Dynamical model 1:
ARBOTHAI Dengue Forecasting Framework

The ARBOTHAI modeling framework is a stochastic, population-based epidemiological model designed to simulate and forecast dengue transmission across diverse climatic and demographic settings. Its core architecture captures the complex interplay between host immunity, virus dynamics, and environmental drivers. The model is specifically built to support regional dengue forecasting efforts in Thailand and potentially other dengue-endemic regions.

1. Model Structure

At its foundation, the model adopts a discrete-time stochastic compartmental approach. The human population is divided into a set of epidemiological states that account for infection with two distinct dengue virus strains, along with immunological responses such as temporary cross-immunity and antibody-dependent enhancement (ADE) during secondary infections. The compartments include:

  • Sboth: Susceptible to both strains.
  • E, I, A: Exposed, infected (symptomatic), and asymptomatic individuals for each strain.
  • CI, S1; S2: Individuals with temporary cross-immunity or partial susceptibility after primary infection.
  • E12, E21, I12, I21, A12, A21: Secondary infections from strain 1 to 2 and vice versa, including ADE pathways.0
  • R: Recovered individuals.
  • D: Deaths attributable to dengue.

The model tracks transitions between compartments at each time step using probabilistic rules, typically sampled from binomial distributions, with transition probabilities derived from biological parameters (e.g., incubation rate, recovery rate) and the time-varying force of infection.

2. Transmission Rate Modulation

A key innovation of the ARBOTHAI model is its flexible formulation of the transmission rate (𝛽),  which varies depending on the scenario:

  • Constant scenario: 𝛽(t) is fixed and does not vary in time.
  • Seasonal scenario: 𝛽(t) follows a sinusoidal function to capture annual or semi-annual cycles.
  • Climate-driven scenario: 𝛽(t) is dynamically modulated by climatic variables, specifically temperature and precipitation, using smoothed moving averages and delayed effects (lags). The general formulation is:

β(t)=β0​⋅(1+αT​fT​(T(t−lagT​))+αP​fP​(P(t−lagP​)))

where:

  • T(t) and P(t) are temperature and precipitation,
  • αT and αP are sensitivity coefficients,
  • fT and fP are normalized transformation functions (e.g., logistic or scaled),
  • lag terms account for biological response delays.

This structure enables 𝛽(t)  to adapt to both short- and long-term climatic variability, resulting in a better fit to observed case data, especially in regions where mosquito activity and dengue risk are closely linked to climate.

3. Fitting and Forecasting Approach

The model is calibrated individually for each province or locality using historical dengue surveillance data (typically from 2009-2023). Parameter estimation relies on fitting probabilistic distributions to weekly case data, adjusting for local transmission dynamics, seasonality, and environmental conditions.

Forecasts are generated for 3-, 6-, and 12-month horizons under three configurations:

  • Short-term (3-6 months): Using best-fit parameter distributions and corrected initial conditions (y0) from recent observed data.
  • Long-term (12 months): Using fitted parameter distributions, but sampling y0 probabilistically based on recent historical compartment estimates (e.g., average infected, recovered, etc.).

Model output includes expected incidence trajectories and uncertainty bounds derived from multiple stochastic realizations.

4. Regional Customization and Climate Integration

The model is customized per province, allowing each region to have distinct transmission dynamics (e.g., stronger or weaker seasonality). This localized approach increases realism and improves fit quality. Climate effects are incorporated through the integration of region-specific ERA5 datasets, including daily or weekly resolution of temperature and rainfall.

5. Operational Relevance

ARBOTHAI is designed to inform public health decisions by providing:

  • Scenario-based simulations (e.g., high rainfall year vs. dry year),
  • Early warning signals for outbreak onset and severity,
  • Region-specific forecasts accounting for climatic and epidemiological variability.

Its reliance on routinely available climate data and moderate entomological inputs makes the model scalable and applicable in data-limited contexts, while its modular structure allows adaptation to evolving knowledge and surveillance infrastructure.

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