China, as well as many other populous countries in Asia, continues to face a dual burden of high pulmonary tuberculosis (PTB) incidence and severe air pollution, especially fine particulate matter with a diameter ≤ 2.5 μm (PM2.5). However, few studies have explored the effect of ambient PM2.5, especially its specific components, on the risk of developing PTB. We obtained active PTB cases from 37 prefectures in China over a 7-year period. Prefecture-level monthly PTB counts were analyzed in relation to population-weighted mean concentrations of PM2.5 and its major components. Exposure data were derived from the Tracking Air Pollution in China (TAP) dataset and spatiotemporally linked to each case by prefecture of residence and month of diagnosis. To investigate the lagged exposure-response relationship, we applied an integrated spatiotemporal Bayesian hierarchical model within a distributed lag non-linear modeling framework. We further evaluated potential effect modification by sex, age, local PTB transmission intensity, greenness level, and temperature to assess effect heterogeneity and provide preliminary insights into potential underlying pathways. Mixed-effects meta-regression models were used to evaluate potential effect modification, incorporating interaction terms between the pollutant and each potential modifier. 636,391 PTB cases were reported in our study area during the study period. We found positive associations between all components and PTB incidence and the effects are stronger and last longer at higher concentrations. At the 95th percentile of the concentrations the strongest effect occurring at a one-month lag and lasted for up to three months, the maximum incidence rate ratio were 1.069 (95

The leaf-onset date is sensitive to climate warming. It is widely reported that the temperature sensitivity of the leaf-onset date (ST) of deciduous broadleaf forest (DBF) may decrease under dormancy-period warming. However, evidence of how boreal-DBF ST may generally change under dormancy-period warming is still lacking. Here, by analysing climate and satellite data, we find that, between 1982-1996 and 1998-2012, 74% of all 0.5 degrees x 0.5 degrees boreal-DBF-containing grid cells with a rise in boreal-DBF dormancy-period temperature exhibited an increase in boreal-DBF ST. We demonstrate that the observed general increase in boreal-DBF ST is largely attributable to a warming-related enhancement in dormancy-period chilling accumulation. Furthermore, we show that phenology models systematically underestimated the magnitude of the observed change in the mean boreal-DBF ST across all boreal-DBF-containing grid cells by a mean of 85%. This study has implications for improving phenology models and understanding the carbon cycle in boreal regions.

Although prenatal exposure to famine is known to increase the risk of various noncommunicable conditions, its effects on infectious diseases remain poorly understood. We examined the effect of prenatal exposure to the Great Chinese Famine on risks for 11 notifiable infectious diseases in two consecutive generations through the analysis of >4.4 million surveillance records in Sichuan Province, China. Using an approach combining cohort models with counterfactual effect imputation, we estimated the ratio of observed to expected incidence rates (incidence rate ratio, IRR) in the absence of famine among both the directly affected birth cohort (F1) and their possible offspring (F2). We further examined whether there was a dose-response relationship between famine severity and infectious disease outcomes using meta-regression at the prefecture level. We found that the risk of acquiring any of the 11 infectious diseases studied increased significantly among both the F1 (IRR: 1.13, 95% CI: 1.04 to 1.21) and the F2 cohort (IRR: 1.08, 95% CI: 1.00 to 1.15). IRRs for the F1 generation were higher in prefectures that experienced more severe famine, with one interquartile range increase in famine severity associated with a 3.95% (95% CI: 1.87 to 6.05%) increase in F1 IRR. Positive associations of varying magnitudes were estimated between increased risks in the F1 cohort and famine intensity for most individual diseases. Prenatal exposure to famine may have long-term and potential intergenerational impacts on the risk of a broad range of infectious diseases. Ensuring adequate prenatal nutrition may be crucial in reducing the burden of infectious diseases across generations.

Severe fever with thrombocytopenia syndrome (SFTS) is a tick-borne emerging infectious disease with a reported mortality rate of up to 30% in hospitalized patients. The causative agent, SFTS virus (SFTSV) is maintained in nature through a transmission cycle involving animal hosts and ticks. Therefore, effective control of SFTS in nature environments necessitates a comprehensive understanding of the tick-host circulation patterns that sustain viral persistence. We developed and calibrated mathematical models using seroprevalence survey data collected across China to assess the relative contributions of diverse domestic and wildlife host species to transmission, their determinants, and the effectiveness of various interventions. Our analysis identified poultry, previously unrecognized, as the most important species across the majority of survey sites, followed by goat/sheep, cattle, and rodents. These rankings remained robust even when parameter values were perturbed or certain host species were omitted from the survey. Across all sites, increasing tick mortality rate [Formula: see text] and reducing transovarial transmission efficiency [Formula: see text] consistently ranked among the top five interventions that led to the most significant reduction in the overall [Formula: see text]. Understanding the relative host contributions is crucial for developing interventions. Our simulation results indicated that halving the contact rate of the most important species with ticks could yield a 25-fold greater reduction in transmission intensity compared to halving that of the second most important species.

Emerging infectious diseases (EIDs), whether newly identified or re-emerging in human and animal populations, pose significant threats to global public health. China has experienced multiple EIDs outbreaks in recent years, underscoring the need for robust surveillance and early warning systems. Although China has established surveillance systems for events affecting climate, wildlife, livestock and poultry, and humans, the current systems remain inadequate for the early detection, monitoring, and prevention of zoonotic spillover events. The “One Health” approach, which integrates human, animal, and environmental health, offers a comprehensive strategy for mitigating EIDs risks. This study reviews China’s national-level surveillance and early warning systems from a “One Health” perspective, highlighting key limitations and proposing future directions to enhance preparedness and response capabilities. The findings are intended to inform policy improvements and strengthen interdisciplinary collaboration for effective EIDs management.

Dengue fever has been spreading rapidly worldwide, with a notably high prevalence in South American countries such as Brazil. Its transmission dynamics are governed by the vector population dynamics and the interactions among humans, vectors, and pathogens, which are further shaped by environmental factors. Calculating these environmental indicators is challenging due to the limited spatial coverage of weather station observations and the time-consuming processes involved in downloading and processing local data, such as satellite imagery. This issue is exacerbated in large-scale studies, making it difficult to develop comprehensive and publicly accessible datasets of disease-influencing factors. Addressing this challenge necessitates the efficient data integration methods and the assembly of multi-factorial datasets to aid public health authorities in understanding dengue transmission mechanisms and improving risk prediction models. In response, we developed a population-weighted dataset of 12 dengue risk factors, covering 558 microregions in Brazil over 1252 epidemiological weeks from 2001 to 2024. This dataset and the associated methodology streamline data processing for researchers and can be adapted for other vector-borne disease studies.

Severe fever with thrombocytopaenia syndrome virus (SFTSV) was identified by the World Health Organization as a priority pathogen due to its high case-fatality rate in humans and rapid spread. It is maintained in nature through three transmission pathways: systemic, non-systemic and transovarial. Understanding the relative contributions of these transmission pathways is crucial for developing evidence-informed public health interventions to reduce its spillover risks to humans. Using next-generation matrices, sensitivity analyses, elasticity analyses and random forest models, we estimated the basic reproduction number R0, relative contribution of each pathway, and identified the most sensitive model parameters across 27 scenarios. Results showed that R0 ranged from 0.72 to 2.08 across scenarios, increasing with higher tick abundance and longer viraemia. Transovarial transmission dominated in 26 scenarios, while the importance of the other two varied, with non-systemic transmission more important under high tick abundance, short viraemia or aggregated tick distribution. R0 dropped below 1 in all scenarios when transovarial transmission was excluded. These findings emphasize the need for interventions targeting transovarial transmission, such as reducing female adult tick survival and limiting large vertebrate host movement, and underscore the importance of laboratory studies measuring sensitive parameters including transovarial transmission efficiency, tick survival probabilities and the duration of viraemia and potential for non-systemic transmission for key animal host species with high seroprevalence rates.

Introduction Given the rapid geographic spread of dengue and the growing frequency and intensity of heavy rainfall events, it is imperative to understand the relationship between these phenomena in order to propose effective interventions. However, studies exploring the association between heavy rainfall and dengue infection risk have reached conflicting conclusions, potentially due to the neglect of prior water availability in mosquito breeding sites as an effect modifier. Methods In this study, we addressed this research gap by considering the impact of prior water availability for the first time. We measured prior water availability as the cumulative precipitation over the preceding 8 weeks and utilized a distributed lag non-linear model stratified by the level of prior water availability to examine the association between dengue infection risk and heavy rainfall in Guangzhou, a dengue transmission hotspot in southern China. Results Our findings suggest that the effects of heavy rainfall are likely to be modified by prior water availability. A 24–55 day lagged impact of heavy rainfall was associated with an increase in dengue risk when prior water availability was low, with the greatest incidence rate ratio (IRR) of 1.37 [95% credible interval (CI): 1.02–1.83] occurring at a lag of 27 days. In contrast, a heavy rainfall lag of 7–121 days decreased dengue risk when prior water availability was high, with the lowest IRR of 0.59 (95% CI: 0.43–0.79), occurring at a lag of 45 days. Discussion These findings may help to reconcile the inconsistent conclusions reached by previous studies and improve our understanding of the complex relationship between heavy rainfall and dengue infection risk.

Modeling the global dynamics of emerging infectious diseases (EIDs) like COVID-19 can provide important guidance in the preparation and mitigation of pandemic threats. While age-structured transmission models are widely used to simulate the evolution of EIDs, most of these studies focus on the analysis of specific countries and fail to characterize the spatial spread of EIDs across the world. Here, we developed a global pandemic simulator that integrates age-structured disease transmission models across 3,157 cities and explored its usage under several scenarios. We found that without mitigations, EIDs like COVID-19 are highly likely to cause profound global impacts. For pandemics seeded in most cities, the impacts are equally severe by the end of the first year. The result highlights the urgent need for strengthening global infectious disease monitoring capacity to provide early warnings of future outbreaks. Additionally, we found that the global mitigation efforts could be easily hampered if developed countries or countries near the seed origin take no control. The result indicates that successful pandemic mitigations require collective efforts across countries. The role of developed countries is vitally important as their passive responses may significantly impact other countries.

Although several pathways have been proposed as the prerequisite for a safe phase-out in China, it is not clear which of them are the most important for keeping the mortality rate low, what thresholds should be achieved for these most important interventions, and how the thresholds change with the assumed key epidemiological parameters and population characteristics. We developed an individual-based model (IBM) to simulate the transmission of the Omicron variant in the synthetic population, accounting for the age-dependent probabilities of severe clinical outcomes, waning vaccine-induced immunity, increased mortality rates when hospitals are overburdened, and reduced transmission when self-isolated at home after testing positive. We applied machine learning algorithms on the simulation outputs to examine the importance of each intervention parameter and the feasible intervention parameter combinations for safe exits, which is defined as having mortality rates lower than that of influenza in China (14.3 per 100, 000 persons). We identified vaccine coverage in those above 70 years old, number of ICU beds per capita, and the availability of antiviral treatment as the most important interventions for safe exits across all studied locations, although the thresholds required for safe exits vary remarkably with the assumed vaccine effectiveness, as well as the age structure, age-specific vaccine coverage, community healthcare capacity of the studied locations. The analytical framework developed here can provide the basis for further policy decisions that incorporate considerations about economic costs and societal impacts. Achieving safe exits from the Zero-COVID policy is possible, but challenging for China’s cities. When planning for safe exits, local realities such as the age structure and current age-specific vaccine coverage must be taken into consideration.

With the aid of laboratory typing techniques, infectious disease surveillance networks have the opportunity to obtain powerful information on the emergence, circulation, and evolution of multiple genotypes, serotypes or other subtypes of pathogens, informing understanding of transmission dynamics and strategies for prevention and control. The volume of typing performed on clinical isolates is typically limited by its ability to inform clinical care, cost and logistical constraints, especially in comparison with the capacity to monitor clinical reports of disease occurrence, which remains the most widespread form of public health surveillance. Viewing clinical disease reports as arising from a latent mixture of pathogen subtypes, laboratory typing of a subset of clinical cases can provide inference on the proportion of clinical cases attributable to each subtype (i.e., the mixture components). Optimizing protocols for the selection of isolates for typing by weighting specific subpopulations, locations, time periods, or case characteristics (e.g., disease severity), may improve inference of the frequency and distribution of pathogen subtypes within and between populations. Here, we apply the Disease Surveillance Informatics Optimization and Simulation (DIOS) framework to simulate and optimize hand foot and mouth disease (HFMD) surveillance in a high-burden region of western China. We identify laboratory surveillance designs that significantly outperform the existing network: the optimal network reduced mean absolute error in estimated serotype-specific incidence rates by 14.1%; similarly, the optimal network for monitoring severe cases reduced mean absolute error in serotype-specific incidence rates by 13.3%. In both cases, the optimal network designs achieved improved inference without increasing subtyping effort. We demonstrate how the DIOS framework can be used to optimize surveillance networks by augmenting clinical diagnostic data with limited laboratory typing resources, while adapting to specific, local surveillance objectives and constraints.

Many studies have illustrated adverse effects of short-term exposure to air pollution on human health, which usually assumes a linear exposure-response (E-R) function in the delineation of health effects due to air pollution. However, nonlinearity may exist in the association between air pollutant concentrations and health outcomes such as adult pneumonia hospital visits, and there is a research gap in understanding the nonlinearity. Here, we utilized both the distributed lag model (DLM) and nonlinear model (DLNM) to compare the linear and nonlinear impacts of air pollution on adult pneumonia hospital visits in the coastal city of Qingdao, China. While both models show adverse effects of air pollutants on adult pneumonia hospital visits, the DLNM shows an attenuation of E-R curves at high concentrations. Moreover, the DLNM may reveal delayed health effects that may be missed in the DLM, e.g., ozone exposure and pneumonia hospital visits. With the stratified analysis of air pollutants on adult pneumonia hospital visits, both models consistently reveal that the influence of air pollutants is higher during the cold season than during the warm season. Nevertheless, they may behave differently in terms of other subgroups, such as age, gender and visit types. For instance, while no significant impact due to PM2.5 in any of the subgroups abovementioned emerges based on DLM, the results from DLNM indicate statistically significant impacts for the subgroups of elderly, female and emergency department (ED) visits. With respect to adjustment by two-pollutants, PM10 effect estimates for pneumonia hospital visits were the most robust in both DLM and DLNM, followed by NO2 and SO2 based on the DLNM. Considering the estimated health effects of air pollution relying on the assumed E-R functions, our results demonstrate that the traditional linear association assumptions may overlook some potential health risks.

A simulation model was developed aimed at assisting local public health authorities in exploring strategies for the suppression of SARS-CoV-2 transmission. A mechanistic modeling framework is utilized based on the daily airborne exposure of individuals defined in terms of inhaled viruses. Comparison of model outputs and observed data confirms that the model can generate realistic patterns of secondary cases. In the example investigated, the highest risk of being newly infected was among young adults, males, and people living in large households. Among risky occupations are food preparation and serving, personal care and service, sales, and production-related occupations. Results also show a pattern consistent with superspreading with 70% of initial cases who do not transmit at all while 13.4% of primary cases contribute 80% of secondary cases. The impacts of school closure and masking on the synthetic population are very small, but for students, school closure resulted in more time at home and increased secondary cases among them by over 25%. Requiring masks at schools decreased the case count by 80%. We conclude that the simulator can be useful in exploring local intervention scenarios and provides output useful in assessing the confidence that might be placed on its predictions.

The distribution of bamboo is sensitive to climate change and is also potentially affected by increasing atmospheric CO2 concentrations due to its C3 photosynthetic pathway. Yet the effect of CO2 in climate impact assessments of potential changes in bamboo distribution has to date been overlooked. In this study, we proposed a simple and quantitative method to incorporate the impact of atmospheric CO2 concentration into a species distribution modeling framework. To do so, we implemented 10 niche modeling algorithms with regionally downscaled climatic variables and combined field campaign observations. We assessed future climate impacts on the distribution of an economically and ecologically important and widely distributed bamboo species in Madagascar, and examined the effect of increasing CO2 on future projections. Our results suggested that future climatic changes negatively impact potential bamboo distribution in Madagascar, leading to a decline of 34.8% of climatic suitability and a decline of 63.6 ± 3.2% in suitable areas towards 2100 under RCP 8.5. However, increasing atmosphere CO2 offsets the climate impact for bamboo, and led to a smaller reduction of 19.8% in suitability and a potential distribution expansion of +111.6 ± 9.8% in newly suitable areas. We also found that the decline in climatic suitability for bamboo was related to increasing monthly potential evapotranspiration of the warmest quarter and minimum temperature of the warmest month. Conversely, the decreasing isothermality and increasing precipitation of the warmest quarter contributed to projected increase in bamboo-suitable areas. Our study suggested that elevated CO2 may mitigate the decrease in climatic suitability and increase bamboo-suitable areas, through enhancing water use efficiency and decreasing potential evapotranspiration. Our results highlight the importance of accounting for the CO2 effect on future plant species distributions, and provide a mechanistic approach to do so for ecosystems constrained by water.

Passive surveillance systems are widely used to monitor diseases occurrence over wide spatial areas due to their cost-effectiveness and integration into broadly distributed healthcare systems. However, such systems are generally associated with imperfect ascertainment of disease cases and with heterogeneous capture probabilities arising from factors such as differential access to care. Augmenting passive surveillance systems with other surveillance efforts provides a way to estimate the true number of incident cases. We develop a hierarchical modeling framework for analyzing data from multiple surveillance systems that allows for individual-level covariate-dependent heterogeneous capture probabilities, and borrows information across surveillance sites to improve estimation of the true number of incident cases. Inference is carried out via a two-stage Bayesian procedure. Simulation studies illustrated superior performance of the proposed approach with respect to bias, root mean square error, and coverage compared to a model that does not borrow information across sites. We applied the proposed model to data from three surveillance systems reporting pulmonary tuberculosis (PTB) cases in a major center of ongoing transmission in China. The analysis yielded bias-corrected estimates of PTB cases from the passive system and led to the identification of risk factors associated with PTB rates, as well as factors influencing the operating characteristics of the implemented surveillance systems.

Background: With the progress of urbanization, the mobility of people has gradually increased, which has led to the further spread of dengue fever. This study evaluated the transmissibility of dengue fever within districts and between different districts in Zhanjiang City to provide corresponding advice for cross-regional prevention and control. Methods: A mathematical model of transmission dynamics was developed to explore the transmissibility of the disease and to compare that between different regions. Results: A total of 467 DF cases (6.38 per 100,000 people) were reported in Zhanjiang City in 2018. In the model, without any intervention, the number of simulated cases in this epidemic reached about 950. The dengue fever transmissions between districts varied within and between regions. When the spread of dengue fever from Chikan Districts to other districts was cut off, the number of cases in other districts dropped significantly or even to zero. When the density of mosquitoes in Xiashan District was controlled, the dengue fever epidemic in Xiashan District was found to be significantly alleviated. Conclusions: When there is a dengue outbreak, timely measures can effectively control it from developing into an epidemic. Different prevention and control measures in different districts could efficiently reduce the risk of disease transmission.

School closures may reduce the size of social networks among children, potentially limiting infectious disease transmission. To estimate the impact of K–12 closures and reopening policies on children's social interactions and COVID-19 incidence in California's Bay Area, we collected data on children's social contacts and assessed implications for transmission using an individual-based model. Elementary and Hispanic children had more contacts during closures than high school and non-Hispanic children, respectively. We estimated that spring 2020 closures of elementary schools averted 2167 cases in the Bay Area (95% CI: −985, 5572), fewer than middle (5884; 95% CI: 1478, 11.550), high school (8650; 95% CI: 3054, 15 940) and workplace (15 813; 95% CI: 9963, 22 617) closures. Under assumptions of moderate community transmission, we estimated that reopening for a four-month semester without any precautions will increase symptomatic illness among high school teachers (an additional 40.7% expected to experience symptomatic infection, 95% CI: 1.9, 61.1), middle school teachers (37.2%, 95% CI: 4.6, 58.1) and elementary school teachers (4.1%, 95% CI: −1.7, 12.0). However, we found that reopening policies for elementary schools that combine universal masking with classroom cohorts could result in few within-school transmissions, while high schools may require masking plus a staggered hybrid schedule. Stronger community interventions (e.g. remote work, social distancing) decreased the risk of within-school transmission across all measures studied, with the influence of community transmission minimized as the effectiveness of the within-school measures increased.

Respiratory virus infections in humans are a significant global health concern, causing a wide range of diseases with substantial morbidity and mortality worldwide. This underscores the urgent need for effective interventions to reduce the ...Respiratory virus infections in humans cause a broad-spectrum of diseases that result in substantial morbidity and mortality annually worldwide. To reduce the global burden of respiratory viral diseases, preventative and therapeutic interventions that are ...

Air pollution has been widely associated with adverse effects on the respiratory and cardiovascular systems. We investigated the relationship between acute myocardial infarction (AMI), chronic obstructive pulmonary disease (COPD) and air pollution exposure in the coastal city of Qingdao, China. Air pollution in this region is characterized by inland and oceanic transportation sources in addition to local emission. We examined the influence of PM2.5, PM10, NO2, SO2, CO and O3 concentrations on hospital admissions for AMI and COPD from October 1, 2014, to September 30, 2018, in Qingdao using a Poisson generalized additive model (GAM). We found that PM2.5, PM10, NO2, SO2 and CO exhibited a significant short-term (lag 1 day) association with AMI in the single-pollutant model among older adults (>65 years old) and females, especially during the cold season (October to March). In contrast, only NO2 and SO2 had clear cumulative lag associations with COPD admission for females and those over 65 years old at lag 01 and lag 03, respectively. In the two-pollutant model, the exposure-response relationship fitted by the two-pollutant model did not change significantly. Our findings indicated that there is an inflection point between the concentration of certain air pollutants and the hospital admissions of AMI and COPD even under the linear assumption, indicative of the benefits of reducing air pollution vary with pollution levels. This study has important implications for the development of policy for air pollution control in Qingdao and the public health benefits of reducing air pollution levels.

Background Due to an increase in mosquito habitats and the lack facilities to carry out basic mosquito control, construction sites in China are more likely to experience secondary dengue fever infection after importation of an initial infection, which may then increase the number of infections in the neighboring communities and the chance of community transmission. The aim of this study was to investigate how to effectively reduce the transmission of dengue fever at construction sites and the neighboring communities. Methods The Susceptible-Exposed-Infectious/Asymptomatic-Recovered (SEIAR) model of human and SEI model of mosquitoes were developed to estimate the transmission of dengue virus between humans and mosquitoes within the construction site and within a neighboring community, as well between each of these. With the calibrated model, we further estimated the effectiveness of different intervention scenarios targeting at reducing the transmissibility at different locations (i.e. construction sites and community) with the total attack rate (TAR) and the duration of the outbreak (DO). Results A total of 102 construction site-related and 131 community-related cases of dengue fever were reported in our area of study. Without intervention, the number of cases related to the construction site and the community rose to 156 (TAR: 31.25%) and 10,796 (TAR: 21.59%), respectively. When the transmission route from mosquitoes to humans in the community was cut off, the number of community cases decreased to a minimum of 33 compared with other simulated scenarios (TAR: 0.068%, DO: 60 days). If the transmission route from infectious mosquitoes in the community and that from the construction site to susceptible people on the site were cut off at the same time, the number of cases on the construction site dropped to a minimum of 74 (TAR: 14.88%, DO: 66 days). Conclusions To control the outbreak of dengue fever effectively on both the construction site and in the community, interventions needed to be made both within the community and from the community to the construction site. If interventions only took place within the construction site, the number of cases on the construction site would not be reduced. Also, interventions implemented only within the construction site or between the construction site and the community would not lead to a reduction in the number of cases in the community. Graphical Abstract