Vaccination is the most effective way to prevent influenza infection and adverse outcomes; despite global recommendations to vaccinate pregnant persons, access to influenza vaccines remains low. We explored knowledge, attitudes, and practices of pregnant persons to inform actions to improve vaccine uptake. We pooled data from cross-sectional surveys assessing pregnant persons’ attitudes toward influenza vaccines in 8 low- and middle-income countries. Countries used standard methods to measure attitudes and intents toward influenza vaccination. We stratified by presence/absence of a national influenza vaccination program, income group, geographic region, and individual-level factors. Our analysis included 8556 pregnant persons from 8 countries. Most pregnant persons (6323, 74%) were willing to receive influenza vaccine if it was offered for free. Willingness differed by presence of an existing influenza vaccination program; acceptance was higher in countries without programs (2383, 89%) than in those with programs (3940, 67%, P < .001). Most pregnant persons in middle-income countries, regardless of influenza vaccination program status, were willing to be vaccinated against influenza if the vaccine was provided free of charge. National investments in influenza vaccination programs present an opportunity to avert illness both in pregnant persons themselves and in their newborn babies.

Background: COVID-19 vaccine effectiveness (VE) studies leveraging systematic surveillance in sub-Saharan Africa are limited. We assessed the effectiveness of two vaccines (Pfizer BNT162b2 and Johnson & Johnson Ad26.COV2.S) against SARS-CoV-2-associated hospitalization in South African adults aged ≥18 years.Methods: We conducted a test-negative case-control study using pneumonia surveillance data in South Africa. Inpatients with physician-diagnosed lower respiratory tract infection or suspected COVID-19, testing SARS-CoV-2 positive or negative from June 2021–March 2022, were cases or controls, respectively. Fully vaccinated individuals received one Ad26.COV2.S dose or two BNT162b2 doses ≥14-days before enrollment. VE was estimated using multivariable logistic regression for Delta- and Omicron BA.1/BA.2-predominant periods, stratified by age and HIV status.Results: The study included 925 cases and 1890 controls; 38 (4%) cases and 186 (10%) controls were fully vaccinated with BNT162b2, and 30 (3%) cases and 94 (5%) controls with Ad26.COV2.S. The vaccine effectiveness of BNT162b2 against SARS-CoV-2-associated hospitalization over Delta and Omicron BA.1/BA.2 periods was 91% (95% CI: 52%, 98%) and 33% (-16%, 86%), respectively. The vaccine effectiveness of Ad26.COV2.S against hospitalization over Delta and Omicron BA.1/BA.2 periods was 72% (-36% ,94%), and -19% (-130%, 39%), respectively. The vaccine effectiveness of BNT162b2 against hospitalization over the Delta period was 94% (50%, 99%) and 89% (27%, 98%) among adults aged ≥60 years and HIV-uninfected, respectively.Conclusions: The BNT162b2 vaccine was effective against SARS-CoV-2-associated hospitalization during the Delta period for adults aged ≥18 years, ≥60 years and those HIV-uninfected. VE for Ad26.COV2.S was inconclusive, potentially due to limited sample size or residual confounding. These findings highlight the utility of sentinel surveillance for estimating VE.

Background: Public health agencies employ case definitions for influenza virus and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections to optimize surveillance, monitor disease trends, and inform decision-making, particularly in limited resource settings. Methods: Using a prospective healthcare personnel cohort in two hospitals in Lima, Peru (July 2022–June 2023), we compared and evaluated the performance of three symptom profiles of three common surveillance case definitions to identify influenza and SARS-CoV-2 infections: influenza-like illness, coronavirus disease 2019–like illness (CLI), and acute respiratory infection. Participants with an acute respiratory illness reported symptom progression twice weekly. Participants self-collected nasal swabs for SARS-CoV-2 and influenza reverse transcription real-time polymerase chain reaction testing. Sensitivity, specificity, and positive and negative predictive values of each case definition were evaluated. Results: Of 1623 participants enrolled, 1450 illness episodes were reported; 92 (6%) were positive for influenza, 350 (24%) for SARS-CoV-2, and 3 (0.2%) were positive for both. For the four influenza-like illness case definitions evaluated, all yielded low sensitivity for detection of influenza (points estimate range, 23%–35%) and SARS-COV-2 (range, 21%–29%), but high specificity (83%–90% and 86%–92%, respectively). In contrast, the acute respiratory infection and coronavirus disease 2019–like illness definitions both yielded high sensitivity (83%–100%) and low specificity (2%–41%) for detection of both influenza and SARS-CoV-2. Conclusions: The performance of case definitions was similar for influenza and SARS-CoV-2 supporting the use of the same definitions by surveillance networks. The substantial differences in sensitivity and specificity across the case definitions necessitate surveillance objectives and cost considerations in selecting a case definition fit for purpose.

Background: Seasonal influenza exhibits distinct spatiotemporal patterns across the United States, yet the geographic clustering of influenza activity remains incompletely understood. This study aims to identify jurisdictions with similar patterns of seasonal influenza epidemics by exploring spatiotemporal dynamics across the United States after the 2009 H1N1 pandemic. Methods: We analyzed data from U.S. influenza surveillance systems, including outpatient illness surveillance and virologic surveillance. The outpatient illness data included weekly proportions of outpatient visits for influenza-like illness from jurisdictions including all 50 states, while virologic data comprised influenza test positivity results from U.S. public health and clinical laboratories covering all 50 states. We calculated Moran’s I statistics to assess spatial autocorrelation in peak timing. We also performed k-means clustering on z-normalized time series data and determined optimal clusters using the silhouette method. We then conducted an analysis of variance (ANOVA) to evaluate differences among clusters based on the Moran’s I statistics and the relative proportions of influenza virus types and subtypes. Results: Our analysis revealed distinct spatial clusters with significant geographic patterns. We found a consistent grouping of Southeastern states (Georgia, Alabama, Mississippi, Louisiana, and Florida). This clustering pattern was partially explained by earlier seasonal peaks in these jurisdictions and supported by significant spatial autocorrelation in peak timing. While Southeastern states maintained stable cluster associations, Western and Central states showed greater variation in cluster membership across seasons. We also found significant differences between clusters in the Moran’s I statistics and the proportion of all influenza A virus detections that were influenza A/H1 viruses. However, no significant differences were found in the proportion of all influenza A and B virus detections that were influenza A viruses. Conclusions: These findings quantify the distinct spatiotemporal patterns of seasonal influenza in the Southeastern United States compared to other regions. Understanding these regional clustering patterns can enhance preparations for upcoming changes in influenza activity and inform targeted public health interventions such as timing of vaccination campaigns. Robust surveillance systems, adaptive approaches, and stable long-term data are essential for effectively addressing regional differences and ultimately strengthening nationwide preparedness for seasonal influenza.

Introduction: Modeling when influenza epidemics typically occur can help countries optimize surveillance, time clinical and public health interventions, and reduce the burden of influenza. Methods: We used influenza virus detections reported during 2011–2024 by 180 countries to the Global Influenza Surveillance and Response System, excluding years impacted by the COVID-19 pandemic (2020–2023). We analyzed data by calendar year (week 1–52) or shifted year (week 30–29) time windows, based on when most influenza detections occurred in each country. For countries with sufficient data, we computed generalized additive models (GAMs) of each country’s weekly influenza-positive tests to smooth and impute time series distributions. From these GAMs, we calculated each country’s weekly influenza burden. Country-specific time series were grouped using ensemble consensus k-means clustering, reducing the Euclidean distance between time series within clusters. We calculated cluster-specific GAMs to estimate average seasonal timing. Countries without sufficient data were assigned to a cluster based on population-weighted latitudinal distance to a cluster’s mean latitude. Results: We identified five clusters, or epidemic zones, from 111 countries with sufficient data. The influenza burden in epidemic zones A and B was consistent with a northern hemisphere pattern, with most influenza detections occurring during November–May, while epidemic zones D and E were characterized by southern hemisphere-like seasonal timing, with most influenza burden occurring during June–November (D) and May–November (E). Epidemic zone C had most influenza burden occurring during September–March; most countries assigned to this cluster were in the tropics. Conclusion: Epidemic zones may serve as a useful tool to strengthen and optimize influenza surveillance for global health decision-making (e.g., during vaccine strain composition discussions) and to guide country preparedness efforts for seasonal influenza epidemics, including timing enhanced surveillance and informing the availability of prevention and control efforts. What is already known on this topic: Previous initiatives to provide a framework for describing global influenza patterns and to support national and regional prevention and control strategies have classified countries into influenza transmission zones, based primarily on geographic proximity. Many countries have improved their surveillance systems for respiratory viruses, providing an opportunity to re-assess patterns of influenza dynamics using analytic approaches focused on influenza detection data and maximizing global coverage. What this study adds: Optimal clustering of influenza surveillance data from 111 countries and proximal assignment by latitude of countries lacking sufficient data grouped countries into five epidemic zones. How this study might affect research, practice or policy: By providing improved resolution on the temporal and geographical dynamics of influenza activity, this study offers an evidence base to aid national and global decision-making on enhanced surveillance strategies, seasonal epidemic preparedness, and resource allocation, thereby strengthening efforts to prevent and control influenza worldwide. This data-driven framework for characterizing global patterns of influenza virus circulation can be leveraged to reexamine patterns as new data become available.

Background: The coronavirus disease 2019 (COVID-19) pandemic placed significant pressure on healthcare workers (HCWs) globally, exposing them to high risks of infection and psychological stress. This study assessed dynamic risks and protective factors for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection among HCWs in Lima, Peru. Methods: We conducted the Peru Healthcare Worker COVID Cohort (PHCWCC), a prospective cohort study during the pre-COVID-19 vaccination period (August 2020–May 2021) and the post-vaccination period (August 2021–May 2022). Data included weekly nasal swabs for SARS-CoV-2 testing, reports on exposures, symptoms, perceived risks, and vaccination status. Incidence and factors associated with SARS-CoV-2 positivity were analyzed. Results: Among 1369 HCWs, the infection rate increased from 1.15 (95% confidence interval [CI]: .90–1.14) to 1.71 (95% CI: 1.41–2.05) per 1000 HCW-days between the pre- and post-vaccination phases (P = .01). Despite 97% receiving the primary vaccine series and 75% a booster, perceived high infection risk rose from 26% to 35% (P < .001). Close contact with infected colleagues remained a consistent risk factor (odds ratio [OR] = 2.74, 95% CI: 1.85–4.05; P < .001). In the second phase, direct patient contact (OR = 1.92, 95% CI: 1.20–3.08; P = .006) and patient care environments (OR = 2.14, 95% CI: 1.35–3.40; P = .001) were linked to higher infection risk. Perceived infection risk was inversely associated with positivity (OR = 0.76, 95% CI: 0.63–0.91; P = .003). Conclusions: Evolving infection risks highlight the need for sustained infection prevention, including during non-patient care activities. Fostering risk awareness may reduce infection rates and strengthen healthcare system resilience.