Patterns of Influenza Vaccination Coverage in the United States from 2009 to 2015

Background: Globally, influenza is a major cause of morbidity, hospitalization and mortality. Influenza vaccination has shown substantial protective effectiveness in the United States. We investigated state-level patterns of coverage rates of seasonal and pandemic influenza vaccination, among the overall population in the U.S. and specifically among children and the elderly, from 2009/10 to 2014/15, and associations with ecological factors. Methods and Findings: We obtained state-level influenza vaccination coverage rates from national surveys, and state-level socio-demographic and health data from a variety of sources. We employed a retrospective ecological study design, and used mixed-model regression to determine the levels of ecological association of the state-level vaccinations rates with these factors, both with and without region as a factor for the three populations. We found that health-care access is positively and significantly associated with mean influenza vaccination coverage rates across all populations and models. We also found that prevalence of asthma in adults are negatively and significantly associated with mean influenza vaccination coverage rates in the elderly populations. Conclusions: Health-care access has a robust, positive association with state-level vaccination rates across different populations. This highlights a potential population-level advantage of expanding health-care access.Comment: 10 pages, 2 figure

The impact of COVID-19 vaccination campaign in Hong Kong SAR China and Singapore

Background: Vaccination has been the most important measure to mitigate the COVID-19 pandemic. The vaccination coverage was relatively low in Hong Kong Special Administrative Region China, compared to Singapore, in early 2022. Hypothetically, if the two regions, Hong Kong (HK) and Singapore (SG), swap their vaccination coverage rate, what outcome would occur? Method: We adopt the Susceptible – Vaccinated – Exposed – Infectious – Hospitalized – Death - Recovered model with a time-varying transmission rate and fit the model to weekly reported COVID-19 deaths (the data up to 2022 Nov 4) in HK and SG using R package POMP. After we obtain a reasonable fitting, we rerun our model with the estimated parameter values and swap the vaccination rates between HK and SG to explore what would happen. Results: Our model fits the data well. The reconstructed transmission rate was higher in HK than in SG in 2022. With a higher vaccination rate as in SG, the death total reported in HK would decrease by 37.5% and the timing of the peak would delay by 3 weeks. With a lower vaccination rate as in HK, the death total reported in SG would increase to 5.5-fold high with a peak 6 weeks earlier than the actual during the Delta variant period. Conclusions: Vaccination rate changes in HK and SG may lead to very different outcomes. This is likely due that the estimated transmission rates were very different in HK and SG which reflect the different control policies and dominant variants. Because of strong control measures, HK avoided large-scale community transmission of the Delta variant. Given the high breakthrough infection rate and transmission rate of the Omicron variant, increasing the vaccination rate in HK will likely yield a mild (but significant) contribution in terms of lives saved. While in SG, lower vaccination coverage to the level of HK will be disastrous

Effects of reactive social distancing on the 1918 influenza pandemic.

The 1918 influenza pandemic was characterized by multiple epidemic waves. We investigated reactive social distancing, a form of behavioral response where individuals avoid potentially infectious contacts in response to available information on an ongoing epidemic or pandemic. We modelled its effects on the three influenza waves in the United Kingdom. In previous studies, human behavioral response was modelled by a Power function of the proportion of recent influenza mortality in a population, and by a Hill function, which is a function of the number of recent influenza mortality. Using a simple epidemic model with a Power function and one common set of parameters, we provided a good model fit for the observed multiple epidemic waves in London boroughs, Birmingham and Liverpool. We further applied the model parameters from these three cities to all 334 administrative units in England and Wales and including the population sizes of individual administrative units. We computed the Pearson's correlation between the observed and simulated for each administrative unit. We found a median correlation of 0.636, indicating that our model predictions are performing reasonably well. Our modelling approach is an improvement from previous studies where separate models are fitted to each city. With the reduced number of model parameters used, we achieved computational efficiency gain without over-fitting the model. We also showed the importance of reactive behavioral distancing as a potential non-pharmaceutical intervention during an influenza pandemic. Our work has both scientific and public health significance

Cumulative number of weekly mortality using different values of <i>κ</i> and <i>λ</i>.

<p>With <i>N</i> = 2,000,000, <i>S</i>₀ = 0.8<i>N</i>, <i>I</i>₀ = 100, <i>g</i>⁻¹ = 8, <i>γ</i>⁻¹ = 4 and <i>ϕ</i> = 0.01, the effects of <i>κ</i> and <i>λ</i> on the simulated weekly mortalities are shown in panels (a) and (b) respectively. In panels (a), we fixed <i>λ</i>⁻¹ = 10 days, and the cumulative weekly mortalities are 16% smaller when we have <i>κ</i> = 10,000 than <i>κ</i> = 1,000. In panel (b), when we fixed <i>κ</i> = 10,000, the cumulative weekly mortalities will be 27% smaller when we have <i>λ</i>⁻¹ = 5 days than <i>λ</i>⁻¹ = 20 days.</p

Summary of all parameters estimated in the best-fit model using the Power function.

<p>Distinct parameters could have different values for the three cities. Common parameters have the same values for all three cities.</p

Estimated daily basic reproductive number (thin red curve), effective reproductive number (bold blue curve) and weekly influenza mortality (shaded region).

<p>The average basic reproductive number is 3.24 in the three cities.</p

Comparison between the observed and simulated patterns of influenza mortality in 334 administrative units.

<p>(a) Observed data. (b) Simulated data that considers school term, temperature, and behavioral changes. (c) Without behavioral changes. Administrative units are ordered in descending population sizes from top to bottom.</p

Contour plots of the cumulative number of deaths with (panel a) and (panel b).

<p><i>N</i> = 2,000,000, <i>S</i>₀ = 0.8<i>N</i>, <i>I</i>₀ = 100, <i>g</i>⁻¹ = 8, <i>γ</i>⁻¹ = 4, <i>ϕ</i> = 0.01. <i>κ</i> represents the intensity of reactive social distancing behavior, and <i>λ</i> represents the rate of decay of reactive social distancing behavior.</p

Simulation comparison of the three behavioral functions using the same parameter settings: <i>N</i> = 4000,000, <i>κ</i> = 1350.

<p>Black line, red dashed line and blue dotted line represent Power function, Hill function and modified-Hill function, respectively.</p