Epidemiology of Kyasanur forest disease in India, 2000–2025: a systematic review and meta-analysis

Summary

Background

Kyasanur forest disease (KFD) is a tick-borne viral haemorrhagic fever that has recently expanded beyond its historically endemic focus in Shimoga district, Karnataka, India. Robust estimates of disease burden, distribution and clinical manifestations remain limited. This systematic review and meta-analysis aimed to estimate pooled KFD positivity, case-fatality rates and to characterise the distribution and clinical features of KFD across India.

Methods

Four databases including PubMed, Scopus, Web of Science, Embase along with grey literature (Google Scholar) were searched (January 1, 2000–January 15, 2025). Non-English publications, studies published prior to 2000, reviews, systematic reviews, meta-analyses, seropositivity assessments, and investigations restricted to diagnostic test comparisons were excluded. Eligible studies included those evaluating KFD diagnostic positivity among clinically suspected patients, tick and non-human primate infectivity, outbreak investigations, symptoms, and mortality. Following duplicate removal, study quality was verified utilising NIH Quality Assessment Tool, resulting in exclusion of poor-quality papers. Primary outcomes, diagnostic positivity among clinically suspected KFD patients and case fatality ratio (CFR), were pooled using random-effects meta-analysis with logit transformation. PROSPERO registration: CRD42024621971.

Findings

Of the records screened, 31 studies met the inclusion criteria. Meta-analysis of 18 studies included 7907 suspected patients with 1431 individuals tested positive, yielding a 21% pooled diagnostic positivity rate (95% CI: 14–30). Positivity varied substantially by district. Of 2798 cumulative cases, specific gender details were available for 1422, comprising 738 females and 684 males. The pooled CFR was 4% (95% CI: 2–8). KFDV circulation in ticks was reported in 11 studies (3466 tick pools tested, 116 positive), predominantly Haemaphysalis spinigera and Haemaphysalis turturis. Fever, myalgia and headache were the most consistently reported symptoms.

Interpretation

KFD has spread significantly from its initial origin to neighbouring states, and intensifying surveillance of new foci is critical. Monkeys appear to seed new foci for KFD, while ticks are responsible for sustaining transmission. Early diagnosis and case management can significantly improve patient outcomes.

Funding

None.

Research in context.

Evidence before this study

We searched PubMed, Scopus, Web of Science, and Embase, with grey literature surveyed via Google Scholar, for studies published between Jan 1, 2000, and January 15, 2025, using the terms “Kyasanur forest disease”, “Kyasanur forest disease virus”, “KFD”, and “KFDV” combined with “India”. Existing literature included narrative reviews addressing the virology, immunology, and therapeutic landscape of KFD, but we found no prior systematic review or meta-analysis that assessed outcomes, such as diagnostic positivity, case fatality, clinical presentation, or risk factors for KFD in India.

Added value of this study

This systematic review and meta-analysis included thirty-one studies from India published between 2000 and 2025. We estimated a pooled diagnostic positivity of 21% (95% CI: 14–30) among clinically suspected patients and a pooled case fatality ratio of 4% (95% CI: 2–8). The analysis identified fever, headache, and myalgia as the most consistently documented clinical presentation. KFDV circulation in ticks was predominantly reported in Haemaphysalis spinigera and Haemaphysalis turturis. The study characterizes geographic and temporal variations and highlights the high-risk occupational exposures. It complements existing narrative reviews of KFD virology, immunology, and therapeutics with an epidemiological picture grounded in pooled estimates rather than descriptive summary.

Implications of all the available evidence

This meta-analysis provides an epidemiological foundation for KFD surveillance and research prioritisation in endemic regions of India. The region-stratified and temporally disaggregated CFR estimates highlight geographic heterogeneity in disease outcomes that may assist in identifying areas where clinical surveillance and healthcare preparedness require strengthening. The findings could inform decisions regarding surveillance prioritisation, ecological risk assessment, and targeted public health interventions in KFD-endemic zones. Future research on clinical management, antiviral development, and vaccine research should include experimental and clinical investigations that extends beyond the scope of the present work.

Introduction

Kyasanur forest disease (KFD) is an emerging tick-borne viral haemorrhagic fever that is prevalent in Karnataka state, India and its neighbouring states along the Western Ghats of India. Commonly referred to as monkey fever, the disease is caused by Kyasanur forest disease virus (KFDV), a member of the genus Flavivirus (family Flaviviridae) closely resembling Alkhurma virus.^1,2 KFD is primarily transmitted through the bite of Haemaphysalis spinigera ticks with several other Haemaphysalis species also implicated in transmission.3, 4, 5, 6, 7, 8 The life cycle of ticks includes three distinct stages i.e., larvae, nymph and adults, each requiring a blood meal from a vertebrate host to moult to next stage. There is no documented evidence of human-to-human transmission (Supplementary Fig. S1).^4,5,8,9

KFDV was first identified in 1957 from dead and sick monkeys in the Kyasanur forest of Shimoga district, Karnataka state, India,¹⁰ and is known to infect both primates and non-primates, including birds, rodents, and squirrels. Monkeys (black-faced langurs and red-faced bonnets) are highly susceptible amplifying hosts that die shortly after infection and often signal viral activity ahead of human cases,¹¹ while small mammals sustain the tick-borne transmission cycle between outbreaks; cattle sustain tick populations but do not amplify the virus.¹²

Historically, KFD was restricted to five districts of Karnataka, but its documented activity has spread to neighbouring regions along the Western Ghats with outbreaks reported in states of Tamil Nadu, Maharashtra, Goa Kerala and Karnataka in recent years.² Reduced rainfall and rising heat in southern states may have promoted tick proliferation and elevated the risk of KFDV transmission.13, 14, 15 Moreover, deforestation in the Western Ghats, driven by agricultural expansion and human activities, has disrupted ecosystems, promoting tick proliferation and expanding KFD transmission into new areas.¹⁶

Existing literature on KFD in India has largely comprised outbreak investigations restricted to individual districts or states, narrative reviews of KFDV virology and transmission biology, and reports on the therapeutic and vaccine landscape.17, 18, 19, 20 However, no study to date has quantitatively synthesised the human epidemiological evidence across this literature. Robust, pooled estimates of diagnostic positivity, clinical presentation, and case fatality rates across the country’s expanding endemic zones remain absent. Despite its rapid geographic spread and appreciable fatality, KFD remains a neglected disease with insufficient surveillance infrastructure and limited funding for research. To address this gap, we conducted a systematic review and meta-analysis of KFD in India (2000–2025) with the aim of generating pooled estimates of diagnostic positivity among clinically suspected patients and case fatality ratio (CFR), and to characterise the clinical manifestations, vector/host dynamics, and occupational risk factors associated with KFD.

Methods

The systematic review was designed and conducted following Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) guidelines,²¹ while, meta-analysis was performed implementing the MOOSE protocol (Meta-analysis Of Observational Studies in Epidemiology).²² The study was registered on PROSPERO prior to collection of data and synthesis (registration number CRD42024621971).

Data sources and search strategy

A comprehensive search strategy was developed to search for relevant peer-reviewed literature from four electronic databases: PubMed (699), Scopus (200), Web of Science (161) and Embase (78), while grey literature was surveyed on Google Scholar, with the first 100 results screened, as saturation was reached with no new relevant studies identified beyond this point. The exact Boolean search strings for each database including filters and date limits are provided in Supplementary Table S1. In brief, the terms “Kyasanur forest disease”, “Kyasanur forest disease virus”, “KFD”, and “KFDV” were used in conjunction with “India”. Searches were restricted to publications from 1st January 2000 to 15th January 2025 to highlight the recent geographical expansion of infection. We also reviewed the reference list from all subsequent articles to identify additional potential information sources. The search strategy was specifically designed to be sensitive rather than specific to minimise the risk of missing relevant studies and yielded a total of 1238 (Fig. 1).

Fig. 1.

PRISMA flow chart for the study design.

CoCoPop framework (Condition, Context, Population) was used for inclusion and exclusion criteria, which is well suited for prevalence, incidence or aetiology based meta-analysis.²³ In the present study, CoCoPop was applied to estimate diagnostic positivity rates among clinically suspected KFD patients rather than population-level prevalence. Studies were included if they reported relevant data on either KFDV infections in suspected acute undifferentiated febrile illness (AUFI) patients confirmed by standardised laboratory tests i.e., RT-PCR or IgM ELISA, described clinical manifestations and/or mortality in KFDV positive humans or tested the presence of KFDV in field collected samples of dead monkeys and ticks from KFD endemic zones. Cross-sectional and outbreak investigation studies, case studies, cohorts, and case reports were included for the analysis, while reviews, systematic reviews, meta-analyses, seropositivity, and diagnostic studies that only compared different diagnostic tests were excluded. Additionally, studies not in English and conducted before 2000 were also excluded. Duplicate studies were removed using Rayyan AI tool and verified by the reviewers.²⁴ As KFD has not been reported anywhere outside of India, all studies conducted in India meeting the inclusion criteria were included. The authors, SSAH and MK, conducted the data search and verified the publications’ eligibility. Each included paper underwent two reviews, and any disagreements were resolved by consensus between the reviewers (RC and SSAH) and the senior reviewers (BM and AS).

Two authors (SSAH and MK) independently assessed the quality of included articles using the US National Institutes of Health (NIH) Quality Assessment Tool developed by the National Heart, Lung and Blood Institute (NHLBI) from the National Institute of Health (Version July 2021).²⁵ Responses to each question in the tool were limited to “yes,” “no,” or “cannot determine/not reported/not applicable”. Based on the responses to questionnaire, the risk of bias in each study was evaluated, and studies were rated as ‘Good’, ‘Fair’ or ‘Poor’ (indicating a fatal flaw and excluded from analysis). Any disagreements were resolved by consensus with a third reviewer (RC). Cohen’s Kappa was calculated to estimate degree of agreement between two reviewers on study quality. Finally, the study quality questionnaire for each type of study was converted into twelve risk of bias domains and visualised in the form of a traffic light plot.

KFD transmission is complex with multiple stages involved in different species. Therefore, the analyses were organised into four modules to capture distinct dimensions of KFD transmission and clinical burden. Module 1 (M1: 11 studies) relates to KFD vector and measures the proportion of captured tick pools that were found positive. Module 2 (M2: 10 studies) assesses the occurrence of KFD-associated monkey deaths. These two modules identify the environmental evidence of virus circulation and were described narratively. Module 3 is associated with human burden of KFD and includes two outcomes: the proportion of laboratory confirmed KFD cases among clinically suspected human patients (M3a: 18 studies), and severity and extent of KFD outbreaks, which filters the studies that specifically conducted outbreak investigations (M3b: 13 studies). For Module 3a, studies were included only if they reported both the number of laboratory-confirmed KFD positive cases and total number of clinically suspected patients tested, allowing calculation of a diagnostic positivity with a clearly defined denominator. For Module 3b, outbreak data derived from peer-reviewed literature and government Integrated Disease Surveillance Programme (IDSP) surveillance records were cross-checked to identify duplicate outbreak events. IDSP-derived counts, which do not consistently report the number of suspected cases tested, were excluded from pooled analysis and are presented descriptively as part of outbreak chronology only. Module 4 is related to clinical burden of KFD and includes two outcomes: the various clinical manifestations of KFD (M4a: 15 studies) and overall case fatality ratio (CFR) (M4b: 18 studies) (Fig. 1). The four modules are described in more detail in the Supplementary materials along with studies included in each module and analytical methods used (Supplementary Table S2). In total, including all four modules, 31 unique studies were included in the study, individual studies could contribute to more than one module/outcome. As a secondary outcome, the occupational/exposure risks of KFD were also described.

A standardised template was prepared for data extraction, which included following items – study ID, study title, date of issue, year of publication, study period, study design, state name, district name, specific location of collection, number of human samples tested, number of humans positive, number of humans dead, type of symptom/complication/co-morbidity/co-infection, number with symptom/complication/co-morbidity/co-infection, number of dead monkey samples collected, number of dead monkey samples positive for KFDV, number of tick samples collected, number of tick samples positive for KFDV and occupational/exposure risk. Data were extracted from each article by two reviewers (SSAH and MK) independently entered into standardised forms, and any discrepancies were settled by a third reviewer (RC).

Statistical analysis

Selected studies were grouped under the four modules and six outcomes based on availability of relevant data. Study eligibility was also determined based on sample size to ensure that they were sufficiently informative for selected analysis. Modules 1 and 2 were described narratively and a minimum sample size of two was selected for inclusion. Modules 3a, 4a and 4b were pooled using meta-analysis, and included only those studies that had a minimum sample size of three. A minimum sample size of three was selected as the threshold for inclusion in pooled analysis, as studies with fewer observations provide unstable proportion estimates and disproportionately inflate heterogeneity.

Data for modules 3a, 4a and 4b were imported into R software and pooled using meta-analysis with metaprop function from meta package in R. The available data was unsuitable for pooling for remaining studies, and these were described narratively. A logit transformation was applied to the data prior to meta-analysis to stabilize variances due to extreme proportions, prevalence studies are likely to have significant variations in their true effect sizes, as two distinct populations can never have the same characteristics. Thus, to determine the heterogeneity among studies (both inter-study variances and sampling errors), a random effects model was used, reflecting the assumption that effect sizes may vary across different geographical regions and study populations. Estimates were pooled across multiple studies using a generalized linear mixed model (GLMM), which is considered to yield relatively more accurate results when variance between studies is high, as it is based on the binomial distribution model.²⁶ Maximum likelihood was used to estimate between study variance. A Baujat plot, which charts the impact of each study on the overall effect size against its impact on heterogeneity, was used to analyse the effect of outliers. Publication bias was assessed using DOI plots, which are recommended over funnel plots for proportion and GLMM-based meta-analysis.²⁷ For modules 3a and 4b subgroup analysis was done based on districts to explore geographic variation. It is important to note that pooled estimates derived from Module 3a reflect diagnostic yield of the National Centre for Disease Control (NCDC) KFD surveillance case definition across varied transmission contexts and study settings, and should not be interpreted as population-level prevalence or disease burden estimates.²⁸ A key methodological challenge for pooled reporting frequency of symptoms (module 4a) was that symptoms not reported in a given study may reflect either true absence or failure to document it. To address this, two complementary analytical approaches were adopted – the reported case analysis (RCA) which includes only those studies that reported the particular symptom (indicating that non-reporting represents incomplete ascertainment), and zero imputation sensitivity analysis (ZIS) that assigns a frequency of zero to symptoms that are not reported in a given study (indicating that non-reporting represents true absence). Together, these two estimates define the plausible range within which the true pooled frequency of each manifestation likely lies. Exposure/occupational risk for KFD was reported in the form of relevant graphs, figures, and tables and were analysed descriptively. All figures were generated using the ggplot package in R and/or QGIS software.

Role of the funding source

The study is not supported by any funding body.

Results

The search retrieved 1238 publications. After deleting duplicates, 225 articles underwent full-text screening. 194 articles were excluded; thirty-one studies met inclusion criteria and were included in systematic review (Fig. 1). One comprehensive review article on KFD outbreaks was retained for comparative analysis² (Table 1, Supplementary Table S3).

Table 1.

Characteristics of included studies, arranged by study period.

Study period	Author (Year)	Study design	Geographic unit	Outbreak status
2011–2012	Kasabi et al. (2013b)29	Case-control study	Shimoga district, Karnataka	Outbreak
2012–2013	Mourya et al. (2013)30	Cross-sectional field investigation	Chamarajanagar district, Karnataka	Outbreak (new geographic focus)
2013–2023	Gladson et al. (2024)31	Retrospective Case series	Kozhikode district, Kerala	Non-outbreak (sporadic cases)
2013–2014	Kiran et al. (2015)32	Cross-sectional outbreak investigation	Shimoga district, Karnataka	Outbreak
2014–2016	Sadanandane et al. (2018)33	Cross-sectional entomological survey	Shimoga (Karnataka), Chamarajanagar (Karnataka), Nilgiris (Tamil Nadu), Wayanad and Malappuram (Kerala)	Non-outbreak
2014	Tandale et al. (2015)34	Cross-sectional outbreak investigation	Malappuram district, Kerala	Outbreak (new geographic focus)
2014	Yadav et al. (2014)35	Case report/outbreak investigation	Shimoga and Chikmagalur districts, Karnataka	Outbreak (new geographic focus)
2015	Chandran et al. (2016)36	Cross-sectional outbreak investigation	Wayanad district, Kerala	Outbreak
2015–2021	Gladson et al. (2021)37	Retrospective Observational	Kurichiad Forest, Wayanad district, Kerala	Non-Outbreak
2015	Kuchelkar et al. (2024)38	Case series (mixed methods)	North Goa district, Goa	Outbreak (first Goa outbreak)
2015–2018	Oliveira et al. (2020)39	Cross-sectional surveillance/geospatial	North Goa district, Goa	Spans multiple outbreak years
2015	Sadanandane et al. (2017)40	Cross-sectional outbreak investigation	Wayanad and Malappuram districts, Kerala	Outbreak
2015	Thippeswamy et al. (2017)41	Case report/outbreak report	Shimoga district, Karnataka	Outbreak
2015–2017	Yadav et al. (2018)42	Case series (newer area detection)	Sindhudurg district, Maharashtra	Long-term (Non-outbreak)
2016	Awate et al. (2016)43	Cross-sectional outbreak investigation	Sindhudurg district, Maharashtra	Outbreak
2016–2019	Devadiga et al. (2020)44	Prospective immunological study	Shimoga district, Karnataka	Non-outbreak
2016–2019	Devadiga et al. (2022)45	Observational Cohort	Thirthahalli (Karnataka), South Goa, Dodamarg (Maharashtra)	Non-outbreak
2016	Gurav et al. (2018)46	Cross-sectional outbreak investigation	Sindhudurg district, Maharashtra	Outbreak
2016	Patil et al. (2017)47	Cross-sectional	North Goa district, Goa	Non-outbreak
2016–2018	Rathinam et al. (2022)48	Cross-sectional entomological survey	Malappuram district, Kerala	Non-outbreak
2016–2017	Yadav et al. (2019)49	Observational cohort	Sindhudurg district, Maharashtra	Outbreak (2016–2017 Maharashtra outbreak)
2017–2018	Balasubramanian et al. (2021)50	Cross-sectional field survey	Wayanad district, Kerala	Non-outbreak
2018–2019	Bhat et al. (2021)51	Cross-sectional mixed-methods study	Shimoga district, Karnataka	Non-outbreak
2018–2019	Gupta et al. (2021)52	Retrospective Observational	Shimoga, Uttar Kannada, Udupi, Karnataka	Spans multiple years/outbreaks
2018–2020	Gupta et al. (2022)53	Retrospective cohort (neurological manifestations)	Manipal (Udupi district), Karnataka	Long-term (Non-Outbreak)
2018–2019	Munivenkatappa et al. (2024)54	Cross-sectional multi-district surveillance	14 districts, Karnataka	Non-outbreak
2018–2019	Satapathy et al. (2021)55	Cross-sectional outbreak investigation	Shimoga district, Karnataka	Outbreak
2019–2020	Gupta et al. (2020)56	Retrospective Case Series	Shimoga, Uttar Kannada, Chikmagalur, Karnataka	Non-outbreak (retrospective)
2019	Marja et al. (2021)57	Cross-sectional	Wayanad district, Kerala	Non-Outbreak
2020–2021	Gupta et al. (2024)58	Case-control study	Manipal (Udupi district), Karnataka	Non-outbreak
2022	Vedachalam et al. (2024)59	Case-control study	Shimoga district, Karnataka	Outbreak

Summarizes the design, geographic setting, outbreak status, and analytical contribution of the 31 included studies, arranged by study period. A fully standardized study-level dataset is provided in Supplementary Table S3. Most studies were single-district outbreak investigations or hospital-based case series concentrated in Karnataka, Kerala, Goa, and Maharashtra, conducted between 2011 and 2022, with study quality generally rated good on the NIH Quality Assessment Tool (inter-rater agreement, Cohen’s κ = 0.67). No study was excluded for a fatal flaw, though sample-size justification was commonly unreported (Supplementary Fig. S2).

Among 2798 cumulative KFD cases, gender was reported for 1422 individuals: 738 females and 684 males (Supplementary Table S3). The pooled diagnostic positivity of KFD among 7907 clinically suspected patients (across 18 studies) calculated using a random effects model was 21% (I²: 96.6%, 95% CI: 14–30). Positivity varied substantially by district: 47% in Wayanad district (Kerala state) (I²: 0%, 95% CI: 37–58), 29% in North Goa district, Goa state (I²: 88%, 95% CI: 0–98), 27% in Shimoga, Karnataka (I²: 98.3%, 95% CI: 15–45), and 21% in Sindhudurg, Maharashtra (I²: 97.9%, 95% CI: 5–56), with higher heterogeneity between study effects (Fig. 2A, Supplementary Fig. S3A). Further, highest number of cumulative cases (n = 666) were reported from Shimoga (Karnataka) (with second highest diagnostic positivity of 27%), while Sindhudurg, Maharashtra reported 454 cumulative cumulative cases (Supplementary Fig. S4A). The calculated pooled CFR using a random effect model across 18 studies (1799 KFD-positive patients, 78 deaths), was 4% (I²: 67.4%, 95% CI: 2–8). CFR was relatively higher in Wayanad, Kerala (10%, 95% CI: 6–18), while other districts reported range from 1 to 5% (Fig. 2B, Supplementary Fig. S3B).

Fig. 2.

(A) District-wise pooled test positivity rate for KFDV in suspected patients in India since 2000 using a random effects model. (B) District-wise pooled case fatality ratio in KFD positive patients in India since 2000 using a random effects model. Districts that contain only a single study were not subjected to meta-analysis, and their effect size is displayed as reported. However, single study subgroups do contribute to the overall pooled estimate shown at the base of the figure, which represents the pooled positivity rate across all studies irrespective of the subgroups.

Evidence of KFDV circulation in ticks was reported in 11 studies (3466 tick pools tested, 116 positive), predominantly H. spinigera and H. turturis; monkey carcass surveillance across 10 studies (808 carcasses tested, 125 positive) showed higher positivity in non-outbreak settings (24.9%, 97 of 389 carcasses) than outbreak settings (6.7%, 28 of 419 carcasses), consistent with proposed role of monkey mortality as sentinels seeding new foci of KFD transmission (Fig. 3, Supplementary Table S3), though this could also be a result of more selective carcass testing in sentinel sites that are more likely to test positive. The minimum infection rate in ticks was markedly higher in outbreak investigation studies (7.3%, 43 of 587 tick pools) as compared to non-outbreak studies (2.5%, 73 of 2879 tick pools) (Supplementary Table S3).

Fig. 3.

Spatiotemporal distribution of KFDV infectivity in tick pools and dead monkey carcasses representing environmental circulation of the virus along with the corresponding human cases of KFD in literature from 2012 to 2022. Each panel represents a single year. Transmission of KFDV in ticks (yellow) and transmission in monkeys (green) is represented as a pie chart displaying the percentage of samples positive and the size of the chart equal to the sample size. The number of human cases in the corresponding year is represented as a circle (red) of size proportional to the number of cases. Crosses indicate that humans were tested for KFDV but none were found positive.

During the 2012–2013 KFD outbreak in Shimoga and Chamarajanagar districts, Karnataka, higher infection rates in tick pools (1 of 9) and monkey carcasses (7 of 16) were found (Supplementary Table S3). In 2013, KFD-positive monkeys in neighbouring district of Nilgiris, Tamil Nadu state suggested primate-mediated expansion of KFDV. During 2014–2015 outbreaks, negative neighbouring tick pools highlighted focal KFDV transmission in ticks. However, endemic regions (Shimoga, Wayanad) maintained low, stable tick infection rates (0.5–5%) between outbreak years (2017–2018, 2022), confirming persistent background viral circulation without associated human cases (Fig. 3).

Of 13 symptoms reported in more than five studies analysed using random effects meta-analysis, fever was reported in almost all (RCA: 99%, 95% CI: 96–100) KFD positive patients. Headache (RCA: 76%; 95% CI: 62–85) and myalgia (RCA: 76%; 95% CI: 57–88) were also consistently reported with concordant estimates across sensitivity analyses, indicating minimal under-reporting bias (Fig. 4A; Supplementary Table S4; Supplementary Forest plots 1 and 2). Contrastingly, symptoms such as asthenia/weakness and altered sensorium showed marked divergence between analytical approaches, suggesting substantial under-ascertainment in primary literature. Further, KFD being considered a viral haemorrhagic fever, haemorrhage was reported in more than 80% of studies (9/11) with a pooled 4% RCA estimate. The remaining 31 symptoms, each reported in five or fewer studies, were classified as rare and included non-specific complaints (e.g., respiratory symptoms, photophobia) and complication-related findings (e.g., hepatomegaly, splenomegaly); only three studies documented CNS involvement despite meningoencephalitis being increasingly recognized as a distinct disease phase (Fig. 4B).

Fig. 4.

(A) Pooled frequency of reporting of common symptoms in KFDV positive patients in India since 2000 and (B) Frequency and individual study observations of rare symptoms reported in less than five studies with each dot represents a single study, dot size proportional to the study sample size, “ | ” representing the median observed frequency of the symptom and “k” = total number of studies that reported the particular symptom.

Outbreak chronology traced progressive geographic expansion from Shimoga, Karnataka (first reported 1957) into Kerala (2014), Goa (2015), and Maharashtra (2016–17), with the largest single-year expansion in 2014 spanning four Karnataka districts and marking KFD’s first documented occurrence in Kerala. Since 2019, IDSP has detected further outbreaks across endemic districts in Karnataka, Kerala, Goa, and Maharashtra, all of which have likewise gone unreported in published studies (Fig. 5, Supplementary Fig. S5, Supplementary Table S5). Among 2082 KFD-positive patients with exposure data, agricultural work (32.3%) and forest visits (30.3%) were leading reported exposures (Supplementary Fig. S4B and C).

Fig. 5.

Spatiotemporal distribution of outbreaks of KFD cases in India from the year 2000–2025 based on IDSP data and published literature.

No asymmetry was observed for diagnostic positivity or symptom frequency (Luis Furuya-Kanamori index (LFK) −0.34 and 0.98, respectively), but CFR showed substantial asymmetry (LFK –2.75) with a significant Peter’s test for small-study effects, likely driven by the high Wayanad (Kerala) estimate. Overall, results did not appear to be disproportionately influenced by individual studies, except in cases where the number of studies was limited (Supplementary Figs. S6 and S7; Supplementary Table S6). However, these assessments must be interpreted with caution as the LFK index may have limited power when number of studies are few. GOSH plots showed a single, relatively homogeneous cluster of effect sizes for diagnostic positivity and symptom frequency, but two distinct clusters for CFR, again consistent with the Wayanad estimate as an influential subgroup (Supplementary Fig. S8). Leave-one-out sensitivity analyses showed only minimal changes (1–3%) in the pooled estimate for each of two primary outcomes when any single study was omitted (Supplementary Forest plots 3–5). Baujat plots confirmed no single study exerted outsized influence (overall influence < 0.25) (Supplementary Fig. S7).

Discussion

This study was conducted to develop a comprehensive understanding of different aspects of KFD in India that may support its surveillance and control. In the current study, a meta-analysis approach was adopted to compile the results of 31 studies to provide insights on positivity, case-fatality, distribution, clinical manifestations, and risk factors of KFD. Surveys of dead monkeys and ticks for KFDV were mostly reported only in endemic districts. The pooled diagnostic positivity of KFD among 7907 clinically suspected patients using a random effects model was 21% (I²: 96.6%, 95% CI: 14–30). Positivity varied substantially by district, KFDV circulation in ticks was reported in 11 studies (3466 tick pools tested, 116 positive), predominantly H. spinigera and H. turturis.

In this study we show that since its first report in Shimoga, Karnataka, in 1957, KFD has progressively expanded from Karnataka into Goa, Maharashtra, Kerala, and Tamil Nadu, with substantial diagnostic positivity rates among clinically suspected KFD patients, particularly in Wayanad, Kerala (47%, I²: 0%), North Goa, Goa (29%, I²: 88%), and Sindhudurg, Maharashtra (21%, I²: 97.9%). This indicates that future surveillance activities ought to be focused specifically in these areas. In Karnataka, the 11 districts where KFD diagnostic positivity studies were conducted; positive cases were found in six (Fig. 2A, Supplementary Fig. S3 and Table S3). Over the past two decades, there were only 59 patients tested in the non-positive districts adjacent to endemic regions (Supplementary Fig. S3) highlighting a lack of robust surveillance. In districts of Karnataka, where diagnostic positivity studies were carried out, overall diagnostic positivity ranged from 9% (CI: 7–12%) in Uttar Kannada to 58% (CI: 33–80%) in Chamarajanagar, however the estimates are from a single study. Multiple studies have been reported only from Shimoga, Karnataka, depicting lack of robust surveillance from other districts in the state.

Regarding the vector distribution, a study on potential distribution of H. spinigera ticks has shown that increased aridity along the western ghats may have resulted in increase in tick populations across the western districts of Karnataka and Kerala states, and may support KFDV transmission in future.¹⁵ Furthermore, the vast majority of ticks tested positive for KFDV belonged to species H. spinigera, while some infections were reported in H. turturis suggesting that there are two major ticks responsible for KFDV transmission reported in India. The results of above studies are in alignment with existing literature emphasising the significance of H. spinigera species in the KFDV transmission.^{30,35,40,42,43,48,50,54,58} The available ecological evidence is consistent with ticks sustaining enzootic transmission in endemic areas, while monkey mortality may serve as an early indicator of KFDV activity in new foci.^12,60

The results of KFD clinical manifestations reveal presence of common symptoms like fever, myalgia, headache, and vomiting in initial stages of infection in most patients (Supplementary Forest plots 1 and 2). Cardiac, respiratory, inflammatory, and other symptoms are rarely reported. While the disease can lead to severe clinical manifestations in some cases, majority of affected individuals recover with supportive care. Neurological symptoms, that are recognised as a distinct feature of the second phase of KFD, were reported in only three studies and need more focus in future research. The pooled CFR for KFD across studies was 4% (95% CI: 2–8%); however, this estimate must be interpreted with considerable caution as it is influenced by multiple context-specific factors, including case ascertainment rates, healthcare access, outbreak severity, severity of reported cases, availability of supportive care, and completeness of follow-up. Consequently, pooled CFR should not be interpreted as a fixed biological parameter of disease lethality, but rather as a broad epidemiological summary that reflects heterogeneity of reporting contexts across studies. Region-specific CFRs are likely more meaningful for local public health planning than the pooled estimate. The highest case fatality ratio (10%; 95% CI: 6–18) was observed in Wayanad (Kerala) based on four studies conducted at different time periods (2015, 2017, and 2021) with 253 participants, with the study-wise CFR ranging from 7% to 29% (Fig. 3B). This result is in agreement with the existing literature,^{31,34,38,39,58,61} indicating that early diagnosis and clinical management can result in positive patient outcomes. The studies used for diagnostic positivity analysis generally present a symmetric and normal distribution in GOSH plot, albeit with a larger variation in effect sizes. This indicates that, results are largely consistent and reliable, and there are no distinct subgroups that present a different diagnostic positivity (Supplementary Fig. S8).

The findings on exposure risk for KFD revealed that individuals working as farmers or labourers visiting forests for collection of forest products, individuals exposed to forests, vegetation, and cattle were at higher risk of infection. Proximity to forest areas, handling forest products like dry leaves, etc., and contact with dead monkeys and tick bites exacerbate risk of infection, highlighting the critical need for implementing targeted preventive interventions, such as the use of personal protective equipment (PPE), effective tick management strategies, and community-based awareness initiatives (Supplementary Fig. S4B). Several cross-sectional studies have emphasised the importance of these associated risk factors in transmission of KFDV.^31,34,36,38, 39, 40^,43,47,58,62

Despite the comprehensive analysis adopted in this study, there are several limitations that must be acknowledged. Studies included in analysis show large variations in effect sizes leading to high heterogeneity and publication bias in some pooled estimates. Prevalence based meta-analysis is inherently plagued with high heterogeneity as populations in which prevalence studies are conducted can never be completely identical, which invariably affects pooled estimates. At the same time, due to differences in study designs (hospital-based vs community surveillance), diagnostic positivity rates can be biased across studies. Additionally, the number of included studies within each subgroup was insufficient for conducting meta-regression for investigating the sources of heterogeneity. In several districts, there was only a single study conducted, due to which random effects model could not be applied. These districts have been marked in the figures, and the diagnostic positivity estimates in these districts have low reliability. It is important to distinguish between serological and molecular methods for diagnosis of KFD. To assess true diagnostic positivity, we only used studies in which either ELISA and/or RT-PCR were employed for diagnosis of KFD and excluded sero-surveillance based studies that only assessed the presence of antibodies against previous infections. The present study included diagnostic positivity of KFD infections only among patients with undifferentiated febrile illness which could influence diagnostic positivity. In most studies, KFD cases from hospitals were reported, but there was no information on the actual place where the infection occurred. In the present analysis, it was assumed that patients reporting to hospital were infected in same district in which hospital was located, though this may not always be true, and while unlikely, patients may have travelled from other districts where they were infected. Finally, the study also does not account for vaccine coverage of the formalin inactivates KFD vaccine used in Karnataka, due to lack of data, which could have affected the positivity rates detected.

This study examines various aspects of Kyasanur forest disease virus including its epidemiology, mortality, clinical features and distribution. The findings highlight interdistrict variation of KFDV distribution and substantial disease positivity and case fatality. We recommend that following measures can be considered to avoid further spread of KFDV and to enhance our understanding of this disease. Surveillance needs to be strengthened using molecular diagnostics (like loop-mediated isothermal amplification (LAMP) and other sensitive diagnostic techniques) and priority must be placed on non-endemic regions, particularly districts bordering endemic areas. Monkey surveillance could be explored as sentinels to detect KFD transmission to new foci. Studying genetic diversity of KFDV strains across different regions may help in improving efficacy of existing vaccine. Antiviral treatments and anti-tick vaccines could be explored to reduce tick feeding and human transmission which could ensure broader protection against tick-borne diseases. Future research should aim at understanding the clinical spectrum of disease, improved diagnostic techniques, long-term effects, and the ecological and environmental factors driving KFD transmission which could inform development of context specific policies for KFD control in India.

Contributors

SSAH, RC –data curation, directly accessed and verified data, formal analysis, investigation, methodology, visualization, writing – original draft, and writing– review & editing; MK—data curation; RC and SSAH—revising the draft critically; BRM –resources, software, supervision, visualization, writing; and AS—writing—review & editing, validation, guarantor of the study, conceptualizing and revising the final draft. All authors read and approved the final version of the manuscript.

Data sharing statement

All data relevant to the study are included in the article or uploaded as Supplementary information. Dataset available from the Mendeley repository. chaturvedi, rini; Hussain, Syed Shah Areeb (2025), “Kyasanur forest disease in India”, Mendeley Data, V2, https://doi.org/10.17632/jxhvx7y4tm.21.

Editor note

The Lancet Group takes a neutral position with respect to territorial claims in published maps and institutional affiliations.

Declaration of interests

No conflicts of interests.