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Accurate prediction of epidemics is pivotal for making well-informed decisions for the control of infectious diseases, but addressing heterogeneity in the system poses a challenge. In this study, we propose a novel modeling framework integrating the spatio-temporal heterogeneity of susceptible individuals into homogeneous models, by introducing a continuous recruitment process for the susceptibles. A neural network approximates the recruitment rate to develop a Universal Differential Equations (UDE) model. Simultaneously, we pre-set a specific form for the recruitment rate and develop a mechanistic model. Data from a COVID Omicron variant outbreak in Shanghai are used to train the UDE model using deep learning methods and to calibrate the mechanistic model using MCMC methods. Subsequently, we project the attack rate and peak of new infections for the first Omicron wave in China after the adjustment of the dynamic zero-COVID policy. Our projections indicate an attack rate and a peak of new infections of 80.06% and 3.17% of the population, respectively, compared with the homogeneous model's projections of 99.97% and 32.78%, thus providing an 18.6% improvement in the prediction accuracy based on the actual data. Our simulations demonstrate that heterogeneity in the susceptibles decreases herd immunity for ~37.36% of the population and prolongs the outbreak period from ~30 days to ~70 days, also aligning with the real case. We consider that this study lays the groundwork for the development of a new class of models and new insights for modelling heterogeneity. Author summary: This study developed a novel modeling framework that explicitly integrates spatial heterogeneity into traditionally homogeneous compartmental models for precise and efficient prediction of epidemics of infectious diseases. We employed the deep learning method to learn the recruitment rate of susceptible individuals from data given its inherent complexity and limited information. Moreover, we predefined the mechanistic form of the recruitment rate to facilitate interpretation and scenario analysis, where we fitted the recruitment rate of mechanistic model to those obtained from deep learning-based approach. Compared to the homogeneous model, our mechanistic model predicts significantly lower peak and final sizes of the epidemic, as well as reduced levels of herd immunity. Using real data as a benchmark, our model's predictive accuracy has markedly improved. The framework of our mechanistic model employs a straightforward approach to integrate heterogeneity into compartment models, thereby facilitating the extension of the modelling framework to encompass various aspects of infectious disease transmission with spatio-temporal heterogeneity among the susceptible population. [ABSTRACT FROM AUTHOR]

Simple Summary: Blackflies are small blood-sucking flies that can carry and transmit the parasitic worm Onchocerca volvulus, the causative agent of human onchocerciasis, also known as "river blindness". As gravid blackfly females oviposit their eggs in the fast-moving water of rivers and streams, the geographic distributions of both the vectors and the parasite that they transmit are closely linked with river basins. How blackflies disperse from and return to their breeding sites, the spatial distribution of their movements, and the distances that they can travel are crucial factors influencing transmission. As many endemic countries strive for elimination, understanding these patterns is important for guiding the design of monitoring and surveillance efforts. Here, we summarize the literature on the short-range and long-range dispersal of members of the Simulium damnosum complex and other onchocerciasis vectors and how this may influence exposure to blackfly bites, highlight significant gaps in existing knowledge, and propose possible directions for future research. This is particularly important in view of current global onchocerciasis elimination programs. Blackfly members of the Simulium damnosum complex are major vectors of the parasite that causes onchocerciasis in Africa and Yemen, with other vector species involved in a few localized areas of Africa and in the Neotropics. Although the life cycle of these blackflies is linked to fast-flowing rivers, they can travel long distances (up to at least 500 km), calling into question how transmission zones are defined. Knowledge of the short- and long-range dispersal of these vectors could inform where control interventions and monitoring are necessary if targets for onchocerciasis elimination are to be met. Yet, research on blackfly dispersal has been limited and fragmented over the last 70 years. Here, we review the literature on the dispersal of onchocerciasis vectors, and we show the need for further research to establish how far larvae can travel downstream; the extent to which adults invade transmission zones; and whether adults migrate in a series of successive short movements or in single long-distance shifts, or use both methods. [ABSTRACT FROM AUTHOR]