Epidemiological and economic impact of pandemic influenza in Chicago: Priorities for vaccine interventions

Abstract

The study objective is to estimate the epidemiological and economic impact of vaccine interventions during influenza pandemics in Chicago, and assist in vaccine intervention priorities. Scenarios of delay in vaccine introduction with limited vaccine efficacy and limited supplies are not unlikely in future influenza pandemics, as in the 2009 H1N1 influenza pandemic. We simulated influenza pandemics in Chicago using agent-based transmission dynamic modeling. Population was distributed among high-risk and non-high risk among 0–19, 20–64 and 65+ years subpopulations. Different attack rate scenarios for catastrophic (30.15%), strong (21.96%), and moderate (11.73%) influenza pandemics were compared against vaccine intervention scenarios, at 40% coverage, 40% efficacy, and unit cost of $28.62. Sensitivity analysis for vaccine compliance, vaccine efficacy and vaccine start date was also conducted. Vaccine prioritization criteria include risk of death, total deaths, net benefits, and return on investment. The risk of death is the highest among the high-risk 65+ years subpopulation in the catastrophic influenza pandemic, and highest among the high-risk 0–19 years subpopulation in the strong and moderate influenza pandemics. The proportion of total deaths and net benefits are the highest among the high-risk 20–64 years subpopulation in the catastrophic, strong and moderate influenza pandemics. The return on investment is the highest in the high-risk 0–19 years subpopulation in the catastrophic, strong and moderate influenza pandemics. Based on risk of death and return on investment, high-risk groups of the three age group subpopulations can be prioritized for vaccination, and the vaccine interventions are cost saving for all age and risk groups. The attack rates among the children are higher than among the adults and seniors in the catastrophic, strong, and moderate influenza pandemic scenarios, due to their larger social contact network and homophilous interactions in school. Based on return on investment and higher attack rates among children, we recommend prioritizing children (0–19 years) and seniors (65+ years) after high-risk groups for influenza vaccination during times of limited vaccine supplies. Based on risk of death, we recommend prioritizing seniors (65+ years) after high-risk groups for influenza vaccination during times of limited vaccine supplies.

Medical Costs of Keeping the US Economy Open During COVID-19

“…We build on our modeling and simulation framework for epidemic spread [3][4][5][6][7][8][9] using an individual level synthetic social contact network 5,10 -which represents each individual in the population along with their demographic attributes (e.g., age, gender, income), and their social interactions. The main steps in the first-principles based construction of synthetic populations and social contact networks are: (i) construct a synthetic population by using US Census and other commercial databases; (ii) assign daily activities to individuals within each household using activity and time-use surveys (American Time Use Survey data and National Household Travel Survey Data); (iii) assign a geo-location to each activity of each person based on data from Dun and BradStreet, land-use, Open Street Maps etc.…”

Feedback Between Behavioral Adaptations and Disease Dynamics

“…The methodology used to generate the networks is described in 29 – 33 , and these networks have been used in a variety of public health policy studies e.g. 32 , 34 – 37 , and most recently in 38 – 41 .…”

“…These include structural validity of models, matching the data produced to field data, and formal specifications of these models for software verification. For further information on validation of social contact network, we refer the reader to 29 , 31 , 32 , 34 , 38 , 39 , 56 . Models of within host disease progression and between host disease spread, parameters for the size of the antiviral stockpile, compliance to interventions, attack rate, price of antivirals etc.…”

Using Paired Agent-Based Simulations To Test Strategies For Limiting The Effects Of Epidemics

“…Agent-based simulations are widely used nowadays for modelling the propagation of infectious diseases, for comparing the different strategies for mitigating epidemics and for planning appropriate responses in the aftermath of crises in large urban areas, because they can capture fine scale heterogeneities that may have important non-linear effects on the results. In public health research, they have been used to evaluate different strategies for mitigating the severity of influenza-type pandemics [31][32][33], for modelling measles outbreaks [23], for modelling hospital-acquired infections [34], for determining the best way to allocate vaccines [35], [36] and for modelling the impact of sexual transmission of the Ebola virus [37]. Agent-based simulations have been used in research on malaria to model the infectious reservoir in humans [38] and to study the spatial and temporal heterogeneities of malaria incidence in a rainforest environment [39,40].…”