The research team, led by Teena Mariam Thomas and John Antony Jude Prakash of the Department of Clinical Microbiology at Christian Medical College, Vellore, together with colleagues from the hospital’s Department of Medicine, set out to answer three linked questions: where cases cluster, whether those clusters are statistically meaningful, and whether the disease follows a predictable seasonal rhythm. To do so, they combined laboratory diagnostics with geographic information systems, spatial statistics and time-series modeling, an approach that treats disease data not as a flat list of numbers but as events anchored in both place and time.

Defining a case required laboratory confirmation rather than clinical suspicion alone. A patient presenting with fever and rash was counted as a spotted fever case only if serological testing, using either an enzyme-linked immunosorbent assay or an immunofluorescence assay specific for spotted fever group rickettsiae, returned a positive result, or if molecular assays such as nested polymerase chain reaction or quantitative PCR detected the pathogen’s genetic material. This dual diagnostic strategy matters because rickettsial infections are notoriously difficult to distinguish clinically, and antibody-based tests can be influenced by prior exposure in endemic areas. Demographic details, including age, sex and residential location, were extracted from the hospital’s electronic medical records, allowing each confirmed case to be geocoded to its district of origin.

The spatial analysis relied on two complementary software platforms. Maps were generated using QGIS version 3.34.3, an open-source geographic information system, while spatial autocorrelation and hotspot detection were carried out in GeoDa version 1.22.0.21. Spatial autocorrelation asks a deceptively simple question: are cases located near other cases more often than chance would predict? The team employed Local Indicators of Spatial Association, or LISA, a statistical technique that classifies each area as a cluster of high values, a cluster of low values, or a spatial outlier. Hotspot analysis then refined this picture by identifying districts where case concentrations were statistically significant rather than random fluctuations. Seasonal patterns were quantified with R software version 4.5.2, which allowed the researchers to model monthly case counts across nearly two decades and pinpoint the peak period of transmission.

The geographic findings were striking in their concentration. Vellore district accounted for 39.9 percent of all confirmed cases, while neighboring Chittoor district, just across the state border in Andhra Pradesh, contributed 38.8 percent. Together, these two adjacent districts produced nearly four out of every five cases in the dataset. Tirupattur added 12.5 percent, and the remaining cases were distributed between Ranipet at 4.5 percent and Tiruvannamalai at 4.3 percent. This pattern suggests that the ecological conditions favoring the tick and mite vectors of spotted fever group rickettsiae are concentrated in a relatively compact zone straddling the Tamil Nadu–Andhra Pradesh boundary, rather than being evenly spread across the hospital’s catchment area.

The temporal analysis revealed an equally clear signal. Cases rose sharply between October and March, the cooler and drier months of the year in this part of peninsular India, and peaked in January. This seasonality is consistent with the ecology of the arthropod vectors responsible for transmission. Tick survival, abundance and host-seeking activity are all sensitive to temperature and humidity, and the post-monsoon cool season appears to create conditions in which both the vectors and the humans who encounter them, often through agricultural work, outdoor labor or domestic activities, come into contact more frequently. The finding that the disease peaks in the coolest months rather than the monsoon itself is a practical clue for clinicians, who might otherwise lower their guard for tick-borne illness once the rains subside.

Demographically, the study identified two groups at elevated risk: children under ten years of age and housewives. Both findings carry interpretive weight. Young children may be exposed through outdoor play, contact with domestic animals, or peridomestic environments where ticks and mites thrive, and their developing immune systems may also influence how infection presents. Housewives, meanwhile, may acquire infection close to home, through contact with vegetation, livestock, pets or infested structures in the domestic environment, rather than through occupational exposure in fields or forests. The pattern implies that spotted fever in this region is not confined to rural laborers or forest workers but is a genuinely household-level threat, one that pediatricians and primary care providers should keep firmly in view.

The clinical implications of the study center on a category of illness known as acute undifferentiated febrile illness, or AUFI, the diagnostic puzzle that occupies much of tropical medicine. Patients with AUFI present with fever but no obvious localizing signs, and the differential diagnosis spans dengue, chikungunya, malaria, leptospirosis, typhoid and rickettsial infections, among others. Because spotted fever typically announces itself with fever and an eschar or rash, and because it responds well to doxycycline when treated early, missing the diagnosis can mean prolonged illness, unnecessary investigations and, in severe cases, complications affecting the kidneys, lungs or central nervous system. The authors argue that clinicians evaluating AUFI patients with rash, particularly children under ten who live in or near the identified hotspot districts, should consider spotted fever a serious possibility during the October-to-March window, and that diagnostic capacity for rickettsial testing should be strengthened accordingly.

The methodological framework of the study also demonstrates the growing value of spatial epidemiology in resource-limited settings. By pairing routine hospital laboratory data with freely available geographic and statistical tools, the researchers converted eighteen years of clinical records into actionable public health intelligence without the expense of a dedicated surveillance network. The identification of statistically significant spatial clusters provides a rational basis for targeting interventions, whether those involve vector control campaigns, community education about tick avoidance, or the strategic placement of diagnostic services. It also generates hypotheses for future field studies: what vegetation types, land-use patterns, animal reservoirs or microclimates sustain the vector populations in Vellore and Chittoor, and why do the surrounding districts show lower transmission despite their proximity?

The authors are careful to frame their conclusions within the limits of a retrospective, single-center design. The dataset reflects patients who reached a tertiary care hospital, which means milder cases managed elsewhere are absent, and the catchment geography may partly reflect healthcare-seeking behavior as well as true disease distribution. They call for prospective, preferably multi-centric studies to confirm the hotspots, quantify the environmental drivers of transmission, and elaborate the full range of risk factors. Funded by the Indian Council of Medical Research and approved by the institutional ethics committee, the study nonetheless delivers an immediate and practical message: in the cooler months of southern India, when a child under ten arrives at a clinic with fever and a rash from one of the identified hotspot districts, spotted fever deserves a prominent place on the differential diagnosis list, and timely treatment with appropriate antibiotics could spare families the consequences of a missed diagnosis.

Subject of Research: Spatio-temporal epidemiology of tick-borne spotted fever group rickettsioses in southern India

Article Title: Spatio-temporal analysis of spotted fever cases reported to a tertiary care hospital in Southern India

Article References: Thomas, T. M., D’Cruz, S., Perumalla, S. K., Gunasekaran, K., & Prakash, J. A. J. (2026). Spatio-temporal analysis of spotted fever cases reported to a tertiary care hospital in Southern India. BMC Infectious Diseases. https://doi.org/10.1186/s12879-026-14466-1

Image Credits: AI Generated

DOI: 10.1186/s12879-026-14466-1

Keywords: spotted fever, rickettsia, tick-borne disease, spatio-temporal analysis, seasonality, India, Vellore, fever and rash, acute undifferentiated febrile illness, hotspot analysis, children, public health