The Impacts Of Simultaneous Disease Intervention Decisions On Epidemic Outcomes

The final publication is available at Elsevier via http://dx.doi.org/10.1016/j.jtbi.2016.01.027 © 2016. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/Mathematical models of the interplay between disease dynamics and human behavioural dynamics can improve our understanding of how diseases spread when individuals adapt their behaviour in response to an epidemic. Accounting for behavioural mechanisms that determine uptake of infectious disease interventions such as vaccination and non-pharmaceutical interventions (NPIs) can significantly alter predicted health outcomes in a population. However, most previous approaches that model interactions between human behaviour and disease dynamics have modelled behaviour of these two interventions separately. Here, we develop and analyze an agent based network model to gain insights into how behaviour toward both interventions interact adaptively with disease dynamics (and therefore, indirectly, with one another) during the course of a single epidemic where an SIRV infection spreads through a contact network. In the model, individuals decide to become vaccinated and/or practice NPIs based on perceived infection prevalence (locally or globally) and on what other individuals in the network are doing. We find that introducing adaptive NPI behaviour lowers vaccine uptake on account of behavioural feedbacks, and also decreases epidemic final size. When transmission rates are low, NPIs alone are as effective in reducing epidemic final size as NPIs and vaccination combined. Also, NPIs can compensate for delays in vaccine availability by hindering early disease spread, decreasing epidemic size significantly compared to the case where NPI behaviour does not adapt to mitigate early surges in infection prevalence. We also find that including adaptive NPI behaviour strongly mitigates the vaccine behavioural feedbacks that would otherwise result in higher vaccine uptake at lower vaccine efficacy as predicted by most previous models, and the same feedbacks cause epidemic final size to remain approximately constant across a broad range of values for vaccine efficacy. Finally, when individuals use local information about others' behaviour and infection prevalence, instead of population-level information, infection is controlled more efficiently through ring vaccination, and this is reflected in the time evolution of pair correlations on the network. This model shows that accounting for both adaptive NPI behaviour and adaptive vaccinating behaviour regarding social effects and infection prevalence can result in qualitatively different predictions than if only one type of adaptive behaviour is modelled.Natural Sciences and Engineering Research Council (NSERC) Individual Discovery Gran

Pandemic (H1N1) 2009 Vaccination and Class Suspensions after Outbreaks, Taipei City, Taiwan

In Taipei City, class suspensions were implemented beginning September 1, 2009 when transmission of pandemic (H1N1) 2009 infection was suspected. The uptake rate of pandemic (H1N1) 2009 vaccination (starting on November 16, 2009) among students 7–18 years of age was 74.7%. Outbreaks were mitigated after late November 2009

Cost-Effectiveness of Introducing Nuvaxovid to COVID-19 Vaccination in the United Kingdom: A Dynamic Transmission Model

Background/Objectives: Vaccination against SARS-CoV-2 remains a key measure to control COVID-19. Nuvaxovid, a recombinant Matrix-M–adjuvanted protein-based vaccine, showed similar efficacy to mRNA vaccines in clinical trials and real-world studies, with lower rates of reactogenicity. Methods: To support decision making on UK vaccine selection, a population-based compartmental dynamic transmission model with a cost-utility component was developed to evaluate the cost-effectiveness of Nuvaxovid compared with mRNA vaccines from a UK National Health Service perspective. The model was calibrated to official epidemiology statistics for mortality, incidence, and hospitalisation. Scenario and sensitivity analyses were conducted. Results: In the probabilistic base case, a Nuvaxovid-only strategy provided total incremental cost savings of GBP 1,338,323 and 1558 additional quality-adjusted life years (QALYs) compared with an mRNA-only vaccination strategy. Cost savings were driven by reduced cold chain-related operational costs and vaccine wastage, while QALY gains were driven by potential differences in vaccine tolerability. Probabilistic sensitivity analysis indicated an approximately 70% probability of cost-effectiveness with Nuvaxovid-only versus mRNA-only vaccination across most cost-effectiveness thresholds (up to GBP 300,000/QALY gained). Conclusions: Nuvaxovid remained dominant over mRNA vaccines in scenario analyses assessing vaccine efficacy waning, Nuvaxovid market shares, and the vaccinated population

Clinical outcomes of seasonal influenza and pandemic influenza A (H1N1) in pediatric inpatients

<p>Abstract</p> <p>Background</p> <p>In April 2009, a novel influenza A H1N1 (nH1N1) virus emerged and spread rapidly worldwide. News of the pandemic led to a heightened awareness of the consequences of influenza and generally resulted in enhanced infection control practices and strengthened vaccination efforts for both healthcare workers and the general population. Seasonal influenza (SI) illness in the pediatric population has been previously shown to result in significant morbidity, mortality, and substantial hospital resource utilization. Although influenza pandemics have the possibility of resulting in considerable illness, we must not ignore the impact that we can experience annually with SI.</p> <p>Methods</p> <p>We compared the outcomes of pediatric patients ≤18 years of age at a large urban hospital with laboratory confirmed influenza and an influenza-like illness (ILI) during the 2009 pandemic and two prior influenza seasons. The primary outcome measure was hospital length of stay (LOS). All variables potentially associated with LOS based on univariable analysis, previous studies, or hypothesized relationships were included in the regression models to ensure adjustment for their effects.</p> <p>Results</p> <p>There were 133 pediatric cases of nH1N1 admitted during 2009 and 133 cases of SI admitted during the prior 2 influenza seasons (2007-8 and 2008-9). Thirty-six percent of children with SI and 18% of children with nH1N1 had no preexisting medical conditions (p = 0.14). Children admitted with SI had 1.73 times longer adjusted LOS than children admitted for nH1N1 (95% CI 1.35 - 2.13). There was a trend towards more children with SI requiring mechanical ventilation compared with nH1N1 (16 vs.7, p = 0.08).</p> <p>Conclusions</p> <p>This study strengthens the growing body of evidence demonstrating that SI results in significant morbidity in the pediatric population. Pandemic H1N1 received considerable attention with strong media messages urging people to undergo vaccination and encouraging improved infection control efforts. We believe that this attention should become an annual effort for SI. Strong unified messages from health care providers and the media encouraging influenza vaccination will likely prove very useful in averting some of the morbidity related to influenza for future epidemics.</p

Swine-Origin Influenza A Outbreak 2009 at Shinshu University, Japan

<p>Abstract</p> <p>Background</p> <p>A worldwide outbreak of swine flu H1N1 pandemic influenza occurred in April 2009. To determine the mechanism underlying the spread of infection, we prospectively evaluated a survey implemented at a local university.</p> <p>Methods</p> <p>Between August 2009 and March 2010, we surveyed 3 groups of subjects: 2318 children in six schools attached to the Faculty of Education, 11424 university students, and 3344 staff members. Subjects with influenza-like symptoms who were diagnosed with swine flu at hospitals or clinics were defined as swine flu patients and asked to make a report using a standardized form.</p> <p>Results</p> <p>After the start of the pandemic, a total of 2002 patients (11.7%) were registered in the survey. These patients included 928 schoolchildren (40.0%), 1016 university students (8.9%), and 58 staff members (1.7%). The incidence in schoolchildren was significantly higher than in the other 2 groups (<it>P </it>< 0.0001) but there were no within group differences in incidence rate between males and females. During the period of the survey, three peaks of patient numbers were observed, in November 2009, December 2009, and January 2010. The first peak consisted mainly of schoolchildren, whereas the second and third peaks included many university students. Staff members did not contribute to peak formation. Among the university students, the most common suspected route of transmission was club activity. Interventions, such as closing classes, schools, and clubs, are likely to affect the epidemic curves.</p> <p>Conclusion</p> <p>Schoolchildren and university students are vulnerable to swine flu, suggesting that avoidance of close contact, especially among these young people, may be effective way in controlling future severe influenza pandemics, especially at educational institutions.</p

Influenza Pandemic Waves under Various Mitigation Strategies with 2009 H1N1 as a Case Study

A significant feature of influenza pandemics is multiple waves of morbidity and mortality over a few months or years. The size of these successive waves depends on intervention strategies including antivirals and vaccination, as well as the effects of immunity gained from previous infection. However, the global vaccine manufacturing capacity is limited. Also, antiviral stockpiles are costly and thus, are limited to very few countries. The combined effect of antivirals and vaccination in successive waves of a pandemic has not been quantified. The effect of acquired immunity from vaccination and previous infection has also not been characterized. In times of a pandemic threat countries must consider the effects of a limited vaccine, limited antiviral use and the effects of prior immunity so as to adopt a pandemic strategy that will best aid the population. We developed a mathematical model describing the first and second waves of an influenza pandemic including drug therapy, vaccination and acquired immunity. The first wave model includes the use of antiviral drugs under different treatment profiles. In the second wave model the effects of antivirals, vaccination and immunity gained from the first wave are considered. The models are used to characterize the severity of infection in a population under different drug therapy and vaccination strategies, as well as school closure, so that public health policies regarding future influenza pandemics are better informed

Determinants of the Spatiotemporal Dynamics of the 2009 H1N1 Pandemic in Europe: Implications for Real-Time Modelling

Influenza pandemics in the last century were characterized by successive waves and differences in impact and timing between different regions, for reasons not clearly understood. The 2009 H1N1 pandemic showed rapid global spread, but with substantial heterogeneity in timing within each hemisphere. Even within Europe substantial variation was observed, with the UK being unique in experiencing a major first wave of transmission in early summer and all other countries having a single major epidemic in the autumn/winter, with a West to East pattern of spread. Here we show that a microsimulation model, parameterised using data about H1N1pdm collected by the beginning of June 2009, explains the occurrence of two waves in UK and a single wave in the rest of Europe as a consequence of timing of H1N1pdm spread, fluxes of travels from US and Mexico, and timing of school vacations. The model provides a description of pandemic spread through Europe, depending on intra-European mobility patterns and socio-demographic structure of the European populations, which is in broad agreement with observed timing of the pandemic in different countries. Attack rates are predicted to depend on the socio-demographic structure, with age dependent attack rates broadly agreeing with available serological data. Results suggest that the observed heterogeneity can be partly explained by the between country differences in Europe: marked differences in school calendars, mobility patterns and sociodemographic structures. Moreover, higher susceptibility of children to infection played a key role in determining the epidemiology of the 2009 pandemic. Our work shows that it would have been possible to obtain a broad-brush prediction of timing of the European pandemic well before the autumn of 2009, much more difficult to achieve with simpler models or pre-pandemic parameterisation. This supports the use of models accounting for the structure of complex modern societies for giving insight to policy makers

Can Interactions between Timing of Vaccine-Altered Influenza Pandemic Waves and Seasonality in Influenza Complications Lead to More Severe Outcomes?

Vaccination can delay the peak of a pandemic influenza wave by reducing the number of individuals initially susceptible to influenza infection. Emerging evidence indicates that susceptibility to severe secondary bacterial infections following a primary influenza infection may vary seasonally, with peak susceptibility occurring in winter. Taken together, these two observations suggest that vaccinating to prevent a fall pandemic wave might delay it long enough to inadvertently increase influenza infections in winter, when primary influenza infection is more likely to cause severe outcomes. This could potentially cause a net increase in severe outcomes. Most pandemic models implicitly assume that the probability of severe outcomes does not vary seasonally and hence cannot capture this effect. Here we show that the probability of intensive care unit (ICU) admission per influenza infection in the 2009 H1N1 pandemic followed a seasonal pattern. We combine this with an influenza transmission model to investigate conditions under which a vaccination program could inadvertently shift influenza susceptibility to months where the risk of ICU admission due to influenza is higher. We find that vaccination in advance of a fall pandemic wave can actually increase the number of ICU admissions in situations where antigenic drift is sufficiently rapid or where importation of a cross-reactive strain is possible. Moreover, this effect is stronger for vaccination programs that prevent more primary influenza infections. Sensitivity analysis indicates several mechanisms that may cause this effect. We also find that the predicted number of ICU admissions changes dramatically depending on whether the probability of ICU admission varies seasonally, or whether it is held constant. These results suggest that pandemic planning should explore the potential interactions between seasonally varying susceptibility to severe influenza outcomes and the timing of vaccine-altered pandemic influenza waves

Modelling the strategies for age specific vaccination scheduling during influenza pandemic outbreaks

Finding optimal policies to reduce the morbidity and mortality of the ongoing pandemic is a top public health priority. Using a compartmental model with age structure and vaccination status, we examined the effect of age specific scheduling of vaccination during a pandemic influenza outbreak, when there is a race between the vaccination campaign and the dynamics of the pandemic. Our results agree with some recent studies on that age specificity is paramount to vaccination planning. However, little is known about the effectiveness of such control measures when they are applied during the outbreak. Comparing five possible strategies, we found that age specific scheduling can have a huge impact on the outcome of the epidemic. For the best scheme, the attack rates were up to 10% lower than for other strategies. We demonstrate the importance of early start of the vaccination campaign, since ten days delay may increase the attack rate by up to 6%. Taking into account the delay between developing immunity and vaccination is a key factor in evaluating the impact of vaccination campaigns. We provide a general framework which will be useful for the next pandemic waves as well