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BACKGROUND: Air pollution is a major risk factor for planetary health and has long been suspected of predisposing humans to respiratory diseases induced by pathogens like influenza viruses. However, epidemiological evidence remains elusive due to lack of longitudinal data from large cohorts. OBJECTIVE: Our aim is to quantify the short-term association of influenza incidence with exposure to ambient air pollutants in Chinese cities. METHODS: Based on air pollutant data and influenza surveillance data from 82 cities in China over a period of 5 years, we applied a two-stage time series analysis to assess the association of daily incidence of reported influenza cases with six common air pollutants [particulate matter with aerodynamic diameter [less than or equal to]2.5 [micro]m (P[M.sub.2.5]), particulate matter with aerodynamic diameter [less than or equal to]10 [micro]m (P[M.sub.10]), N[O.sub.2], S[O.sub.2], CO, and [O.sub.3]], while adjusting for potential confounders including temperature, relative humidity, seasonality, and holiday effects. We built a distributed lag Poisson model for one or multiple pollutants in each individual city in the first stage and conducted a meta-analysis to pool city-specific estimates in the second stage. RESULTS: A total of 3,735,934 influenza cases were reported in 82 cities from 2015 to 2019, accounting for 72.71% of the overall case number reported in the mainland of China. The time series models for each pollutant alone showed that the daily incidence of reported influenza cases was positively associated with almost all air pollutants except for ozone. The most prominent short-term associations were found for S[O.sub.2] and N[O.sub.2] with cumulative risk ratios of 1.094 [95% confidence interval (CI): 1.054, 1.136] and 1.093 (95% CI: 1.067, 1.119), respectively, for each 10 [micro]g/[m.sup.3] increase in the concentration at each of the lags of 1-7 d. Only N[O.sub.2] showed a significant association with the daily incidence of influenza cases in the multipollutant model that adjusts all six air pollutants together. The impact of air pollutants on influenza was generally found to be greater in children, in subtropical cities, and during cold months. DISCUSSION: Increased exposure to ambient air pollutants, particularly N[O.sub.2], is associated with a higher risk of influenza-associated illness. Policies on reducing air pollution levels may help alleviate the disease burden due to influenza infection. https://doi.org/10.1289/EHP12146, Introduction Rapid economic development and urbanization are often achieved at the expense of air quality. A growing body of evidence indicates significant associations between exposure to air pollutants and adverse [...]

Background: Understanding and mapping the distribution of sandflies and sandfly-associated pathogens (SAPs) is crucial for guiding the surveillance and control effort. However, their distribution and the related risk burden in China remain poorly understood. Methods: We mapped the distribution of sandflies and SAPs using literature data from 1940 to 2022. We also mapped the human visceral leishmaniasis (VL) cases using surveillance data from 2014 to 2018. The ecological drivers of 12 main sandfly species and VL were identified by applying machine learning, and their distribution and risk were predicted in three time periods (2021–2040, 2041–2060, and 2061–2080) under three scenarios of climate and socioeconomic changes. Results: In the mainland of China, a total of 47 sandfly species have been reported, with the main 12 species classified into three clusters according to their ecological niches. Additionally, 6 SAPs have been identified, which include two protozoa, two bacteria, and two viruses. The incidence risk of different VL subtypes was closely associated with the distribution risk of specific vectors. The model predictions also revealed a substantial underestimation of the current sandfly distribution and VL risk. The predicted areas affected by the 12 major species of sandflies and the high-risk areas for VL were found to be 37.9–1121.0% and 136.6% larger, respectively, than the observed range in the areas. The future global changes were projected to decrease the risk of mountain-type zoonotic VL (MT-ZVL), but anthroponotic VL (AVL) and desert-type zoonotic VL (DT-ZVL) could remain stable or slightly increase. Conclusions: Current field observations underestimate the spatial distributions of main sandfly species and VL in China. More active surveillance and field investigations are needed where high risks are predicted, especially in areas where the future risk of VL is projected to remain high or increase. Author summary: Our research provides a comprehensive understanding of the distribution of sandflies and SAPs in China. We have identified 47 sandfly species and 6 SAPs, with 12 main sandfly species forming three distinct ecological clusters. Our machine learning models predict the distribution and risk of sandfly species and VL under various climate and socioeconomic scenarios. The model predictions reveal a significant underestimation of the current sandfly distribution and VL risk. Future global changes are projected to decrease the risk of MT-ZVL, but AVL and DT-ZVL could remain stable or slightly increase. These findings underscore the need for more active surveillance and field investigations of vectors, especially where the future risk of VL is projected to remain high or increase. This study provides important theoretical support for guiding the surveillance and control of sandflies and SAPs. [ABSTRACT FROM AUTHOR]

After the termination of zero‐COVID‐19 policy, the populace in China has experienced both Omicron BA.5 and XBB waves. Considering the poor antibody responses and severe outcomes observed among the elderly following infection, we conducted a longitudinal investigation to examine the epidemiological characteristics and antibody kinetics among 107 boosted elderly participants following the Omicron BA.5 and XBB waves. We observed that 96 participants (89.7%) were infected with Omicron BA.5, while 59 (55.1%) participants were infected with Omicron XBB. Notably, 52 participants (48.6%) experienced dual infections of both Omicron BA.5 and XBB. The proportion of symptomatic cases appeared to decrease following the XBB wave (18.6%) compared to that after the BA.5 wave (59.3%). Omicron BA.5 breakthrough infection induced lower neutralizing antibody titers against XBB.1.5, BA.2.86, and JN.1, while reinfection with Omicron XBB broadened the antibody responses against all measured Omicron subvariants and may alleviate the wild type‐vaccination induced immune imprinting. Boosted vaccination type and comorbidities were the significant factors associated with antibody responses. Updated vaccines based on emerging severe acute respiratory syndrome coronavirus 2 variants are needed to control the Coronavirus Disease 2019 pandemic in the elderly. [ABSTRACT FROM AUTHOR]

ObjectivesThe rapid emergence, spread, and disease severity of avian influenza A (H7N9) in China has prompted concerns about a possible pandemic and regional spread in the coming months. The objective of this study was to predict the risk of future human infections with H7N9 in China and neighboring countries by assessing the association between H7N9 cases at sentinel hospitals and putative agricultural, climatic, and demographic risk factors.MethodsThis cross-sectional study used the locations of H7N9 cases and negative cases from China's influenza-like illness surveillance network. After identifying H7N9 risk factors with logistic regression, we used Geographic Information Systems (GIS) to construct predictive maps of H7N9 risk across Asia.ResultsLive bird market density was associated with human H7N9 infections reported in China from March-May 2013. Based on these cases, our model accurately predicted the virus' spread into Guangxi autonomous region in February 2014. Outside China, we find there is a high risk that the virus will spread to northern Vietnam, due to the import of poultry from China.ConclusionsOur risk map can focus efforts to improve surveillance in poultry and humans, which may facilitate early identification and treatment of human cases.

Background: A viral infection can modify the risk to subsequent viral infections via cross‐protective immunity, increased immunopathology, or disease‐driven behavioral change. There is limited understanding of virus–virus interactions due to lack of long‐term population‐level data. Methods: Our study leverages passive surveillance data of 10 human acute respiratory viruses from Beijing, Chongqing, Guangzhou, and Shanghai collected during 2009 to 2019: influenza A and B viruses; respiratory syncytial virus A and B; human parainfluenza virus (HPIV), adenovirus, metapneumovirus (HMPV), coronavirus, bocavirus (HBoV), and rhinovirus (HRV). We used a multivariate Bayesian hierarchical model to evaluate correlations in monthly prevalence of test‐positive samples between virus pairs, adjusting for potential confounders. Results: Of 101,643 lab‐tested patients, 33,650 tested positive for any acute respiratory virus, and 4,113 were co‐infected with multiple viruses. After adjusting for intrinsic seasonality, long‐term trends and multiple comparisons, Bayesian multivariate modeling found positive correlations for HPIV/HRV in all cities and for HBoV/HRV and HBoV/HMPV in three cities. Models restricted to children further revealed statistically significant associations for another ten pairs in three of the four cities. In contrast, no consistent correlation across cities was found among adults. Most virus–virus interactions exhibited substantial spatial heterogeneity. Conclusions: There was strong evidence for interactions among common respiratory viruses in highly populated urban settings. Consistent positive interactions across multiple cities were observed in viruses known to typically infect children. Future intervention programs such as development of combination vaccines may consider spatially consistent virus–virus interactions for more effective control. [ABSTRACT FROM AUTHOR]