Serotype-specific surveillance for invasive pneumococcal disease (IPD) is essential for assessing the impact of 10- and 13-valent pneumococcal conjugate vaccines (PCV10/13). The Pneumococcal Serotype Replacement and Distribution Estimation (PSERENADE) project aimed to evaluate the global evidence to estimate the impact of PCV10/13 by age, product, schedule, and syndrome. Here we systematically characterize and summarize the global landscape of routine serotype-specific IPD surveillance in PCV10/13-using countries and describe the subset that are included in PSERENADE. Of 138 countries using PCV10/13 as of 2018, we identified 109 with IPD surveillance systems, 76 of which met PSERENADE data collection eligibility criteria. PSERENADE received data from most (n = 63, 82.9%), yielding 240,639 post-PCV10/13 introduction IPD cases. Pediatric and adult surveillance was represented from all geographic regions but was limited from lower income and high-burden countries. In PSERENADE, 18 sites evaluated PCV10, 42 PCV13, and 17 both; 17 sites used a 3 + 0 schedule, 38 used 2 + 1, 13 used 3 + 1, and 9 used mixed schedules. With such a sizeable and generally representative dataset, PSERENADE will be able to conduct robust analyses to estimate PCV impact and inform policy at national and global levels regarding adult immunization, schedule, and product choice, including for higher valency PCVs on the horizon.

Streptococcus pneumoniae serotype 1 (ST1) was an important cause of invasive pneumococcal disease (IPD) globally before the introduction of pneumococcal conjugate vaccines (PCVs) containing ST1 antigen. The Pneumococcal Serotype Replacement and Distribution Estimation (PSERENADE) project gathered ST1 IPD surveillance data from sites globally and aimed to estimate PCV10/13 impact on ST1 IPD incidence. We estimated ST1 IPD incidence rate ratios (IRRs) comparing the pre-PCV10/13 period to each post-PCV10/13 year by site using a Bayesian multi-level, mixed-effects Poisson regression and all-site IRRs using a linear mixed-effects regression (N = 45 sites). Following PCV10/13 introduction, the incidence rate (IR) of ST1 IPD declined among all ages. After six years of PCV10/13 use, the all-site IRR was 0.05 (95% credibility interval 0.04–0.06) for all ages, 0.05 (0.04–0.05) for <5 years of age, 0.08 (0.06–0.09) for 5–17 years, 0.06 (0.05–0.08) for 18–49 years, 0.06 (0.05–0.07) for 50–64 years, and 0.05 (0.04–0.06) for ≥65 years. PCV10/13 use in infant immunization programs was followed by a 95% reduction in ST1 IPD in all ages after approximately 6 years. Limited data availability from the highest ST1 disease burden countries using a 3 + 0 schedule constrains generalizability and data from these settings are needed.

Improving air quality is an important environmental challenge of our time. Chile currently has one of the most stable and emerging economies in Latin America, where human impact on natural resources and air quality does not go unperceived. Santiago, the capital of Chile, is one of the cities in which particulate matter (PM) levels exceed national and international limits. Its location and climate cause critical conditions for human health when interaction with anthropogenic emissions is present. In this paper, we propose a predictive model based on bivariate regression to estimate PM levels, related to PM2.5 and PM10, simultaneously. Birnbaum-Saunders distributions are used in the joint modeling of real-world PM2.5 and PM10 data by considering as covariates some relevant meteorological variables employed in similar studies. The Mahalanobis distance is utilized to assess bivariate outliers and to detect suitability of the distributional assumption. In addition, we use the local influence technique for analyzing the impact of a perturbation on the overall estimation of model parameters. In the predictions, we check the categorization for the observed and predicted cases of the model according to the primary air quality regulations for PM.

Introduction: Severe acute respiratory syndrome virus 2 (SARS-CoV-2) has caused over million deaths worldwide, with more than 61,000 deaths in Chile. The Chilean government has implemented a vaccination program against SARS-CoV-2, with over 17.7 million people receiving a complete vaccination scheme. The final target is 18 million individuals. The most common vaccines used in Chile are CoronaVac (Sinovac) and BNT162b2 (Pfizer-Biotech). Given the global need for vaccine boosters to combat the impact of emerging virus variants, studying the immune response to SARS-CoV-2 is crucial. In this study, we characterize the humoral immune response in inoculated volunteers from Chile who received vaccination schemes consisting of two doses of CoronaVac [CoronaVac (2x)], two doses of CoronaVac plus one dose of BNT162b2 [CoronaVac (2x) + BNT162b2 (1x)], and three doses of BNT162b2 [BNT162b2 (3x)].Methods: We recruited 469 participants from Clínica Dávila in Santiago and the Health Center Víctor Manuel Fernández in the city of Concepción, Chile. Additionally, we included participants who had recovered from COVID-19 but were not vaccinated (RCN). We analyzed antibodies, including anti-N, anti-S1-RBD, and neutralizing antibodies against SARS-CoV-2.Results: We found that antibodies against the SARS-CoV-2 nucleoprotein were significantly higher in the CoronaVac (2x) and RCN groups compared to the CoronaVac (2x) + BNT162b2 (1x) or BNT162b2 (3x) groups. However, the CoronaVac (2x) + BNT162b2 (1x) and BNT162b2 (3x) groups exhibited a higher concentration of S1-RBD antibodies than the CoronaVac (2x) group and RCN group. There were no significant differences in S1-RBD antibody titers between the CoronaVac (2x) + BNT162b2 (1x) and BNT162b2 (3x) groups. Finally, the group immunized with BNT162b2 (3x) had higher levels of neutralizing antibodies compared to the RCN group, as well as the CoronaVac (2x) and CoronaVac (2x) + BNT162b2 (1x) groups.Discussion: These findings suggest that vaccination induces the secretion of antibodies against SARS-CoV-2, and a booster dose of BNT162b2 is necessary to generate a protective immune response. In the current state of the pandemic, these data support the Ministry of Health of the Government of Chile’s decision to promote heterologous vaccination as they indicate that a significant portion of the Chilean population has neutralizing antibodies against SARS-CoV-2.

Human mobility is strongly associated with the spread of SARS-CoV-2 via air travel on an international scale, and with population mixing and the number of people moving between locations on a local scale. However, these conclusions are drawn mostly from observations in the context of the global north where international and domestic connectivity is heavily influenced by the air travel network; scenarios where land-based mobility can also dominate viral spread remain understudied. Furthermore, research on the effects of non-pharmaceutical interventions (NPIs) has mostly focused on national- or regional-scale implementations, leaving gaps in our understanding of the potential benefits of implementing NPIs at higher granularity. Here, we use Chile as a model to explore the role of human mobility on disease spread within the global south; the country implemented a systematic genomic surveillance program and NPIs at a very high spatial granularity. We combine viral genomic data, anonymised human mobility data from mobile phones and official records of international travellers entering the country to characterise the routes of importation of different variants, the relative contributions of airport and land border importations and the real-time impact of the country’s mobility network on the diffusion of SARS-CoV-2. The introduction of variants which are dominant in neighbouring countries (and not detected through airport genomic surveillance) is predicted by land border crossings and not by air travellers, and the strength of connectivity between comunas (Chile’s lowest administrative divisions) predicts the time of arrival of imported lineages to new locations. A higher stringency of local NPIs was also associated with fewer domestic viral importations. Our analysis sheds light on the drivers of emerging respiratory infectious disease spread outside of air travel, and on the consequences of disrupting regular movement patterns at lower spatial scales.