Title: Prevention of seasonal influenza outbreak via healthcare insurance
The outbreak of seasonal flu costs billions of dollars in health care utilization and lost productivity. Despite the effectiveness of vaccination and antiviral medications to prevent serious flu-related complications and slow down the spread of an influenza epidemic, only 52% of the U.S. population aged 6 months and older received flu vaccines in the 2019-20 flu season. In addition, a costly out-of-pocket expense results in fewer patients seeking treatment, leading to potential hospitalizations and even flu-related deaths. In this study, we develop an integrated healthcare insurance mechanism that optimizes two incentive policies, vaccination reward and cost-sharing, to alleviate the medical cost and disease burden while preventing the outbreak of seasonal influenza. We model the dynamic interaction between a single insurer and multiple insureds as a Stackelberg vaccination game; we then embed the game into an agent-based simulation to model the spread of flu in a population under different policies. Finally, we apply machine learning and simulation optimization to optimize healthcare incentive policies in a large-scale flu transmission simulation. Simulation results indicate that the proposed methodology efficiently identifies a set of good incentive policies under different scenarios of flu vaccine efficacy and reproduction numbers. more »« less
Augsburger, Ian B.; Galanthay, Grace K.; Tarosky, Jacob H.; Rychtář, Jan; Taylor, Dewey
(, PLOS Neglected Tropical Diseases)
Carabali, Mabel
(Ed.)
Monkeypox (MPX) is a viral zoonotic disease that was endemic to Central and West Africa. However, during the first half of 2022, MPX spread to almost 60 countries all over the world. Smallpox vaccines are about 85% effective in preventing MPX infections. Our objective is to determine whether the vaccines should be mandated or whether voluntary use of the vaccine could be enough to stop the MPX outbreak. We incorporate a standard SVEIR compartmental model of MPX transmission into a game-theoretical framework. We study a vaccination game in which individuals decide whether or not to vaccinate by assessing their benefits and costs. We solve the game for Nash equilibria, i.e., the vaccination rates the individuals would likely adopt without any outside intervention. We show that, without vaccination, MPX can become endemic in previously non-endemic regions, including the United States. We also show that to “not vaccinate” is often an optimal solution from the individual’s perspective. Moreover, we demonstrate that, for some parameter values, there are multiple equilibria of the vaccination game, and they exhibit a backward bifurcation. Thus, without centrally mandated minimal vaccination rates, the population could easily revert to no vaccination scenario.
Fitzpatrick, Meagan C.; Moghadas, Seyed M.; Vilches, Thomas N.; Shah, Arnav; Pandey, Abhishek; Galvani, Alison P.
(, JAMA Network Open)
Importance Adverse outcomes of COVID-19 in the pediatric population include disease and hospitalization, leading to school absenteeism. Booster vaccination for eligible individuals across all ages may promote health and school attendance. Objective To assess whether accelerating COVID-19 bivalent booster vaccination uptake across the general population would be associated with reduced pediatric hospitalizations and school absenteeism. Design, Setting, and Participants In this decision analytical model, a simulation model of COVID-19 transmission was fitted to reported incidence data from October 1, 2020, to September 30, 2022, with outcomes simulated from October 1, 2022, to March 31, 2023. The transmission model included the entire age-stratified US population, and the outcome model included children younger than 18 years. Interventions Simulated scenarios of accelerated bivalent COVID-19 booster campaigns to achieve uptake that was either one-half of or similar to the age-specific uptake observed for 2020 to 2021 seasonal influenza vaccination in the eligible population across all age groups. Main Outcomes and Measures The main outcomes were estimated hospitalizations, intensive care unit admissions, and isolation days of symptomatic infection averted among children aged 0 to 17 years and estimated days of school absenteeism averted among children aged 5 to 17 years under the accelerated bivalent booster campaign simulated scenarios. Results Among children aged 5 to 17 years, a COVID-19 bivalent booster campaign achieving age-specific coverage similar to influenza vaccination could have averted an estimated 5 448 694 (95% credible interval [CrI], 4 936 933-5 957 507) days of school absenteeism due to COVID-19 illness. In addition, the booster campaign could have prevented an estimated 10 019 (95% CrI, 8756-11 278) hospitalizations among the pediatric population aged 0 to 17 years, of which 2645 (95% CrI, 2152-3147) were estimated to require intensive care. A less ambitious booster campaign with only 50% of the age-specific uptake of influenza vaccination among eligible individuals could have averted an estimated 2 875 926 (95% CrI, 2 524 351-3 332 783) days of school absenteeism among children aged 5 to 17 years and an estimated 5791 (95% CrI, 4391-6932) hospitalizations among children aged 0 to 17 years, of which 1397 (95% CrI, 846-1948) were estimated to require intensive care. Conclusions and Relevance In this decision analytical model, increased uptake of bivalent booster vaccination among eligible age groups was associated with decreased hospitalizations and school absenteeism in the pediatric population. These findings suggest that although COVID-19 prevention strategies often focus on older populations, the benefits of booster campaigns for children may be substantial.
Zath, Geoffrey K; Thomas, Mallory M; Loveday, Emma K; Bikos, Dimitri A; Sanche, Steven; Ke, Ruian; Brooke, Christopher B; Chang, Connie B
(, PLOS Pathogens)
Lowen, Anice C
(Ed.)
An important aspect of how viruses spread and infect is the viral burst size, or the number of new viruses produced by each infected cell. Surprisingly, this value remains poorly characterized for influenza A virus (IAV), commonly known as the flu. In this study, we screened tens of thousands of cells using a microfluidic method called droplet quantitative PCR (dqPCR). The high-throughput capability of dqPCR enabled the measurement of a large population of infected cells producing progeny virus. By measuring the fully assembled and successfully released viruses from these infected cells, we discover that the viral burst sizes for both the seasonal H3N2 and the 2009 pandemic H1N1 strains vary significantly, with H3N2 ranging from 101to 104viruses per cell, and H1N1 ranging from 101to 103viruses per cell. Some infected cells produce average numbers of new viruses, while others generate extensive number of viruses. In fact, we find that only 10% of the single-cell infections are responsible for creating a significant portion of all the viruses. This small fraction produced approximately 60% of new viruses for H3N2 and 40% for H1N1. On average, each infected cell of the H3N2 flu strain produced 709 new viruses, whereas for H1N1, each infected cell produced 358 viruses. This novel method reveals insights into the flu virus and can lead to improved strategies for managing and preventing the spread of viruses.
Phadke, I.; McKee, A.; Conway, J.M.; Shea, K.
(, Epidemiology and Infection)
Abstract During a disease outbreak, healthcare workers (HCWs) are essential to treat infected individuals. However, these HCWs are themselves susceptible to contracting the disease. As more HCWs get infected, fewer are available to provide care for others, and the overall quality of care available to infected individuals declines. This depletion of HCWs may contribute to the epidemic's severity. To examine this issue, we explicitly model declining quality of care in four differential equation-based susceptible, infected and recovered-type models with vaccination. We assume that vaccination, recovery and survival rates are affected by quality of care delivered. We show that explicitly modelling HCWs and accounting for declining quality of care significantly alters model-predicted disease outcomes, specifically case counts and mortality. Models neglecting the decline of quality of care resulting from infection of HCWs may significantly under-estimate cases and mortality. These models may be useful to inform health policy that may differ for HCWs and the general population. Models accounting for declining quality of care may therefore improve the management interventions considered to mitigate the effects of a future outbreak.
Huang, Xiaolei; Smith, Michael C; Jamison, Amelia M; Broniatowski, David A; Dredze, Mark; Quinn, Sandra Crouse; Cai, Justin; Paul, Michael J
(, BMJ Open)
Introduction The Centers for Disease Control and Prevention (CDC) spend significant time and resources to track influenza vaccination coverage each influenza season using national surveys. Emerging data from social media provide an alternative solution to surveillance at both national and local levels of influenza vaccination coverage in near real time. Objectives This study aimed to characterise and analyse the vaccinated population from temporal, demographical and geographical perspectives using automatic classification of vaccination-related Twitter data. Methods In this cross-sectional study, we continuously collected tweets containing both influenza-related terms and vaccine-related terms covering four consecutive influenza seasons from 2013 to 2017. We created a machine learning classifier to identify relevant tweets, then evaluated the approach by comparing to data from the CDC’s FluVaxView. We limited our analysis to tweets geolocated within the USA. Results We assessed 1 124 839 tweets. We found strong correlations of 0.799 between monthly Twitter estimates and CDC, with correlations as high as 0.950 in individual influenza seasons. We also found that our approach obtained geographical correlations of 0.387 at the US state level and 0.467 at the regional level. Finally, we found a higher level of influenza vaccine tweets among female users than male users, also consistent with the results of CDC surveys on vaccine uptake. Conclusion Significant correlations between Twitter data and CDC data show the potential of using social media for vaccination surveillance. Temporal variability is captured better than geographical and demographical variability. We discuss potential paths forward for leveraging this approach.
Ho, Ting-Yu, Zabinsky, Zelda B., Fishman, Paul A., and Liu, Shan. Prevention of seasonal influenza outbreak via healthcare insurance. Retrieved from https://par.nsf.gov/biblio/10383487. IISE Transactions on Healthcare Systems Engineering . Web. doi:10.1080/24725579.2022.2145393.
Ho, Ting-Yu, Zabinsky, Zelda B., Fishman, Paul A., & Liu, Shan. Prevention of seasonal influenza outbreak via healthcare insurance. IISE Transactions on Healthcare Systems Engineering, (). Retrieved from https://par.nsf.gov/biblio/10383487. https://doi.org/10.1080/24725579.2022.2145393
@article{osti_10383487,
place = {Country unknown/Code not available},
title = {Prevention of seasonal influenza outbreak via healthcare insurance},
url = {https://par.nsf.gov/biblio/10383487},
DOI = {10.1080/24725579.2022.2145393},
abstractNote = {The outbreak of seasonal flu costs billions of dollars in health care utilization and lost productivity. Despite the effectiveness of vaccination and antiviral medications to prevent serious flu-related complications and slow down the spread of an influenza epidemic, only 52% of the U.S. population aged 6 months and older received flu vaccines in the 2019-20 flu season. In addition, a costly out-of-pocket expense results in fewer patients seeking treatment, leading to potential hospitalizations and even flu-related deaths. In this study, we develop an integrated healthcare insurance mechanism that optimizes two incentive policies, vaccination reward and cost-sharing, to alleviate the medical cost and disease burden while preventing the outbreak of seasonal influenza. We model the dynamic interaction between a single insurer and multiple insureds as a Stackelberg vaccination game; we then embed the game into an agent-based simulation to model the spread of flu in a population under different policies. Finally, we apply machine learning and simulation optimization to optimize healthcare incentive policies in a large-scale flu transmission simulation. Simulation results indicate that the proposed methodology efficiently identifies a set of good incentive policies under different scenarios of flu vaccine efficacy and reproduction numbers.},
journal = {IISE Transactions on Healthcare Systems Engineering},
author = {Ho, Ting-Yu and Zabinsky, Zelda B. and Fishman, Paul A. and Liu, Shan},
}
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