COVID-19 Trajectory Research Articles

COVINet: a deep learning-based and interpretable prediction model for the county-wise trajectories of COVID-19 in the United States

The devastating impact of COVID-19 on the United States has been profound since its onset in January 2020. Predicting the trajectory of epidemics accurately and devising strategies to curb their progression are currently formidable challenges. In response to this crisis, we propose COVINet, which combines the architecture of Long Short-Term Memory and Gated Recurrent Unit, incorporating actionable covariates to offer high-accuracy prediction and explainable response. First, we train COVINet models for confirmed cases and total deaths with five input features, and compare Mean Absolute Errors (MAEs) and Mean Relative Errors (MREs) of COVINet against ten competing models from the United States CDC in the last four weeks before April 26, 2021. The results show COVINet outperforms all competing models for MAEs and MREs when predicting total deaths. Then, we focus on prediction for the most severe county in each of the top 10 hot-spot states using COVINet. The MREs are small for all predictions made in the last 7 or 30 days before March 23, 2023. Beyond predictive accuracy, COVINet offers high interpretability, enhancing the understanding of pandemic dynamics. This dual capability positions COVINet as a powerful tool for informing effective strategies in pandemic prevention and governmental decision-making.

The Molecular and Biological Patterns Underlying Sustained SARS-CoV-2 Circulation in the Human Population

For four years, SARS-CoV-2, the etiological agent of COVID-19, has been circulating among humans. By the end of the second year, an absence of immunologically naive individuals was observed, attributable to extensive immunization efforts and natural viral exposure. This study focuses on delineating the molecular and biological patterns that facilitate the persistence of SARS-CoV-2, thereby informing predictions on the epidemiological trajectory of COVID-19 toward refining pandemic countermeasures. The aim of this study was to describe the molecular biological patterns identified that contribute to the persistence of the virus in the human population. For over three years since the beginning of the COVID-19 pandemic, molecular genetic monitoring of SARS-CoV-2 has been conducted, which included the collection of nasopharyngeal swabs from infected individuals, assessment of viral load, and subsequent whole-genome sequencing. We discerned dominant genetic lineages correlated with rising disease incidence. We scrutinized amino acid substitutions across SARS-CoV-2 proteins and quantified viral loads in swab samples from patients with emerging COVID-19 variants. Our findings suggest a model of viral persistence characterized by 1) periodic serotype shifts causing substantial diminutions in serum virus-neutralizing activity (> 10-fold), 2) serotype-specific accrual of point mutations in the receptor-binding domain (RBD) to modestly circumvent neutralizing antibodies and enhance receptor affinity, and 3) a gradually increasing amount of virus being shed in mucosal surfaces within a single serotype. This model aptly accounts for the dynamics of COVID-19 incidence in Moscow. For a comprehensive understanding of these dynamics, acquiring population-level data on immune tension and antibody neutralization relative to genetic lineage compositions is essential.

Integrating socio-psychological factors in the SEIR model optimized by a genetic algorithm for COVID-19 trend analysis

The global spread of COVID-19 has profoundly affected health and economies, highlighting the need for precise epidemic trend predictions for effective interventions. In this study, we used infectious disease models to simulate and predict the trajectory of COVID-19. An SEIR (susceptible, exposed, infected, removed) model was established using Wuhan data to reflect the pandemic. We then trained a genetic algorithm-based SEIR (GA-SEIR) model using data from a specific U.S. region and focused on individual susceptibility and infection dynamics. By integrating socio-psychological factors, we achieved a significant enhancement to the GA-SEIR model, leading to the development of an optimized version. This refined GA-SEIR model significantly improved our ability to simulate the spread and control of the epidemic and to effectively track trends. Remarkably, it successfully predicted the resurgence of COVID-19 in mainland China in April 2023, demonstrating its robustness and reliability. The refined GA-SEIR model provides crucial insights for public health authorities, enabling them to design and implement proactive strategies for outbreak containment and mitigation. Its substantial contributions to epidemic modelling and public health planning are invaluable, particularly in managing and controlling respiratory infectious diseases such as COVID-19.

Protocol for a longitudinal study examining the trajectory of COVID-19, post-COVID, multidimensional disadvantage and health-related quality of life in India: the IndiQol Project

IntroductionThe COVID-19 pandemic has raised concerns about the persistence of symptoms after infection, commonly referred to as ‘post-COVID’ or ‘long-COVID’. While countries in high-resource countries have highlighted the increased risk...

Prevalence and predictors of prenatal depression during the COVID-19 pandemic: A multistage observational study in Beijing, China.

While growing psychological health issues among pregnant women during the COVID-19 pandemic have been clearly validated, most research was conducted in countries with relatively lax quarantine measures. This study aimed to compare the prevalence of prenatal depression among pre-, peak-, and post-COVID-19 in Beijing, the region with a stringent response policy in China. We also explore predictors of prenatal depression throughout the outbreak. We investigated prenatal depression among 742 pregnant women who received antenatal checkups in Beijing from March 28, 2019 to May 07, 2021 using the Edinburgh Postnatal Depression Scale and associative demographic, pregnancy-related, and psychosocial characteristics were measured. The phase was divided into pre-, peak-, and post-COVID-19 in light of the trajectory of COVID-19. Pearson's Chi-square test was used after the examination of confounders homogeneity. The bivariable and multivariable logistic regression was conducted to explore predictors. The pooled prevalence of prenatal depression was 11.9% throughout the COVID-19 pandemic. Rates at different phases were 10.6%, 15.2%, and 11.1% respectively and no significant difference was observed. Multivariable logistic regression revealed that history of mental illness, number of boy-preference from both pregnant women and husband's family, social support, occupation, and living space were independent predictors of prenatal depression in Beijing. Our data suggested that the impact of this pandemic on prenatal depression in Beijing appears to be not significant, which will strengthen confidence in adhering to current policy for decision-makers and provide important guidance for the development of major outbreak control and management policies in the future. Our findings may also provide a more efficient measure to identify high-risk pregnant women for professionals and help raise gender equity awareness of pregnant women and their husbands' families. Future studies should focus on the value of targeted care and family relations on the mental health of pregnant women.

Public interest trends for COVID-19 and pandemic trajectory: A time-series analysis of US state-level data.

Google Trends provides spatiotemporal data for user-specific terms scaled from less than 1 (lowest relative popularity) to 100 (highest relative popularity) as a proxy for the public interest. Here we use US state-level data for COVID-19 to examine popularity trends during the pandemic evolution. We used "coronavirus" and "covid" search terms and set the period up from January 1st, 2020, to November 12, 2022. We measured the agreement on web rankings between states using the nonparametric Kendall's W (0 for no concordance to 1 for perfect agreement). We compiled state-level weekly data on COVID-19 incidence and mortality and scaled state curves from 0 to 100 through a min-max normalization process. We used a dynamic time-warping algorithm to calculate similarities between the popularity, mortality, and incidence of COVID-19. The methodology is a pattern recognition process between time series by distance optimization. The similarity was mapped from 0 to 1, with 1 indicating perfect similarity and 0 indicating no similarity. The peak in popularity was in March 2020, succeeded by a decline and a prolonged period of fluctuation around 20%. Public interest rose briefly at the end of 2021, to fall to a low activity of around 10%. This pattern was remarkably consistent across states (Kendal's W 0.94, p < 0.001). Web search trends were an impression of contagion growth: Overall, popularity-mortality trajectories yielded higher similarity indices (median 0.78; interquartile range 0.75-0.82) compared to popularity-incidence trajectories (median 0.74; interquartile range 0.72-0.76, Wilcoxon's exact p<0.001). The popularity-mortality trajectories had a very strong similarity (>0.80) in 19/51 (37%) regions, as opposed to only 4/51 (8%) for popularity-incidence trajectories. State-level data show a fading public concern about COVID-19, and web-search popularity patterns may reflect the COVID-19 trajectory in terms of cases and mortality.

'There was a pivotal moment'. The dynamics, transitions, adaptations and trajectories of nursing at the front-line in the UK during the COVID-19 pandemic.

Using qualitative interview data (n = 142 interviews) generated with 50 nurses, over the course of the COVID-19 pandemic, this paper traces the trajectories of nurses in the UK and attempts to unpick the interplay between structure and agency in their narratives. Interviews were inductively analysed for themes and an additional narrative analysis was undertaken to preserve the form of each participant's narrative. We argue that nurses' pandemic trajectories occurred within the 'psychological vulnerability-stigma nexus' which operates within health and social care providers in the UK and whilst constraining nurses' agency at times it could also provide an impetus to act agentically. We found that the nurses' COVID-19 trajectories were characterised by: getting by, getting out (job-hopping) getting needs met and getting organised. We call for more considered systemic support to be generated and consistently provided to nurses to ensure retention of nurses and the security of society to avoid exacerbating existing workforce shortages.

Lessons from COVID-19 for GCR governance: a research agenda.

The Lessons from Covid-19 Research Agenda offers a structure to study the COVID-19 pandemic and the pandemic response from a Global Catastrophic Risk (GCR) perspective. The agenda sets out the aims of our study, which is to investigate the key decisions and actions (or failures to decide or to act) that significantly altered the course of the pandemic, with the aim of improving disaster preparedness and response in the future. It also asks how we can transfer these lessons to other areas of (potential) global catastrophic risk management such as extreme climate change, radical loss of biodiversity and the governance of extreme risks posed by new technologies. Our study aims to identify key moments- 'inflection points'- that significantly shaped the catastrophic trajectory of COVID-19. To that end this Research Agenda has identified four broad clusters where such inflection points are likely to exist: pandemic preparedness, early action, vaccines and non-pharmaceutical interventions. The aim is to drill down into each of these clusters to ascertain whether and how the course of the pandemic might have gone differently, both at the national and the global level, using counterfactual analysis. Four aspects are used to assess candidate inflection points within each cluster: 1. the information available at the time; 2. the decision-making processes used; 3. the capacity and ability to implement different courses of action, and 4. the communication of information and decisions to different publics. The Research Agenda identifies crucial questions in each cluster for all four aspects that should enable the identification of the key lessons from COVID-19 and the pandemic response.

Cognitive and mental health trajectories of COVID-19: Role of hospitalisation and long-COVID symptoms.

There is considerable evidence of cognitive impairment post COVID-19, especially in individuals with long-COVID symptoms, but limited research objectively evaluating whether such impairment attenuates or resolves over time, especially in young and middle-aged adults. Follow-up assessments (T2) of cognitive function (processing speed, attention, working memory, executive function, memory) and mental health were conducted in 138 adults (18-69years) who had been assessed 6months earlier (T1). Of these, 88 had a confirmed history of COVID-19 at T1 assessment (≥20days post-diagnosis) and were also followed-up on COVID-19-related symptoms (acute and long-COVID); 50 adults had no known COVID-19 history at any point up to their T2 assessment. From T1 to T2, a trend-level improvement occurred in intra-individual variability in processing speed in the COVID, relative to the non-COVID group. However, longer response/task completion times persisted in participants with COVID-19-related hospitalisation relative to those without COVID-19-related hospitalisation and non-COVID controls. There was a significant reduction in long-COVID symptom load, which correlated with improved executive function in non-hospitalised COVID-19 participants. The COVID group continued to self-report poorer mental health, irrespective of hospitalisation history, relative to non-COVID group. Although some cognitive improvement has occurred over a 6-month period in young and middle-aged COVID-19 survivors, cognitive impairment persists in those with a history of COVID-19-related hospitalisation and/or long-COVID symptoms. Continuous follow-up assessments are required to determine whether cognitive function improves or possibly worsens, over time in hospitalised and long-COVID participants.

Circulating biomarkers and their implications in COVID-19 pathogenesis

COVID-19 is an infectious disease caused by a new SARS-CoV-2 coronavirus. The coronavirus infection emerged in China in 2019 and quickly disseminated globally in a short time. According to WHO about 100 million people were infected in 2021, rising to 500 million in 2022. In most cases, the trajectory of COVID-19 concludes with a positive result. However, the likelihood of experiencing a serious form of the disease is quite high. A severe form of coronavirus infection causes a storm of reactions in the body. The increased release of cytokines into the blood in response to viral infection is called cytokine release syndrome and causes acute respiratory distress syndrome and multi-organ failure. Timely control of cytokine storm with drugs such as immunomodulators and cytokine antagonists, as well as reduction of lung inflammatory cell infiltration is the main way to improve treatment effectiveness and decrease the mortality rate in individuals suffering from COVID-19.

Bloodstream Coinfections and Antimicrobial Resistance in Hospitalized COVID-19 Patients: A Single-center Retrospective Study.

Bloodstream infections in patients with COVID-19 are linked to higher mortality rates, whilst data on epidemiology and resistance patterns remains scarce to guide management and prevent antibiotic resistance. This research focuses on the prevalence, clinical features, causative microorganisms, and antimicrobial susceptibility of bacterial and fungal secondary bloodstream co-infections in hospitalized patients with COVID-19. In this retrospective study analysis of 230 patients with COVID-19 from Central Taiwan (June 2021 to June 2022), pathogens were identified via MALDI-TOF MS and Vitek 2 system with Clinical & Laboratory Standards Institute (CLSI) standards. In the cohort, 17.8% experienced bloodstream infections, resulting in a total of 45 isolates from the 41 bloodstream infection patients: predominantly gram-positive bacteria (Staphylococcus and Enterococcus) at 69%, gram-negative at 29% (Escherichia coli and Klebsiella pneumoniae), and fungi at 2%. Infected patients showed significantly elevated levels of white blood count (WBC), C-reactive protein (CRP) and procalcitonin (PCT). Of note, resistance to common antibiotics, such as fluoroquinolones, cephalosporins, and oxacillin was significant, especially in K. pneumoniae, Acinetobacter species, and S. aureus infections. Our study highlights the influence of bacterial infections in hospitalized patients with COVID-19. The bacterial infections were discovered to impact the clinical trajectory of COVID-19, potentially exacerbating or mitigating its symptoms, severity and fatality. These insights are pivotal to addressing clinical challenges in COVID-19 management and underscoring the need for tailored medical interventions. Understanding these co-infections is thus essential for optimizing patient care and improving overall outcomes in the post COVID-19 pandemic era.

Modeling COVID-19 spreading evidence from Canada

This study delves into the comprehensive examination of the COVID-19 pandemic that has been affecting the global community since late 2019. The repercussions have been ameliorated to some extent with the advent of effective vaccination campaigns, albeit the impact varies across regions and outbreaks. Beginning with an introduction to the fundamental epidemiological SIR (Susceptibility, Infection, Recovery) model, the research extrapolates it to reflect the complex dynamics of the COVID-19 scenario, employing data from Ontario, Canada, to ground the analysis in real-world observations. Several parameters and initial conditions inform the development of differential equations and ensuing line graphs within the scope of the extended VSEAIR (Vaccinated, Susceptible, Exposed, Asymptomatic Infected, Symptomatic Infected, and Recovered) model. The study scrutinizes the interplay of two pivotal aspects: the effectiveness of vaccination and the influence of governmental interventions. It offers a rigorous review of the trajectory of COVID-19 in Ontario, shedding light on potential strategies to optimize the response to the pandemic and contributing to evidence-based policymaking.

Biomathematics and its Applications in Modeling COVID-19 Infections

The emerging field of biomathematics utilizes mathematical techniques to gain quantitative insights into biological systems. Mathematical modeling and computational simulations are becoming increasingly vital tools for elucidating complex infectious disease dynamics. The COVID-19 pandemic has highlighted the applicability of biomathematical methods for informing public health policy decisions. Here we provide an overview of major modeling approaches used to analyze SARS-CoV-2 transmission, forecast COVID-19 trajectories, evaluate intervention strategies, and understand viral evolution. Key model types discussed include compartmental models, agent-based models, and immunological models. We highlight critical model parameters and how their estimation from noisy data presents core challenges. The continued integration of biological mechanisms and epidemiological evidence through iteratively refined models promises more realistic representations of the intricate system interactions underlying the COVID-19 pandemic. While assumptions and uncertainties pervade, mathematical abstractions have already provided invaluable guidance for real-time decision-making in this global health emergency. Collaborative development of versatile biomathematical frameworks for emerging infectious diseases can equip policymakers with prescient, science-based insights for navigating future outbreaks.

An analysis of the nexus between youth urban mobility and COVID-19 trajectories for peace and development in Africa: Perspectives from Tanzania

The outbreak of the COVID-19 pandemic in 2019 posed prominent public policy concerns. It posed many unanswered questions, especially among the youth who require social protection in different contexts. The pandemic spread over 200 countries worldwide, infecting over 640 million individuals and resulting in the deaths of over 6.5 million people. However, the pandemic has a similar fundamental consequence, marked by widespread inequality across households and individuals. It is estimated that its consequences might be more detrimental, particularly among the youth in low-income families like Tanzania. The country received significant global attention for its COVID-19 response during the first year of the pandemic since it did not share pandemic statistics, implement lockdowns, and question vaccine efficacy while emphasizing traditional medicines. Despite the progress made, the ripple effects of the pandemic are still felt in the country and will probably remain for a long and drive policies for generations to come. Still, there is scanty information about how the youth have been affected by the pandemic, particularly in urban areas, and relatively little is known about the relationships between youth, urban mobility, and COVID-19 trajectories for peace and development in the country. This chapter uses Tanzania as a case study to assess the nexus between youth, urban mobility, and COVID-19 trajectories for African peace and development. The chapter relied on secondary data collected through a systematic review. Content analysis was applied to analyze the data. The chapter reveals several studies on youth and COVID-19 in Tanzania. Nevertheless, these studies are very few. Like other parts of Africa and the world over, COVID-19 has significantly impacted the youth in Tanzania. It also revealed determinants and challenges, like social, economic, health, mental, and learning effects, of urban youth mobility in the face of COVID-19. Since the COVID-19 pandemic has largely affected the lives of youth, this chapter provides relevant information that will prompt responsible authorities to adopt effective response measures for addressing the pandemic for enhanced peace and development in Tanzania and Africa.

Extended access to general practice services during weekends in the first wave of the COVID-19 pandemic.

The incidence, symptoms, and trajectories of COVID-19 in the community were unknown in the early phase of the pandemic. Consequently, organizing a primary health care response was challenging. The aim of this study was to investigate whether reorganizing general practice services with extended weekend access for patients was feasible, and to assess the extent to which patients used this service. Observational study with registration after a simple intervention. General practice services in the second half of March 2020 when the first wave of the COVID-19 pandemic hit Bergen, the second largest city in Norway. All general practices in Bergen were asked to be available during weekends for their patients with respiratory tract infections (RTIs), by telephone, video-, or e-consultation. Number of practices participating, patients connected to these practices, and consultations for RTIs and suspected COVID-19. During the first weekend, 33 of 71 practices (45%) covering 51% of the population participated. The following weekend this increased to 39 practices (53%) covering 64% of the population. The first weekend 25 practices reported a total of 336 consultations for RTIs, eight of which were for confirmed and 113 were for suspected COVID-19. The corresponding numbers reported from 23 practices the second weekend were 158 RTI consultations, four for confirmed and 41 for suspected COVID-19. On short notice about half the practices in Bergen were made accessible during weekends for their patients with RTIs. The number of consultations per practice was small, but combined this amounted to a substantial improvement in the emergency services.

Sex differences in COVID-19 symptom severity and trajectories among ambulatory adults.

The ongoing COVID-19 pandemic has led to hundreds of millions of infections worldwide. Although differences in COVID-19 hospitalization rates between males and females have been described, many infections in the general population have been mild, and the severity of symptoms during the course of COVID-19 in non-hospitalized males and females is not well understood. We conducted a case-ascertained study to examine household transmission of SARS-CoV-2 infections in Nashville, Tennessee, between April 2020 and April 2021. Among enrolled ambulatory adult participants with laboratory-confirmed SARS-CoV-2 infections, we assessed the presence and severity of symptoms (total, systemic, and respiratory) daily using a symptoms severity questionnaire, from illness onset and throughout the 2-week follow-up period. We compared the mean daily symptom severity scores (0-3: none, mild, moderate, and severe) and change in symptoms between males and females using a multivariable linear mixed effects regression model. The analysis included 223 enrolled adults with SARS-CoV-2 infection (58% females, mostly white, non-Hispanic) from 146 households with 2917 total daily symptom reports. The overall mean severity of total symptoms reported over the illness period was 1.04 and 0.90 for females and males, respectively. Mean systemic and respiratory scores were higher for females than for males (p< 0.001). In multivariable analyses, females reported more severe total and systemic symptoms during the illness period compared with males. However, no significant differences in reported respiratory symptoms were observed. Our findings indicate that among ambulatory adults with SARS-CoV-2 infections, females reported slightly higher symptom severity during their illness compared with males.

Implications of the COVID-19 trajectory for the evaluation of hand hygiene interventions: Secondary analysis of the Soapp trial.

Hand hygiene behavior is crucial to counter the spread of infectious diseases. However, its adoption during the early stages of the Coronavirus disease (COVID-19) pandemic showed temporal fluctuations associated with the trajectory of the pandemic (e.g. new COVID-19 infections). Such associations can confound conclusions about the effectiveness of interventions aimed at promoting hand hygiene during a pandemic. In this study, we performed a secondary analysis of a dataset from the optimization phase of Soapp, an app to promote hand hygiene during the COVID-19 pandemic. We used a longitudinal study design to test whether the associations between the pandemic trajectory and hand hygiene behavior were still present one year after the outbreak (primary outcome) and whether they impacted conclusions about the effectiveness of Soapp (secondary outcome). Participants (N = 216) were randomized to different versions of Soapp and used an electronic diary to self-report their hand hygiene behavior multiple times during the study. We considered the following indicators of the COVID-19 pandemic from the country of Switzerland in the period between March and August 2021: total cases/deaths, increases in recent new cases/deaths, new cases/deaths, and number of administered doses of vaccine. Data were analyzed using a multilevel approach. Results suggested that there were no significant associations between hand hygiene and the indicators of the pandemic trajectory. However, models including total cases/deaths impacted the conclusions about Soapp's effectiveness. Implications from this study are that the development and evaluation of hand hygiene interventions during a pandemic context should account for the trajectory indicators to maximize their effectiveness and control for confounding effects.

Relationship of Heterologous Virus Responses and Outcomes in Hospitalized COVID-19 Patients.

The clinical trajectory of COVID-19 may be influenced by previous responses to heterologous viruses. We examined the relationship of Abs against different viruses to clinical trajectory groups from the National Institutes of Health IMPACC (Immunophenotyping Assessment in a COVID-19 Cohort) study of hospitalized COVID-19 patients. Whereas initial Ab titers to SARS-CoV-2 tended to be higher with increasing severity (excluding fatal disease), those to seasonal coronaviruses trended in the opposite direction. Initial Ab titers to influenza and parainfluenza viruses also tended to be lower with increasing severity. However, no significant relationship was observed for Abs to other viruses, including measles, CMV, EBV, and respiratory syncytial virus. We hypothesize that some individuals may produce lower or less durable Ab responses to respiratory viruses generally (reflected in lower baseline titers in our study), and that this may carry over into poorer outcomes for COVID-19 (despite high initial SARS-CoV-2 titers). We further looked at longitudinal changes in Ab responses to heterologous viruses, but found little change during the course of acute COVID-19 infection. We saw significant trends with age for Ab levels to many of these viruses, but no difference in longitudinal SARS-CoV-2 titers for those with high versus low seasonal coronavirus titers. We detected no difference in longitudinal SARS-CoV-2 titers for CMV seropositive versus seronegative patients, although there was an overrepresentation of CMV seropositives among the IMPACC cohort, compared with expected frequencies in the United States population. Our results both reinforce findings from other studies and suggest (to our knowledge) new relationships between the response to SARS-CoV-2 and Abs to heterologous viruses.

Negative association between ascaris lumbricoides seropositivity and Covid-19 severity: insights from a study in Benin.

The COVID-19 pandemic has had devastating effects worldwide, but the trajectory of the pandemic has been milder in Low-and-Middle-Income Countries (LMICs), including those in Africa. Co-infection with helminths, such as Ascaris lumbricoides, has been suggested as a possible factor contributing to the reduced severity observed in these regions. The present study investigated the association between Ascaris-specific antibody levels and COVID-19 severity in 276 SARS-CoV-2-infected individuals in Benin. Participants were categorized into asymptomatic (n=100), mild (n=150), and severe (n=26) groups based on clinical disease severity. Sera were collected and analyzed using ELISA to measure Ascaris and SARS-CoV-2-specific antibodies, while Luminex was used to assess cytokines and SARS-CoV-2-specific neutralizing antibody expression. The results demonstrated that asymptomatic SARS-CoV-2 seropositive individuals expressed, on average, 1.7 and 2.2-times higher levels of Ascaris antibodies compared to individuals with mild and severe COVID-19, respectively. This finding suggests an inverse correlation between Ascaris antibody levels and COVID-19 severity. Notably, logistic regression analysis showed that Ascaris seropositivity was significantly associated with a reduced risk of severe COVID-19 (OR = 0.277, p = 0.021). Interestingly, COVID-19 patients with comorbidities such as type 2 diabetes and high blood pressure showed lower expression of Ascaris antibodies. Strikingly, no correlation was observed between Ascaris antibody levels and SARS-CoV-2-specific neutralizing antibodies. On the other hand, individuals seronegative for Ascaris displayed significantly higher levels of systemic pro-inflammatory markers compared to seropositive individuals. These findings suggest that higher expression of Ascaris antibodies is associated with asymptomatic SARS-CoV-2 infections and may contribute to the reduction of the risk to develop severe COVID-19. The beneficial effect of Ascaris seropositivity on COVID-19 outcomes in Benin may be attributed to a decrease in comorbidities and pro-inflammatory markers. These observations provide valuable insights into the milder COVID-19 trajectory observed in Africa and may have implications for future therapeutic strategies.

Personality predicts pre-COVID-19 to COVID-19 trajectories of transdiagnostic anxiety and depression symptoms.

This study aimed to characterize within-person pre-COVID-19 and coronavirus pandemic (COVID-19) transdiagnostic anxiety and depression symptom trajectories in emerging adults and determine the roles of neuroticism and behavioral activation in predicting these COVID-19-related changes. We recruited a sample of 342 emerging adults (aged 18-19 at baseline) who were screened on neuroticism and behavioral activation and completed symptom questionnaires on multiple occasions before and after the start of the pandemic. We examined estimates of the symptom factors of General Distress, Anhedonia-Apprehension, and Fears at each wave. The stress amplification model predicts a multiplicative neuroticism-adversity interaction with those high on neuroticism showing the greatest symptom increases to the pandemic. The stably elevated negative affect model is an additive model and predicts that persons high on neuroticism will display elevated symptoms at every wave. General Distress and Anhedonia-Apprehension showed large increases from the pre-COVID-19 to COVID-19 transition then decreased thereafter. The increase brought the average General Distress score to clinical levels at the first COVID-19 wave. There was a small decrease in Fears from the pre-COVID-19 to COVID-19 transition followed by a large increase. Thus, COVID-19 was associated with both increases in psychological symptoms and some resilience. Neuroticism positively predicted the pre-COVID-19 to COVID-19 transition change in Fears but was associated with a dampening of increases in General Distress and Anhedonia-Apprehension. The results disconfirmed the stress amplification model of neuroticism but partially supported the stably elevated negative affect model. (PsycInfo Database Record (c) 2023 APA, all rights reserved).