The study represents Vellore’s road system as a graph, with intersections modeled as nodes and roadways as linking edges. Selected urban sectors—including public administration, retail, healthcare and education—are assigned specific time slots according to their operating schedules. A sector-level routing layer then produces a circulation sequence based on a Hamiltonian cycle over the modeled sectors and assigns feasible shortest-path routes across the physical road network to individual commuters.

Researchers compared two modeled scenarios. Scenario A used conventional operating hours and shortest-path routing. Scenario B combined modified, sector-specific timings with Hamiltonian Circuit optimization. Performance was evaluated using end-to-end delay and agent queue count, both of which were lower in Scenario B than in Scenario A over the active-agent ratios examined in the abstract. The results therefore describe the performance of the integrated temporal-spatial routing framework within the simulation model.

The available evidence is limited to the article abstract, highlights and study summary; it does not provide the full methods, model validation, underlying observations or details of the active-agent ratios. The research concerns a modeled traffic system for Vellore and does not establish that the approach has been implemented at city scale or produced the reported reductions in real traffic. The abstract characterizes the combined framework as promising and potentially workable, scalable and affordable, but those broader conclusions remain tied to the simulated setting described here.

Subject of Research: Graph-theoretic simulation of staggered sector schedules and Hamiltonian-inspired routing for urban traffic in Vellore.

Article Title: Graph theoretic simulation based approach for urban traffic optimization using Hamiltonian inspired routing and sector based time staggering in Vellore

Article References: S, P., & A, P. (2026). Graph theoretic simulation based approach for urban traffic optimization using Hamiltonian inspired routing and sector based time staggering in Vellore. Discover Sustainability. https://doi.org/10.1007/s43621-026-04962-4

Image Credits: AI Generated

DOI: 10.1007/s43621-026-04962-4

Keywords: Vellore, traffic simulation, graph theory, Hamiltonian routing, sector timing, shortest-path routing, end-to-end delay, agent queue count, Graph, theoretic, simulation, based

The research, published on 20 October 2026 in Frontiers in Sustainability, modeled waste moving backward through the healthcare system. The case study assumed that infectious waste generated at diagnostic centers would be transported to treatment plants within 24 hours, after which recyclable material could proceed to recovery facilities and non-recyclable or residual waste could go to final disposal. The model minimized two competing objectives: total system cost, including facilities, transport, processing, disposal, energy and recovery revenue, and environmental risk from transportation and treatment. Energy consumption entered the economic objective through treatment costs rather than forming a separate third environmental objective.

The proposed framework was designed for infectious waste arising at laboratories and diagnostic centers, an area the authors identified as comparatively underexplored in earlier medical-waste network research. Its four principal network components were waste-generating sources, processing facilities, recovery facilities and a centralized disposal site. Flow constraints connected each stage, facility-opening variables represented operating decisions, binary variables constrained treatment technologies to waste categories they could handle, and capacity limits constrained processing, recovery and disposal. Such structure turned the general objective into a testable mathematical design problem for the selected Vellore data rather than a direct evaluation of an operating waste system.

Uncertainty was represented with trapezoidal fuzzy numbers for waste generation and transportation costs. Each fuzzy parameter was described by lower and upper bounds and a central range of more plausible values. A necessity-based transformation converted the fuzzy constraints into deterministic equivalents, after which optimality and feasibility robustness terms penalized poor objective performance and constraint violations under unfavorable conditions. The researchers used a fuzzy confidence value of 0.5 and a robustness parameter of 0.1 for the main experiments, a combination they selected as balanced rather than maximally conservative. Other model parameters were treated as deterministic, so the modeled uncertainty did not encompass every potential source of operational variation.

Solutions were generated with the Non-dominated Sorting Genetic Algorithm II, or NSGA-II, an evolutionary algorithm that searches for trade-offs among conflicting objectives. It combines selection, crossover and mutation with elitism, fast non-dominated sorting and a crowding-distance mechanism intended to preserve diversity along the Pareto front. The implementation ran in Python through the pymoo package and Google Colab, using a population of 500, simulated binary crossover with probability 0.95, polynomial mutation with probability 0.20 and 1,000 generations. Each optimization run required roughly 34 to 36 minutes, while a scalability test found that average runtime rose from 9.92 minutes for the smallest benchmark network to 48.88 minutes for the largest.

Because NSGA-II returns a set of non-dominated alternatives rather than one definitive network, the researchers applied the Technique for Order Preference by Similarity to Ideal Solution, or TOPSIS. This method normalized the cost and risk values, compared every Pareto solution with the ideal and least desirable alternatives and ranked them by their relative closeness. The final reported solution averaged the highest-ranked TOPSIS results across repeated runs, reducing the influence of the algorithm’s random initialization and stochastic search. Equal weights for cost and risk produced a compromise rather than privileging either objective. Tests using cost-risk weight combinations of 0.3 and 0.7, 0.5 and 0.5, and 0.7 and 0.3 produced only small changes in the selected cost and risk values.

The Vellore network covered 60 administrative wards. One diagnostic center was assigned to approximately every four wards using a P-median model, while the treatment network included one existing facility and two candidates selected through the same location method. Road-network shortest-path distances were calculated in ArcGIS. Four treatment technologies—autoclaving, incineration, microwave treatment and chemical disinfection—were matched to five waste categories according to applicability and regulatory constraints. Two waste streams were assumed to be recyclable, and 20% of material sent to recycling was modeled as residue requiring disposal. The treatment facilities had respective capacities of 3,600, 3,240 and 2,880 kg for autoclaving, while the two recovery facilities had capacities of 3,500 and 2,800 kg and the disposal facility had a total capacity of 12,000 kg.

Several inputs came from operating information in Vellore, including treatment capacities and distances, while missing cost, operational and exposure parameters were represented by synthetic values chosen to resemble realistic conditions. Ward population data came from the 2011 Census of India. Route exposure estimates ranged from 700 to 2,800 people, and facility exposure estimates ranged from 1,400 to 2,500 people. These assumptions mean the numerical results should be interpreted as a case-study model rather than a direct measurement of contamination incidents, clinical outcomes or carbon emissions. The authors stated that detailed route-level exposure and facility-specific environmental risk data were unavailable, and they did not conduct a field implementation of the optimized network.

Across ten independent comparisons, NSGA-II produced the strongest overall Pareto-front metrics among the three tested algorithms. Its hypervolume was 0.0323, compared with 0.0318 for NSGA-III and 0.0283 for MOEA/D; its inverted generational distance was 0.0105, lower than NSGA-III’s 0.0247 and MOEA/D’s 0.0376. NSGA-II also had the lowest spacing value, 0.0137, and a mean TOPSIS closeness coefficient of 0.9647, the highest of the three. The authors attributed NSGA-III’s relative weakness to the test problem having only two objectives, whereas its reference-point diversity mechanism was designed especially for many-objective cases. MOEA/D attained the greatest individual closeness coefficient in one run, but the authors reported greater fluctuation across the ten runs.

Sensitivity analysis showed a clear but limited robustness trade-off. Raising the robustness parameter from 0 to 1 increased average cost by about 16.63%, from 11,023,496 to 12,855,670, while average risk fell by only about 0.51%, from 273,680,193 to 272,290,679. By contrast, changing the recycling-residue fraction from 0 to 0.5 kept average cost within roughly 0.2% and produced only small changes in risk. Joint variation in fuzzy confidence and robustness parameters also left solution quality broadly stable, although cost generally rose as robustness increased. The selected main setting of fuzzy confidence 0.5 and robustness 0.1 was retained as a compromise that avoided an overly conservative solution.

The authors interpreted the optimized architecture as a way to shorten modeled routes, move infectious waste promptly to compatible treatment, recover recyclable material and reduce the quantity sent to final disposal. These are model outputs, not evidence that the framework reduced disease transmission or greenhouse-gas emissions in Vellore. The study did not test the network in operation, and its six-day horizon, synthetic inputs, assumed energy and transport parameters, and uncertainty limited to selected variables constrain generalization. Future work could add fluctuating energy prices, treatment capacities, vehicle fleets, recovery revenues, stochastic or robust multi-period models and additional waste categories. Within those limitations, the analysis provides a transparent computational framework for comparing cost and contamination risk when medical-waste flows and operating conditions are uncertain.

Subject of Research: Case-study optimization of a medical-waste reverse supply chain for cost, environmental risk, energy use and uncertainty

Article Title: Integrated cost–risk optimization of sustainable reverse supply chain networks for medical waste using artificial intelligence with energy consumption and uncertainty considerations

Article References: S., R., & K., K. (2026). Integrated cost–risk optimization of sustainable reverse supply chain networks for medical waste using artificial intelligence with energy consumption and uncertainty considerations. Frontiers in Sustainability, 7, Article 1927379. https://doi.org/10.3389/frsus.2026.1927379

Image Credits: AI Generated

DOI: 10.3389/frsus.2026.1927379

Keywords: medical waste, reverse supply chain, NSGA-II, TOPSIS, fuzzy robust optimization, environmental risk, energy consumption, Vellore, diagnostic centers, network design, Integrated, cost

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