Quantifying the Investment Gap, Cost of Malaria Elimination, and Returns on Investment in India: A Macroeconomic Analysis

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Although India has committed to achieving zero indigenous malaria cases by 2027 and complete elimination by 2030 under the National Strategic Plan (NSP) 2023–2027, current investments may be inadequate to meet these targets. This study quantifies the economic burden of malaria, estimates the investment required for elimination, and assesses the potential returns on investment using a macroeconomic modelling approach. Methods A district-level SEIR model stratified by endemicity (high, low, and malaria-free districts) was applied to estimate intervention costs under three scenarios: ( 1 ) present investment; ( 2 ) NSP 2023–2027–aligned investment; and ( 3 ) intensified interventions modelled on the Mandla Malaria Elimination Demonstration Project (MEDP) plus Sri Lanka and Bhutan–based approaches. The cost-of-illness method was used to estimate the national economic burden, including treatment costs, productivity losses, premature mortality, and personal protection expenditures. Forecasting models were applied to project malaria incidence through 2035, while Disability-Adjusted Life Years (DALYs) and return on investment (ROI) were calculated to assess health and economic outcomes. Results The findings indicate that the current investment of approximately INR 6 per capita annually is insufficient. The required annual investment is estimated at INR 16,414 million, compared with INR 4,451 million allocated in 2024. Under current trends, malaria transmission would persist beyond 2035. Scaling up to NSP targets could reduce cases to two digits by 2032, eliminate deaths by 2029, and yield an ROI of 3.18. The intensified MEDP-plus scenario projected near elimination by 2030, zero deaths by 2028, and a higher ROI of 3.76. Conclusion These findings demonstrate that closing India’s malaria investment gap is both a public health and macroeconomic priority. Malaria Elimination ROI Investment Case MEDP Figures Figure 1 INTRODUCTION Malaria continues to pose a formidable global health challenge despite decades of concerted control efforts. In 2024, an estimated 282 million cases and 610,000 deaths were recorded worldwide ( 1 ). The disease burden remains heavily skewed toward the WHO African Region, which accounts for approximately 94% of global cases and 95% of malaria deaths. The WHO South-East Asia Region contributes a smaller proportion - around 0.96% of global cases and 0.64% of deaths, but within this region, India bears a disproportionately higher share of the burden ( 1 ). In 2024, India contributed 73% of all estimated malaria cases and 89% of deaths in the South-East Asia Region ( 1 ). However, India ranked as the first-largest contributor of reported malaria cases (53%) and deaths (89%) in the SEA region. The gap between reported and estimated cases is due to adjustments made for care-seeking and diagnostic testing rates ( 1 ). Although the country has made substantial progress in reducing malaria incidence and mortality over the past two decades, the disease remains endemic in several regions, particularly among tribal populations, in forested zones, and in hard-to-reach areas. In line with the WHO Global Technical Strategy for Malaria ( 2 ), the Government of India launched the National Framework for Malaria Elimination (NFME) 2016–2030 ( 3 ), followed by two phases of the National Strategic Plan (NSP) for Malaria Elimination: 2017–2022 and 2023–2027. These initiatives aim to achieve zero indigenous cases by 2027 and a malaria-free India by 2030 ( 4 ). A deteriorating global financing landscape increasingly threatens progress toward these milestones. In 2024, global malaria funding fell short by 42% (US $ 3.9 billion) of the resources required to stay on track for elimination. The South-East Asia Region received only US $ 104.5 million, representing a 49% decline since 2010 ( 5 ). Such contraction in external support leaves national programmes vulnerable, particularly those heavily reliant on donor contributions. Ensuring the long-term sustainability of malaria control and elimination, therefore, demands increased domestic investment, complemented by international technical and, to some extent, financial assistance. Evidence from global and regional studies shows that investments in malaria elimination yield substantial returns through reduced disease burden, economic growth, and productivity gains ( 6 ). India’s history offers a cautionary reminder of the costs of underinvestment. Following a dramatic reduction in malaria cases to roughly 100,000 annually by the early 1960s, complacency and funding shortfalls led to a resurgence in the 1970s, when cases rose to 6.45 million by 1976. This experience underscores the fragility of malaria control gains and the need for sustained political and financial commitment. Achieving elimination by 2030 will require closing significant gaps in focus and investment, particularly in high-burden districts. Current per capita spending on malaria control in India averages INR 6.0 between 2009 and 2024, among the lowest in the region and far below levels observed in countries that have successfully eliminated malaria ( 5 ). Public expenditure constitutes only a small share of total malaria-related costs, with households bearing a substantial burden through out-of-pocket payments. Studies indicate that increased public investment could substantially reduce these costs and generate significant economic returns: every dollar invested in malaria elimination yields an estimated six-fold return and up to US $ 90 billion in cumulative economic gains ( 7 – 9 ). Against this backdrop, the present study quantifies the economic burden of malaria and the investment required to achieve elimination in India. By combining macroeconomic modelling with national and subnational data, it highlights the magnitude and distribution of investment shortfalls and assesses the potential returns of scaling up interventions to NSP 2023–2027 levels. Bridging India’s malaria investment gap is therefore not merely a public health necessity but a strategic economic imperative for sustainable and equitable national development. MATERIAL AND METHODS Cost Estimate A district-level compartmental model (SEIR: Susceptible–Exposed–Infectious–Recovered) was applied to estimate the optimum investment required to achieve malaria elimination in India by 2030. Districts were stratified into three epidemiological clusters: zero (malaria-free), low, and high endemicity, based on malaria incidence rates reported by the National Centre for Vector Borne Disease Control (NCVBDC) in 2024 (10). The costing framework integrated key epidemiological and programmatic parameters, including projected population at risk, total number of households, intervention coverage, commodity unit costs, and human resource requirements. These parameters were adapted from established costing methodologies used in global malaria investment analyses (11). Expenditure estimates were organised into three broad categories: (1) intervention costs, encompassing prevention, surveillance, diagnosis, and treatment; (2) operational costs, including human resource deployment, capacity building, monitoring and evaluation systems, communication, and advocacy; and (3) other costs, such as the development of technical guidelines, training manuals, and standard operating procedures (SOPs), along with research activities, impact assessments, and survey operations. Together, these elements captured both the direct and indirect resource requirements for sustaining a robust elimination programme. Cost projections were generated for three intervention scenarios. Scenario 1 (present investment) assumed continuation of prevailing investment trends and current levels of intervention coverage, reflecting the existing operational environment of India’s national malaria programme without significant scale-up. Scenario 2 (scale-up to NSP 2023–2027 targets) modelled an expansion of malaria control and elimination activities to achieve the coverage levels specified in the NSP 2023–2027, including enhanced surveillance, improved diagnostic and treatment coverage, strengthened vector control, and upgraded programme management capacity. Scenario 3 (MEDP plus intensified intervention model) extended beyond NSP targets by applying lessons from the Mandla Malaria Elimination Demonstration Project (MEDP), implemented from 2017 to 2021 in a high-burden tribal district of Madhya Pradesh. The MEDP invested approximately INR 180 million to safeguard a population of 1.2 million. Its operational strategy emphasised the “T4 approach” of tracking every individual, early testing, prompt treatment, and tracking drug compliance, supported by integrated vector management, biannual mass surveillance and treatment, intensive community mobilisation, capacity strengthening of field staff, and rigorous managerial oversight (12). This scenario further incorporated optimal LLIN coverage following the Bhutan example (1 LLIN per 1.5 persons), high-quality assurance, and near-universal compliance with LLIN and IRS through community mobilisation, as observed in Bhutan and Sri Lanka (13,14). This structured framework enabled estimation of financial requirements across varying implementation intensities. By comparing outputs across scenarios, the analysis quantified the resource gap and investment thresholds required to achieve malaria elimination by 2035. Economic Burden Estimate The cost-of-illness (COI) method was used to estimate the economic burden of malaria in India. Components included treatment costs (medical and non-medical), earnings foregone due to lost workdays, the value of lives lost due to malaria-attributable premature deaths, and personal protection costs. Personal protection costs included expenditures on mosquito repellents, netting, insecticide sprays, smoke, and similar measures, excluding programme-sponsored LLINs and IRS, and were estimated for populations living in high-endemic districts. All values were adjusted using the current-price GDP deflator from the Ministry of Statistics and Programme Implementation (15). The total economic burden was calculated as: Total Economic Burden = Treatment cost (medical + non-medical) + Earning foregone + Value of lives lost + Personal protection cost 1. Treatment cost = Per illness cost of treatment sought from different sources (including doctor’s fee, cost of medicine, laboratory charges for diagnosis, etc) x Number of estimated or reported malaria cases. 2. Earning foregone = (Work days lost due to illness (time period of treatment sought after onset of symptoms + treatment days + supplemented days of absenteeism due to illness) x Average per capita daily income) x Number of estimated or reported malaria cases (The average work days lost was assumed to be 10 days as per the National Sample Survey (NSS), 2018. Workday loss was considered for caregivers in cases of children’s illness. 3. Value of lives lost (Economic loss per malaria attributable deaths) = Per capita average annual income x Year of Potential Productive Life Lost (YPPLL) (An average of 15 years of YPPLL for adults was assumed, assuming 45 years of average age at malarial death among adults, an average age of work potency of 60 years or age of retirement from active work, and an average of 35 years of YPPLL was assumed for children’s death cases. 4. Personal protection cost = Per capita average expenditure on personal protection (kind of mosquito repellents, insecticide, smoke, window screen, etc) x Population living at high malaria risk Malaria cases and deaths (point estimates) were obtained from WHO World Malaria Reports (2024–2025) and NCVBDC (2023–2024). Age-specific proportions of malaria-attributable deaths were derived from Dhingra et al. (2010) (16). Treatment costs by source of care (public facilities, private hospitals, charitable hospitals, private clinics, and informal providers) were taken from the 75th NSS round (2018) (17). The value of life loss was calculated using the Human Capital Method, and per capita income data were obtained from the Reserve Bank of India (18). Personal protection costs were based on earlier-reported per-capita estimates (19). Direct health system costs were taken from the national malaria programme budget and Global Fund contributions as reported in the WHO World Malaria Report, while elimination costs were derived from the NSP for Malaria Elimination (4). Disease Forecast Time-series data on reported annual malaria cases in India from 1990 to 2024 were used to forecast incidence for 2025–2035. Key intervention milestones were included as covariates, including the Enhanced Malaria Control Programme (1997); introduction of Accredited Social Health Activists (ASHA) workers, Long-Lasting Insecticidal Nets (LLINs), and Artemisinin-based Combination Therapy (ACT) in 2010; deployment of bivalent RDTs and artemisinin–lumefantrine in the North-East (2013); initiation of NFME (2016); and MEDP-type intensified investment scenarios Multiple forecasting models were applied: Moving Average (MA), LOWESS, Exponential Smoothing, ARIMAX, Bayesian Structural Time Series (BSTS), Extreme Gradient Boosting (XGBoost), Holt’s additive and multiplicative methods, and Poisson and Negative Binomial regressions. Due to the unavailability of seasonal malaria data, annual time-series observations were used. Stationarity of the series was assessed using the Augmented Dickey-Fuller (ADF) test, and regular differencing was applied where necessary to achieve stationarity. Model performance was evaluated based on Root Mean Square Error (RMSE), Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Mean Absolute Per cent Error (MAPE), and Mean Absolute Scaled Error (MASE), with the best-fitting model determined by the lowest error values (20). Disability-Adjusted Life Year (DALY) DALYs were calculated as the sum of years of life lost (YLL) and years lived with disability (YLD): DALY = YLL + YLD Where, YLL (Years of Life Lost) measures premature death caused by malaria and YLD (Years Lived with Disability) measures the burden from non-fatal cases of malaria. YLL =N x L Where, N = Number of malaria deaths and L = Standard life expectancy at the age of death (21). YLD = I x DW x D Where, I = Number of malaria cases, DW = Disability weight (0.191 constant). D = Average duration of illness until recovery or death (10 days constant). Return on Investment Return on investment (ROI) was calculated as net benefit (gain minus cost) divided by cost. Probabilistic Sensitivity Analysis (PSA) using Monte Carlo simulation was conducted to account for uncertainty and variation in ROI estimates for the optimum and MEDP-based enhanced investment scenarios. Statistical analyses were performed using R version 4.4.3. (Foundation for Statistical Computing, Vienna, Austria). RESULTS The World Health Organization (WHO) estimated 2,007,280 malaria cases and 3,431 deaths in India during 2024 (5). In contrast, India officially reported 255,500 cases and 86 deaths in the same year. District-wise malaria prevalence data for 2019–2024 indicated that approximately 2% of the population resided in high-burden districts with an annual parasite incidence (API) ≥1, 92% lived in low-burden districts (API <1), and the remaining 6% were in malaria-free areas (10). Based on this stratification, projected populations and households were categorised into three clusters: zero (malaria-free), low (API < 1), and high (API ≥ 1) burden districts. The optimal annual investment required for malaria elimination was estimated for intervention sub-heads under the NSP 2023–2027, using 2024 malaria incidence as the reference and the unit costs described by Patouillard et al. (11) (Tables 1 and 2). The estimated optimum annual investment required was INR 16,414 million in the initial year of implementation (2026). By comparison, the reported national budget for malaria elimination in 2024 was INR 4,451 million (5). Scenario 1: Present investment - Analysis of the economic burden of malaria in India, based on programme-reported cases and deaths for 2024 and forecasted values for 2025–2035, estimated the national economic burden at approximately INR 15–16 billion annually. Using WHO-estimated cases and deaths, the 2024 burden was approximately INR 42 billion. Overall, the cost of malaria was nearly four times higher than the country’s current investment in elimination efforts. DALYs were projected to decline from 5,161 in 2024 to 1,040 by 2035. Forecasts from the best-fitting Holt’s linear multiplicative model indicated that, under existing intervention strategies and current investment trends, India is unlikely to achieve its malaria elimination goal by 2035 (Table 3, Figure 1). This model showed the best statistical accuracy, with the lowest MAPE (33.05) and MASE (0.44) values (Table 6) Scenario 2: NSP 2023–2027–based scale-up - Forecasts incorporating enhanced intervention coverage at the estimated optimal investment level under the NSP 2023–2027 showed substantial reductions in malaria transmission and burden. Malaria cases were projected to decline from 255,500 in 2024 to 1,429 in 2030 and reach double-digit levels (31 cases) by 2032. Malaria-related deaths were expected to decline from 86 in 2024 to zero by 2029. Correspondingly, DALYs were projected to decline from 5,161 in 2024 to 7 by 2030. The economic burden was projected to fall sharply, from approximately INR 16 billion in 2024 to INR 600 million by 2035. This scenario yielded an estimated ROI of 3.18 (95% CI: 3.12–3.24) (Table 4, Figure 1). Scenario 3: MEDP-plus intensified investment - The enhanced investment scenario modelled on the MEDP-plus approach showed even more rapid reductions. Malaria cases were projected to decline from 255,500 in 2024 to 142 by 2030, while deaths were projected to reach zero by 2028. DALYs declined from 5,161 in 2024 to 1 by 2030. The associated economic burden decreased from INR 16 billion in 2024 to INR 138 million by 2035, generating a higher ROI of 3.76 (95% CI: 3.70–3.83) (Table 5, Figure 1). Comparative outcomes across scenarios - Across the three investment scenarios, the modelling revealed a clear gradient between financing intensity and elimination outcomes. Under the present investment trajectory, malaria transmission is projected to persist beyond 2035, with an economic burden nearly four times higher than current programme spending. Scaling up to NSP 2023–2027 investment levels would reduce malaria cases to two digits by 2032 and achieve zero deaths by 2029, with an ROI of 3.18. The intensified MEDP-plus model provided the fastest pathway to elimination, with near-zero transmission by 2030, zero deaths by 2028, and the highest economic return (ROI 3.76), indicating that only a front-loaded, intensified investment strategy places India on a credible elimination trajectory. DISCUSSION This study provides the first district-stratified macroeconomic analysis of malaria elimination investment needs in India, integrating epidemiological forecasting, cost-of-illness estimation, and return-on-investment modelling. The results demonstrate that India’s current investment in malaria control, averaging INR 6.0 (US $ 0.07) per capita annually, is grossly inadequate to achieve elimination by 2030 ( 1 ). An estimated INR 16,414 million (US $ 193 million) in annual funding - more than four times current allocations would be required to sustain an elimination trajectory. This finding aligns with the 2025 financial gap analysis conducted by the National Health Systems Resource Centre, which reported that the allocation for all vector-borne disease programmes under the Ministry of Health and Family Welfare covered only one-third of the normative requirement ( 22 ). Our findings, based on reported 2024 malaria prevalence, differ from those of Gupta and Chowdhury, who used 2012 data ( 7 ). Their estimate of India’s national economic burden of malaria, approximately US $ 1.94 billion, used a cost-of-illness framework applied to the population at risk, based on WHO estimates ( 8 ). While their estimates were higher, reflecting broader inclusion of household expenditures and indirect productivity losses at the national scale, both analyses converge in demonstrating that malaria imposes a substantial macroeconomic cost on India. Methodologically, Gupta and Chowdhury’s work was static and retrospective, whereas our analysis employs dynamic epidemiological forecasting via a district-stratified SEIR model, linking transmission trends to investment needs and returns. Enhanced financing yields disproportionate benefits for both health and the economy. Under the present investment trajectory, malaria is projected to persist beyond 2030, with an economic burden nearly four times higher than current programme spending. Scaling interventions to the targets outlined in the National Strategic Plan (2023–2027) would avert approximately 1,429 cases annually by 2030, reduce DALYs to single digits, eliminate deaths by 2029, and yield an ROI of 3.18. An intensified scenario modelled on the MEDP-plus approach projects near elimination by 2030, with 142 cases, zero deaths, and a higher ROI of 3.76. Among the three scenarios, only the intensified investment pathway places India on a credible trajectory toward elimination. Sustaining these gains will require consistent programmatic success nationwide, as even limited outbreaks could compromise efforts toward elimination. An earlier forecasting study using data from 1990 to 2022 suggested that India could eliminate malaria by 2027 ( 17 ). However, the rise in cases during 2023 and 2024 necessitated a recalibration of projections, with only the intensified scenario indicating near elimination by 2030. These results are consistent with regional evidence showing that each US $ 1 invested in malaria elimination yields US $ 19–31 in economic returns through reduced treatment costs, productivity gains, and macroeconomic growth ( 5 , 9 , 23 ). The relationship between malaria reduction and economic advancement is well established: cross-country analyses show that malaria-endemic nations experience annual GDP growth rates approximately 1.3% lower than their non-endemic peers, and that elimination contributes to improved educational attainment, workforce participation, and poverty reduction ( 6 , 24 ). For India, these findings underscore the urgent need to strengthen domestic financial stewardship and reduce dependence on external assistance. Historical experience offers a cautionary precedent: the resurgence of malaria in the 1970s, following premature withdrawal of funding and weakened surveillance systems, reversed earlier gains ( 25 , 26 ). Without sustained political and fiscal commitments, similar vulnerabilities may re-emerge. Ensuring continuity of interventions will require ring-fenced malaria budgets at both central and state levels, alongside innovative mechanisms such as performance-linked financing, public health impact bonds, and private-sector engagement through corporate social responsibility frameworks. The policy implications extend beyond the health sector. Malaria elimination should be framed as a macroeconomic and social development strategy rather than a disease-specific expenditure. Evidence from the Greater Mekong Subregion and sub-Saharan Africa shows that elimination accelerates progress toward multiple Sustainable Development Goals by improving educational outcomes, promoting gender equity, and strengthening household economic resilience ( 5 , 6 ). Achieving similar synergies in India will require not only increased funding but also efficient resource utilisation, accountability mechanisms, and adaptive planning tailored to district-level heterogeneity. It has been argued that malaria elimination does not necessarily require greater financial investment than efficient malaria control. However, sustained funding to support an agile programme, flexible resource allocation, strengthened workforce capacity, and robust monitoring and evaluation systems are essential for achieving and maintaining elimination ( 27 ). Bhutan’s experience illustrates the value of intensified vector control and sustained external financial support. Between 2006 and 2014, nationwide scale-up of long-lasting insecticidal nets (approximately one net per 1.5 persons), indoor residual spraying, and GIS-based surveillance systems contributed substantially to malaria reduction ( 14 ). In contrast, India currently distributes one LLIN per 2.5 persons, suggesting that higher coverage may be necessary to achieve comparable effectiveness ( 28 ). This analysis has limitations. It relies on secondary data sources, including WHO World Malaria Reports and National Sample Surveys, which may under- or overestimate the true burden due to incomplete case detection or care-seeking biases. Forecasting was based on annual rather than seasonal data, which may have underestimated short-term fluctuations. Economic burden estimates excluded intangible and long-term costs, such as caregiver stress and post-infectious sequelae, which would further increase estimated returns if included. Nevertheless, triangulation of multiple models, probabilistic sensitivity analysis, and district-level stratification strengthen confidence in the robustness of these findings. Political feasibility, health system constraints, and behavioural compliance were not explicitly modelled. India stands at a critical juncture. Achieving near elimination by 2030 is technically feasible but contingent on sustained funding for all components of elimination, including human resources, supply chain systems, accountability frameworks, and independent oversight. While near elimination may be achievable by 2030, a malaria-free India, defined as zero indigenous transmission, may be more realistic by 2035. Failure to act risks not only resurgence but also the erosion of broader health and economic gains made over the past two decades. Strengthening domestic financing and aligning investments with evidence-based strategies will be essential to convert political commitment into sustainable outcomes. Malaria elimination should therefore be recognised not merely as a public health goal but as an investment in national productivity, resilience, and equitable development. CONCLUSION India stands at a critical juncture in its malaria elimination trajectory. This analysis shows that near elimination by 2030 is technically feasible, but only under conditions of sustained, front-loaded, and strategically targeted investment. Current spending levels are insufficient to meet national goals and risk prolonging transmission well beyond the stated timeline. In contrast, scaling up to NSP-aligned investment levels and especially adopting intensified, district-focused approaches such as the MEDP-plus model could rapidly reduce malaria burden, avert premature deaths, and generate substantial economic returns. Malaria elimination should therefore be viewed not merely as a health-sector objective but as a macroeconomic development investment. Sustained domestic financing, strengthened accountability mechanisms, and adaptive district-level implementation will be essential to prevent resurgence and protect past gains. Aligning political commitment with evidence-based financing strategies offers India a credible pathway toward a malaria-free future and broader, more equitable economic development. Abbreviations ACT – Artemisinin-based Combination Therapy ADF – Augmented Dickey-Fuller (test) AIC – Akaike Information Criterion API – Annual Parasite Incidence ARIMAX – Autoregressive Integrated Moving Average with Exogenous Variables ASHA – Accredited Social Health Activist BSTS – Bayesian Structural Time Series CI – Confidence Interval COI – Cost of Illness DALY – Disability-Adjusted Life Year DW – Disability Weight GDP – Gross Domestic Product GIS – Geographic Information System IRS – Indoor Residual Spraying LLIN – Long-Lasting Insecticidal Net LOWESS – Locally Weighted Scatterplot Smoothing MA – Moving Average MAPE – Mean Absolute Percentage Error MASE – Mean Absolute Scaled Error MEDP – Malaria Elimination Demonstration Project MoHFW – Ministry of Health and Family Welfare MoSPI – Ministry of Statistics and Programme Implementation NFME – National Framework for Malaria Elimination NSP – National Strategic Plan NSS – National Sample Survey NSSO – National Sample Survey Office PSA – Probabilistic Sensitivity Analysis RDT – Rapid Diagnostic Test RMSE – Root Mean Square Error ROI – Return on Investment SEIR – Susceptible–Exposed–Infectious–Recovered SOP – Standard Operating Procedure SRS – Sample Registration System WHO – World Health Organisation XGBoost – Extreme Gradient Boosting YLD – Years Lived with Disability YLL – Years of Life Lost YPPLL – Years of Potential Productive Life Lost Declarations Ethics approval and consent to participate: Not applicable Consent for publication: All authors have approved and consented the manuscript for publication. Availability of data and materials: All data and materials available upon request from the corresponding author. Competing interests: None Funding: Study funded by Foundation for Disease Elimination and Control of India Authors' contributions: MPS: Conceptualisation, Methodology, Formal analysis, Data curation, Validation, Writing – original draft; HR: Methodology, Formal analysis, Software, Visualisation, Writing – original draft, Writing – review & editing; PKB: Investigation, Data curation, Validation, Writing – review & editing; HJ: Writing – review & editing; AK: Writing – review & editing; YKG: Writing – review & editing; NKG: Writing – review & editing; SKG: Investigation, Resources, Writing – review & editing; AAL: Conceptualisation, Supervision, Funding acquisition, Project administration, Writing – review & editing. References World Malaria Report. 2025 [Internet]. 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Indian J Med Res. 1996;103:26–45. Mendis K, Wickremasinghe R, Premaratne R. Malaria elimination does not cost more than malaria control: Sri Lanka a case in point. Malar J. 2022;21(1):231. Operational Manual for Implementation of Malaria Programme. 2009 [Internet]. NVBDCP, Ministry of Health & Family Welfare, Government of India; Available from: https://ncvbdc.mohfw.gov.in/WriteReadData/l892s/Malaria-Operational-Manual-2009.pdf Tables Table 1 to 6 are available in the Supplementary Files section.</p Additional Declarations No competing interests reported. Supplementary Files Additionalfile1PolicySummaryBox.docx Table16.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 30 Apr, 2026 Reviews received at journal 13 Apr, 2026 Reviews received at journal 07 Apr, 2026 Reviewers agreed at journal 20 Mar, 2026 Reviewers agreed at journal 19 Mar, 2026 Reviewers agreed at journal 16 Mar, 2026 Reviewers invited by journal 06 Mar, 2026 Editor assigned by journal 06 Feb, 2026 Submission checks completed at journal 04 Feb, 2026 First submitted to journal 03 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8778324","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":604039265,"identity":"10a52df3-c3fa-4d44-85ff-91e71fa3f9ea","order_by":0,"name":"Mrigendra P Singh","email":"","orcid":"","institution":"Foundation for Disease Elimination and Control of India","correspondingAuthor":false,"prefix":"","firstName":"Mrigendra","middleName":"P","lastName":"Singh","suffix":""},{"id":604039266,"identity":"8732decf-6b94-427e-bec0-afbcdc43587a","order_by":1,"name":"Harsh Rajvanshi","email":"","orcid":"","institution":"Foundation for Disease Elimination and Control of India","correspondingAuthor":false,"prefix":"","firstName":"Harsh","middleName":"","lastName":"Rajvanshi","suffix":""},{"id":604039267,"identity":"fcef514c-ded5-45bd-9a88-1fab4da08ee5","order_by":2,"name":"Praveen K Bharti","email":"","orcid":"","institution":"Indian Council of Medical Research - National Institute of Malaria Research","correspondingAuthor":false,"prefix":"","firstName":"Praveen","middleName":"K","lastName":"Bharti","suffix":""},{"id":604039268,"identity":"ef6f33f8-12b1-40ab-bdb3-d624a15d4315","order_by":3,"name":"Himanshu Jayswar","email":"","orcid":"","institution":"Government of Madhya Pradesh","correspondingAuthor":false,"prefix":"","firstName":"Himanshu","middleName":"","lastName":"Jayswar","suffix":""},{"id":604039269,"identity":"98793087-ec7a-41a8-a0e4-7ba1512245ad","order_by":4,"name":"Azadar Khan","email":"","orcid":"","institution":"Foundation for Disease Elimination and Control of India","correspondingAuthor":false,"prefix":"","firstName":"Azadar","middleName":"","lastName":"Khan","suffix":""},{"id":604039270,"identity":"6242c3a7-e4ff-4ab2-8581-346abc5bbc62","order_by":5,"name":"Yogendra K Gupta","email":"","orcid":"","institution":"Foundation for Disease Elimination and Control of India","correspondingAuthor":false,"prefix":"","firstName":"Yogendra","middleName":"K","lastName":"Gupta","suffix":""},{"id":604039271,"identity":"ebada3f0-9d17-4574-bc8a-0b6077e3d957","order_by":6,"name":"Nirmal K Ganguly","email":"","orcid":"","institution":"Foundation for Disease Elimination and Control of India","correspondingAuthor":false,"prefix":"","firstName":"Nirmal","middleName":"K","lastName":"Ganguly","suffix":""},{"id":604039273,"identity":"0bcb0019-dd8b-45d6-83dc-36ea1324d104","order_by":7,"name":"Santosh K Gautam","email":"","orcid":"","institution":"University of Notre Dame","correspondingAuthor":false,"prefix":"","firstName":"Santosh","middleName":"K","lastName":"Gautam","suffix":""},{"id":604039274,"identity":"ac76aab4-ef25-4e01-88c1-aaddf40da746","order_by":8,"name":"Altaf A Lal","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA30lEQVRIiWNgGAWjYDACdiCuYLBg4GNvALIMLIjQwszMwHCGQYKBjecASIsEKVokEkBcIrTwN/Mf/HCAQUKeTfL51Q0/CiQY+Nu7E/BqkTjMzCwB1GLYJp1TdrMH6DCJM2c34LfmMDOD9AcGCUaglrQbPEAtBhK5+LXIA235AbTFvk3yTNrNP8RoMTjMzAZyWGKbBPux20TZYniY2czigIFEchtPDtttGQMJHoJ+kTve+PjGgQob2372489uvvljI8ff3kvA+xDngQgeCEmEcjhgf0CK6lEwCkbBKBhBAAA5ST8H8zRV2AAAAABJRU5ErkJggg==","orcid":"","institution":"Foundation for Disease Elimination and Control of India","correspondingAuthor":true,"prefix":"","firstName":"Altaf","middleName":"A","lastName":"Lal","suffix":""}],"badges":[],"createdAt":"2026-02-03 16:23:53","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8778324/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8778324/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104471998,"identity":"8e37c27a-34fc-4993-b524-5a45ad881f6e","added_by":"auto","created_at":"2026-03-12 07:28:45","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":46305,"visible":true,"origin":"","legend":"\u003cp\u003eForecast analysis of annual time series malaria cases (1990 – 2024) in India using three investment scenarios (Holt’s linear multiplicative model)\u003c/p\u003e","description":"","filename":"groupimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8778324/v1/d47816a7c3c3e356838beaee.jpeg"},{"id":104785805,"identity":"b2387a1b-e98d-467a-a35b-137a49b157e9","added_by":"auto","created_at":"2026-03-17 08:13:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":567693,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8778324/v1/a262a8d3-2b47-47b1-86e1-1ef801691110.pdf"},{"id":104783549,"identity":"296640d2-1530-41c9-8741-ab7ac4dff08d","added_by":"auto","created_at":"2026-03-17 08:01:14","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":19402,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile1PolicySummaryBox.docx","url":"https://assets-eu.researchsquare.com/files/rs-8778324/v1/f7cb95fa119a452a7af20b40.docx"},{"id":104471992,"identity":"6b8b3f1a-2a77-4205-8bf5-654c27405a19","added_by":"auto","created_at":"2026-03-12 07:28:38","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":40489,"visible":true,"origin":"","legend":"","description":"","filename":"Table16.docx","url":"https://assets-eu.researchsquare.com/files/rs-8778324/v1/c7058d9bcd572f84f5061e05.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eQuantifying the Investment Gap, Cost of Malaria Elimination, and Returns on Investment in India: A Macroeconomic Analysis\u003c/p\u003e","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eMalaria continues to pose a formidable global health challenge despite decades of concerted control efforts. In 2024, an estimated 282\u0026nbsp;million cases and 610,000 deaths were recorded worldwide (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). The disease burden remains heavily skewed toward the WHO African Region, which accounts for approximately 94% of global cases and 95% of malaria deaths. The WHO South-East Asia Region contributes a smaller proportion - around 0.96% of global cases and 0.64% of deaths, but within this region, India bears a disproportionately higher share of the burden (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn 2024, India contributed 73% of all estimated malaria cases and 89% of deaths in the South-East Asia Region (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). However, India ranked as the first-largest contributor of reported malaria cases (53%) and deaths (89%) in the SEA region. The gap between reported and estimated cases is due to adjustments made for care-seeking and diagnostic testing rates (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Although the country has made substantial progress in reducing malaria incidence and mortality over the past two decades, the disease remains endemic in several regions, particularly among tribal populations, in forested zones, and in hard-to-reach areas.\u003c/p\u003e \u003cp\u003eIn line with the WHO Global Technical Strategy for Malaria (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e), the Government of India launched the National Framework for Malaria Elimination (NFME) 2016\u0026ndash;2030 (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e), followed by two phases of the National Strategic Plan (NSP) for Malaria Elimination: 2017\u0026ndash;2022 and 2023\u0026ndash;2027. These initiatives aim to achieve zero indigenous cases by 2027 and a malaria-free India by 2030 (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA deteriorating global financing landscape increasingly threatens progress toward these milestones. In 2024, global malaria funding fell short by 42% (US\u003cspan\u003e$\u003c/span\u003e3.9\u0026nbsp;billion) of the resources required to stay on track for elimination. The South-East Asia Region received only US\u003cspan\u003e$\u003c/span\u003e104.5\u0026nbsp;million, representing a 49% decline since 2010 (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Such contraction in external support leaves national programmes vulnerable, particularly those heavily reliant on donor contributions. Ensuring the long-term sustainability of malaria control and elimination, therefore, demands increased domestic investment, complemented by international technical and, to some extent, financial assistance. Evidence from global and regional studies shows that investments in malaria elimination yield substantial returns through reduced disease burden, economic growth, and productivity gains (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIndia\u0026rsquo;s history offers a cautionary reminder of the costs of underinvestment. Following a dramatic reduction in malaria cases to roughly 100,000 annually by the early 1960s, complacency and funding shortfalls led to a resurgence in the 1970s, when cases rose to 6.45\u0026nbsp;million by 1976. This experience underscores the fragility of malaria control gains and the need for sustained political and financial commitment.\u003c/p\u003e \u003cp\u003eAchieving elimination by 2030 will require closing significant gaps in focus and investment, particularly in high-burden districts. Current per capita spending on malaria control in India averages INR 6.0 between 2009 and 2024, among the lowest in the region and far below levels observed in countries that have successfully eliminated malaria (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Public expenditure constitutes only a small share of total malaria-related costs, with households bearing a substantial burden through out-of-pocket payments. Studies indicate that increased public investment could substantially reduce these costs and generate significant economic returns: every dollar invested in malaria elimination yields an estimated six-fold return and up to US\u003cspan\u003e$\u003c/span\u003e90\u0026nbsp;billion in cumulative economic gains (\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAgainst this backdrop, the present study quantifies the economic burden of malaria and the investment required to achieve elimination in India. By combining macroeconomic modelling with national and subnational data, it highlights the magnitude and distribution of investment shortfalls and assesses the potential returns of scaling up interventions to NSP 2023\u0026ndash;2027 levels. Bridging India\u0026rsquo;s malaria investment gap is therefore not merely a public health necessity but a strategic economic imperative for sustainable and equitable national development.\u003c/p\u003e"},{"header":"MATERIAL AND METHODS","content":"\u003cp\u003e\u003cstrong\u003eCost Estimate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA district-level compartmental model (SEIR: Susceptible\u0026ndash;Exposed\u0026ndash;Infectious\u0026ndash;Recovered) was applied to estimate the optimum investment required to achieve malaria elimination in India by 2030. Districts were stratified into three epidemiological clusters: zero (malaria-free), low, and high endemicity, based on malaria incidence rates reported by the National Centre for Vector Borne Disease Control (NCVBDC) in 2024 (10).\u003c/p\u003e\n\u003cp\u003eThe costing framework integrated key epidemiological and programmatic parameters, including projected population at risk, total number of households, intervention coverage, commodity unit costs, and human resource requirements. These parameters were adapted from established costing methodologies used in global malaria investment analyses (11).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eExpenditure estimates were organised into three broad categories: (1) intervention costs, encompassing prevention, surveillance, diagnosis, and treatment; (2) operational costs, including human resource deployment, capacity building, monitoring and evaluation systems, communication, and advocacy; and (3) other costs, such as the development of technical guidelines, training manuals, and standard operating procedures (SOPs), along with research activities, impact assessments, and survey operations. Together, these elements captured both the direct and indirect resource requirements for sustaining a robust elimination programme.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCost projections were generated for three intervention scenarios.\u003c/p\u003e\n\u003cp\u003eScenario 1 (present investment) assumed continuation of prevailing investment trends and current levels of intervention coverage, reflecting the existing operational environment of India\u0026rsquo;s national malaria programme without significant scale-up.\u003c/p\u003e\n\u003cp\u003eScenario 2 (scale-up to NSP 2023\u0026ndash;2027 targets) modelled an expansion of malaria control and elimination activities to achieve the coverage levels specified in the NSP 2023\u0026ndash;2027, including enhanced surveillance, improved diagnostic and treatment coverage, strengthened vector control, and upgraded programme management capacity.\u003c/p\u003e\n\u003cp\u003eScenario 3 (MEDP plus intensified intervention model) extended beyond NSP targets by applying lessons from the Mandla Malaria Elimination Demonstration Project (MEDP), implemented from 2017 to 2021 in a high-burden tribal district of Madhya Pradesh. The MEDP invested approximately INR 180 million to safeguard a population of 1.2 million. Its operational strategy emphasised the \u0026ldquo;T4 approach\u0026rdquo; of tracking every individual, early testing, prompt treatment, and tracking drug compliance, supported by integrated vector management, biannual mass surveillance and treatment, intensive community mobilisation, capacity strengthening of field staff, and rigorous managerial oversight (12). This scenario further incorporated optimal LLIN coverage following the Bhutan example (1 LLIN per 1.5 persons), high-quality assurance, and near-universal compliance with LLIN and IRS through community mobilisation, as observed in Bhutan and Sri Lanka (13,14).\u003c/p\u003e\n\u003cp\u003eThis structured framework enabled estimation of financial requirements across varying implementation intensities. By comparing outputs across scenarios, the analysis quantified the resource gap and investment thresholds required to achieve malaria elimination by 2035.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEconomic Burden Estimate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe cost-of-illness (COI) method was used to estimate the economic burden of malaria in India. Components included treatment costs (medical and non-medical), earnings foregone due to lost workdays, the value of lives lost due to malaria-attributable premature deaths, and personal protection costs. Personal protection costs included expenditures on mosquito repellents, netting, insecticide sprays, smoke, and similar measures, excluding programme-sponsored LLINs and IRS, and were estimated for populations living in high-endemic districts. All values were adjusted using the current-price GDP deflator from the Ministry of Statistics and Programme Implementation (15).\u003c/p\u003e\n\u003cp\u003eThe total economic burden was calculated as:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTotal Economic Burden\u003cstrong\u003e\u0026nbsp;=\u003c/strong\u003e Treatment cost (medical + non-medical) + Earning foregone + Value of lives lost + Personal protection cost\u003c/p\u003e\n\u003cp\u003e1. \u0026nbsp; Treatment cost\u003cstrong\u003e\u0026nbsp;=\u003c/strong\u003e Per illness cost of treatment sought from different sources (including doctor\u0026rsquo;s fee, cost of medicine, laboratory charges for diagnosis, etc) x Number of estimated or reported malaria cases.\u003c/p\u003e\n\u003cp\u003e2. \u0026nbsp; Earning foregone\u003cstrong\u003e\u0026nbsp;=\u003c/strong\u003e (Work days lost due to illness (time period of treatment sought after onset of symptoms + treatment days + supplemented days of absenteeism due to illness) x Average per capita daily income) x Number of estimated or reported malaria cases\u003c/p\u003e\n\u003cp\u003e(The average work days lost was assumed to be 10 days as per the National Sample Survey (NSS), 2018. Workday loss was considered for caregivers in cases of children\u0026rsquo;s illness.\u003c/p\u003e\n\u003cp\u003e3. \u0026nbsp; Value of lives lost (Economic loss per malaria attributable deaths)\u003cstrong\u003e\u0026nbsp;=\u003c/strong\u003e Per capita average annual income x Year of Potential Productive Life Lost (YPPLL)\u003c/p\u003e\n\u003cp\u003e(An average of 15 years of YPPLL for adults was assumed, assuming 45 years of average age at malarial death among adults, an average age of work potency of 60 years or age of retirement from active work, and an average of 35 years of YPPLL was assumed for children\u0026rsquo;s death cases.\u003c/p\u003e\n\u003cp\u003e4.\u0026nbsp; \u0026nbsp;\u0026nbsp;Personal protection cost\u003cstrong\u003e\u0026nbsp;=\u003c/strong\u003e Per capita average expenditure on personal protection (kind of mosquito repellents, insecticide, smoke, window screen, etc) x Population living at high malaria risk\u003c/p\u003e\n\u003cp\u003eMalaria cases and deaths (point estimates) were obtained from WHO World Malaria Reports (2024\u0026ndash;2025) and NCVBDC (2023\u0026ndash;2024). Age-specific proportions of malaria-attributable deaths were derived from Dhingra et al. (2010) (16). Treatment costs by source of care (public facilities, private hospitals, charitable hospitals, private clinics, and informal providers) were taken from the 75th NSS round (2018) (17). The value of life loss was calculated using the Human Capital Method, and per capita income data were obtained from the Reserve Bank of India (18).\u003c/p\u003e\n\u003cp\u003ePersonal protection costs were based on earlier-reported per-capita estimates (19). Direct health system costs were taken from the national malaria programme budget and Global Fund contributions as reported in the WHO World Malaria Report, while elimination costs were derived from the NSP for Malaria Elimination (4).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisease Forecast\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTime-series data on reported annual malaria cases in India from 1990 to 2024 were used to forecast incidence for 2025\u0026ndash;2035. Key intervention milestones were included as covariates, including the Enhanced Malaria Control Programme (1997); introduction of Accredited Social Health Activists (ASHA) workers, Long-Lasting Insecticidal Nets (LLINs), and Artemisinin-based Combination Therapy (ACT) in 2010; deployment of bivalent RDTs and artemisinin\u0026ndash;lumefantrine in the North-East (2013); initiation of NFME (2016); and MEDP-type intensified investment scenarios\u003c/p\u003e\n\u003cp\u003eMultiple forecasting models were applied: Moving Average (MA), LOWESS, Exponential Smoothing, ARIMAX, Bayesian Structural Time Series (BSTS), Extreme Gradient Boosting (XGBoost), Holt\u0026rsquo;s additive and multiplicative methods, and Poisson and Negative Binomial regressions.\u003c/p\u003e\n\u003cp\u003eDue to the unavailability of seasonal malaria data, annual time-series observations were used. Stationarity of the series was assessed using the Augmented Dickey-Fuller (ADF) test, and regular differencing was applied where necessary to achieve stationarity. Model performance was evaluated based on Root Mean Square Error (RMSE), Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Mean Absolute Per cent Error (MAPE), and Mean Absolute Scaled Error (MASE), with the best-fitting model determined by the lowest error values (20).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisability-Adjusted Life Year (DALY)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDALYs were calculated as the sum of years of life lost (YLL) and years lived with disability (YLD):\u003c/p\u003e\n\u003cp\u003eDALY = YLL + YLD\u003c/p\u003e\n\u003cp\u003eWhere, YLL (Years of Life Lost) measures premature death caused by malaria and YLD (Years Lived with Disability) measures the burden from non-fatal cases of malaria.\u003c/p\u003e\n\u003cp\u003eYLL =N x L\u003c/p\u003e\n\u003cp\u003eWhere, N = Number of malaria deaths and L = Standard life expectancy at the age of death (21).\u003c/p\u003e\n\u003cp\u003eYLD = I x DW x D\u003c/p\u003e\n\u003cp\u003eWhere, I = Number of malaria cases, DW = Disability weight (0.191 constant). D = Average duration of illness until recovery or death (10 days constant).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eReturn on Investment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eReturn on investment (ROI) was calculated as net benefit (gain minus cost) divided by cost. Probabilistic Sensitivity Analysis (PSA) using Monte Carlo simulation was conducted to account for uncertainty and variation in ROI estimates for the optimum and MEDP-based enhanced investment scenarios. Statistical analyses were performed using R version 4.4.3. (Foundation for Statistical Computing, Vienna, Austria).\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003eThe World Health Organization (WHO) estimated 2,007,280 malaria cases and 3,431 deaths in India during 2024 (5). In contrast, India officially reported 255,500 cases and 86 deaths in the same year. District-wise malaria prevalence data for 2019\u0026ndash;2024 indicated that approximately 2% of the population resided in high-burden districts with an annual parasite incidence (API) \u0026ge;1, 92% lived in low-burden districts (API \u0026lt;1), and the remaining 6% were in malaria-free areas (10).\u003c/p\u003e\n\u003cp\u003eBased on this stratification, projected populations and households were categorised into three clusters: zero (malaria-free), low (API \u0026lt; 1), and high (API \u0026ge; 1) burden districts. The optimal annual investment required for malaria elimination was estimated for intervention sub-heads under the NSP 2023\u0026ndash;2027, using 2024 malaria incidence as the reference and the unit costs described by Patouillard et al. (11) (Tables 1 and 2). The estimated optimum annual investment required was INR 16,414 million in the initial year of implementation (2026). By comparison, the reported national budget for malaria elimination in 2024 was INR 4,451 million (5).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eScenario 1: Present investment\u003c/em\u003e\u003cstrong\u003e\u0026nbsp;-\u0026nbsp;\u003c/strong\u003eAnalysis of the economic burden of malaria in India, based on programme-reported cases and deaths for 2024 and forecasted values for 2025\u0026ndash;2035, estimated the national economic burden at approximately INR 15\u0026ndash;16 billion annually. Using WHO-estimated cases and deaths, the 2024 burden was approximately INR 42 billion. Overall, the cost of malaria was nearly four times higher than the country\u0026rsquo;s current investment in elimination efforts.\u003c/p\u003e\n\u003cp\u003eDALYs were projected to decline from 5,161 in 2024 to 1,040 by 2035. Forecasts from the best-fitting Holt\u0026rsquo;s linear multiplicative model indicated that, under existing intervention strategies and current investment trends, India is unlikely to achieve its malaria elimination goal by 2035 (Table 3, Figure 1). This model showed the best statistical accuracy, with the lowest MAPE (33.05) and MASE (0.44) values (Table 6)\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eScenario 2: NSP 2023\u0026ndash;2027\u0026ndash;based scale-up\u003c/em\u003e\u003cstrong\u003e\u0026nbsp;-\u0026nbsp;\u003c/strong\u003eForecasts incorporating enhanced intervention coverage at the estimated optimal investment level under the NSP 2023\u0026ndash;2027 showed substantial reductions in malaria transmission and burden. Malaria cases were projected to decline from 255,500 in 2024 to 1,429 in 2030 and reach double-digit levels (31 cases) by 2032. Malaria-related deaths were expected to decline from 86 in 2024 to zero by 2029.\u003c/p\u003e\n\u003cp\u003eCorrespondingly, DALYs were projected to decline from 5,161 in 2024 to 7 by 2030. The economic burden was projected to fall sharply, from approximately INR 16 billion in 2024 to INR 600 million by 2035. This scenario yielded an estimated ROI of 3.18 (95% CI: 3.12\u0026ndash;3.24) (Table 4, Figure 1).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eScenario 3: MEDP-plus intensified investment\u003c/em\u003e -\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eThe enhanced investment scenario modelled on the MEDP-plus approach showed even more rapid reductions. Malaria cases were projected to decline from 255,500 in 2024 to 142 by 2030, while deaths were projected to reach zero by 2028. DALYs declined from 5,161 in 2024 to 1 by 2030.\u003c/p\u003e\n\u003cp\u003eThe associated economic burden decreased from INR 16 billion in 2024 to INR 138 million by 2035, generating a higher ROI of 3.76 (95% CI: 3.70\u0026ndash;3.83) (Table 5, Figure 1).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eComparative outcomes across scenarios -\u0026nbsp;\u003c/em\u003eAcross the three investment scenarios, the modelling revealed a clear gradient between financing intensity and elimination outcomes. Under the present investment trajectory, malaria transmission is projected to persist beyond 2035, with an economic burden nearly four times higher than current programme spending. Scaling up to NSP 2023\u0026ndash;2027 investment levels would reduce malaria cases to two digits by 2032 and achieve zero deaths by 2029, with an ROI of 3.18. The intensified MEDP-plus model provided the fastest pathway to elimination, with near-zero transmission by 2030, zero deaths by 2028, and the highest economic return (ROI 3.76), indicating that only a front-loaded, intensified investment strategy places India on a credible elimination trajectory.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis study provides the first district-stratified macroeconomic analysis of malaria elimination investment needs in India, integrating epidemiological forecasting, cost-of-illness estimation, and return-on-investment modelling. The results demonstrate that India\u0026rsquo;s current investment in malaria control, averaging INR 6.0 (US\u003cspan\u003e$\u003c/span\u003e0.07) per capita annually, is grossly inadequate to achieve elimination by 2030 (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). An estimated INR 16,414\u0026nbsp;million (US\u003cspan\u003e$\u003c/span\u003e193\u0026nbsp;million) in annual funding - more than four times current allocations would be required to sustain an elimination trajectory. This finding aligns with the 2025 financial gap analysis conducted by the National Health Systems Resource Centre, which reported that the allocation for all vector-borne disease programmes under the Ministry of Health and Family Welfare covered only one-third of the normative requirement (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur findings, based on reported 2024 malaria prevalence, differ from those of Gupta and Chowdhury, who used 2012 data (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Their estimate of India\u0026rsquo;s national economic burden of malaria, approximately US\u003cspan\u003e$\u003c/span\u003e1.94\u0026nbsp;billion, used a cost-of-illness framework applied to the population at risk, based on WHO estimates (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). While their estimates were higher, reflecting broader inclusion of household expenditures and indirect productivity losses at the national scale, both analyses converge in demonstrating that malaria imposes a substantial macroeconomic cost on India. Methodologically, Gupta and Chowdhury\u0026rsquo;s work was static and retrospective, whereas our analysis employs dynamic epidemiological forecasting via a district-stratified SEIR model, linking transmission trends to investment needs and returns.\u003c/p\u003e \u003cp\u003eEnhanced financing yields disproportionate benefits for both health and the economy. Under the present investment trajectory, malaria is projected to persist beyond 2030, with an economic burden nearly four times higher than current programme spending. Scaling interventions to the targets outlined in the National Strategic Plan (2023\u0026ndash;2027) would avert approximately 1,429 cases annually by 2030, reduce DALYs to single digits, eliminate deaths by 2029, and yield an ROI of 3.18. An intensified scenario modelled on the MEDP-plus approach projects near elimination by 2030, with 142 cases, zero deaths, and a higher ROI of 3.76. Among the three scenarios, only the intensified investment pathway places India on a credible trajectory toward elimination.\u003c/p\u003e \u003cp\u003eSustaining these gains will require consistent programmatic success nationwide, as even limited outbreaks could compromise efforts toward elimination. An earlier forecasting study using data from 1990 to 2022 suggested that India could eliminate malaria by 2027 (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). However, the rise in cases during 2023 and 2024 necessitated a recalibration of projections, with only the intensified scenario indicating near elimination by 2030.\u003c/p\u003e \u003cp\u003eThese results are consistent with regional evidence showing that each US\u003cspan\u003e$\u003c/span\u003e1 invested in malaria elimination yields US\u003cspan\u003e$\u003c/span\u003e19\u0026ndash;31 in economic returns through reduced treatment costs, productivity gains, and macroeconomic growth (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). The relationship between malaria reduction and economic advancement is well established: cross-country analyses show that malaria-endemic nations experience annual GDP growth rates approximately 1.3% lower than their non-endemic peers, and that elimination contributes to improved educational attainment, workforce participation, and poverty reduction (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFor India, these findings underscore the urgent need to strengthen domestic financial stewardship and reduce dependence on external assistance. Historical experience offers a cautionary precedent: the resurgence of malaria in the 1970s, following premature withdrawal of funding and weakened surveillance systems, reversed earlier gains (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Without sustained political and fiscal commitments, similar vulnerabilities may re-emerge. Ensuring continuity of interventions will require ring-fenced malaria budgets at both central and state levels, alongside innovative mechanisms such as performance-linked financing, public health impact bonds, and private-sector engagement through corporate social responsibility frameworks.\u003c/p\u003e \u003cp\u003eThe policy implications extend beyond the health sector. Malaria elimination should be framed as a macroeconomic and social development strategy rather than a disease-specific expenditure. Evidence from the Greater Mekong Subregion and sub-Saharan Africa shows that elimination accelerates progress toward multiple Sustainable Development Goals by improving educational outcomes, promoting gender equity, and strengthening household economic resilience (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Achieving similar synergies in India will require not only increased funding but also efficient resource utilisation, accountability mechanisms, and adaptive planning tailored to district-level heterogeneity.\u003c/p\u003e \u003cp\u003eIt has been argued that malaria elimination does not necessarily require greater financial investment than efficient malaria control. However, sustained funding to support an agile programme, flexible resource allocation, strengthened workforce capacity, and robust monitoring and evaluation systems are essential for achieving and maintaining elimination (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBhutan\u0026rsquo;s experience illustrates the value of intensified vector control and sustained external financial support. Between 2006 and 2014, nationwide scale-up of long-lasting insecticidal nets (approximately one net per 1.5 persons), indoor residual spraying, and GIS-based surveillance systems contributed substantially to malaria reduction (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). In contrast, India currently distributes one LLIN per 2.5 persons, suggesting that higher coverage may be necessary to achieve comparable effectiveness (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis analysis has limitations. It relies on secondary data sources, including WHO World Malaria Reports and National Sample Surveys, which may under- or overestimate the true burden due to incomplete case detection or care-seeking biases. Forecasting was based on annual rather than seasonal data, which may have underestimated short-term fluctuations. Economic burden estimates excluded intangible and long-term costs, such as caregiver stress and post-infectious sequelae, which would further increase estimated returns if included. Nevertheless, triangulation of multiple models, probabilistic sensitivity analysis, and district-level stratification strengthen confidence in the robustness of these findings. Political feasibility, health system constraints, and behavioural compliance were not explicitly modelled.\u003c/p\u003e \u003cp\u003eIndia stands at a critical juncture. Achieving near elimination by 2030 is technically feasible but contingent on sustained funding for all components of elimination, including human resources, supply chain systems, accountability frameworks, and independent oversight. While near elimination may be achievable by 2030, a malaria-free India, defined as zero indigenous transmission, may be more realistic by 2035. Failure to act risks not only resurgence but also the erosion of broader health and economic gains made over the past two decades. Strengthening domestic financing and aligning investments with evidence-based strategies will be essential to convert political commitment into sustainable outcomes. Malaria elimination should therefore be recognised not merely as a public health goal but as an investment in national productivity, resilience, and equitable development.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eIndia stands at a critical juncture in its malaria elimination trajectory. This analysis shows that near elimination by 2030 is technically feasible, but only under conditions of sustained, front-loaded, and strategically targeted investment. Current spending levels are insufficient to meet national goals and risk prolonging transmission well beyond the stated timeline. In contrast, scaling up to NSP-aligned investment levels and especially adopting intensified, district-focused approaches such as the MEDP-plus model could rapidly reduce malaria burden, avert premature deaths, and generate substantial economic returns.\u003c/p\u003e \u003cp\u003eMalaria elimination should therefore be viewed not merely as a health-sector objective but as a macroeconomic development investment. Sustained domestic financing, strengthened accountability mechanisms, and adaptive district-level implementation will be essential to prevent resurgence and protect past gains. Aligning political commitment with evidence-based financing strategies offers India a credible pathway toward a malaria-free future and broader, more equitable economic development.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003eACT\u003c/strong\u003e \u0026ndash; Artemisinin-based Combination Therapy\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eADF\u003c/strong\u003e \u0026ndash; Augmented Dickey-Fuller (test)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAIC\u003c/strong\u003e \u0026ndash; Akaike Information Criterion\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAPI\u003c/strong\u003e \u0026ndash; Annual Parasite Incidence\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eARIMAX\u003c/strong\u003e \u0026ndash; Autoregressive Integrated Moving Average with Exogenous Variables\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eASHA\u003c/strong\u003e \u0026ndash; Accredited Social Health Activist\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBSTS\u003c/strong\u003e \u0026ndash; Bayesian Structural Time Series\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCI\u003c/strong\u003e \u0026ndash; Confidence Interval\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCOI\u003c/strong\u003e \u0026ndash; Cost of Illness\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDALY\u003c/strong\u003e \u0026ndash; Disability-Adjusted Life Year\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDW\u003c/strong\u003e \u0026ndash; Disability Weight\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGDP\u003c/strong\u003e \u0026ndash; Gross Domestic Product\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGIS\u003c/strong\u003e \u0026ndash; Geographic Information System\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIRS\u003c/strong\u003e \u0026ndash; Indoor Residual Spraying\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLLIN\u003c/strong\u003e \u0026ndash; Long-Lasting Insecticidal Net\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLOWESS\u003c/strong\u003e \u0026ndash; Locally Weighted Scatterplot Smoothing\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMA\u003c/strong\u003e \u0026ndash; Moving Average\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMAPE\u003c/strong\u003e \u0026ndash; Mean Absolute Percentage Error\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMASE\u003c/strong\u003e \u0026ndash; Mean Absolute Scaled Error\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMEDP\u003c/strong\u003e \u0026ndash; Malaria Elimination Demonstration Project\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMoHFW\u003c/strong\u003e \u0026ndash; Ministry of Health and Family Welfare\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMoSPI\u003c/strong\u003e \u0026ndash; Ministry of Statistics and Programme Implementation\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNFME\u003c/strong\u003e \u0026ndash; National Framework for Malaria Elimination\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNSP\u003c/strong\u003e \u0026ndash; National Strategic Plan\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNSS\u003c/strong\u003e \u0026ndash; National Sample Survey\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNSSO\u003c/strong\u003e \u0026ndash; National Sample Survey Office\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePSA\u003c/strong\u003e \u0026ndash; Probabilistic Sensitivity Analysis\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRDT\u003c/strong\u003e \u0026ndash; Rapid Diagnostic Test\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRMSE\u003c/strong\u003e \u0026ndash; Root Mean Square Error\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eROI\u003c/strong\u003e \u0026ndash; Return on Investment\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSEIR\u003c/strong\u003e \u0026ndash; Susceptible\u0026ndash;Exposed\u0026ndash;Infectious\u0026ndash;Recovered\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSOP\u003c/strong\u003e \u0026ndash; Standard Operating Procedure\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSRS\u003c/strong\u003e \u0026ndash; Sample Registration System\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWHO\u003c/strong\u003e \u0026ndash; World Health Organisation\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eXGBoost\u003c/strong\u003e \u0026ndash; Extreme Gradient Boosting\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eYLD\u003c/strong\u003e \u0026ndash; Years Lived with Disability\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eYLL\u003c/strong\u003e \u0026ndash; Years of Life Lost\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eYPPLL\u003c/strong\u003e \u0026ndash; Years of Potential Productive Life Lost\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cem\u003eEthics approval and consent to participate:\u003c/em\u003e Not applicable\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConsent for publication:\u003c/em\u003e All authors have approved and consented the manuscript for publication.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAvailability of data and materials:\u003c/em\u003e All data and materials available upon request from the corresponding author.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCompeting interests:\u003c/em\u003e None\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFunding:\u003c/em\u003e Study funded by Foundation for Disease Elimination and Control of India\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAuthors\u0026apos; contributions:\u003c/em\u003e MPS: Conceptualisation, Methodology, Formal analysis, Data curation, Validation, Writing \u0026ndash; original draft; HR: Methodology, Formal analysis, Software, Visualisation, Writing \u0026ndash; original draft, Writing \u0026ndash; review \u0026amp; editing; PKB: Investigation, Data curation, Validation, Writing \u0026ndash; review \u0026amp; editing; HJ: Writing \u0026ndash; review \u0026amp; editing; AK: Writing \u0026ndash; review \u0026amp; editing; YKG: Writing \u0026ndash; review \u0026amp; editing; NKG: Writing \u0026ndash; review \u0026amp; editing; SKG: Investigation, Resources, Writing \u0026ndash; review \u0026amp; editing; AAL: Conceptualisation, Supervision, Funding acquisition, Project administration, Writing \u0026ndash; review \u0026amp; editing.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWorld Malaria Report. 2025 [Internet]. 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New Delhi: Ministry of Statistics and Programme Implementation (MoSPI), Government of India; 2019.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHandbook of statistics on Indian economy [Internet]. New Delhi: Reserve Bank of India; Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://rbi.org.in/Scripts/AnnualPublications.aspx?head=Handbook%20of%20Statistics%20on%20Indian%20Economy\u003c/span\u003e\u003cspan address=\"https://rbi.org.in/Scripts/AnnualPublications.aspx?head=Handbook%20of%20Statistics%20on%20Indian%20Economy\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMills A, Lubell Y, Hanson K. Malaria eradication: the economic, financial and institutional challenge. Malar J. 2008;7(Suppl 1):S11.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSingh MP, Rajvanshi H, Bharti PK, Anvikar AR, Lal AA. Time series analysis of malaria cases to assess the impact of various interventions over the last three decades and forecasting malaria in India towards the 2030 elimination goals. Malar J. 2024;23(1):50.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSample Registration System (SRS). - Abridged Life Tables 2016\u0026ndash;2020 [Internet]. Office of the Registrar General \u0026amp; Census Commissioner, India; Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://censusindia.gov.in/nada/index.php/catalog/44377\u003c/span\u003e\u003cspan address=\"https://censusindia.gov.in/nada/index.php/catalog/44377\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFinancial Gap Analysis of five National Health Programmes [Internet]. National Health Systems Resource Centre, Government of India. 2025 Oct. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://nhsrcindia.org/sites/default/files/2025-05/Financial%20Gap%20Analysis%20of%205%20National%20Health%20Programmes.pdf\u003c/span\u003e\u003cspan address=\"https://nhsrcindia.org/sites/default/files/2025-05/Financial%20Gap%20Analysis%20of%205%20National%20Health%20Programmes.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThe economic impact. of US funding for malaria. Oxford Economics and Malaria No More US.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGallup JL, Sachs JD. The economic burden of malaria. 2000.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSharma V, Mehrotra K. Malaria resurgence in India: a critical study. Soc Sci Med. 1986;22(8):835\u0026ndash;45.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSharma VP. Re-emergence of malaria in India. Indian J Med Res. 1996;103:26\u0026ndash;45.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMendis K, Wickremasinghe R, Premaratne R. Malaria elimination does not cost more than malaria control: Sri Lanka a case in point. Malar J. 2022;21(1):231.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOperational Manual for Implementation of Malaria Programme. 2009 [Internet]. NVBDCP, Ministry of Health \u0026amp; Family Welfare, Government of India; Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ncvbdc.mohfw.gov.in/WriteReadData/l892s/Malaria-Operational-Manual-2009.pdf\u003c/span\u003e\u003cspan address=\"https://ncvbdc.mohfw.gov.in/WriteReadData/l892s/Malaria-Operational-Manual-2009.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 to 6 are available in the Supplementary Files section.\u003c/p"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"malaria-journal","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"malj","sideBox":"Learn more about [Malaria Journal](http://malariajournal.biomedcentral.com/)","snPcode":"12936","submissionUrl":"https://submission.nature.com/new-submission/12936/3","title":"Malaria Journal","twitterHandle":"@malariajournal","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Malaria Elimination, ROI, Investment Case, MEDP","lastPublishedDoi":"10.21203/rs.3.rs-8778324/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8778324/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eMalaria continues to impose a significant health and economic burden in India, accounting for over half of cases and deaths in the WHO South-East Asia Region despite substantial progress in recent decades. Although India has committed to achieving zero indigenous malaria cases by 2027 and complete elimination by 2030 under the National Strategic Plan (NSP) 2023\u0026ndash;2027, current investments may be inadequate to meet these targets. This study quantifies the economic burden of malaria, estimates the investment required for elimination, and assesses the potential returns on investment using a macroeconomic modelling approach.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA district-level SEIR model stratified by endemicity (high, low, and malaria-free districts) was applied to estimate intervention costs under three scenarios: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) present investment; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) NSP 2023\u0026ndash;2027\u0026ndash;aligned investment; and (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) intensified interventions modelled on the Mandla Malaria Elimination Demonstration Project (MEDP) plus Sri Lanka and Bhutan\u0026ndash;based approaches. The cost-of-illness method was used to estimate the national economic burden, including treatment costs, productivity losses, premature mortality, and personal protection expenditures. Forecasting models were applied to project malaria incidence through 2035, while Disability-Adjusted Life Years (DALYs) and return on investment (ROI) were calculated to assess health and economic outcomes.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe findings indicate that the current investment of approximately INR 6 per capita annually is insufficient. The required annual investment is estimated at INR 16,414\u0026nbsp;million, compared with INR 4,451\u0026nbsp;million allocated in 2024. Under current trends, malaria transmission would persist beyond 2035. Scaling up to NSP targets could reduce cases to two digits by 2032, eliminate deaths by 2029, and yield an ROI of 3.18. The intensified MEDP-plus scenario projected near elimination by 2030, zero deaths by 2028, and a higher ROI of 3.76.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThese findings demonstrate that closing India\u0026rsquo;s malaria investment gap is both a public health and macroeconomic priority.\u003c/p\u003e","manuscriptTitle":"Quantifying the Investment Gap, Cost of Malaria Elimination, and Returns on Investment in India: A Macroeconomic Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-12 07:27:53","doi":"10.21203/rs.3.rs-8778324/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-04-30T19:12:58+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-13T14:09:02+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-07T14:51:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"138057106963583768667268809792020856229","date":"2026-03-20T18:34:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"256992762447197514962533535402134474717","date":"2026-03-19T13:50:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"207630473535592170208417218393993824970","date":"2026-03-16T08:38:52+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-06T09:44:37+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-06T16:03:08+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-04T18:05:19+00:00","index":"","fulltext":""},{"type":"submitted","content":"Malaria Journal","date":"2026-02-03T16:09:08+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"malaria-journal","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"malj","sideBox":"Learn more about [Malaria Journal](http://malariajournal.biomedcentral.com/)","snPcode":"12936","submissionUrl":"https://submission.nature.com/new-submission/12936/3","title":"Malaria Journal","twitterHandle":"@malariajournal","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"80f18c98-8e52-46e4-be21-59ac080e42dd","owner":[],"postedDate":"March 12th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-04-30T19:12:58+00:00","index":28,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-12T07:27:53+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-12 07:27:53","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8778324","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8778324","identity":"rs-8778324","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}