About STA'RLab

Time-varying transmissibility: Several epidemiological parameters, including serial interval (SI), incubation period (IP), latent period (LP), generation time (GT), and super-spreading parameter (k), are assumed to be fixed for a specific virus and are often key information for understanding the transmissibility and severity of the underlying virus. We coined the measure effective serial interval to illustrate the time-varying transmission characteristics of respiratory diseases (Ali ST et al. Science 2020). The time-varying SI, IP, LP, GT, and k(t) were assessed with their driving factors (Chen D et al. Nat. Comm. 2022, Ali ST et al. AJE 2025, Chen D et al. Nat. Comm. 2025, and Hossain MP et al. JID 2026).

Time-varying severity: Similarly, the method for estimating severity is often retrospective, and the time-varying measure of severity needs to consider the temporal variations in delay distribution and true denominator reconstruction. Outcomes of our studies on COVID-19 encouraged us to undertake a series of projects to improve the time-varying inference of transmissibility and severity by refining at data and parametric levels at a real-time scale. 

The generalization of these approaches to vector-borne and other zoonotic diseases is not only novel but also interesting to assess these parametric definitions under the mechanisms of host and vector, often driven by environmental factors in addition. We established settings in Bangladesh for dengue (Hossain MP et al PloS Com Bio 2026) and Mainland China to assess these parameters. The team aims to provide novel improved inference for estimating the effective reproduction number (Rt) and time-varying severity for these viruses. 

Supports: General Research Fund (GRF) and Health and Medical Research Fund (HMRF), Hong Kong 

Often, traditional surveillance systems (including passive surveillance, active surveillance, sentinel surveillance, syndromic surveillance, genomic & laboratory surveillance) are not always available at the same scale for each of the locations across the globe for these viruses.  However, alternative surveillance systems like digital surveillance (search engines), Language Model-based surveillance  (AI models), wastewater surveillance, Digital Health Interventions, etc., could be used to find proxies of their activities and burden (Ali ST et al Ann. Rev. of Pub. Health ).  The key ongoing projects are  

-- Data discovery (data grabbing) as an alternative surveillance for early detection and reconstruction of epi-curves (AI4IDs)

-- Assessment of the data integration for predicting the importation risk of these viruses. 

Supports: General Research Fund (GRF), Hong Kong 

We develop Statistical, Mechanistic, Semi-Mechanistic, and Artificial Intelligence model-based frameworks, and their ensemble and interfaces, for addressing various dynamical characteristics of outbreaks with the aim of predicting/forecasting epidemics/ pandemics burden and control (Dong et al. MedRxiv 2025; Lau YC et al. PLOS Comp Bio 2024; Ali et al. Lancet GH 2022; Ali ST et al. ARPH 2021).

-- Seasonality, co-circulation, dynamic interface of multiple pathogens.

-- Time-varying population immunity and strain-specific epidemic and pandemic risks

-- Time-varying importation risk for cross-border interventions

-- Prediction and forecasting

The current focus is on how AI frontier models could be carefully used with the mechanistic grounding under these traditional models for disease dynamics (Chen J et al. 2026). Using the predicted or forecasted disease burden, a series of counterfactual analyses are usually conducted to optimise the intervention timing and duration for proactive mitigation and control strategies. In recent years, along with respiratory viruses, we aimed to forecast dengue and other vector-borne viruses' activity in Asian countries, with a primary focus on the Greater Bay Area (GBA).

Supports: General Research Fund (GRF) and Health and Medical Research Fund (HMRF), Hong Kong

The above predictive models could be further improved by assessing the impact of various meteorological and environmental drivers on these infectious disease outcomes. We illustrated how the year-round multiple peaks of respiratory viruses (including influenza, RSV and HFMD) in tropics and subtropics could be explained by U-shape association of absolute humidity and nonlinear negative association of ambient Ozone on the disease outcomes (Yao Z et al JIPH 2025, Guo F et al Nat Comm 2024, Ali ST et al Sc. Tot Envs 2022, Ali ST et al Scientific Report 2020, Ali ST et al ERJ 2018). Key questions yet to be explored include:

-- The role of meteorological drivers (humidity, temperature etc.) and environmental drivers (ozone and other pollutants) in shaping their host- and vector-based impacts on the transmissibility/ severity/ burden of these infectious diseases

-- Along with these local drivers, how the hemispheric pressure systems could shape the disease dynamics of these viruses, particularly for highly intercontinentally connected locations like Hong Kong

-- How climate and climate change could drive the dynamics of various respiratory and vector-borne diseases in particular!

Supports: Health and Medical Research Fund (HMRF), Hong Kong

Social behavior and spatial dynamics of individuals in the population effectively shape infectious disease transmission and burden. Mask-wearing and personal hygiene reduce transmission; however, transmission risk can’t be avoided in many settings where face masks are not usually worn (Martín-Sánchez M et al. Journal of Infections). Heterogeneity in these settings may lead to variation in superspreading potential and transmissibility of COVID-19 (Chen D et al. Nature Communications). We further developed higher-order modelling frameworks to illustrate how collective interactions, population mobility, and spatial dynamics can better assess the transmission of these viruses (Wang D et al. medRxiv 2025 and Wang Y et al. medRxiv 2026).

From a global and public health perspective, community-based and cross-border interventions, along with vaccination, are effective at various scales. However, the timing, duration, and targeted enforcement of these interventions should be proactive or at least a real-time for practical effectiveness (Ali ST et al. EID 2018).

We continue to explore several research questions:

-- Prediction of setting-specific clustering and superspreading potentials to strengthen the case for target-based and setting-specific interventions over those for the general population.

-- How collective interactions, population mobility, and genetic mutation shape the transmission dynamics of these viruses.

-- Optimizing the timing and duration of target-based interventions (school closure, cross-border interventions, vaccination schemes, and PHSMs) for proactive and real-time approaches.

Supports: Health and Medical Research Fund (HMRF), Hong Kong

As an emerging area of implementation research, we have several initiatives on Health Informatics and Digital Health Interventions (DHIs). Dashboards are often retrospective in nature and are usually active during the outbreaks of interest (e.g., https://covid19.sph.hku.hk/). We are developing infectious disease dashboard with proactive information (projections/forecasts) on the upcoming burden, severity, and risk of the NEXT epidemic and pandemic of multi-pathogens, integrating the outcomes from the above projects for the community, public health agencies, and governments. We aim to extend this information by establishing the tools (e.g., mHealth apps) for immediate users (health personnel, doctors, and patients), which could allow us to assess early indicators and temporal and setting- or case-profile-based variations in parametric distributions of various epidemiological characteristics.

-- Self-management tools (apps) for early case detection and tracking unobserved cases for proactive prevention and mitigation policies.

-- Industrial collaboration is a priority to achieve this goal.