Health and economic burden of chikungunya infection and potential benefits of vaccination in 32 countries: a vaccine impact modelling study

Health and economic burden of chikungunya infection and potential benefits of vaccination in 32 countries: a vaccine impact modelling study | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Health and economic burden of chikungunya infection and potential benefits of vaccination in 32 countries: a vaccine impact modelling study View ORCID Profile Junwen Zhou , Natasha Salant , View ORCID Profile Hale-Seda Radoykova , Janey Messina , View ORCID Profile William Wint , View ORCID Profile Joshua Longbottom , Katherine M Holohan , View ORCID Profile Katelyn A Dinkel , View ORCID Profile Mira L T Sytsma , View ORCID Profile Andrew A Torkelson , Luciano Pamplona de Góes Cavalcanti , View ORCID Profile T Déirdre Hollingsworth , View ORCID Profile Jennifer Lord , View ORCID Profile David R M Smith , View ORCID Profile Koen B. Pouwels doi: https://doi.org/10.1101/2025.09.26.25336724 Junwen Zhou 1 Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford , Oxford, UK PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Junwen Zhou For correspondence: junwen.zhou{at}ndph.ox.ac.uk Natasha Salant 1 Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford , Oxford, UK MSc Find this author on Google Scholar Find this author on PubMed Search for this author on this site Hale-Seda Radoykova 2 EPFL – Swiss Federal Technology Institute of Lausanne , Lausanne, Switzerland MSc Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Hale-Seda Radoykova Janey Messina 3 School of Geography and the Environment and the Oxford School of Global and Area Studies, University of Oxford , Oxford, UK PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site William Wint 4 Environmental Research Group Oxford Limited (ERGO) , Oxford, UK PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for William Wint Joshua Longbottom 5 Liverpool School of Tropical Medicine , Liverpool, UK PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Joshua Longbottom Katherine M Holohan 6 Li Ka Shing Centre for Health Information and Discovery, Big Data Institute, University of Oxford , Oxford, UK 7 NDM Centre for Global Health Research, Nuffield Department of Medicine, University of Oxford , Oxford, UK PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Katelyn A Dinkel 8 Linksbridge SPC , Seattle, WA, USA MSc Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Katelyn A Dinkel Mira L T Sytsma 8 Linksbridge SPC , Seattle, WA, USA MSc Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Mira L T Sytsma Andrew A Torkelson 8 Linksbridge SPC , Seattle, WA, USA PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Andrew A Torkelson Luciano Pamplona de Góes Cavalcanti 9 Department of Community Health, Federal University of Ceara , Fortaleza, Brazil PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site T Déirdre Hollingsworth 6 Li Ka Shing Centre for Health Information and Discovery, Big Data Institute, University of Oxford , Oxford, UK 7 NDM Centre for Global Health Research, Nuffield Department of Medicine, University of Oxford , Oxford, UK PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for T Déirdre Hollingsworth Jennifer Lord 5 Liverpool School of Tropical Medicine , Liverpool, UK PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jennifer Lord David R M Smith 10 Epidemiology and Modelling of Antibiotic Evasion Unit, Institut Pasteur, Université Paris Cité , Paris, France 11 Anti-Infective Evasion and Pharmacoepidemiology Team, Inserm U1018, CESP, UVSQ, Université Paris-Saclay , Montigny-le-Bretonneux, France 12 Nuffield Department of Primary Care Health Sciences, University of Oxford , Oxford, UK PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for David R M Smith Koen B. Pouwels 12 Nuffield Department of Primary Care Health Sciences, University of Oxford , Oxford, UK PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Koen B. Pouwels Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF ABSTRACT Background Chikungunya is an emerging vector-borne disease caused by Alphavirus chikungunya (CHIKV). As the first CHIKV vaccines gain regulatory approval, estimates of their health-economic impact are needed to guide investments and decision-making. Methods We developed a modelling framework comprising a CHIKV environmental suitability map, a serocatalytic model estimating force of infection, and a decision-analytic disease progression model to project the health-economic burden of chikungunya across 32 countries. Societal costs (2023 International dollars) included healthcare use, productivity losses and monetised disability-adjusted life-years (DALYs). We simulated several preventive CHIKV vaccination campaigns, conservatively assuming no impact on transmission. We estimated threshold vaccine costs as the cumulative societal costs averted per vaccine dose. Results From 2025 to 2050, our model projected a cumulative 862 million (95% uncertainty interval: 737 million-1.08 billion) CHIKV infections, 1.95 million (1.62 million-2.49 million) hospitalisations and 66,900 (53,000-88,000) deaths across 32 countries, leading to 7.21 million (5.64 million-9.66 million) DALYs and $106 billion ($72.6 billion-$148 billion) in societal costs. A five-year population-wide campaign reaching 50% of individuals aged 12 years and older, followed by ten years of annual routine adolescent vaccination, averted 881,000 (673,000-1.21 million) DALYs and $16.8 billion ($11.3 billion-$23.9 billion) in societal costs. This strategy was the most cost-efficient, with threshold vaccine costs ranging from $0.30 ($0.14-$0.57) in Chad to $55.5 ($39.4-$78.9) in Panama. Conclusion CHIKV vaccination could substantially reduce the future health and economic burden of chikungunya, supporting its consideration in national and regional immunisation programmes. Funding Coalition for Epidemic Preparedness Innovations. INTRODUCTION Chikungunya is a mosquito-borne disease caused by Alphavirus chikungunya (CHIKV), an arbovirus transmitted primarily through infected Aedes species. First isolated in Tanzania in 1953, 1 CHIKV has since spread globally through international travel and trade, with outbreaks reported in Africa, Asia, the Americas, Europe and various islands in the Caribbean Sea and Indian and Pacific Oceans. 2 CHIKV infection poses a substantial burden on healthcare systems, quality of life and economic productivity. 3 Acute infection is characterised by fever and joint pain and has a similar clinical presentation to several other acute arboviral diseases, such as Zika and dengue. Approximately half of symptomatic cases lead to chronic disease characterised by painful and persistent post-acute arthralgia. 4 The impact of chronic chikungunya on quality of life is substantial, with 94% of participants with post-acute arthralgia in a longitudinal study in Brazil reporting difficulty with ordinary daily activities, 88% reporting difficulty walking and 62% reporting mental distress. 4 In February 2023, the Pan American Health Organization reported an increase in reported chikungunya cases and deaths and recommended that affected states intensify preparation and response to outbreaks. 5 In November 2023, the world’s first CHIKV vaccine, VLA1553, was approved by the United States Food and Drug Administration, and in July 2024 received market authorisation in Europe. In early 2025, a second, recombinant CHIKV vaccine, PXVX0317, received market authorisation in Europe and the United States. However, access remains limited in many low- and middle-income countries (LMIC) that bear the highest burden, such as Brazil, Paraguay and India. To inform policy and investment decisions, we developed a modelling framework to project the health-economic burden of chikungunya from 2025 to 2050 and to evaluate the potential impacts of preventive vaccination campaigns across 32 countries in 5 continents. METHODS Model overview We developed a modelling framework to project the health-economic burden of CHIKV infection from 2025 to 2050 and to estimate the potential health-economic benefits of a series of preventive CHIKV vaccination campaigns administered globally. The analytical time horizon was chosen to fully capture the impacts of included vaccination campaigns (see below). This analysis focuses on 32 countries across 5 continents with evidence of substantial CHIKV transmission: Belize, Bolivia, Brazil, Democratic Republic of the Congo, Republic of the Congo, Colombia, Costa Rica, Djibouti, Dominican Republic, Ecuador, Ethiopia, Grenada, Guatemala, Guyana, Honduras, Haiti, Indonesia, India, Cambodia, St. Lucia, Mexico, Malaysia, Nicaragua, Panama, Peru, Philippines, Paraguay, Sudan, El Salvador, Chad, Thailand, and Venezuela. These countries were selected due to reporting either substantial and recent outbreaks (>1,000 cases from 2019 to 2022, with >2,000 cases overall) or less recent but substantial outbreaks (12,000 cases overall) according to case counts from 1952 to 2022. 6 Countries reporting only sporadic cases and overseas territories were excluded. Maldives and Tonga were excluded from the list due to unreliable estimation of CHIKV suitability across archipelagos consisting of many small islands. Overall, the model consists of five main components. First, global CHIKV suitability was estimated using boosted regression trees (BRT), recorded transmission events and environmental covariates. Second, historical patterns of the force of infection (FoI) in different areas of the world were estimated from age-seroprevalence data. Third, the relationship between suitability and FoI was estimated using a logistic growth model. Fourth, this relationship and estimates of the population at risk, derived from antibody prevalence estimates and a temperature-based mask to exclude areas deemed unlikely suitable for sustained mosquito survival and virus replication, were used to project average annual CHIKV infection incidence forward in time from 2025 to 2050. Finally, a decision-analytic model was used to estimate country-specific health-economic outcomes of CHIKV infection with and without CHIKV vaccine administration. Efficacy estimates for vaccines such as VLA1553 and PXVX0317 are not yet available, so in this study a hypothetical vaccine was considered with characteristics following WHO’s target product profile for a CHIKV vaccine. We conservatively assumed the vaccine to have a direct impact on disease prevention but no impact on CHIKV transmission. An illustrated model schematic is provided in Figure S1 and additional methodological details for each model component are provided in Supplementary methods . CHIKV suitability CHIKV suitability was estimated as the probability of CHIKV occurrence, using BRT species distribution modelling. The model was trained on a global occurrence database (describing instances where at least one person became infected with CHIKV) containing 1,801 geolocated points and polygons, and excluding polygons greater than 2,500 square kilometres to reduce uncertainty. Pseudo-absences were generated and weighted according to an estimated Ae. aegypti temperature niche. The covariates used included precipitation, land surface temperature and vegetation greenness, as well as population density. The CHIKV suitability model was then masked using the probability of presence of both Ae. aegypti and Ae. albopictus , so that CHIKV could only occur where environmental conditions were likely suitable for vectors. The vector distributions were calculated as an ensemble of BRT and random forest models, again trained on global vector distribution data, using indices of vegetation greenness, day and night land surface temperature and middle infrared derived from a 2012-2021 timeseries, as well as land use, elevation and human population density. Both CHIKV and vector models were produced at a 5-kilometre spatial resolution. Force of infection (FoI) FoI was defined as the annual probability of a susceptible individual becoming infected. Cross-sectional age-stratified seroprevalence datasets from two recent studies by Kang et al . 7 and Dos Santos et al . 8 were combined, resulting in a pooled dataset including 68 locations in 29 countries and territories. These data were used to estimate the FoI of CHIKV using a Bayesian serocatalytic model. This model made the following assumptions: 1) infection risk was not age-dependent; 2) infection risk was potentially time-varying; 3) there was no seroreversion; and 4) there was no bearing of human migration on CHIKV seroprevalence status. To reflect that CHIKV is often associated with explosive outbreaks in non-endemic areas, the model assumes a time-varying FoI, modelled as a Gaussian random walk on the logarithmic scale. The average of posterior draws for the FoI of CHIKV since the year 2000 was carried forward to estimate average annual FoI. This more recent period was selected under the hypothesis that more recent transmission activity is more likely to reflect future dynamics, while avoiding the need to simultaneously estimate the annual risk of future outbreaks occurring and their magnitudes. Mapping CHIKV suitability to FoI The relationship between CHIKV suitability and FoI was estimated in order to extrapolate infection incidence to areas without seroprevalence data. A logistic growth curve model was fitted using estimated suitability as the only covariate and estimated FoI as the outcome. Additional datapoints for locations with no evidence of local CHIKV transmission were imputed to better inform the logistic growth curve. To propagate uncertainty in both suitability and FoI, the logistic growth curve model was fitted on a combination of 100 bootstrapped suitability estimates from the boosted regression trees and 100 posterior draws from the serocatalytic model. Projecting infections through time As CHIKV antibodies provide long-lasting immunity, 9 only seronegative individuals were considered at risk for CHIKV infection. Annual infection incidence in each country was estimated based on the country’s estimated FoI, population size and seroprevalence. Annual country-level population estimates from 2025-2050 were taken from the UN World Population Prospects 10 , while WHO region-specific averages of pre-existing antibody levels were generated for 2025 using antibody prevalence estimates from the serocatalytic model. Annual infection incidence was then projected annually while accounting for time-varying change in seroprevalence due to births, deaths and infections. Age- and sex-specific disease risk Evidence shows CHIKV infection risk is similar regardless of age and sex, 11 , 12 but illness severity varies 12 , 13 . Age- and sex-specific risks of symptomatic infection were estimated based on data from Nicaragua (2015) 14 , scaled to match the age and sex distribution of 635,195 cases reported in Brazil (2015-2021). 15 In sensitivity analyses, alternative data from the Philippines (2012-2013) 16 and Maldives (2019) 17 were considered. Symptomatic cases were divided into detected (healthcare-seeking) and undetected groups. In Paraguay (2023), 6% of all infections were detected, 8 , 12 while in Brazil (2017-2025), 3.2% of detected infections were hospitalised 18 . The model assumes mild/moderate illness among non-hospitalised cases, and severe illness among hospitalised cases, with age- and sex-specific risks scaled to hospitalisation patterns from Paraguay (2022-2023) 19 . Risks of death were scaled to age- and sex-specific fatality rates from Brazil (2016-2022). 13 For chronic chikungunya, a meta-analysis found 51.0% of detected symptomatic cases develop ≥3 months of symptoms, 7 applied to detected cases only in the base-case analysis, with sensitivity analysis extending this to undetected cases. Vaccine characteristics We considered a hypothetical single-dose CHIKV vaccine licensed for prophylactic administration to adolescents and adults. The vaccine was assumed to provide 10 years of partial protection against disease, and was conservatively assumed to have no impact on infection or transmission. In the base-case scenario, the vaccine was 70% effective against all symptomatic disease, while efficacies of 50% and 90% were considered in sensitivity analyses. There was assumed to be no association between vaccine uptake and disease risk or serostatus, and potential side-effects or serious adverse events were not considered. Vaccination campaigns Three main vaccination strategies were simulated in which doses of a hypothetical CHIKV vaccine were allocated preventively to target groups living in all 32 countries. The vaccination strategies include: (1) population-wide campaigns, defined as one-off vaccine administration aiming to reach a high level of whole-population vaccine coverage over a fixed timeline; (2) routine campaigns, defined as sustained vaccine administration aiming to reach a high level of vaccine coverage among individuals of a specific age each year; and (3) the combination of both campaign types ( Figure S2-S3 ). For population-wide campaigns, we simulated three scenarios targeting individuals aged 12-99 over five years (2025-2029), with annual coverage targets of 2%, 4% and 10%, reaching, respectively, 10%, 20% and 50% coverage after five years. For routine campaigns, we simulated one scenario targeting individuals aged 12 with an annual coverage target of 65% over 2025-2040. For combined campaigns, we combined this routine campaign with each of the three preventive campaign coverage scenarios, assuming an initial phase of general population vaccination over five years followed by routine vaccination for the remaining 11 years. In all scenarios, the number of vaccine doses delivered was reduced by 5% relative to stated vaccine coverage targets to account for vaccine wastage. Table S1 shows the total vaccine demand forecast for each scenario. Health and economic outcomes The following health outcomes were included: (i) CHIKV infections, (ii) symptomatic chikungunya, defined as symptomatic CHIKV infections and stratified into mild/moderate symptoms or severe symptoms, (iii) chikungunya hospitalisations (due to acute severe symptoms), (iv) chikungunya deaths, (v) chronic chikungunya, and (vi) disability-adjusted life-years (DALYs). Economic outcomes were estimated in all 32 countries except Venezuela, which was excluded due to unavailability of reliable economic parameter estimates. The following economic outcomes were included: (i) healthcare costs, stratified by setting (outpatient/inpatient) and payer (government-reimbursed/out-of-pocket (OOP)); (ii) instances of catastrophic healthcare expenditure or impoverishing healthcare expenditure resulting from OOP healthcare costs; (iii) productivity losses due to reduced labour force participation because of acute symptomatic chikungunya, chronic chikungunya or death; (iv) monetised DALYs, quantified using country-specific health opportunity costs; 20 and (v) the value of statistical life (VSL) and value of statistical life-years (VSLY) lost due to chikungunya mortality; (vi) societal costs, defined as the sum of all healthcare costs, productivity losses and monetised DALYs; and (vii) dose-weighted mean threshold vaccine costs (TVCs), calculated for each vaccination scenario by dividing societal costs averted by the number of doses administered. Costs beyond the first year of the study horizon (2025) are discounted at 3.5% annually, or 0% in sensitivity analysis. All economic outcomes are reported in International dollars ($) 2023. Simulation and statistical reporting Parameter uncertainty was incorporated by randomly drawing parameter values from estimated distributions for each of 1000 Monte Carlo simulations. Random parameters varied in each run include: CHIKV suitability; posterior draws of the serocatalytic model; and health outcome probabilities, their distributions by age and sex and their durations and associated disability weights. Final health and economic outcomes, as well as outcomes averted by vaccination, are reported as means and 95% uncertainty intervals (UIs) of outcome distributions across all simulations. Disease burden estimates are reported in accordance with the GATHER statement ( Table S2 ). All analyses were run using R version 4.3.1. Role of the funder The Coalition for Epidemic Preparedness Innovations (CEPI) commissioned this analysis. RESULTS Chikungunya burden without vaccination From 2025-2050 in the 32 included countries reporting recent evidence of CHIKV outbreaks, our model predicted a cumulative 862 million (95% UI: 737 million–1.08 billion) CHIKV infections. In the absence of vaccination, these infections led to substantial cases of symptomatic chikungunya, hospitalisation, death and chronic chikungunya, resulting in a cumulative 7.21 million (5.64 million–9.66 million) DALYs. ( Table 1 ) Chronic chikungunya caused the greatest share of chikungunya DALYs, followed by death. ( Figure 1A-1B , Table S3 ) View this table: View inline View popup Download powerpoint Table 1. Projected cumulative total health and economic burden of CHIKV infection from 2025 to 2050 across 32 countries in the absence of vaccination. Future costs are discounted annually at 3.5%/year. CHIKV = Chikungunya virus, DALY = disability-adjusted life-year, VSL = value of statistical life, VSLY = value of statistical life-years, I$ = International dollar, K = thousand, M = million, B = billion, T = trillion. Download figure Open in new tab Figure 1. Projected burden of CHIKV infection from 2025 to 2050 across 32 countries in the absence of vaccination and associations with age. ( A ) Change over time in the annual number of CHIKV infection outcomes. ( B ) Change over time in annual DALYs associated with chikungunya. ( C ) The cumulative total number of symptomatic chikungunya cases, stratified by age group. ( D ) The cumulative total number of chikungunya hospitalisations (severe symptoms), stratified by age group.( E ) The cumulative total number of chikungunya deaths, stratified by age group. ( F ) The cumulative incidence of symptomatic chikungunya per 100,000 person-years, stratified by age group. ( G ) The cumulative incidence of chikungunya hospitalisation (severe symptoms) per 100,000 person-years, stratified by age group. ( H ) The cumulative incidence of chikungunya deaths per 100,000 person-years, stratified by age group. In panels A and B, lines represent means and shading represents 95% uncertainty intervals. In panels C through G, bar heights represent means and error bars represent 95% uncertainty intervals. The projected burden of chikungunya varied greatly across age groups. ( Figure 1C-1H ) Annual symptomatic chikungunya per 100,000 population was lowest in children aged 0-9 [245 (186-334)], and similar across the other age groups ranging from 416 (315-579) in those aged 10-19 to 646 (480-870) in those aged 50-59. In terms of hospitalisation and death, adults aged 70+ had by far the greatest incidence. ( Table S4 ) The cumulative societal costs of chikungunya - was estimated at $106 billion ($72.6 billion–$148 billion). Productivity losses were the main driver of societal costs, followed by monetised DALYs, and lastly healthcare expenditure. ( Table 1 ) Healthcare seeking for mild/moderate symptoms was the main driver of healthcare expenditure and productivity losses, whereas chronic chikungunya made the largest contributions to monetised DALYs. ( Table S5 ) Estimated DALYs per 100,000 person-years ranged from 1.39 (0.71-2.54) in Chad to 12.8 (10.2-17.1) in Thailand, whereas estimated societal costs ranged from $2,360 ($1,160-$4,310) in Chad to $521,000 ($379,000-$724,000) in Panama. ( Table S6 ) Vaccine impact The benefits of a population-wide vaccination campaign targeting individuals aged 12+ over 5 years depended on the share of the population vaccinated annually. Under base case assumptions of 70% vaccine efficacy and a vaccine-induced immune response lasting 10 years, campaigns reaching 2% and 10% annual coverage, respectively, required 236 million and 1.18 billion doses (Venezuela accounting for about 1%), and averted 146,000 (111,000-202,000) and 731,000 (554,000-1.01 million) DALYs, as well as $3.13 billion ($2.10 billion-$4.47 billion) and $15.7 billion ($10.5 billion-$22.3 billion) in societal costs across the 32 countries. A routine campaign targeting 65% of children on their 12 th birthday over a period of 16 years required a similar number of vaccine doses (511 million) as the population-wide campaign with 4% annual coverage (473 million doses), but averted a smaller number of symptomatic cases, hospitalisations and deaths than this campaign. Combined campaigns required the most doses and averted the greatest share of health and economic outcomes. The most ambitious combined campaign with the greatest annual coverage during the initial population-wide phase (10%) led to the greatest health and economic benefits, averting 881,000 (673,000-1.21 million) DALYs and $16.8 billion ($11.3 billion-$23.9 billion) in societal costs. By contrast, economic benefits were always smallest in the routine campaign, which prevented $1.87 billion ($1.30 billion-$2.66 billion) societal costs. ( Table 2 ) View this table: View inline View popup Table 2. Projected cumulative total health outcomes and economic costs averted due to CHIKV vaccination from 2025 to 2050 across 32 countries, depending on the vaccination campaign. Vaccine was base-case assumed 70% efficacy and future costs are discounted annually at 3.5%/year. DALY = disability-adjusted life-year, VSL = value of statistical life, VSLY = value of statistical life-years, I$ = International dollar, K = thousand, M = million, B = billion. Vaccination campaigns varied considerably in terms of cost-efficiency (the health-economic benefits accrued per dose of vaccine). Population-wide campaigns had the highest cost-efficiency across all health-economic outcomes, with a dose-weighted mean TVC equal to $14.3 ($9.62-$20.4), while routine campaigns were least efficient, with the TVC equal to $4.70 ($3.28-$6.70). ( Table 3 ) Population-wide campaigns with different annual coverages had the same TVCs, as per-dose vaccine impacts scaled linearly with vaccine coverage in the absence of an impact on transmission. Combined campaigns had intermediate cost-efficiency, with TVCs ranging from $9.09 ($6.28-$12.9) to $12.5 ($8.45-$17.8), respectively, under assumptions of 2% and 10% annual coverage during the population-wide phase of the campaign. View this table: View inline View popup Download powerpoint Table 3. Efficiency in averting health outcomes, economic costs and threshold vaccine costs (TVC) under different vaccination campaign scenarios. Base case assumptions include 70% vaccine efficacy against disease and an annual 3.5% discount rate for future costs. DALY = disability-adjusted life-year, TVC = threshold vaccine costs, VSL = value of statistical life, VSLY = value of statistical life-years, I$ = International dollar. Vaccine efficiency was estimated to vary greatly across countries in averting DALYs ( Figure S4 ) and societal costs ( Figure 2 ). TVCs for population wide-campaigns were above $20 and $40 in 13 (41.9%) and 4 (12.9%) countries, respectively, whereas TVCs for routine campaigns were above $20 only in Panama [$23.4 ($17.0-$33.2)] ( Table S7 ). TVCs for population-wide campaigns ranged from $0.30 ($0.14-$0.57) in Chad to $55.5 ($39.4-$78.9) in Panama ( Figure 2 ) Download figure Open in new tab Figure 2. Threshold vaccine costs by different vaccination strategies in each of the 31 countries analysed. Combined campaign analysed starts with the population-wide campaign over 5 years with 20% coverage in total. Venezuela was excluded from health-economic analyses due to a lack of reliable estimates of economic parameters due to its economic collapse in the recent years. Future costs and life-years are discounted annually at 3.5%/year. I$ = International dollar. Sensitivity analyses varying vaccine efficacy, parameter inputs and model assumptions had important impacts on projected vaccine benefits and, thus, TVC. ( Figure S5, Table S8-S13 ) The sensitivity analysis using an estimate of symptom probability from the Philippines (0.180) instead of Nicaragua (0.505) led to a roughly 3-fold reduction in estimated burden, vaccine impact and TVCs. The sensitivity analysis assuming undetected symptomatic infections share the same disability weight and probability of developing chronic symptoms as detected mild/moderate symptomatic infections led to an approximately 6-fold increase in DALYs and a 4-fold increase in societal costs. However, the order of vaccination campaigns in terms of TVC was robust to sensitivity analyses, with population-wide campaigns always leading to greatest per-dose benefits. ( Table S12-S13 ) DISCUSSION This study projected the health-economic burden of chikungunya across 32 countries, mostly LMICs, from 2025-2050, and estimated the health-economic benefits of different vaccine rollout strategies. We estimated that CHIKV could cause 862 million infections, 66,900 deaths and 7.12 million DALYs over this period, generating $106 billion in societal costs. A combined vaccination strategy of an initial population-wide campaign followed by annual routine adolescent vaccination had the greatest impact, averting a cumulative estimated 7,630 deaths, 881,000 DALYs and $16.8 billion in societal costs. The population-wide campaign alone was the most cost-effective strategy, with TVC values ranging from $0.30 ($0.14-$0.57) in Chad to $55.5 ($39.4-$78.9) in Panama. Our projections rely on conservative vaccine efficacy assumptions, reflecting limited real-world data. While recent phase 3 trials for VLA1553 and PXVX0317 21 , 22 reported high immunogenicity, the durability of protection, effect on clinical disease, and impact on transmission remain uncertain. Our results may therefore underestimate the benefits of vaccination, particularly if vaccines reduce transmission or provide longer-lasting immunity. Future cost-effectiveness analyses incorporating real-world data on vaccine effectiveness, duration of protection, and safety will be essential. These modelling results are sensitive to certain assumptions and uncertainties. There is recognised concern that chikungunya severity and mortality risk may be underestimated, 23 , 24 and the evolution of new lineages or emergence of existing lineages in areas with low pre-existing immunity could result in outbreaks that are more severe than those characterised by our model. 25 Assumptions regarding symptom probability and disease risk in detected versus undetected symptomatic infections had the largest impacts on our results. However, despite variation in total cumulative chikungunya burden and vaccine impact across population groups in these sensitivity analyses, the relative efficiency of included vaccine strategies was consistent, with population-wide campaigns always leading to the greatest cost-efficiency. Our health burden projections are comparable to a recent model-based analysis of chikungunya’s global burden by dos Santos et al . 8 , which estimated fewer average annual infections (20.5 million) than our study (33.2 million) across the 31 countries included in both studies (theirs excluded Grenada), although their estimates did not take future projected population growth into account and treated outbreak probability and size as stochastic events, independent of underlying country characteristics such as CHIKV suitability. In turn, they estimated fewer annual symptomatic infections (10.2 million vs. 16.4 million), and infections resulting in chronic symptoms (491,000 vs. 1.01 million), but a similar number of deaths (2,460 vs. 2,570). Most markedly, our study estimated a substantially larger annual DALY burden (67,800 vs. 277,000), although their reported duration (1 year) and disability weight (0.233) for chronic disease appear inconsistent with an estimated 67,800 total DALYs, even before taking DALYs due to mild disease and death into account. Importantly, our study provides the first estimates of chikungunya healthcare costs and the cost-efficiency of vaccination across multiple settings. Our estimates of the health-economic burden of chikungunya and benefits of vaccination may inform decision-making regarding the rollout of both currently licensed and forthcoming CHIKV vaccines. Estimates of vaccine impact in our analysis varied greatly across included countries, suggesting that the returns-on-investment and therefore health-economic prioritisation of CHIKV vaccination is likely to be highly context specific. The greatest challenge to long-term prospective arbovirus burden modelling is fundamental uncertainty regarding epidemic risk. We made conservative assumptions throughout our modelling pipeline to avoid overestimating future burden and have not taken into account future climate and environmental change affecting CHIKV suitability, leading to potential underestimation of future burden in several, but not all, considered countries 26 . We also excluded countries reporting no evidence of substantial outbreaks up to 2022. We assumed that natural CHIKV infection confers lifelong immune protection against reinfection, while vaccination only protects against disease, therefore representing a cautious scenario for both infection burden estimation and vaccine impact. Finally, we did not consider the emergence of novel variants with vaccine-escape properties. In conclusion, this study has provided detailed projections of the burden of chikungunya across 32 countries and has estimated the potential returns-on-investment of several proposed strategies for CHIKV vaccine administration. These results may inform investment decisions for current and forthcoming CHIKV vaccines. Data Availability All data used in this study are publicly available. The code and minimum dataset required to reproduce results are available at https://github.com/zhoujunwen/HE_MODELS_CHIK_VacDisease. Declaration of interest Authors declare no other competing interests. Data sharing All data used in this study are publicly available. The code and minimum dataset required to reproduce results are available at https://github.com/zhoujunwen/HE_MODELS_CHIK_VacDisease . Contributions KBP, JeL and TDH acquired funding and supervised the work. NS, DRMS, HSR, JZ, KMH and LPGC reviewed the literature and synthesised data. DRMS, NS, KBP, AAT, MLTS and KAD developed the vaccine administration strategies. JM, WW, JL, JeL, and KBP conducted CHIKV suitability modelling. HSR and KBP conducted serocatalytic modelling. JZ and KBP conducted chikungunya and force of infection mapping. JZ, NS and DRMS developed all other model components, conducted the health economic analyses and produced results. JZ, DRMS, KBP and TDH interpreted results. The underlying data were verified by JZ, DRMS, KBP and TDH, and all authors had full access to the study data and accept responsibility to submit for publication. JZ wrote the first draft with supervision from DRMS and KBP. The final version of this manuscript was reviewed and approved by all authors. Acknowledgements This work was conducted by the OxLiv Consortium and funded by the Coalition for Epidemic Preparedness Innovations (CEPI) through Vaccine Impact Assessment project funding. We acknowledge the CEPI project team (Project Lead: Christinah Mukandavire) for continuous support and the project’s external advisory team for their invaluable feedback. KBP and DRMS are supported by the Medical Research Foundation (MRF-160-0017-ELP-POUW-C0909). TDH thanks the Li Ka Shing Foundation for institutional funding. The views expressed are those of the authors and not necessarily those of the institutions with which they are affiliated. References 1. ↵ Ross RW . The Newala epidemic. III. The virus: isolation, pathogenic properties and relationship to the epidemic . J Hyg (Lond) 1956 ; 54 ( 2 ): 177 – 91 . OpenUrl CrossRef PubMed 2. ↵ British Heart Foundation . BHF UK CVD Factsheet 2025 . https://wwwbhforguk/-/media/files/for-professionals/research/heart-statistics/bhf-cvd-statistics-uk-factsheetpdf (Accessed on 2025/06/15 ) 2025 . 3. ↵ Costa LB , Barreto FKA , Barreto MCA , et al. Epidemiology and Economic Burden of Chikungunya: A Systematic Literature Review . 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