Predictability of human mobility during the COVID-19 pandemic in the United States.

Human mobility is fundamental to a range of applications including epidemic control, urban planning, and traffic engineering. While laws governing individual movement trajectories and population flows across locations have been extensively studied, the predictability of population-level mobility during the COVID-19 pandemic driven by specific activities such as work, shopping, and recreation remains elusive. Here we analyze mobility data for six place categories at the US county level from 2020 February 15 to 2021 November 23 and measure how the predictability of these mobility metrics changed during the COVID-19 pandemic. We quantify the time-varying predictability in each place category using an information-theoretic metric, permutation entropy. We find disparate predictability patterns across place categories over the course of the pandemic, suggesting differential behavioral changes in human activities perturbed by disease outbreaks. Notably, predictability change in foot traffic to residential locations is mostly in the opposite direction to other mobility categories. Specifically, visits to residences had the highest predictability during stay-at-home orders in March 2020, while visits to other location types had low predictability during this period. This pattern flipped after the lifting of restrictions during summer 2020. We identify four key factors, including weather conditions, population size, COVID-19 case growth, and government policies, and estimate their nonlinear effects on mobility predictability. Our findings provide insights on how people change their behaviors during public health emergencies and may inform improved interventions in future epidemics.

Quantifying suicide contagion at population scale.

The spread of suicidal behavior among individuals is often described as a contagion; however, rigorous modeling of suicide as a dynamic, contagious process is minimal. Here, we develop and validate a model-inference system depicting suicide ideation and death and use it to quantify the contagion processes in the US associated with two prominent celebrity suicide events: Robin Williams during 2014 and Kate Spade and Anthony Bourdain, which occurred 3 days apart during 2018. We show that both events produced large transient increases of suicide contagion contact rates, i.e., the spread of suicidal thought and behavior, and a period of elevated suicidal ideation in the general population. Our modeling approach provides a framework for quantifying suicidal contagion and better understanding, preventing, and containing its spread.

Title evaluation of FluSight influenza forecasting in the 2021-22 and 2022-23 seasons with a new target laboratory-confirmed influenza hospitalizations.

Accurate forecasts can enable more effective public health responses during seasonal influenza epidemics. For the 2021-22 and 2022-23 influenza seasons, 26 forecasting teams provided national and jurisdiction-specific probabilistic predictions of weekly confirmed influenza hospital admissions for one-to-four weeks ahead. Forecast skill is evaluated using the Weighted Interval Score (WIS), relative WIS, and coverage. Six out of 23 models outperform the baseline model across forecast weeks and locations in 2021-22 and 12 out of 18 models in 2022-23. Averaging across all forecast targets, the FluSight ensemble is the 2nd most accurate model measured by WIS in 2021-22 and the 5th most accurate in the 2022-23 season. Forecast skill and 95% coverage for the FluSight ensemble and most component models degrade over longer forecast horizons. In this work we demonstrate that while the FluSight ensemble was a robust predictor, even ensembles face challenges during periods of rapid change.

Healthcare system resilience and adaptability to pandemic disruptions in the United States.

Understanding healthcare system resilience has become paramount, particularly in the wake of the COVID-19 pandemic, which imposed unprecedented burdens on healthcare services and severely impacted public health. Resilience is defined as the system's ability to absorb, recover from and adapt to disruptions; however, despite extensive studies on this subject, we still lack empirical evidence and mathematical tools to quantify its adaptability (the ability of the system to adjust to and learn from disruptions). By analyzing millions of patients' electronic medical records across US states, we find that the COVID-19 pandemic caused two successive waves of disruptions within the healthcare systems, enabling natural experiment analysis of the adaptive capacity of each system to adapt to past disruptions. We generalized the quantification framework and found that the US healthcare systems exhibit substantial adaptability (ρ = 0.58) but only a moderate level of resilience (r = 0.70). When considering system responses across racial groups, Black and Hispanic groups were more severely impacted by pandemic disruptions than white and Asian groups. Physician abundance was the key characteristic for determining healthcare system resilience. Our results offer vital guidance in designing resilient and sustainable healthcare systems to prepare for future waves of disruptions akin to COVID-19 pandemics.

Synthesis of Indole-Fused Pyrazino[1,2-a]quinazolinones by Copper(I)-Catalyzed Selective Hydroamination-Cyclization of Alkynyl-tethered Quinazolinones.

We described a copper(I)-catalyzed atom economic and selective hydroamination-cyclization of alkynyl-tethered quinazolinones to prepare a variety of indole-fused pyrazino[1,2-a]quinazolinones in good to excellent yields ranging from 39 %-99 % under mild reaction conditions. Control experiments revealed that coordination-directed method of quinazolinone moiety with copper(I) was important for the selective hydroamination-cyclization of alkynes at the N1-atom instead of N3-atom of quinazolinone. The reaction could be easily performed at gram scales and some prepared indole-fused pyrazino[1,2-a]quinazolinones with donating groups on the indole moiety showed a distinct fluorescence emission wavelength with blue shift under the acid conditions.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.