Recent outbreaks, including the coronavirus disease 2019 (COVID-19) pandemic, have highlighted the importance of mathematical and statistical modelling in understanding the drivers of transmission and how to tailor responses. Improving future responses requires lessons from these previous outbreaks to be learned and the remaining challenges overcome. This requires developing appropriate mathematical, statistical and computational frameworks that accurately capture the studied mechanisms, leading to a better understanding of how different data can affect these formulations or the simulated interventions. Furthermore, improving model validation and quantifying uncertainty in the model parameter estimates and projections when applied responsively across settings and diseases is also required. Via a collection of 22 papers, this special issue brings together both theoretical advances and applied modelling innovations aimed at improving epidemic and pandemic preparedness and response. It includes articles that develop novel statistical inference approaches, as well as sophisticated and data-informed mathematical models, enhanced simulation techniques, exploration of heterogeneities in disease transmission both across ages and settings and the use of modelling techniques to evaluate intervention strategies across different diseases and settings to improve public health outcomes.
Subaerial biofilms (SABs) are microbial communities that colonize exposed surfaces, playing a role in biogeochemical cycles and the deterioration of built heritage. Their functioning is tightly coupled to environmental conditions, particularly moisture and carbon availability. This work presents a predictive framework to assess the long-term stability of SABs in response to future environmental changes - specifically, variations in temperature (T), air relative humidity (RH), and atmospheric CO2 partial pressure. The approach is based on a system of ordinary differential equations that describe the dynamics of key SAB components such as cyanobacteria, heterotrophs and polysaccharides. Using daily environmental profiles representative of summer and winter conditions, the model explores both the individual and combined impacts of T, RH and CO2 on microbial metabolism, productivity, and ecosystem composition. Results suggest that while temperature increases negatively affect inorganic carbon availability, they do not cause substantial shifts in SAB structure - consistent with the wide thermal tolerance typical of these communities. In contrast, elevated atmospheric CO2 may enhance carbon fixation and overall productivity. However, air relative humidity emerges as the primary regulator of microbial viability: even small fluctuations significantly alter the duration of metabolically active periods, with declines potentially leading to ecosystem collapse. Notably, the interaction between CO2 and water activity is synergistic: increases in atmospheric CO2 together with temperature-driven changes in relative humidity - either upward or downward - can in combination significantly influence SAB dynamics. These findings highlight the central role of water activity in maintaining SAB viability and suggest that even moderate shifts in microclimatic moisture availability could have profound impacts on these microbial communities.

