Modeling the Effect of Disease Characteristics on the Outcomes of Interventions

Many infectious diseases have common features in terms of their natural history; however, quantifiable characteristics, such as transmissibility, the incubation period, and infectiousness profile, set them apart. These characteristics play a pivotal role in determining whether a disease might trigger a local outbreak or evolve into a global pandemic. Consequently, understanding these characteristics and discerning their variations across diseases is critical in devising effective public health policies to prevent the spread of infection in the population and mitigate potential outcomes and socioeconomic repercussions, as observed during the COVID-19 pandemic. This study introduces a general modeling framework for transmission dynamics of two respiratory viruses, namely, influenza and SARS-CoV-2, to investigate the effect their characteristics have on intervention outcomes. Performing simulations and sensitivity analysis, we show that the length of infectious period and infectiousness profile during various stages of illness have a remarkable influence on the outcome of interventions. With appropriate parameterization of the model, the results show that beyond isolation of infectious individuals, school closure can lead to different mitigating effects for the two diseases, likely due to the longer and more infectious pre-symptomatic stage in the SARS-CoV-2 infection compared to influenza. This is further demonstrated by the results from the sensitivity analyses, indicating that the duration of pre-symptomatic and symptomatic periods consistently exhibits a negative linear correlation with the relative reduction in attack rate (i.e., the proportion of the population infected throughout the outbreak), regardless of the disease or the type of intervention.