Modeling the dynamics of disease elimination

Elimination of an infectious disease is often a goal of the public health community. Although that goal is rarely
achieved, the tremendous expansion of epidemiological databases provides new opportunities to test
hypotheses concerning elimination with mathematical modeling. Besides improving our scientific
understanding of disease transmission, hypotheses validated through mathematical modeling provide public
health practitioners with a more structured, quantitative assessment of how elimination of specific pathogens
can be achieved. This proposal aims to develop an interconnected set of modeling tools to support elimination
of communicable diseases. A variety of processes used to achieve disease elimination will be considered
including use of mass drug administration to eliminate neglected tropical diseases such as trachoma,
vaccination for preventable diseases such as SARS-CoV-2, and antibiotic stewardship efforts to curtail drug
resistant infections such as methicillin-resistant Staphylococcus aureus (MRSA). A key theme is the
requirement of subcritical transmission for disease elimination, meaning that the average number of new
infections each case causes is less than one. A major goal is to elucidate the transmission dynamics of
subcritical diseases on the brink of elimination. Transmission heterogeneity may arise from many
mechanisms including super-shedding of certain individuals, pockets of susceptibility such as in a community
with low vaccine uptake, and contact structure in which some individuals have the potential to infect many
others. Simulations of various patterns of disease transmission will be used to develop distinct measurements
of transmission heterogeneity. In addition, new techniques to infer and compensate for observation error will be
developed that integrate data on the observation process, such as the proportion of cases identified
retrospectively via contact tracing programs. Models of transmission dynamics will be used to identify
transmission-hotspots and superspreaders that can jeopardize elimination. People, areas, or events that
have increased transmission potential can maintain endemic disease transmission even though the population-
level average value of R may be less than one. In the first stage of this objective, we will use existing models to
construct a suite of in silico simulations to compare the performance of various scan statistics designed to
detect disease burden beyond what is expected by chance. In the second stage, we will apply these scan
statistics to observational data. Identification of transmission-hotspots and supersreaders permits optimization
of disease elimination strategies. To eliminate disease, it is insufficient to merely identify transmission-
hotspots or superspreading activity. A strategy is needed for suppressing the sites, events, or people that
cause higher levels of transmission. We will use mathematical and computational models for disease
elimination to address 1) the impact of control interventions, 2) the optimal distribution of a limited treatment
supply, and 3) monitoring of treatment efficacy.

Grant Number: 5R35GM147702-04
NIH Institute/Center: NIH
Principal Investigator: Seth Blumberg