Supplemental measles vaccine antibody response among HIV-infected and -uninfected children in Malawi after 1- and 2-dose primary measles vaccination schedules.

BACKGROUND: The long-term antibody response to measles vaccine (MV) administered at age 6 months with or without subsequent doses is not well documented. METHODS: Measles serum antibody responses were evaluated after a supplemental dose of measles vaccine (sMV) administered at a median age of 20 months among Malawian children who had previously received 2 doses of measles vaccine (MV) at ages 6 and 9 months (HIV-infected and random sample of HIV-uninfected) or 1 dose at age 9 months (random sample of HIV-uninfected). We compared measles antibody seropositivity between groups by enzyme linked immunoassay and seroprotection by plaque reduction neutralization geometric mean concentrations. RESULTS: Of 1756 children enrolled, 887 (50.5%) received a sMV dose following MV at 9 months of age and had specimens available after sMV receipt, including 401 HIV-uninfected children who received one MV dose at 9 months, 464 HIV-uninfected and 22 HIV-infected children who received two doses of MV at ages 6 and 9 months. Among HIV-uninfected children, protective levels of antibody were found post sMV in 90-99% through ages 24-36 months and were not affected by MV schedule. Geometric mean concentration levels of measles antibody were significantly increased post-sMV among those HIV-uninfected children previously non-responsive to vaccination. Among HIV-infected children, the proportion seroprotected increased initially but by 9 months post-sMV was no higher than pre-sMV. CONCLUSIONS: Our findings support early 2-dose MV to provide measles immunity for young infants without risk of interference with antibody responses to subsequent MV doses administered as part of SIAs

Assessing Google Flu Trends Performance in the United States during the 2009 Influenza Virus A (H1N1) Pandemic

Google Flu Trends (GFT) uses anonymized, aggregated internet search activity to provide near-real time estimates of influenza activity. GFT estimates have shown a strong correlation with official influenza surveillance data. The 2009 influenza virus A (H1N1) pandemic [pH1N1] provided the first opportunity to evaluate GFT during a non-seasonal influenza outbreak. In September 2009, an updated United States GFT model was developed using data from the beginning of pH1N1.We evaluated the accuracy of each U.S. GFT model by comparing weekly estimates of ILI (influenza-like illness) activity with the U.S. Outpatient Influenza-like Illness Surveillance Network (ILINet). For each GFT model we calculated the correlation and RMSE (root mean square error) between model estimates and ILINet for four time periods: pre-H1N1, Summer H1N1, Winter H1N1, and H1N1 overall (Mar 2009–Dec 2009). We also compared the number of queries, query volume, and types of queries (e.g., influenza symptoms, influenza complications) in each model. Both models' estimates were highly correlated with ILINet pre-H1N1 and over the entire surveillance period, although the original model underestimated the magnitude of ILI activity during pH1N1. The updated model was more correlated with ILINet than the original model during Summer H1N1 (r = 0.95 and 0.29, respectively). The updated model included more search query terms than the original model, with more queries directly related to influenza infection, whereas the original model contained more queries related to influenza complications.Internet search behavior changed during pH1N1, particularly in the categories “influenza complications” and “term for influenza.” The complications associated with pH1N1, the fact that pH1N1 began in the summer rather than winter, and changes in health-seeking behavior each may have played a part. Both GFT models performed well prior to and during pH1N1, although the updated model performed better during pH1N1, especially during the summer months

COVID-19 vaccine perceptions and uptake in a national prospective cohort of essential workers

INTRODUCTION: In a multi-center prospective cohort of essential workers, we assessed knowledge, attitudes, and practices (KAP) by vaccine intention, prior SARS-CoV-2 positivity, and occupation, and their impact on vaccine uptake over time. METHODS: Initiated in July 2020, the HEROES-RECOVER cohort provided socio-demographics and COVID-19 vaccination data. Using two follow-up surveys approximately three months apart, COVID-19 vaccine KAP, intention, and receipt was collected; the first survey categorized participants as reluctant, reachable, or endorser. RESULTS: A total of 4,803 participants were included in the analysis. Most (70%) were vaccine endorsers, 16% were reachable, and 14% were reluctant. By May 2021, 77% had received at least one vaccine dose. KAP responses strongly predicted vaccine uptake, particularly positive attitudes about safety (aOR = 5.46, 95% CI: 1.4-20.8) and effectiveness (aOR = 5.0, 95% CI: 1.3-19.1). Participants' with prior SARS-CoV-2 infection were 22% less likely to believe the COVID-19 vaccine was effective compared with uninfected participants (aOR 0.78, 95% CI: 0.64-0.96). This was even more pronounced in first responders compared with other occupations, with first responders 42% less likely to believe in COVID-19 vaccine effectiveness (aOR = 0.58, 95% CI 0.40-0.84). Between administrations of the two surveys, 25% of reluctant, 56% reachable, and 83% of endorser groups received the COVID-19 vaccine. The reachable group had large increases in positive responses for questions about vaccine safety (10% of vaccinated, 34% of unvaccinated), and vaccine effectiveness (12% of vaccinated, 27% of unvaccinated). DISCUSSION: Our study demonstrates attitudes associated with COVID-19 vaccine uptake and a positive shift in attitudes over time. First responders, despite potential high exposure to SARS-CoV-2, and participants with a history of SARS-CoV-2 infection were more vaccine reluctant. CONCLUSIONS: Perceptions of the COVID-19 vaccine can shift over time. Targeting messages about the vaccine's safety and effectiveness in reducing SARS-CoV-2 virus infection and illness severity may increase vaccine uptake for reluctant and reachable participants

Monitoring Influenza Activity in the United States: A Comparison of Traditional Surveillance Systems with Google Flu Trends

Google Flu Trends was developed to estimate US influenza-like illness (ILI) rates from internet searches; however ILI does not necessarily correlate with actual influenza virus infections.Influenza activity data from 2003-04 through 2007-08 were obtained from three US surveillance systems: Google Flu Trends, CDC Outpatient ILI Surveillance Network (CDC ILI Surveillance), and US Influenza Virologic Surveillance System (CDC Virus Surveillance). Pearson's correlation coefficients with 95% confidence intervals (95% CI) were calculated to compare surveillance data. An analysis was performed to investigate outlier observations and determine the extent to which they affected the correlations between surveillance data. Pearson's correlation coefficient describing Google Flu Trends and CDC Virus Surveillance over the study period was 0.72 (95% CI: 0.64, 0.79). The correlation between CDC ILI Surveillance and CDC Virus Surveillance over the same period was 0.85 (95% CI: 0.81, 0.89). Most of the outlier observations in both comparisons were from the 2003-04 influenza season. Exclusion of the outlier observations did not substantially improve the correlation between Google Flu Trends and CDC Virus Surveillance (0.82; 95% CI: 0.76, 0.87) or CDC ILI Surveillance and CDC Virus Surveillance (0.86; 95%CI: 0.82, 0.90).This analysis demonstrates that while Google Flu Trends is highly correlated with rates of ILI, it has a lower correlation with surveillance for laboratory-confirmed influenza. Most of the outlier observations occurred during the 2003-04 influenza season that was characterized by early and intense influenza activity, which potentially altered health care seeking behavior, physician testing practices, and internet search behavior

Evaluation of Jackknife and Bootstrap for Defining Confidence Intervals for Pairwise Agreement Measures

Several research fields frequently deal with the analysis of diverse classification results of the same entities. This should imply an objective detection of overlaps and divergences between the formed clusters. The congruence between classifications can be quantified by clustering agreement measures, including pairwise agreement measures. Several measures have been proposed and the importance of obtaining confidence intervals for the point estimate in the comparison of these measures has been highlighted. A broad range of methods can be used for the estimation of confidence intervals. However, evidence is lacking about what are the appropriate methods for the calculation of confidence intervals for most clustering agreement measures. Here we evaluate the resampling techniques of bootstrap and jackknife for the calculation of the confidence intervals for clustering agreement measures. Contrary to what has been shown for some statistics, simulations showed that the jackknife performs better than the bootstrap at accurately estimating confidence intervals for pairwise agreement measures, especially when the agreement between partitions is low. The coverage of the jackknife confidence interval is robust to changes in cluster number and cluster size distribution

Time series plots of ILINet data and category-level GFT estimates.

<p>Category-level estimates are created by applying the GFT methodology to a subset of the queries in a given model. A) ILINet data and GFT estimates based on original model queries related to influenza complications. B) ILINet data and GFT estimates based on updated model queries related to specific influenza symptoms.</p

Time series plots of ILINet data and original and updated GFT estimates.

<p>A) ILINet data and GFT estimates from 2009. B) ILINet data and GFT estimates for the entire time period where GFT estimates are available: 2003–2009.</p

Comparison of relative query category volume in original and updated United States GFT models.

<p>*Search users often misspell the word <i>pneumonia</i>.</p

Correlation and RMSE between United States Google Flu Trends estimates and ILINet data.

<p>*The overall correlation during pH1N1 is not an average of the Waves 1 and 2 correlations. The range of ILI rates was larger in Wave 2 than in Wave 1, causing the Wave 2 data to contribute more than the Wave 1 data to the overall correlation during pH1N1.</p