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arxiv: 2605.17975 · v1 · pith:VTTP5YX6new · submitted 2026-05-18 · 🧬 q-bio.PE

M-SDT: A modelling framework for dengue transmission, forecasting, and intervention strategies in Ahmedabad Municipal Corporation

Sourav Roy , Rajendra Gadhavi , Bhavin Solanki , Chirag Shah , Raj C. Sharma , Indrajit Ghosh This is my paper

Pith reviewed 2026-05-20 00:34 UTC · model grok-4.3

classification 🧬 q-bio.PE
keywords dengue transmissionvector control strategiescompartmental modelAhmedabadforecastingspatial heterogeneityintervention evaluation
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The pith

A mechanistic model for dengue in Ahmedabad shows sustained residual spraying reduces incidence by over 80 percent while periodic fogging has cumulative yearly effects.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper develops a data-driven compartmental model to simulate dengue transmission across zones in Ahmedabad Municipal Corporation. It calibrates this model to zone-wise case data from 2020 to 2024, including asymptomatic infections, and generates forecasts for 2026 to 2028. The work then compares seasonal vector control options and identifies which factors most influence long-term burden. A sympathetic reader would care because the results point to concrete differences in how specific interventions perform over time and across locations in an urban setting.

Core claim

The M-SDT model, which incorporates both symptomatic and asymptomatic infections, when calibrated to zone-wise dengue case data from 2020-2024 using bootstrap sampling with negative binomial noise, reveals pronounced spatial heterogeneity across AMC zones with persistent hotspots. Forecasts for 2026-2028 indicate continued endemic circulation with moderate inter-annual variability. Sensitivity analysis identifies the mosquito biting rate and vector mortality as dominant drivers. Evaluating seasonal vector control strategies shows that periodic fogging has a cumulative effect over the years, while sustained residual spraying can quickly curb outbreaks and decrease incidence by over 80 percent

What carries the argument

The M-SDT model, a mechanistic seasonal dengue transmission compartmental framework that incorporates symptomatic and asymptomatic infections and is calibrated zone-wise with uncertainty quantification via bootstrap sampling.

Load-bearing premise

The transmission parameters fitted to 2020-2024 zone data including unobserved asymptomatic cases are assumed to remain unchanged for predicting dynamics and intervention responses in 2026-2028.

What would settle it

Collecting actual dengue incidence data in Ahmedabad zones during 2026-2028 and checking whether observed reductions from implemented spraying or fogging strategies match the model's predicted over-80-percent decrease or cumulative effects would test the claims.

Figures

Figures reproduced from arXiv: 2605.17975 by Bhavin Solanki, Chirag Shah, Indrajit Ghosh, Raj C. Sharma, Rajendra Gadhavi, Sourav Roy.

Figure 1
Figure 1. Figure 1: (a) Map of Gujarat highlighting the Ahmedabad District. (b) Map of AMC divided into seven regions of study. (c) Schematic representation of the M-SDT model. The human population is divided into susceptible (S), exposed (E), symptomatic infected (I), asymptomatic infected (A), and recovered (R) compartments. Infection is acquired through bites from infected mosquitoes (MI ), while susceptible mosquitoes (MS… view at source ↗
Figure 2
Figure 2. Figure 2: Global sensitivities of the model parameters to the cumulative symptomatic infections for the period 2026-2030 at AMC. Notable PRCC values are marked by * (p-value < 0.01). Parameters that remain fixed are considered from [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Bootstrap uncertainty quantification and parameter inference in the [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Probabilistic prediction of annual dengue incidence in 2026–2028. [PITH_FULL_IMAGE:figures/full_fig_p009_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Forecasting of dengue incidence rate per region of AMC zones [PITH_FULL_IMAGE:figures/full_fig_p011_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Violin plots depicting the distribution of the number of yearly reported [PITH_FULL_IMAGE:figures/full_fig_p012_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Dengue cases per year, zoned-wise division of dengue patients in [PITH_FULL_IMAGE:figures/full_fig_p015_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Time-wise deployment of vector control measures during three [PITH_FULL_IMAGE:figures/full_fig_p018_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Spatiotemporal pattern of dengue incidence in government hospitals. [PITH_FULL_IMAGE:figures/full_fig_p023_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Spatio-temporal distribution of dengue incidence at private hospitals. [PITH_FULL_IMAGE:figures/full_fig_p024_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Central zone. (a) Best-fit curve of reported dengue cases in the years 2021–2024, where black circles represent the reported data points, the red line shows the best-fit median curve, and gray curves represent the bootstrapped realizations. The dotted red lines show the 95% confidence intervals for the best-fit curve. (b) Bootstrapped distribution of the baseline biting rate parameter b0, with an estimate… view at source ↗
Figure 12
Figure 12. Figure 12: West zone. (a) Fit model depicting the evolution of the number of dengue cases over time, where bootstrap samples reflect the variability around the median path. (b) Frequency histogram of the estimated baseline biting rate b0, revealing a narrow distribution around the moderate parameter value. (c) The distribution of the proportion symptomatic p, which is more dispersed and skewed to the right. North zo… view at source ↗
Figure 13
Figure 13. Figure 13: Northern region. (a) Reconstruction of a model of dengue cases with a regulated growth and subsequent stabilization for the entire period considered, with the bootstrap paths not exceeding the boundaries of a small confidence band. (b) Distribution of the biting rate b0 with values predominantly close to larger ones, representing a low variation of human-mosquito interaction rates. (c) Distribution of the… view at source ↗
Figure 14
Figure 14. Figure 14: East zone. (a) Goodness of fit corresponding to a high incidence regime with a clear peak and subsequent decline, with a broad spread for the bootstrap distribution indicative of high variability. (b) Distribution of the biting rate per unit time b0, with a more spread-out distribution and high means, signifying variability in contact rate. (c) Distribution of the proportion symptomatic p, which is right-… view at source ↗
Figure 15
Figure 15. Figure 15: South zone. (a) Model fit depicting a high initial disease burden, then steady decrease in dengue cases reported, where the bootstrap sample replicates the general trend even when variability is fairly large. (b) Baseline biting rate b0 distribution, which shows moderate spread but symmetric distribution and thus indicates that contact rates are stable. (c) Symptomatic fraction p distribution, which is mo… view at source ↗
Figure 16
Figure 16. Figure 16: North-west zone. (a) Temporal dynamics of model fitness that show non-monotonicity with periods of decay followed by revival, together with a broad variability of bootstrap paths which reflects strong dependency on parameter changes. (b) Probability distribution of b0, a baseline biting rate value. The value is moderate on average, but a relatively broad distribution indicates significant variability of t… view at source ↗
Figure 17
Figure 17. Figure 17: South-west zone. (a) Model goodness of fit, where the cases increase gradually and stabilize, after which they start falling, while the bootstrap distributions show moderate variability around the median curve. (b) Biting rate distribution b0, centered on moderate values, indicative of stable human-mosquito contacts. (c) Symptomatic proportion p distribution, with mild right skewness. in the parameters b0… view at source ↗
read the original abstract

Dengue fever poses a persistent public health challenge in rapidly urbanizing Indian cities such as Ahmedabad, where spatial heterogeneity and seasonal variability complicate forecasting and control. In this study, we develop a data-driven compartmental framework to simulate transmission dynamics, generate forecasts, and evaluate intervention strategies across the Ahmedabad Municipal Corporation (AMC). We employ a Mechanistic Seasonal Dengue Transmission (M-SDT) model that incorporates symptomatic and asymptomatic infections. We calibrated the proposed model using zone-wise dengue case data during 2020--2024. Parameter uncertainty is rigorously quantified using a bootstrap sampling framework with negative binomial noise. The calibrated model reveals pronounced spatial heterogeneity across AMC zones, with persistent hotspots and distinct transmission regimes. Forecasts for 2026--2028 indicate continued endemic circulation with moderate inter-annual variability. Sensitivity analysis identifies the mosquito biting rate and vector mortality as dominant drivers of long-term disease burden, highlighting the central role of vector ecology in shaping epidemic outcomes. Evaluating seasonal vector control strategies shows a notable difference in operation; periodic fogging has a cumulative effect over the years, while sustained residual spraying can quickly curb outbreaks and decrease incidence by over 80%. The zone-wise analysis reveals that the mosquito-to-human ratio governs not only the baseline outbreak potential but also each zone's responsiveness to control strategies. Overall, the M-SDT modelling framework enables reconstruction of unobserved dynamics, rigorous uncertainty quantification, and evaluation of targeted, zone-specific interventions, underscoring the importance of integrating fine-scale surveillance data with mechanistic modelling for adaptive urban dengue control.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The manuscript develops a Mechanistic Seasonal Dengue Transmission (M-SDT) compartmental model that includes symptomatic and asymptomatic infections to simulate dengue dynamics, produce forecasts, and assess interventions in Ahmedabad Municipal Corporation zones. The model is calibrated to zone-wise case reports from 2020–2024 via bootstrap sampling under negative-binomial noise; spatial heterogeneity is characterized, 2026–2028 forecasts are generated, sensitivity analysis identifies mosquito biting rate and vector mortality as dominant drivers, and seasonal vector-control scenarios are compared, with the claim that sustained residual spraying reduces incidence by more than 80 % while periodic fogging produces cumulative effects over years.

Significance. If the mappings from control measures to parameter shifts prove robust and the forecasts receive independent validation, the zone-specific framework could inform adaptive urban dengue management. The bootstrap uncertainty quantification and negative-binomial observation model are methodologically sound, yet the absence of held-out validation and external entomological constraints on intervention effects limits the strength of the quantitative efficacy claims.

major comments (2)
  1. [Intervention strategies section] Intervention evaluation: the reported >80 % incidence reduction under sustained residual spraying and the cumulative effect of periodic fogging depend on externally imposed, time-varying changes to biting rate, vector mortality, or mosquito-to-human ratio. No independent entomological data, coverage estimates, or resistance parameters are cited to justify these shifts; because the underlying parameters were fitted only to the 2020–2024 case series, the quantitative contrast between strategies remains an untested extrapolation rather than a model-derived prediction.
  2. [Forecasting and sensitivity analysis] Forecasting and calibration: 2026–2028 incidence projections and the ranking of biting rate and vector mortality as dominant drivers are obtained from parameters calibrated exclusively to the same 2020–2024 zone-wise data. Without explicit held-out validation, cross-validation, or comparison against independent surveillance, these long-term forecasts and sensitivity rankings constitute extrapolations whose reliability cannot be assessed from the calibration alone.
minor comments (1)
  1. [Abstract] The abstract states the model structure and claims but supplies neither governing equations nor a parameter table, making it difficult for readers to judge the precise mechanistic assumptions before reaching the results.

Simulated Author's Rebuttal

2 responses · 2 unresolved

We thank the referee for the constructive comments, which help clarify the scope of our modeling results. We respond to each major comment below and indicate the revisions we will incorporate.

read point-by-point responses

  1. Referee: [Intervention strategies section] Intervention evaluation: the reported >80 % incidence reduction under sustained residual spraying and the cumulative effect of periodic fogging depend on externally imposed, time-varying changes to biting rate, vector mortality, or mosquito-to-human ratio. No independent entomological data, coverage estimates, or resistance parameters are cited to justify these shifts; because the underlying parameters were fitted only to the 2020–2024 case series, the quantitative contrast between strategies remains an untested extrapolation rather than a model-derived prediction.

    Authors: We agree that the intervention results are scenario-based extrapolations that rely on hypothesized shifts in vector parameters rather than direct empirical measurements of control efficacy. The M-SDT framework is designed to compare the relative performance of different strategies under explicit assumptions about their impact on biting rate, mortality, and mosquito density. We will revise the Intervention strategies section and the Discussion to (i) state these assumptions explicitly, (ii) note the absence of independent entomological or coverage data, and (iii) frame the >80 % reduction and cumulative fogging effects as illustrative contrasts that can inform the design of future field studies. Additional sensitivity analyses on the magnitude of the assumed parameter changes will also be added. revision: yes

  2. Referee: [Forecasting and sensitivity analysis] Forecasting and calibration: 2026–2028 incidence projections and the ranking of biting rate and vector mortality as dominant drivers are obtained from parameters calibrated exclusively to the same 2020–2024 zone-wise data. Without explicit held-out validation, cross-validation, or comparison against independent surveillance, these long-term forecasts and sensitivity rankings constitute extrapolations whose reliability cannot be assessed from the calibration alone.

    Authors: The referee is correct that both the 2026–2028 forecasts and the sensitivity rankings are derived solely from the 2020–2024 calibration. The bootstrap procedure quantifies uncertainty conditional on the fitted model and observation process, but does not provide out-of-sample validation. We will add a new Limitations subsection that (i) explicitly acknowledges the extrapolative nature of the forecasts, (ii) discusses the implications for long-term reliability, and (iii) outlines how future surveillance data could be used for validation. The sensitivity analysis will be reframed as identifying parameters whose improved empirical estimation would most reduce forecast uncertainty. revision: yes

standing simulated objections not resolved
  • Independent entomological or coverage data to constrain intervention parameter shifts are not available to the authors.
  • Held-out surveillance data beyond 2024 for formal validation of forecasts are not available to the authors.

Circularity Check

0 steps flagged

No significant circularity in derivation chain

full rationale

The M-SDT model is calibrated to 2020-2024 zone-wise case data with bootstrap uncertainty quantification, then applied to generate 2026-2028 forecasts and to simulate intervention effects via assumed changes to vector parameters such as biting rate and mortality. This is standard mechanistic modeling workflow with external assumptions for interventions; the forecasts and strategy comparisons are extrapolations from the fitted dynamics rather than tautological reductions to the calibration inputs by construction. No self-definitional steps, fitted-input-called-predictions, or load-bearing self-citations are evident in the abstract or described structure. The central claims remain independent of the input data once the model structure and intervention mappings are accepted.

Axiom & Free-Parameter Ledger

2 free parameters · 1 axioms · 0 invented entities

The central claims rest on standard compartmental assumptions plus parameters fitted to local surveillance data; no new entities are postulated.

free parameters (2)
  • mosquito biting rate
    Identified as a dominant driver in sensitivity analysis and therefore fitted during calibration to match observed incidence.
  • vector mortality rate
    Listed among the main determinants of long-term disease burden and adjusted to historical case data.
axioms (1)
  • domain assumption Dengue transmission can be represented by a compartmental model that includes both symptomatic and asymptomatic human infections and seasonal mosquito dynamics.
    Invoked to structure the M-SDT framework and enable reconstruction of unobserved states.

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Reference graph

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