source data requiredAre sufficient details provided to allow replication of the method development and its use by others?YesReviewer Expertise:health economicsI confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Overview: I would like to commend the authors on this excellent paper, which is the most comprehensive descriptive analysis of healthcare costs for older people that I have encountered in the Irish context. More generally, I believe that it makes a significant contribution to the literature relating to the nature, distribution, composition, and correlates of Irish healthcare costs. In particular, it covers a wide-ranging list of healthcare resources in its estimation of formal healthcare costs. This is far from a minor achievement given the historical paucity of available healthcare resource use and unit cost data in Ireland. The costing approaches applied to value each individual healthcare resource item appears to be pragmatically appropriate. In addition, the study includes an explicit consideration of proximity to death in its examination of the correlates of healthcare cost. This variable has been shown to be a significant driver of costs in the literature, but it is often omitted from applied papers of this kind given the difficulty in acquiring the data. To this end, the research team took on a number of additional and challenging lines of data collection, merging, and analysis, and the final paper is the richer for it. A minor criticism, which is related to these additional efforts in compiling the data, is that the paper does not flow as it might in places, but this is understandable given the need to describe the multiple methods employed. The authors employ appropriate statistical techniques for the multivariate analysis, although in places, they need to provide additional information. The extrapolation approach also appears to be appropriate. The authors clearly consider the limitations of their analysis, undertake sensitivity analysis, and highlight potential lines for future research. Finally, while the major contributions of the paper are descriptive in nature, this is not a flaw or limitation of the work. Indeed, this paper may prove to be a starting point in a future body of research that considers issues of causality as they relate to formal healthcare costs for older people in Ireland. This future work may be more impactful for policy and practice decision-making, but this paper provides a significant step forward. Congratulations to all of the team involved on the project. I have included a number of suggestions below that the authors may wish to consider, as I believe they will go to further enhance the paper. I look forward to reading the authors’ responses and to reviewing the next iteration of the paper.I should note that I was unable to access the appendices (which may well be a problem of my own making). However, I do not believe this is detrimental to my review and recommendation overall. I am aware that some of my suggestions may be addressed in the appendices.Key Points:Main Contribution: The authors make a significant contribution in respect of the nature, distribution, composition and correlates of healthcare costs for older people in Ireland. This is clearly articulated in the paper.  Main Multivariate Regression Result: The authors highlight the both theIADLs andproximity to death findings as the key results from the multivariate regression analysis, given their economic and statistical significance. I wonder would the authors be better served by placing the emphasis on theproximity to death variable? My rationale is as follows, but I would be happy to hear a counter argument from the authors if they wish to make one.predisposing, enabling, need, andprior use, is appropriate and the authors faced the not-insignificant challenge of selecting a list of variables from those available within TILDA to represent these categories. In particular, I would imagine the selection of variables to represent the ‘need’ category was challenging, and the authors went with a limited, informed and sensible set.The authors adopt the Andersen framework to inform their choice of independent variables for the multivariate regression analysis. This approach, which categorises variables asThe selection of IADLs variable proved to be an important one. This is interesting not just of its impact on the formal care system, but also for the potential and related impacts on the informal care system. That said, I do wonder if the inclusion of additional or alternative ‘need’ variables would impact on the economic and statistical significance of this variable? Indeed, the choice of GLM family and link function may also influence this outcome (see below for further comments). My suggestion would be to frame the IADLs finding in the context of the Andersen ‘need’ category: i.e. it indicates that ‘need’ factors appear to be strong predictors of healthcare costs and not to emphasise it individually as the key take-away. proximity to death, and I think this is the key independent variable and should be interpreted as the key finding from the multivariate regression analysis. It proves to be economically and statistically significant, but it also highlights the importance of this variable in such costing studies, and the need for researchers to try to capture it.The authors also include a variable forproximity to death may prove to be a cleaner and more robust foundation stone for the paper.    This is not to say the IADLs and other results should not be highlighted, but I think the focus on  Summary Data for Healthcare Resource Usage and Individual Healthcare Costs: As the authors suggest, their findings will be of interest to and use for future research projects. To this end, it would be helpful to report summary statistics  for individual resource utilisation  and individual resource costs. These could be reported in the appendix .  Descriptive and Distributional Analysis: The authors present descriptive and distributional summary statistics of the total healthcare cost variable in tabular and graphical forms. As the authors suggest, these findings will be of interest to and use for future research projects. To this end, I think it would be valuable to include standard deviations and 95% confidence intervals for each of the mean cost estimates for the CAPI, EOLI, ALL samples in Table 5. The authors could also include 95% confidence intervals to Figure 2.  Compositional Analysis: The authors provide an informative graphical analysis of the distribution and composition of costs in Figures 3, 4 and 5. I wonder is there a way to include additional information to aid interpretation in the form of % of total figures for each decile  of Figure 4, and € values for Figure 5? As mentioned, I could not access the appendix and the appendix may well be the best place for this data, but I think this is valuable information.  Multivariate Regression Analysis: The authors employ a GLM regression framework for the multivariate analyses. This is a standard and appropriate method for the statistical analysis of healthcare cost data, which can exhibit extreme skew and kurtosis in distribution. I think the authors need to provide additional information for the reader on the multivariate regression analysis (some of which may be presented in the appendix). In particular:The authors should provide a rationale for the choice of GLM for the analysis of cost data.GLM regression requires the selection of a ‘family’ and ‘link’ function. The authors used ‘information criteria’ to inform these choices. My understanding is that this approach is not quite correct. That is, the selection of ‘family’ is typically informed by a Modified Park test, and ‘link’ function by a combination of a Pearson correlation test, a Pregibon link test and a Modified Hosmer and Lemeshow test. I think that the authors should undertake these tests to make their ‘family’ and ‘link’ choices, and provide these results along with the regression results in Table 7 (or in the footnotes of Table 7).The authors may wish to add further sensitivity analyses, which assume different ‘family’ and ‘link’ combinations, as results may be sensitive to this choice.The approach adopted for the generation of standard errors in the regression analysis is also of interest. For example, given the nature of the TILDA dataset and how it is compiled, I wonder if there are issues relating to ‘clustering’ or ‘hierarchical data’ that need to be considered in the regression analysis? This information should be included and again, perhaps this is something that could be tested and explored in sensitivity analysis.  Unconditional Quantile Regression: Given that the authors rightly highlight the importance of the distribution of the estimated healthcare costs, and describe these results in the descriptive parts of the paper, I wonder if the authors considered conducting quantile regression to estimate and explore the associations between the independent variables and healthcare costs across the full distribution: e.g. deciles of the total healthcare cost distribution? While this would constitute additional analysis, I think it would provide complementary findings to those reported for research questions 1 and 2.Appendix: Please make these resources availableMinor Points:Abstract:Methods: replace the term ‘main dataset’; use the term ‘2022 prices’;Concussions:align more closely toConclusions section in the Discussion.Methods:Table 2 and Figure 1 could be moved to the appendix.Provide more specific details on the imputation method employed (e.g. multiple imputation?).Results:Update results to reflect suggested changes.Update tables to reflect suggested changes.Add further sensitivity analyses as suggested.Discussion:proximity to death variable should be the key variable of interest.Update as suggested:The Andersen framework should be used to discuss the implications of the other regression findings.Is the rationale for developing the new method (or application) clearly explained?YesIs the description of the method technically sound?YesAre the conclusions about the method and its performance adequately supported by the findings presented in the article?YesIf any results are presented, are all the source data underlying the results available to ensure full reproducibility?No source data requiredAre sufficient details provided to allow replication of the method development and its use by others?PartlyReviewer Expertise:Health EconomicsI confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.


Eonycteris spelaea). This report describes an investigation of MuV-specific data originally collected as part of a longitudinal virome study of apparently healthy, captive lesser dawn bats in Southeast Asia (BioProject ID PRJNA561193) which was the first report of a MuV-like virus, named dawn bat paramyxovirus (DbPV), in bats outside of Africa. More in-depth analysis of these original RNA sequences in the current report reveals that the new DbPV genome shares only 86% amino acid identity with the RNA-dependent RNA polymerase of its closest relative, the African bat-borne mumps virus (AbMuV). While there is no obvious immediate cause for concern, it is important to continue investigating and monitoring bat-borne MuVs to determine the risk of human infection.The importance of genomic surveillance on emerging diseases continues to be highlighted with the ongoing SARS-CoV-2 pandemic. Here, we present an analysis of a new bat-borne mumps virus (MuV) in a captive colony of lesser dawn bats ( Eonycteris spelaea) .,24.23,24While the reads and contigs from one body swab collected from bat 7633EDB in April 2016 covered 76.5% of the DbPV scaffolded composite genome with an average depth of 9.6x , this saPteropus alecto kidney) cells. The urine sample was collected 18 months following the collection of the last DbPV-positive swab from bat 7633EDB. In brief, 5 µL of urine collected in March 2019 from Bat 7633EDB was inoculated onto 1 × 106 cells in 500 µL of Dulbecco’s Modified Eagle Medium  in a 6-well plate. The cells were incubated at 37 °C with 5% CO2 for 2 h. Following incubation, the inoculum was replaced with 1 mL of DMEM . The cells were incubated at 37 °C with 5% CO2 and observed daily for the presence of a cytopathic effect (CPE). Supernatant from PaKi cells was serially passaged three times. Although CPE was observed in PaKi cells in passage 3, paramyxovirus sequences could not be detected by PCR. Due to the exhaustion of primary samples from 7633EDB, we were not able to ascertain whether another bat cell type might have been more permissive for DbPV, and we were unable to isolate this virus.Once the composite genome of the DbPV was identified and it was clear that most MuV-like reads were detected in a swab from bat 7633EDB, isolation of the DbPV was attempted in PaKi . This is consistent with similar studies that report the detection of new bat viruses that were characterized as much as possible but were not finished genomes due to sample limitations . While wPteropus alecto furin and proteases from other mammals [While there is no obvious evidence that the detection of a new bat-borne MuV is an immediate public health concern, it is important to continue to investigate bat-borne MuVs to determine the risk of human infection. By characterizing new viruses that share common ancestors to human pathogens through the lens of One Health, we gain a greater understanding of the differences among viruses that have evolved to become pathogens and those that pose no threat to humans . Our dis mammals , we coul mammals ,25,26.With the full-length genome sequence, the virus could potentially be rescued by reverse genetics and the biology of the virus extensively characterized, as was recently accomplished for a novel bat paramyxovirus, myotis bat morbillivirus (MBaMV) . Using t


Flaviviridae, Togaviridae, Phenuviridae, Peribunyaviridae, Reoviridae, Asfarviridae, Rhabdoviridae, Orthomyxoviridae and Poxviridae families. It is thought that new world arboviruses such as yellow fever virus emerged in the 16th century due to the slave trade from Africa to America. Severe disease-causing viruses in humans include Japanese encephalitis virus (JEV), yellow fever virus (YFV), dengue virus (DENV), West Nile virus (WNV), Zika virus (ZIKV), Crimean–Congo hemorrhagic fever virus (CCHFV), severe fever with thrombocytopenia syndrome virus (SFTSV) and Rift Valley fever virus (RVFV). Numerous methods have been developed to detect the presence of these pathogens in clinical samples, including enzyme-linked immunosorbent assays (ELISAs), lateral flow assays (LFAs) and reverse transcriptase–polymerase chain reaction (RT-PCR). Most of these assays are performed in centralized laboratories due to the need for specialized equipment, such as PCR thermal cyclers and dedicated infrastructure. More recently, molecular methods have been developed which can be performed at a constant temperature, termed isothermal amplification, negating the need for expensive thermal cycling equipment. In most cases, isothermal amplification can now be carried out in as little as 5–20 min. These methods can potentially be used as inexpensive point of care (POC) tests and in-field deployable applications, thus decentralizing the molecular diagnosis of arboviral disease. This review focuses on the latest developments in isothermal amplification technology and detection techniques that have been applied to arboviral diagnostics and highlights future applications of these new technologies.Arboviruses are a diverse family of vector-borne pathogens that include members of the  Arboviral disease in humans can range from asymptomatic to life-threatening conditions, such as hemorrhagic fevers and encephalitis. Arboviruses are distributed worldwide  with somOver the last 150 years, a number of notable epidemics and pandemics have occurred. The influenza A H1N1 pandemic of 1918–1920 resulted in an estimated 50–100 million deaths, the HIV pandemic beginning in the 1980s has affected over 40 million people, and the SARS-CoV-1 epidemic of 1983 and the swine flu pandemic starting in 2009 have caused significant morbidity and mortality. The Ebola outbreaks of 2014–2016 resulted in over 11,000 fatalities, and the Zika outbreaks in 2015–2016 caused a substantial burden of disease globally [Flaviviridae family, such as Spondweni virus (SPOV), Usutu virus (USUV), Ilheus virus (ILHV), Rocio virus (ROCV) and Wesselsbron virus (WSLV), or one of the tick-borne family of flaviviruses could emerge into the human population and cause significant health concerns [Phleboviruses to emerge as an important human threat due to its continued geographical spread [Aedes, and emerge as a more significant human pathogen [Arboviruses will continue to emerge and re-emerge over time, a notable example being Zika virus which, until recent epidemics, was considered a virus that caused relatively mild infection in humans, but it has since been shown to cause microcephaly and Guillain–Barré syndrome . It has concerns . Rift Val spread . Alphavipathogen . Many arFlaviviridae, Togaviridae, Phenuviridae, Peribunyaviridae, Reoviridae, Rhabdoviridae, Orthomyxoviridae and Gammaentomopoxvirus families. The only significant DNA-containing virus is the African swine fever virus that belongs to the Asfarviridae family. The 1992 International Catalogue of Arboviruses registered 535 species of virus belonging to 14 families; however, this number is continually increasing due to improvements in isolation and molecular methods for virus discovery and surveillance [Arboviruses consist of a diverse family of pathogens that can infect a wide range of animals and humans. Arboviruses are predominantly positive or negative single-stranded or double-stranded RNA containing viruses from the eillance .Arboviruses must infect their insect vector prior to transmission to a susceptible host . ArbovirA significant number of arboviruses cause human disease and the morbidity and mortality associated with infection cause a substantial social and economic burden when outbreaks occur. Traditionally, diagnosis of arbovirus infection has been performed by serology. Serological methods include direct test involving virus detection and indirect tests targeting antibodies produced during viral infection. One of the most common techniques used for arboviral diagnosis is enzyme-linked immunosorbent assays, such as (ELISA)-IgM capture, which targets antibodies produced early in response to infection and indirect IgG assays. However, due to cross-reactivity with closely related viruses, confirmatory assays, such as the plaque reduction neutralization test (PRNT), which quantifies neutralizing antibodies in serum or CSF samples, are required. Other techniques, such as immune magnetic agglutination assays and rapid diagnostic tests (RDTs), including lateral flow assays, have been used extensively in arbovirus diagnostics . SerologAlthough serological methods are generally quick and inexpensive, this technology has several drawbacks. Cross-reactivity between related viruses can cause false positive results and this has been noted with Zika, dengue, West Nile virus and Japanese encephalitis virus . SecondlNGS allows for unbiased sequencing of clinical samples and vector pools, which can be helpful in surveillance programs where an unknown pathogen is suspected. Viral metatranscriptomic next-generation sequencing (mNGS) techniques have been used to screen vectors for arboviruses in Australia during adverse weather events, favoring vector activity detecting Ross River virus (RRV), Sindbis virus (SINV), Trubanaman virus (TRUV), Umatilla virus (UMAV), and Wongorr virus (WGRV). The use of NGS in this situation enabled the discovery of multiple virus types in the pools, which would have been more challenging using a targeted RT-PCR approach .Orthomyxoviridae obtained from different geographical areas, resulting in the classification and separation of these viruses into two major clades, namely the Thogoto-like and Dhori-like viruses [This method has been used to identify arboviruses in patients with undiagnosed encephalitis/meningitis and in patients with symptoms consistent with arboviral infection but yielding inconclusive RT-PCR data ,44,45,46 viruses .In a survey of acute febrile illness in Columbia, samples were initially screened by RT-PCR and rapid tests for DENV, ZIKV and CHIKV, followed by testing negative samples with mNGS, resulting in the discovery of undetected Oropouche virus (OROV) circulation. Interestingly, the study confirmed OROV as an emerging pathogen in the area using RT-PCR, finding that virus activity was associated with specific locations, climate and clinical symptoms . mNGS haWhile mNGS is primarily a tool used to monitor the presence of viruses from clinical and environmental samples, which could result in significant public health threats, methods have been developed for the enrichment of specific virus sequences in samples such as vector pools. Viral specific enrichment was successfully used to characterize Crimean-Congo hemorrhagic fever virus (CCHFV) and Jingmen virus (JMTV) in a pooled tick sample  and in aAlthough the costs and labor involved in NGS protocols has dropped dramatically, NGS is still relatively costly, requires extensive equipment and computer networks, as well as highly trained staff, and is unsuitable for use in POC applications and in resource-limited settings.RT-PCR has been used for many years in arbovirus diagnostics and has the advantage of increased sensitivity and specificity compared to serological and NGS methods without prior viral enrichment strategies. RT-PCR has become the gold standard for diagnosing many viral diseases, including respiratory, gastrointestinal, sexually transmitted, meningitis and tropical diseases. RT-PCR has been used to diagnose a wide range of both human and animal arboviral infections.TM form consisting of adenine, thymine and guanine . After conversion to a 3base™, genome members of viral families become more similar at the nucleic acid level  applications, mainly due to the requirement for thermal cycling equipment, infrastructure and trained staff . Novel Rid level . Thus, p® , Sal6830 MicroGEM  and the Cobas™ Liat™ system . These systems are all-in-one devices that simultaneously extract and purify nucleic acids from clinical samples, perform PCR amplification and yield a positive/negative read-out at the end of the reaction. Typical run times are 30–60 min, and sensitivity can range from as low as 12 to 6.4 × 103 copies/mL [To produce RT-PCR assays compatible with POC applications, several sample-to-answer devices have been developed, including the GenXpert™ system , FilmArrayopies/mL . Veredus7-fold amplification efficiency within 60–120 min. Recently, novel technology was developed to amplify RNA in 10–13 min. This method, nicking endonuclease amplification reaction (NEAR), was developed by Abbott laboratories and used in a sample-to-answer device that detected the presence of SARS-CoV-2 in nasopharyngeal swabs. The reaction is carried out in a small lightweight portable instrument requiring minimal hands-on time and training. The technique is capable of a 109 amplification efficiency of starting material in less than 15 min, making it ideal for field-based or POC applications [Walker et al. developed the original strand displacement amplification reaction in 1992. This method relies on generating a hemi-modified restriction site on one DNA strand and the 5′-3′ exonuclease activity of a DNA polymerase. A specific restriction enzyme nicks the DNA, and the strand is then displaced and replaced with a newly copied strand via the strand displacement/polymerase activity of the DNA polymerase. The reaction continues at a single temperature, without the requirement of thermal cycling, leading to exponential amplification of the DNA or RNA target . The metications .6–109 amplification efficiency in 60–90 min. The NASBA reaction produces RNA molecules complementary to the original target RNA sequence. NASBA amplification products can be detected using gel electrophoresis, fluorescence probes with real-time NASBA and colorimetric techniques, such as NASBA-ELISA.One of the first technologies developed for the isothermal amplification of nucleic acids, nucleic acid sequence-based amplification (NASBA), was developed in 1991. This method employs a three-enzymes approach using AMV reverse transcriptase, RNase H and T7 RNA polymerase to efficiently amplify RNA target sequences . Initial6-fold amplification efficiency in 30–90 min. HDA has been adapted for colorimetric, fluorescent and lateral flow read-outs, making it applicable to POC and in-field applications [M. tuberculosis [T. vaginalis [HDA is an isothermal amplification technique that relies on the endogenous properties of a helicase enzyme, and several accessory proteins to unwind double-stranded DNA to generate single-stranded DNA templates for primer binding. HDA is carried out at a constant temperature, removing the requirement of thermal cycling equipment required by conventional PCR methods. After primer binding, the separated strands are copied using a DNA polymerase, resulting in exponential amplification of the target sequence  as shownications . HDA hasications , SARS-Coications , M. tuberculosis , T. vagiaginalis  and yellaginalis .9 amplification efficiency from low copy starting material in as little as 60 min [The LAMP technique first described in 2000  is a mets 60 min . Typicals 60 min . The useLAMP is the one of the most common isothermal methods used as the basis for molecular POC devices ,70. LAMP® colorimetric master mix . Fluorescent probes have been developed for the detection of positive samples, opening the possibility of multiplexing the LAMP reaction [A large number of read-out methods have been developed to visualize the LAMP reaction. The first of these was by turbidity which, as a result of the large amounts of amplification product generated during the reaction, results in the accumulation of pyrophosphate, which turns the reaction cloudy and can be observed by the naked eye . Colorimreaction .One notable method that can be coupled with the LAMP reaction is the CRISPR-Cas12 enzyme complex see , which cThe RPA method was described in 2006 as a method that used a recombinase-primer complex to induce strand exchange at primer recognition sites. This leads to a destabilization of the DNA duplex, and the interaction with single-stranded binding proteins results in strand displacement. DNA is then copied using strand displacing/DNA polymerase enzyme Bst DNA polymerase I, which is also used in the LAMP reaction  as shown7–108 amplification efficiency in as little as 60 min. Recent improvements to the technology have resulted in amplification times as low as 3–15 min, making it suitable for rapid POC and mobile field deployable use [F. tularensis [Like other isothermal methodologies, no thermal cycling is required to denature the copied DNA strands. RPA is carried out at temperatures between 37 °C and 42 °C and has been shown to result in a 10able use . Like LAable use . RPA hasable use , F. tulalarensis , HIV [84larensis  and yelllarensis .RPA has been coupled with the CRISPR-Cas12 system and a lateral flow or fluorescent read-out for the diagnosis of West Nile virus, Zika and all four dengue subtypes .As previously mentioned, RT-PCR methods have become the gold standard for the detection of many infectious agents. RT-PCR is the most used molecular method for the diagnosis of arboviral disease. LAMP, RPA, SDA and NASBA have all been applied to arbovirus diagnoses and have the advantage of being applicable in resource-limited and POC settings.https://lamp.neb.com/#!/ (accessed 15 April 2023), which greatly simplifies the process.Each INAAT method performs amplification in a unique manner that ultimately results in exponential amplification of the nucleic acid sequences of interest. NASBA is the only method that is specific for RNA sequences, while the other methods can be used with both RNA and DNA templates. NASBA, HDA and RPA, similar to RT-PCR, require only two primers for the amplification of target sequences, whereas SDA requires a further two outer (bump) primers to initiate the reaction. The LAMP method is the most complex, requiring 4–6 primers, which can be complicated to design without specific design programs. LAMP primers can be designed using publicly available sites such as INAAT has the advantage that amplification can be carried out very rapidly with common times of 5–30 min using the LAMP, RPA/RAA and NEAR methods. Rapid amplification is important in POC and for field deployable use; thus, these methods are an ideal choice for such applications. In addition, most of these technologies have been adapted so they can be coupled with relatively inexpensive read-outs such as LFDs for use in resource-limited settings. One drawback of some INAAT is that due to the large amounts of product produced during the reaction, non-specific amplification can occur especially with LAMP. The SDA technique, although an isothermal method, requires the DNA strands to be denatured thermally at the beginning of the reaction and has the lowest sensitivity of the methods described.The World Health Organization (WHO) has released guidelines that define the desirable properties of POC devices for resource-limited regions. These guidelines led to the ASSURED and (RE)ASSURED criteria ,88. To qINAAT techniques are ideally suited for adaptation to POC and field deployable settings . A wide Lab on a chip (LOC) devices perform all steps from sample purification to detection and result read-out. These devices are usually fully integrated, relying on microfluidics with all reagents for sample testing loaded onboard the instrument or in cartridges that can be easily inserted into the platform . LOC dev®MDX from Diasorin, which has been used for the detection of Bordetella, Clostridium difficile, Influenza, SARS-CoV-2, CMV, HSV, VZV and dengue . A INAAT LOAD-based device have been designed for the detection of highly pathogenic avian influenza virus using a portable closed computer-controlled device [Plasmodium falciparum, P. vivax, P. ovale, P. malariae; Salmonella enterica Typhi, S. enterica Paratyphi A, Streptococcus pneumoniae, chikungunya virus, dengue 1–4 and Zika virus. Hands-on time is around 5 min to add the sample into the cartridge with results generated in around 2 h using lyophilized amplification reagents, removing the need for cold chain logistics. The cost of the system has not been disclosed but is expected to be cheaper than current approaches [Like LOC devices, LOAD instruments perform all steps of sample preparation, amplification and result read-out. The difference between the two methods is that LOAD utilizes centrifugal forces, which can be coupled with microfluidics  and deted device . A LOAD d device ,96 More proaches .The original method described in 2007 is a simple and inexpensive method that involves patterning paper with millimeter-sized channels . TypicalµPADs require no power source, are inexpensive to produce and are easy to transport; thus, they an ideal alternative to traditional techniques. µPADs have been adapted for the detection of Zika using NS1 protein and chikungunya viruses using a laser cut glass fiber µPAD that was capable of the detection of chikungunya-specific IgM in less than 10 min ,100. A µLateral flow devices (LFDs) have been used in POC applications for many years and are probably the most common platforms used for the development of such assays. Their extensive adoption in POC development is a direct result of their low cost, ease of use, quicker results and simple interpretation. Applications of LFDs include heart disease , monitorT. vaginalis [Mycobacterium tuberculosis [Although LFDs have been extensively used for many years, they generally lack the sensitivity and specificity of molecular methods, such as RT-PCR and INAAT. By incorporation of streptavidin and fluorescein labeled primers into the amplification reaction, lateral flow detection coupled with isothermal amplification has been applied to the HDA reaction for the detection of aginalis  and biotaginalis . LFDs harculosis , Influenrculosis , Japanesrculosis , Zika [1rculosis , SARS-Corculosis , Monkeyprculosis , Africanrculosis  and Hearrculosis .CRISPR-Cas technology for pathogen detection relies on the inherent properties of CRISPR proteins. Clustered regularly interspaced short palindromic repeats (CRISPRs) are a family of proteins that provide an immune response in bacteria, which degrades foreign DNA guided by sequence specific RNA (crRNA) molecules . The CRIThese assays have been paired with INAAT, such as LAMP, RPA, HDA and SDA, to produce assays that are cheap, deployable in the field and suitable for POC applications. To date, the CRISPR-Cas-12/13 systems have been used for the detection of several important arboviruses, including dengue, West Nile virus and Zika , JapanesNGS methods have the advantage that they can be used in viral surveillance programs to provide an unbiased screenshot, detecting any virus that is present in the clinical or environmental sample, which is very difficult to achieve using conventional molecular diagnostic techniques. NGS methods have a high level of sensitivity, which is again useful in viral surveillance programs to identify emerging pathogens in clinical samples. The major drawbacks of NGS are the high cost, dedicated infrastructure requirements and the reliance on highly trained technicians. Unlike serology, NGS technology is not applicable to POC and use in resource-limited settings.RT-PCR is also a well-established technology that is highly sensitive and specific for the detection of specific pathogens in clinical and environmental samples. The technique does not have the high costs associated with NGS and has been adopted worldwide in hospital and pathology laboratories as a rapid method for pathogen detection. The drawbacks of RT-PCR include reliance on thermal cycling equipment and the requirement of trained staff to perform the assays. In general, RT-PCR is not compatible with POC, field deployable applications and resource-limited settings due to the expense of reagents and dependence on thermal cycling equipment.INAAT is a newer technology that until recently has not achieved the widespread adoption of methods such as serology and RT-PCR. Like RT-PCR, INAAT has been shown to have a high level of sensitivity and specificity for the detection of target sequences. The method is rapid and can deliver results in less than 10 min in some cases. Unlike RT-PCR, the technology does not rely on a thermal cycler to achieve amplification; thus, it is more suited to POC and field deployable applications. INAAT has been combined with simple read-outs, such as naked eye visualization, LFD and other POC devices, resulting in assays that can be used in resource-limited settings.The vast majority of arboviruses are endemic in resource-limited countries and the burden of disease in those regions contributes to high levels of morbidity and mortality. Dengue is ubiquitous throughout the tropics, and it has been estimated that some 1–2 billion people are at risk of disease . Over thAs a result of the lack of specific vaccines and anti-viral drugs for many arboviral infections, early detection is critical to enable preventative measures during outbreaks of disease, such as vector control strategies, and to guide patient treatment regimens . In addiUnder the ASSURED guidelines for resource-limited and POC settings, an assay should be Affordable, Sensitive, Specific, User friendly, Rapid and robust, Equipment free or simple and Deliverable. Although RDTs are simple to perform and relatively inexpensive, they lack specificity and sensitivity when compared to molecular diagnostic approaches. INAAT technologies conform to the ASSURED caveats; thus, they are ideal for use in the diagnosis of arboviral disease. The main advantages of these techniques over the more established RT-PCR methods are that they do not require expensive thermal cycling equipment, can be coupled to inexpensive read-outs such as lateral flow or naked eye detection, are more resistant to inhibitors that can be present in the primary sample and are generally faster than RT-PCR with time to results as little as 5 min.Over the past 30 years, many INAAT technologies have been described, including NASBA, SDA, HDA and whole-genome amplification. Perhaps the two most widely adopted techniques are the LAMP and RPA methods which have been applied to a wide range of arboviral diseases. Both can generate results in less than 15 min and have been coupled with a range of simple detection methods, including LFDs, colorimetric, CRISPR-Cas12 and fluorescent-based technologies. Research is ongoing to produce enzymes that are capable of ambient temperature amplification, removing the requirement of a heat source and the lyophilization of reagents, eliminating the necessity of cold chain logistics and prolonging the viability of test kits. These improvements will greatly reduce costs involved and the ease with which they can be delivered and used in resource-limited settings and in the field. Lab on a chip and lab on a disc devices continue to be developed for POC and field deployable applications, thus minimizing the requirement of dedicated facilities and associated expenses to run them. With rapid developments in nanotechnology, microfluidics, miniaturization, 3D printing, prefabrication of components and the reduction in costs associated with these technologies, several devices have been constructed that only require the end user to add the primary sample. The simplicity of such devices mean that users will require limited formal training to perform assays removing the need for highly trained technicians and increasing the possibility of more convenient POC approaches.During the SARS-CoV-2 pandemic, a plethora of research was carried out using the INAAT method for disease diagnosis. This greatly accelerated the acceptance of INAAT methods for use in routine molecular diagnostics. Many clinical trials were conducted comparing the sensitivity and specificity of INAAT to RT-PCR showing that these methods could be, in some cases, just as sensitive as more established techniques. Over the coming years, it is expected that new sample-to-answer INAAT-based devices will be produced that are cheaper to manufacture, easier to use, applicable as POC and deployable for the diagnosis of arboviral disease.


The application of CRISPR/Cas9 to improve genome engineering efficiency for large dsDNA viruses has been extensively described, but a robust and versatile method for high-throughput generation of marker-free recombinants for a desired locus has not yet been reported. Cytoplasmic-replicating viruses use their own repair enzymes for homologous recombination, while nuclear-replicating viruses use the host repair machinery. This is translated into a wide range of Cas9-induced homologous recombination efficiencies, depending on the virus replication compartment and viral/host repair machinery characteristics and accessibility. However, the use of Cas9 as a selection agent to target parental virus genomes robustly improves the selection of desired recombinants across large dsDNA viruses. We used ectromelia virus (ECTV) and herpes simplex virus (HSV) type 1 and 2 to optimize a CRISPR/Cas9 method that can be used versatilely for efficient genome editing and selection of both cytoplasmic- and nuclear-replicating viruses. We performed a genome-wide genetic variant analysis of mutations located at predicted off-target sequences for 20 different recombinants, showing off-target-free accuracy by deep sequencing. Our results support this optimized method as an efficient, accurate and versatile approach to enhance the two critical factors of high-throughput viral genome engineering: generation and colour-based selection of recombinants. This application of CRISPR/Cas9 reduces the time and labour for screening of desired recombinants, allowing for high-throughput generation of large collections of mutant dsDNA viruses for a desired locus, optimally in less than 2 weeks. Efficient, versatile and reliable genome engineering of large double-stranded DNA (dsDNA) viruses is instrumental for pathogenesis and virulence studies, as well as for vaccine vector and oncolytic virus generation. Genome engineering by CRISPR/Cas9 has been shown in recent years to be a powerful platform for the generation of recombinant dsDNA viruses, such as vaccinia virus (VACV), herpesviruses and African swine fever virus (ASFV) . The CasThe two key steps in dsDNA virus genome engineering by CRISPR/Cas9 are the initial generation of recombinants and the selection of these from a background of parental virus. Initial recombination means to increase rates of HDR, but the utility of CRISPR/Cas9 as an efficient selection tool also lies in its ability to cut parental genomes, leaving most of them unable to propagate . While CHere we report an optimized protocol, termed as the two-progenies method, for generating large collections of recombinants by CRISPR/Cas9 for cytoplasmic- and nuclear-replicating dsDNA viruses. We used ECTV and HSV type 1 and 2 as models of cytoplasmic- and nuclear-replicating viruses, respectively. Our method exploits both the generation and selection of recombinant steps to maximize efficiency and reduce the time and labour involved in screening desired recombinants, allowing for off-target-free high-throughput generation of large collections of mutant dsDNA viruses for a desired locus, optimally in less than 2 weeks.l-glutamine and antibiotics . All mammalian cells were cultured at 37 °C in a CO2-buffered cell incubator and tested regularly for mycoplasma contaminations by PCR with primers Myco_Fw and Myco_Rv . The source of the ECTV Naval isolate (accession no. KJ563295) was described previously [293T (ATCC CRL-3216), BSC-1 (ATCC CCL-26) and Vero cells (ATCC CCL-81) were cultured in Dulbecco's Modified Eagle Medium (DMEM) with 5 % (v/v) foetal bovine serum (FBS), 2 mM eviously . HSV-1 seviously ) and HSVeviously ) were kiUL26-27 Red and HSV-1 Red ∆gG-eGFP have not been described, while HSV-2 UL26-27 Red and HSV-2 Red ∆gG-eGFP have been previously described [US4 locus encoding for glycoprotein G (gG) was replaced by an eGFP cassette in HSV-1/2 Red ∆gG-eGFP . For viral stock production, two confluent 150 mm plates of BSC-1 (for ECTV) or Vero cells (for HSV) were infected at a multiplicity of infection (m.o.i.) of 0.1 plaque-forming units (p.f.u.)/cell and the viral inoculum was removed 2 h post-infection (p.i.). Cells and supernatant were collected when 90–100 % of cytopathic effect was reached and cellular debris was concentrated by centrifugation at 300 g for 5 min. After discarding the supernatants, 0.5 ml of 10 mM Tris/HCl pH 8.8 was added to the pellet, with freezing/thawing three times. The pellet was lysed with a 23 gauge needle and 1 ml syringe 30 times, releasing the intracellular virus. After 10 min of centrifugation at 500 g, the supernatant was collected. This was done another two times. All of the procedures were performed at 4 °C using pre-cooled instrumentation. The supernatants were combined, aliquoted, frozen and titrated twice by serial dilutions in duplicate BSC-1 (for ECTV) or Vero cells (for HSV) monolayers with 2×105 cells/well in 12-well plates seeded the day before. At 2 h p.i., viral inoculum was removed and cells were overlaid with 1 ml of semi-solid carboxymethyl cellulose (1.5 % w/v) 2 % FBS DMEM. Cells were fixed in 10 % formaldehyde at 48 h p.i. and plaques were stained with 0.1 % (w/v) crystal violet for virus plaque counting.Recombinant viruses HSV-1 escribed . Those fAll sequence references correspond to the ECTV Naval isolate complete genome (KJ563295), HSV-1 strain SC16 partial genome (KX946970) and HSV-2 strain 333 complete genome (LS480640). All primer sequences can be found in Table S1.195P pseudogene were amplified from the ECTV Naval genome by PCR. The first region (187 133–187 461) was amplified with primers F_up(195P)_Fw and F_up(195P)_Rv and the second region (187 574–187 890) with primers F_dw(195P)_Fw and F_dw(195P)_Rv. The VACV p7.5 promoter-eGFP cassette (see Methods S1) was amplified from pF_INS-GFP , with primers p7.5-GFP_Fw and p7.5-GFP_Rv. Then, fragments were cloned into a modified version of pcDNA3.1/Zeo(−) (Invitrogen) by In-Fusion cloning , obtaining pF_195P-eGFP. The modified pcDNA3.1/Zeo(−) was generated by PCR amplification, excluding the human cytomegalovirus (CMV) enhancer/promoter – bovine growth hormone polyadenylation signal (bGH) region with primers pcDNA3.1_Fw and pcDNA3.1_Rv.To make the donor vector pF_195P-eGFP, two regions of the ECTV US4 locus from the HSV-1 SC16 genome (136 628–138 344) was amplified with primers F_up_US4(1)_Fw and F_dw_US4(1)_Rv and then, In-Fusion cloned into the modified pcDNA3.1/Zeo(−), obtaining plasmid pF_US4(1)_FL . Similarly, the US4 locus from the HSV-2 333 genome (137 411–140 507) was amplified with primers F_up_US4(2)_Fw and F_dw_US4(2)_Rv and In-Fusion cloned to produce plasmid pF_US4(2)_FL . Each donor plasmid for the US4 locus was generated by using pF_US4_FL as template . Donor plasmids pF_US4(1)/(2)_FL were linearized by PCR with primers pF_US4(1)_KO_Fw and pF_US4(1)_KO_Rv, and primers pF_US4(2)_KO_Fw and pF_US4(2)_KO_Rv, respectively, and In-Fusion circularized to generate pF_US4(1)/(2)_KO (Knock-Out) vectors, where the US4 open reading frame (ORF) was excluded. Vectors pF_US4(1)/(2)_SC (Stop-Codon) were produced with primers pF_US4(1)_SC_Fw and pF_US4(1)_SC_Rv, and primers pF_US4(2)_SC_Fw and pF_US4(2)_SC_Rv, respectively, containing a non-translatable version of the US4 gene. Plasmids pF_US4(1)/(2)_∆cyt (cytoplasmic tail) were generated with primers pF_US4(1)_∆cyt_Fw and pF_US4(1)_∆cyt_Rv; and primers pF_US4(2)_∆cyt_Fw and pF_US4(2)_∆cyt_Rv, respectively, where gG cytoplasmic tail was excluded. Plasmid pF_US4(2)_mgG was made using primers pF_US4(2)_mgG_Fw and pF_US4(2)_mgG_Rv, excluding the secreted gG2 (sgG2) domain . Plasmid pF_US4(1)_gG2 was produced by amplification of gG2 ORF from pF_US4(2)_FL with primers pF_US4(1)_gG2_Fw and pF_US4(1)_gG2_Rv, and then, gG2 ORF was In-Fusion cloned into the linearized pF_US4(1) backbone, with primers F_dw_US4(1)_Fw and F_up_US4(1)_Rv. Similarly, gG1 ORF from pF_US4(1)_FL was amplified with primers pF_US4(2)_gG1_Fw and pF_US4(2)_gG1_Rv and In-Fusion cloned into previously linearized pF_US4(2) backbone, with primers F_dw_US4(2)_Fw and F_up_US4(2)_Rv, generating donor vector pF_US4(2)_gG1. The region of interest in every plasmid was fully Sanger sequenced with primers pZ_Fw and pZ_Rv.The 195P pseudogene of ECTV were based on region of interest (ROI) 187 462–187 573 (Table S2). gRNAs targeting the selected region of the CMV and bGH sequences , were designed in accordance with each ROI (Table S3 and Methods S1). Every gRNA was filtered using the Cas-offinder algorithm (including in the Protospacer Workbench software), allowing up to five mismatches. gRNAs with 0 off-targets were selected based on their Cas9 cleavage activity, defined by the Doench–Root activity score [BbsI sites of pSpCas9(BB)−2A-Puro V2.0 (Addgene #62988) or pSpCas9∆NLS(BB)−2A-Puro plasmids. The latter was generated in-house by removing the nuclear localization signals from pSpCas9(BB)−2A-Puro V2.0 with four primers (pCas9∆NLS_1 to 4) by In-Fusion cloning. gRNA_195 P-2 was constructed with primers gRNA_195 P-2_Fw and gRNA_195 P-2_Rv, while gRNA_195 P-4 was constructed with primers gRNA_195 P-4_Fw and gRNA_195 P-4_Rv. Then, they were cloned separately into plasmid pSpCas9∆NLS(BB)−2A-Puro. gRNA_CMV-3 was produced with primers gRNA_CMV-3_Fw and gRNA_CMV-3_Rv, giving pSpCas9(gRNA_CMV-3), and finally, gRNA_bGH-4 with primers gRNA_bGH-4_Fw and gRNA_bGH-4_Rv, to produce pSpCas9(gRNA_ bGH-4). The inserted gRNA cloned into every plasmid was fully Sanger sequenced with primer hU6_Fw.gRNAs were designed using Protospacer Workbench software . gRNAs dty score , and loc5 cells/well in six-well plates were transfected using FuGENE HD (Promega) with 1 µg of the corresponding first pSpCas9-gRNA plasmid  to target the desired ROI, and 2 µg of the indicated ‘pF’ donor plasmid (i.e. a mass ratio of 1 : 2 for pSpCas9-gRNA : pF). At 8 h post-transfection, transfected cells were selected in the presence of 10 µg ml−1 of puromycin (Sigma). Then, after overnight treatment, selected cells were infected with the indicated virus at an m.o.i. of 0.1 p.f.u./cell, with the viral inoculum removed 2 h later, followed by washing and the addition of 2 ml of fresh 10 % FBS DMEM with puromycin. Both cell-associated and supernatant virus (first progeny) were harvested together 48–72 h p.i. and subjected to three cycles of freezing/thawing. Simultaneously, 293T cell monolayers were transfected with 3 µg of the corresponding second pSpCas9-gRNA (or pSpCas9∆NLS-gRNA) plasmid to target the same desired ROI. Transfected, puromycin-selected cells were infected with the clarified supernatant from the first progeny, being removed after 2 h, washed and overlaid with 2 ml of fresh 10 % FBS DMEM with puromycin. Virus present in both cells and supernatants (second progeny) was harvested 48–72 h p.i. and freeze/thawed three times. The second progeny was serially diluted and used to infect Vero (for HSV) or BSC-1 (for ECTV) cell monolayers in 12-well plates, with semisolid carboxymethyl cellulose (1.5 % w/v) 2 % FBS DMEM added after inoculum removal. Isolated recombinant viral plaques were identified 48–96 h p.i. by fluorescent microscopy in a Leica DM IL LED inverted microscope equipped with a Leica DFC3000-G digital camera, which was also used to take micrographs. Five rounds of plaque isolation, PCR screening, SDS-page, and Sanger and deep sequencing were performed to confirm the desired genomic modification for each case. For PCR screening, primers E194_Fw and 5GFP_Rv were used for ECTV, while primers Up_US4(1)_Fw and Dw_US4(1)_Rv were used for HSV-1, and primers Up_US4(2)_Fw and Dw_US4(2)_Rv were used for HSV-2.Recombinant viruses were produced by following the same general workflow represented in −1, Roche), RNAse A  and nuclease S7  and then incubated 2 h at 37 °C to eliminate cellular DNA/RNA. Sterile EDTA (12 mM) and EGTA (2 mM) at pH 8 were added to inactivate nuclease activity. Then, viral particles were lysed by incubation with proteinase K (200 µg ml−1) and sodium dodecyl sulfate (0.5 %) for 1.5 h at 45 °C. Viral DNA was purified by phenol : chloroform : isoamyl alcohol  extraction. All procedures were performed under sterile conditions. DNA was quantified in a Nanodrop One spectrophotometer (Thermo Fisher Scientific) and by fluorometric quantitation using a PicoGreen device (Invitrogen). Contaminating DNA was checked by PCR against mycoplasma, prokaryotic 16S rRNA (primers 16S_Fw and 16S_Rv) and eukaryotic 18S rRNA (primers 18S_Fw and 18S_Rv) [Viral stocks were treated with DNAse I (0.25 U µl 18S_Rv) . Finallyde novo when, after coverage filtering, their frequency in the previous parental viral population was <0.01. De novo MVs were examined to determine whether they were located within any of the predicted Cas9 off-target sequences for both gRNAs (gRNA_CMV-#3 and gRNA_bGH-#4) used to generate each recombinant. For full lists of MVs detected in each viral population, see Table S5 , Table S6  and Table S7 (recombinant ECTV 195P-eGFP). For a summary list of detected, filtered and categorized MVs for every sample sequenced in this study, see Table S8.Reads from each sequenced sample were trimmed using Trimmomatic v0.36 , qualityt-test or the two-tailed Mann–Whitney U test were used. Differences in both tests were considered statistically significant when P-values were <0.05.Data analyses were performed using GraphPad Prism 8 (v8.4.3) software. Student’s unpaired 195P locus within the ETCV genome . Similar results were obtained when parental Cas9-derived HSV-1/2 Red ΔgG-eGFP were analysed for de novo MVs located in predicted Cas9 off-target sequences for both gRNAs used . Altogether, these results confirm that our method using Cas9 for high-throughput genome engineering of dsDNA viruses offers reliable genome-wide off-target-free accuracy.The fact that large dsDNA viruses have a small genome (150–200 Kbp) compared to bacteria and eukaryotic organisms, minimizes the potential off-target sites for Cas9. However, the presence of complex repeated regions as well as other secondary structures within dsDNA viral genomes might be translated into greater Cas9 promiscuity. Viral factors participating in the replication phase, together with the intrinsic high recombination rate exhibited by poxviruses and herpesviruses, could also reduce Cas9 precision. Moreover, the fact that herpesviruses can generate a great degree of genetic diversity when replicating  viruses . In ordean HSV-1 . The facUS8 knock-out mutants by NHEJ, but <10 % for knock-in recombinants by HDR at the same locus [ICP0. Then, substitution of eGFP by other marker genes, such as mCherry or LacZ genes, gave HDR rates of 10 and 2.1 %, respectively [et al. concluded that Cas9-induced NHEJ or HDR is not efficient at repairing the VACV genome [The ability to quickly identify and select recombinant DNA viruses has wide and profound implications for viral pathogenesis and virulence studies, as well as for vaccine vector and oncolytic virus generation. CRISPR/Cas9 has proven to be groundbreaking for genome engineering of large dsDNA viruses. Several studies have evaluated the specificity and ability of this system to edit the HSV genome . The firme locus . Other ame locus . Optimizectively . Nonhomoectively . In contectively . More reV genome . MoreoveV genome . On top V genome . The potThe generation of knock-out recombinants is often used as a research tool to study the contribution of a certain locus or gene to fundamental aspects of the virus biology. Once a relevant locus is identified, the detailed study of its mechanistic contribution to the virus life cycle requires the generation of additional recombinant viruses expressing variant forms of the protein of interest in order to analyse the role of protein motifs and domains played during infection. This implies a time-consuming, laborious and expensive task when studying large multidomain viral proteins. It can become even more complicated if marker genes remain as a consequence of the genome engineering process, since the presence of those marker genes is typically unacceptable in therapeutics and vaccines. Two consecutive rounds of our two-progenies method allow for a high-throughput generation of large collections of mutant dsDNA viruses for a desired locus in less than 2 weeks, in optimal and ideal conditions . By replAltogether, our refinements contribute by improving and accelerating the accurate genome engineering of large dsDNA viruses through increasing HDR efficiency using Cas9 as the initiator of recombination, and/or by using it as a selection agent to increase the frequency of desired recombinants over a large background of non-desired recombinant and parental viruses.Click here for additional data file.Click here for additional data file.