Challenges of COVID-19 Case Forecasting in the US, 2020-2021.

During the COVID-19 pandemic, forecasting COVID-19 trends to support planning and response was a priority for scientists and decision makers alike. In the United States, COVID-19 forecasting was coordinated by a large group of universities, companies, and government entities led by the Centers for Disease Control and Prevention and the US COVID-19 Forecast Hub (https://covid19forecasthub.org). We evaluated approximately 9.7 million forecasts of weekly state-level COVID-19 cases for predictions 1-4 weeks into the future submitted by 24 teams from August 2020 to December 2021. We assessed coverage of central prediction intervals and weighted interval scores (WIS), adjusting for missing forecasts relative to a baseline forecast, and used a Gaussian generalized estimating equation (GEE) model to evaluate differences in skill across epidemic phases that were defined by the effective reproduction number. Overall, we found high variation in skill across individual models, with ensemble-based forecasts outperforming other approaches. Forecast skill relative to the baseline was generally higher for larger jurisdictions (e.g., states compared to counties). Over time, forecasts generally performed worst in periods of rapid changes in reported cases (either in increasing or decreasing epidemic phases) with 95% prediction interval coverage dropping below 50% during the growth phases of the winter 2020, Delta, and Omicron waves. Ideally, case forecasts could serve as a leading indicator of changes in transmission dynamics. However, while most COVID-19 case forecasts outperformed a naïve baseline model, even the most accurate case forecasts were unreliable in key phases. Further research could improve forecasts of leading indicators, like COVID-19 cases, by leveraging additional real-time data, addressing performance across phases, improving the characterization of forecast confidence, and ensuring that forecasts were coherent across spatial scales. In the meantime, it is critical for forecast users to appreciate current limitations and use a broad set of indicators to inform pandemic-related decision making.

Echinacea purpurea Polysaccharide Ameliorates Dextran Sulfate Sodium-Induced Colitis by Restoring the Intestinal Microbiota and Inhibiting the TLR4-NF-κB Axis.

Ulcerative colitis (UC) is a chronic intestinal ailment which cannot be completely cured. The occurrence of UC has been on the rise in recent years, which is highly detrimental to patients. The effectiveness of conventional drug treatment is limited. The long-term usage of these agents can lead to substantial adverse effects. Therefore, the development of a safe and efficient dietary supplement is important for the prevention of UC. Echinacea purpurea polysaccharide (EPP) is one of the main bioactive substances in Echinacea purpurea. EPP has many favorable effects, such as antioxidative, anti-inflammatory, and antitumor effects. However, whether EPP can prevent or alleviate UC is still unclear. This study aims to analyze the effect and mechanism of EPP on UC in mice using a 3% dextran sulfate sodium (DSS)-induced UC model. The results showed that dietary supplementation with 200 mg/kg EPP significantly alleviated the shortening of colon length, weight loss, and histopathological damage in DSS-induced colitis mice. Mechanistically, EPP significantly inhibits the activation of the TLR4/NF-κB pathway and preserves the intestinal mechanical barrier integrity by enhancing the expression of claudin-1, ZO-1, and occludin and reducing the loss of goblet cells. Additionally, 16S rRNA sequencing revealed that EPP intervention reduced the abundance of Bacteroides, Escherichia-Shigella, and Klebsiella; the abundance of Lactobacillus increased. The results of nontargeted metabonomics showed that EPP reshaped metabolism. In this study, we clarified the effect of EPP on UC, revealed the potential function of EPP, and supported the use of polysaccharide dietary supplements for UC prevention.

Wheat peptide alleviates DSS-induced colitis by activating the Keap1-Nrf2 signaling pathway and maintaining the integrity of the gut barrier.

Inflammatory bowel disease (IBD) is difficult to cure, and formulating a dietary plan is an effective means to prevent and treat this disease. Wheat peptide contains a variety of bioactive peptides with anti-inflammatory and antioxidant functions. The results of this study showed that preventive supplementation with wheat peptide (WP) can significantly alleviate the symptoms of dextran sulfate sodium (DSS)-induced colitis in mice. WP can increase body weight, alleviate colon shortening, and reduce disease activity index (DAI) scores. In addition, WP improved intestinal microbial disorders in mice with colitis. Based on LC-MS, a total of 313 peptides were identified in WP, 4 of which were predicted to be bioactive peptides. The regulatory effects of WP and four bioactive peptides on the Keap1-Nrf2 signaling pathway were verified in Caco-2 cells. In conclusion, this study demonstrated that WP alleviates DSS-induced colitis by helping maintain gut barrier integrity and targeting the Keap1-Nrf2 axis; these results provided a rationale for adding WP to dietary strategies to prevent IBD.

Quantifying the presymptomatic transmission of COVID-19 in the USA.

The emergence of many presymptomatic hidden transmission events significantly complicated the intervention and control of the spread of COVID-19 in the USA during the year 2020. To analyze the role that presymptomatic infections play in the spread of this disease, we developed a state-level metapopulation model to simulate COVID-19 transmission in the USA in 2020 during which period the number of confirmed cases was more than in any other country. We estimated that the transmission rate (i.e., the number of new infections per unit time generated by an infected individual) of presymptomatic infections was approximately 59.9% the transmission rate of reported infections. We further estimated that {at any point in time the} average proportion of infected individuals in the presymptomatic stage was consistently over 50% of all infected individuals. Presymptomatic transmission was consistently contributing over 52% to daily new infections, as well as consistently contributing over 50% to the effective reproduction number from February to December. Finally, non-pharmaceutical intervention targeting presymptomatic infections was very effective in reducing the number of reported cases. These results reveal the significant contribution that presymptomatic transmission made to COVID-19 transmission in the USA during 2020, as well as pave the way for the design of effective disease control and mitigation strategies.

Evaluating completion rates of COVID-19 contact tracing surveys in New York City.

IMPORTANCE: Contact tracing is the process of identifying people who have recently been in contact with someone diagnosed with an infectious disease. During an outbreak, data collected from contact tracing can inform interventions to reduce the spread of infectious diseases. Understanding factors associated with completion rates of contact tracing surveys can help design improved interview protocols for ongoing and future programs. OBJECTIVE: To identify factors associated with completion rates of COVID-19 contact tracing surveys in New York City (NYC) and evaluate the utility of a predictive model to improve completion rates, we analyze laboratory-confirmed and probable COVID-19 cases and their self-reported contacts in NYC from October 1st 2020 to May 10th 2021. METHODS: We analyzed 742,807 case investigation calls made during the study period. Using a log-binomial regression model, we examined the impact of age, time of day of phone call, and zip code-level demographic and socioeconomic factors on interview completion rates. We further developed a random forest model to predict the best phone call time and performed a counterfactual analysis to evaluate the change of completion rates if the predicative model were used. RESULTS: The percentage of contact tracing surveys that were completed was 79.4%, with substantial variations across ZIP code areas. Using a log-binomial regression model, we found that the age of index case (an individual who has tested positive through PCR or antigen testing and is thus subjected to a case investigation) had a significant effect on the completion of case investigation - compared with young adults (the reference group,24 years old < age < = 65 years old), the completion rate for seniors (age > 65 years old) were lower by 12.1% (95%CI: 11.1% - 13.3%), and the completion rate for youth group (age < = 24 years old) were lower by 1.6% (95%CI: 0.6% -2.6%). In addition, phone calls made from 6 to 9 pm had a 4.1% (95% CI: 1.8% - 6.3%) higher completion rate compared with the reference group of phone calls attempted from 12 and 3 pm. We further used a random forest algorithm to assess its potential utility for selecting the time of day of phone call. In counterfactual simulations, the overall completion rate in NYC was marginally improved by 1.2%; however, certain ZIP code areas had improvements up to 7.8%. CONCLUSION: These findings suggest that age and time of day of phone call were associated with completion rates of case investigations. It is possible to develop predictive models to estimate better phone call time for improving completion rates in certain communities.

EGF-driven EGFR/miR-27b-3p/FOXO1 promotes rat FSH synthesis and secretion.

The adenopituitary secretes follicle-stimulating hormone (FSH), which plays a crucial role in regulating the growth, development, and reproductive functions of organisms. Investigating the process of FSH synthesis and secretion can offer valuable insights into potential areas of focus for reproductive research. Epidermal growth factor (EGF) is a significant paracrine/autocrine factor within the body, and studies have demonstrated its ability to stimulate FSH secretion in animals. However, the precise mechanisms that regulate this action are still poorly understood. In this research, in vivo and in vitro experiments showed that the activation of epidermal growth factor receptor (EGFR) by EGF induces the upregulation of miR-27b-3p and that miR-27b-3p targets and inhibits Foxo1 mRNA expression, resulting in increased FSH synthesis and secretion. In summary, this study elucidates the precise molecular mechanism through which EGF governs the synthesis and secretion of FSH via the EGFR/miR-27b-3p/FOXO1 pathway.

Influence maximization based on threshold models in hypergraphs.

Influence maximization problem has received significant attention in recent years due to its application in various domains, such as product recommendation, public opinion dissemination, and disease propagation. This paper proposes a theoretical analysis framework for collective influence in hypergraphs, focusing on identifying a set of seeds that maximize influence in threshold models. First, we extend the message passing method from pairwise networks to hypergraphs to accurately describe the activation process in threshold models. Then, we introduce the concept of hypergraph collective influence (HCI) to measure the influence of nodes. Subsequently, we design an algorithm, HCI-TM, to select the influence maximization set, taking into account both node and hyperedge activation. Numerical simulations demonstrate that HCI-TM outperforms several competing algorithms in synthetic and real-world hypergraphs. Furthermore, we find that HCI can be used as a tool to predict the occurrence of cascading phenomena. Notably, we find that the HCI-TM algorithm works better for larger average hyperdegrees in Erdös-Rényi hypergraphs and smaller power-law exponents in scale-free hypergraphs.

Age-specific transmission dynamics of SARS-CoV-2 during the first 2 years of the pandemic.

During its first 2 years, the SARS-CoV-2 pandemic manifested as multiple waves shaped by complex interactions between variants of concern, non-pharmaceutical interventions, and the immunological landscape of the population. Understanding how the age-specific epidemiology of SARS-CoV-2 has evolved throughout the pandemic is crucial for informing policy decisions. In this article, we aimed to develop an inference-based modeling approach to reconstruct the burden of true infections and hospital admissions in children, adolescents, and adults over the seven waves of four variants (wild-type, Alpha, Delta, and Omicron BA.1) during the first 2 years of the pandemic, using the Netherlands as the motivating example. We find that reported cases are a considerable underestimate and a generally poor predictor of true infection burden, especially because case reporting differs by age. The contribution of children and adolescents to total infection and hospitalization burden increased with successive variants and was largest during the Omicron BA.1 period. However, the ratio of hospitalizations to infections decreased with each subsequent variant in all age categories. Before the Delta period, almost all infections were primary infections occurring in naive individuals. During the Delta and Omicron BA.1 periods, primary infections were common in children but relatively rare in adults who experienced either reinfections or breakthrough infections. Our approach can be used to understand age-specific epidemiology through successive waves in other countries where random community surveys uncovering true SARS-CoV-2 dynamics are absent but basic surveillance and statistics data are available.

System identifiability in a time-evolving agent-based model.

Mathematical models are a valuable tool for studying and predicting the spread of infectious agents. The accuracy of model simulations and predictions invariably depends on the specification of model parameters. Estimation of these parameters is therefore extremely important; however, while some parameters can be derived from observational studies, the values of others are difficult to measure. Instead, models can be coupled with inference algorithms (i.e., data assimilation methods, or statistical filters), which fit model simulations to existing observations and estimate unobserved model state variables and parameters. Ideally, these inference algorithms should find the best fitting solution for a given model and set of observations; however, as those estimated quantities are unobserved, it is typically uncertain whether the correct parameters have been identified. Further, it is unclear what 'correct' really means for abstract parameters defined based on specific model forms. In this work, we explored the problem of non-identifiability in a stochastic system which, when overlooked, can significantly impede model prediction. We used a network, agent-based model to simulate the transmission of Methicillin-resistant staphylococcus aureus (MRSA) within hospital settings and attempted to infer key model parameters using the Ensemble Adjustment Kalman Filter, an efficient Bayesian inference algorithm. We show that even though the inference method converged and that simulations using the estimated parameters produced an agreement with observations, the true parameters are not fully identifiable. While the model-inference system can exclude a substantial area of parameter space that is unlikely to contain the true parameters, the estimated parameter range still included multiple parameter combinations that can fit observations equally well. We show that analyzing synthetic trajectories can support or contradict claims of identifiability. While we perform this on a specific model system, this approach can be generalized for a variety of stochastic representations of partially observable systems. We also suggest data manipulations intended to improve identifiability that might be applicable in many systems of interest.

Lack of causal association between epilepsy and dementia: A Mendelian randomization analysis.

OBJECTIVE: Epidemiological studies have reported an association between epilepsy and dementia. However, the causal relationship between epilepsy and the risk of dementia is not clear. We aimed to inspect the causal effect of epilepsy on memory loss and dementia. METHODS: We analyzed summary data of epilepsy, memory loss, and dementia from the genome-wide association study (GWAS) using the two-sample Mendelian randomization (MR) method. We used the estimated odds ratio of memory loss and dementia associated with each of the genetically defined traits to infer evidence for a causal relationship with the following exposures: all epilepsy, focal epilepsy (including focal epilepsy with hippocampal sclerosis, lesion-negative focal epilepsy, and focal epilepsy with other lesions), and genetic generalized epilepsy (including childhood absence epilepsy, generalized tonic-clonic seizures alone, Juvenile absence epilepsy, and Juvenile myoclonic epilepsy). RESULTS: According to the result of MR using the inverse variance weighted method (IVW), we found that genetically predicted epilepsy did not causally increase the risk of memory loss and dementia (p > 0.05). Results of the MR-Egger and weighted median method were consistent with the IVW method. CONCLUSIONS: No evidence has been found to support the notion that epilepsy can result in memory loss and dementia. The associations observed in epidemiological studies could be attributed, in part, to confounding or nongenetic determinants.

Agaricus blazei Polysaccharide Alleviates DSS-Induced Colitis in Mice by Modulating Intestinal Barrier and Remodeling Metabolism.

Ulcerative colitis (UC) is a chronic noninfectious intestinal disease that severely affects patients' quality of life. Agaricus blazei Murrill polysaccharide (ABP) is an effective active ingredient extracted from Agaricus blazei Murrill (ABM). It has good efficacy in inhibiting tumor cell growth, lowering blood pressure, and improving atherosclerosis. However, its effect on colitis is unclear. The aim of this study was to analyze the protective effects and potential mechanisms of ABP against dextran sulfate sodium (DSS)-induced acute colitis in mice. The results showed that dietary supplementation with ABP significantly alleviated DSS-induced colitis symptoms, inflammatory responses, and oxidative stress. Meanwhile, ABP intervention was able to maintain the integrity of the intestinal mechanical barrier by promoting the expression of ZO-1 and Occludin tight junction proteins and facilitating mucus secretion. Moreover, 16S rRNA sequencing results suggested that ABP intervention was able to alleviate DSS-induced gut microbiota disruption, and nontargeted metabolomics results indicated that ABP was able to remodel metabolism. In conclusion, these results demonstrate that dietary supplementation with ABP alleviated DSS-induced acute colitis by maintaining intestinal barrier integrity and remodeling metabolism. These results improve our understanding of ABP function and provide a theoretical basis for the use of dietary supplementation with ABP for the prevention of ulcerative colitis.

Spatiotemporal variations of "triple-demic" outbreaks of respiratory infections in the United States in the post-COVID-19 era.

BACKGROUND: The US confronted a "triple-demic" of influenza, respiratory syncytial virus (RSV), and COVID-19 in the winter of 2022, leading to increased respiratory infections and a higher demand for medical supplies. It is urgent to analyze these epidemics and their spatial-temporal co-occurrence, identifying hotspots and informing public health strategies. METHODS: We employed retrospective and prospective space-time scan statistics to assess the situations of COVID-19, influenza, and RSV in 51 US states from October 2021 to February 2022, and from October 2022 to February 2023, respectively. This enabled monitoring of spatiotemporal variations for each epidemic individually and collectively. RESULTS: Compared to winter 2021, COVID-19 cases decreased while influenza and RSV infections significantly increased in winter 2022. We found a high-risk cluster of influenza and COVID-19 (not all three) in winter 2021. In late November 2022, a large high-risk cluster of triple-demic emerged in the central US. The number of states at high risk for multiple epidemics increased from 15 in October 2022 to 21 in January 2023. CONCLUSIONS: Our study offers a novel spatiotemporal approach that combines both univariate and multivariate surveillance, as well as retrospective and prospective analyses. This approach offers a more comprehensive and timely understanding of how the co-occurrence of COVID-19, influenza, and RSV impacts various regions within the United States. Our findings assist in tailor-made strategies to mitigate the effects of these respiratory infections.

Circulating Biomarkers and Risk of Hypertension: A Two-Sample Mendelian Randomisation Study.

OBJECTIVE: This study systematically assessed circulating proteins to identify new serum biomarkers and risk of hypertension using Mendelian randomisation. METHODS: The associations between 4,782 human circulating proteins and the risk of hypertension were evaluated using two-sample Mendelian randomisation. The FinnGen study demonstrated a link between genetic predisposition and hypertension in 85,438 cases and 223,663 controls. RESULTS: Inverse variance weighted and sensitivity analysis revealed nine proteins in circulation that have a causative effect on hypertension. SMOC1 and TIE1 were determined to be causative factors in the decreased likelihood of developing hypertension, with odds ratios of 0.86 (95% CI 0.81-0.91; p=1.06e-06) and 0.96 (95% CI 0.94-0.98; p=9.39e-05), respectively. NDUFB4, ETHE1, POFUT2, TRIL, ADAM23, GXYLT1, OXT, TPST2, and TMCC3 showed a possible connection to hypertension. CONCLUSIONS: This two-sample Mendelian randomisation study found that SMOC1 and TIE1 are causally linked to hypertension, making them a promising target for therapy.

Oat Peptides Alleviate Dextran Sulfate Sodium Salt-Induced Colitis by Maintaining the Intestinal Barrier and Modulating the Keap1-Nrf2 Axis.

The prevalence of inflammatory bowel disease (IBD) is progressively rising each year, emphasizing the significance of implementing rational dietary interventions for disease prevention. Oats, being a staple agricultural product, are abundant in protein content. This study aimed to investigate the protective effects and underlying mechanisms of oat peptides (OPs) in a mouse model of acute colitis induced by dextran sulfate sodium salt (DSS) and a Caco-2 cell model. The findings demonstrated that intervention with OPs effectively mitigated the symptoms associated with DSS-induced colitis. The physicochemical characterization analysis demonstrated that the molecular weight of the OPs was predominantly below 5 kDa, with a predominant composition of 266 peptides. This study provides further evidence of the regulatory impact of OPs on the Keap1-Nrf2 signaling axis and elucidates the potential role of WGVGVRAERDA as the primary bioactive peptide responsible for the functional effects of OPs. Ultimately, the results of this investigation demonstrate that OPs effectively mitigate DSS-induced colitis by preserving the integrity of the intestinal barrier and modulating the Keap1-Nrf2 axis. Consequently, these findings establish a theoretical foundation for the utilization of OPs as dietary supplements to prevent the onset of IBD.

Evaluation of the US COVID-19 Scenario Modeling Hub for informing pandemic response under uncertainty.

Our ability to forecast epidemics far into the future is constrained by the many complexities of disease systems. Realistic longer-term projections may, however, be possible under well-defined scenarios that specify the future state of critical epidemic drivers. Since December 2020, the U.S. COVID-19 Scenario Modeling Hub (SMH) has convened multiple modeling teams to make months ahead projections of SARS-CoV-2 burden, totaling nearly 1.8 million national and state-level projections. Here, we find SMH performance varied widely as a function of both scenario validity and model calibration. We show scenarios remained close to reality for 22 weeks on average before the arrival of unanticipated SARS-CoV-2 variants invalidated key assumptions. An ensemble of participating models that preserved variation between models (using the linear opinion pool method) was consistently more reliable than any single model in periods of valid scenario assumptions, while projection interval coverage was near target levels. SMH projections were used to guide pandemic response, illustrating the value of collaborative hubs for longer-term scenario projections.

Treatment with a flow diverter-assisted coil embolization for ruptured blood blister-like aneurysms of the internal carotid artery: a technical note and analysis of single-center experience with pooled data.

Treatment of blood blister-like aneurysms (BBAs) of the supraclinoid internal carotid artery (ICA) with flow diverters (FDs) has become widespread in recent years. However, ruptured blood blister-like aneurysm (BBA) of ICA treatment with flow diverter-assisted coil embolization (FDAC) remains controversial. Moreover, limited direct comparative studies have been conducted between the two treatment modalities, FDs and FDAC, for BBAs. The purpose of this study was to document our experience and evaluate the effectiveness and safety of FDAC. We conducted a retrospective analysis of clinical and radiological information from ten patients who experienced ruptured BBAs of the supraclinoid ICA at our center from January 2021 to February 2023. The technical details of FDAC for ruptured BBAs were described, and the technical steps were named "pipeline embolization device (PED)-Individualized shaping(microcatheter)-Semi deploying-Rivet(coils)-Massage(microwire)" as the PEISSERM technique. Clinical outcomes were assessed using the modified Rankin Scale (mRS), whereas radiological results were determined through angiography. A pooled analysis was implemented, incorporating data from literature sources that reported perioperative and long-term clinical and angiographic outcomes of ruptured BBAs treated with FD and FDAC strategies, along with our data. Data in our analysis pool were categorized into FD and FDAC strategy groups to explore the preferred treatment modalities for BBAs. The PEISSERM technique was utilized to treat ten patients, seven males, and three females, with an average age of 41.7 years. A single PED was deployed in conjunction with coils in all ten patients. All PEDs were documented to have good wall apposition. The immediate postoperative angiograms demonstrated Raymond grade I in ten aneurysms. Angiographic follow-up of nine patients at 4-25 months showed total occlusion of the aneurysms. At the most recent follow-up, the mRS scores of nine patients hinted at a good prognosis. Pooled analysis of 233 ICA-BBA cases of FD revealed a technical success rate of 91% [95% confidence interval (CI), 0.88 to 0.95], a rate of complete occlusion of 79% (95% CI, 0.73 to 0.84), a recurrence rate of 2% (95% CI, 0.00 to 0.04), a rebleed rate of 2% (95% CI, 0.00 to 0.04), and the perioperative stroke rate was 8% (95% CI, 0.04 to 0.11). The perioperative mortality was 4% (95% CI, 0.01 to 0.07). The long-term good clinical outcome rate was 85% (95% CI, 0.80 to 0.90). The mortality rate was 6% (95% CI, 0.03 to 0.09). Results from the subgroup analysis illustrated that the FDAC strategy for BBAs had a significantly higher immediate postoperative complete occlusion rate (P < 0.001), total occlusion rate (P = 0.016), and a good outcome rate (P = 0.041) compared with the FD strategy. The FDAC strategy can yield a higher rate of good outcomes than the FD strategy. The PEISSERM technique employed by the FDAC is a reliable and effective treatment approach as it can minimize the hemodynamic burden of BBA's fragile dome, thereby achieving an excellent occlusion rate. The PEISSERM technique in the FDAC strategy contributes to understanding the BBA's treatment and offers a potentially optimal treatment for BBA.

Community transmission of SARS-CoV-2 during the Delta wave in New York City.

BACKGROUND: Understanding community transmission of SARS-CoV-2 variants of concern (VOCs) is critical for disease control in the post pandemic era. The Delta variant (B.1.617.2) emerged in late 2020 and became the dominant VOC globally in the summer of 2021. While the epidemiological features of the Delta variant have been extensively studied, how those characteristics shaped community transmission in urban settings remains poorly understood. METHODS: Using high-resolution contact tracing data and testing records, we analyze the transmission of SARS-CoV-2 during the Delta wave within New York City (NYC) from May 2021 to October 2021. We reconstruct transmission networks at the individual level and across 177 ZIP code areas, examine network structure and spatial spread patterns, and use statistical analysis to estimate the effects of factors associated with COVID-19 spread. RESULTS: We find considerable individual variations in reported contacts and secondary infections, consistent with the pre-Delta period. Compared with earlier waves, Delta-period has more frequent long-range transmission events across ZIP codes. Using socioeconomic, mobility and COVID-19 surveillance data at the ZIP code level, we find that a larger number of cumulative cases in a ZIP code area is associated with reduced within- and cross-ZIP code transmission and the number of visitors to each ZIP code is positively associated with the number of non-household infections identified through contact tracing and testing. CONCLUSIONS: The Delta variant produced greater long-range spatial transmission across NYC ZIP code areas, likely caused by its increased transmissibility and elevated human mobility during the study period. Our findings highlight the potential role of population immunity in reducing transmission of VOCs. Quantifying variability of immunity is critical for identifying subpopulations susceptible to future VOCs. In addition, non-pharmaceutical interventions limiting human mobility likely reduced SARS-CoV-2 spread over successive pandemic waves and should be encouraged for reducing transmission of future VOCs.

Inference of transmission dynamics and retrospective forecast of invasive meningococcal disease.

The pathogenic bacteria Neisseria meningitidis, which causes invasive meningococcal disease (IMD), predominantly colonizes humans asymptomatically; however, invasive disease occurs in a small proportion of the population. Here, we explore the seasonality of IMD and develop and validate a suite of models for simulating and forecasting disease outcomes in the United States. We combine the models into multi-model ensembles (MME) based on the past performance of the individual models, as well as a naive equally weighted aggregation, and compare the retrospective forecast performance over a six-month forecast horizon. Deployment of the complete vaccination regimen, introduced in 2011, coincided with a change in the periodicity of IMD, suggesting altered transmission dynamics. We found that a model forced with the period obtained by local power wavelet decomposition best fit and forecast observations. In addition, the MME performed the best across the entire study period. Finally, our study included US-level data until 2022, allowing study of a possible IMD rebound after relaxation of non-pharmaceutical interventions imposed in response to the COVID-19 pandemic; however, no evidence of a rebound was found. Our findings demonstrate the ability of process-based models to retrospectively forecast IMD and provide a first analysis of the seasonality of IMD before and after the complete vaccination regimen.

[Water Chemical Isotope Characteristics and Water Transformation Relationship in Mongolian Section of the Yellow River Basin].

The Yellow River in Inner Mongolia was selected as the study area in this study. In July (wet season) and October (dry season) of 2021, the acquisition of seasonal rivers, the Yellow River tributaries and precipitation, the Yellow River, Wuliangsuhai, Lake Hasuhai, Lake Daihai, an irrigation canal system, and underground water and sea water samples were collected to test the water chemical composition and hydrogen and oxygen isotopic values of different water types. Using the Piper triplot, Gibbs plot, ion ratio, and MixSIAR model methods, the evolution of water chemistry in the Mongolian section of the Yellow River Basin was analyzed, and the transformation relationship between precipitation, surface water, and groundwater was revealed. The results showed that both groundwater and surface water in the study area were slightly alkaline; the dominant anion in water was Cl-, and the dominant cation was Na+. The main hydrochemical types of surface water were Cl·SO4-Na·Mg and SO4·HCO3-Na·Mg, whereas those of groundwater were Cl·SO4-Na·Mg and SO4·HCO3-Na·Ca. Groundwater Ca2+ and Mg2+ were primarily derived from the dissolution of silicate and evaporite, and surface water Ca2+ and Mg2+ were primarily derived from carbonate karst dissolution and carbonate and sulfuric acid in water participating in the dissolution process of carbonate and sulfide minerals. Na+ and Cl- in different water bodies were all affected by anthropogenic pollution sources. Owing to the seasonal effect, δD and δ18O of surface water and groundwater were higher in the wet season than in the dry season. The results showed that surface water was affected by evaporative fractionation after receiving precipitation recharge, and the groundwater recharge sources were complex. The MixSIAR model revealed that surface water was the main recharge source of groundwater, accounting for 52.4%-62.2% of the total recharge, and atmospheric precipitation was the main recharge source of surface water, accounting for 85.4%-97.1% of the total recharge.