Modelling microbial infection to address global health challenges

Abstract

The continued growth of the world's population and increased interconnectivity heighten the risk that infectious diseases pose for human health worldwide. Epidemiological modelling is a tool that can be used to mitigate this risk by predicting disease spread or quantifying the impact of different intervention strategies on disease transmission dynamics. We illustrate how four decades of methodological advances and improved data quality have facilitated the contribution of modelling to address global health challenges, exemplified by models for the HIV crisis, emerging pathogens and pandemic preparedness. Throughout, we discuss the importance of designing a model that is appropriate to the research question and the available data. We highlight pitfalls that can arise in model development, validation and interpretation. Close collaboration between empiricists and modellers continues to improve the accuracy of predictions and the optimization of models for public health decision-making.

Fig. 1. Data-driven model prediction to evaluate the impact of manipulatable policy variables.

a, Data from a variety of sources, including surveillance reports, experiments and epidemiological studies, can inform model parameters. b, Rather than extracting single point estimates, modellers can use data more powerfully by constructing data-driven distributions for parameters from which values are sampled for each simulation. c, Every simulation yields a projection, such that multiple runs based on drawing probabilistically from empirical distributions generate a probabilistic distribution of projections. Types of projection that can be generated include outbreak trajectories, disease burdens and economic impact. d, Probabilistic uncertainty analyses convey not only model projections of policy outcomes, but also quantification of confidence in the projections. e, As policies are adopted and the microbiological system is influenced accordingly, the model can be iteratively updated to reflect the shifting status quo, thereby progressively optimizing policies within an evolving system.

Fig. 2. Behavioural changes drive outbreak patterns and also respond to them.

a, Pertussis case notifications, pertussis deaths and the percentage of children completing the full course of vaccines by their second birthday in England and Wales, 1968–2000. A case series describing children with suspected neurological damage from the whole-cell pertussis vaccine was published in 1974 and received widespread media attention. Subsequently, the National Childhood Encephalopathy Study published in 1981 clarified the risks, which motivated public health efforts to boost vaccine uptake. The whole-cell pertussis vaccine was replaced with an acellular formulation in 1996. b, Pertussis case notifications and percentage change in vaccine uptake in successive years during the recovery phase, 1977–1994. Vaccine uptake appears to be entrained by surges in infection incidence. Mathematical models can capture the interplay between natural and human dynamics exemplified in this dataset and a wide variety of other study systems.