Nonlinear mixed models and related approaches in infectious disease modeling: A systematic and critical review.

The level of surveillance and preparedness against epidemics varies across countries, resulting in different responses to outbreaks. When conducting an in-depth analysis of microinfection dynamics, one must account for the substantial heterogeneity across countries. However, many commonly used statistical model specifications lack the flexibility needed for sound and accurate analysis and prediction in such contexts. Nonlinear mixed effects models (NLMMs) constitute a specific statistical tool that can overcome these significant challenges. While compartmental models are well-established in infectious disease modeling and have seen significant advancements, Nonlinear Mixed Models (NLMMs) offer a flexible approach for handling heterogeneous and unbalanced repeated measures data, often with less computational effort than some individual-level compartmental modeling techniques. This study provides an overview of their current use and offers a solid foundation for developing guidelines that may help improve their implementation in real-world situations. Relevant scientific databases in the Research4life Access initiative programs were used to search for papers dealing with key aspects of NLMMs in infectious disease modeling (IDM). From an initial list of 3641 papers, 124 were finally included and used for this systematic and critical review spanning the last two decades, following the PRISMA guidelines. NLMMs have evolved rapidly in the last decade, especially in IDM, with most publications dating from 2017 to 2021 (83.33%). The routine use of normality assumption appeared inappropriate for IDM, leading to a wealth of literature on NLMMs with non-normal errors and random effects under various estimation methods. We noticed that NLMMs have attracted much attention for the latest known epidemics worldwide (COVID-19, Ebola, Dengue and Lassa) with the robustness and reliability of relaxed propositions of the normality assumption. A case study of the application of COVID-19 data helped to highlight NLMMs' performance in modeling infectious diseases. Out of this study, estimation methods, assumptions, and random terms specification in NLMMs are key aspects requiring particular attention for their application in IDM.

Impact of vaccination on Omicron's escape variants: Insights from fine-scale modelling of waning immunity in Hong Kong.

COVID-19 vaccine-induced protection declines over time. This waning of immunity has been described in modelling as a lower level of protection. This study incorporated fine-scale vaccine waning into modelling to predict the next surge of the Omicron variant of the SARS-CoV-2 virus. In Hong Kong, the Omicron subvariant BA.2 caused a significant epidemic wave between February and April 2022, which triggered high vaccination rates. About half a year later, a second outbreak, dominated by a combination of BA.2, BA.4 and BA.5 subvariants, began to spread. We developed mathematical equations to formulate continuous changes in vaccine boosting and waning based on empirical serological data. These equations were incorporated into a multi-strain discrete-time Susceptible-Exposed-Infectious-Removed model. The daily number of reported cases during the first Omicron outbreak, with daily vaccination rates, the population mobility index and daily average temperature, were used to train the model. The model successfully predicted the size and timing of the second surge and the variant replacement by BA.4/5. It estimated 655,893 cumulative reported cases from June 1, 2022 to 31 October 2022, which was only 2.69% fewer than the observed cumulative number of 674,008. The model projected that increased vaccine protection (by larger vaccine coverage or no vaccine waning) would reduce the size of the second surge of BA.2 infections substantially but would allow more subsequent BA.4/5 infections. Increased vaccine coverage or greater vaccine protection can reduce the infection rate during certain periods when the immune-escape variants co-circulate; however, new immune-escape variants spread more by out-competing the previous strain.

The immunological and pharmacokinetic evaluation of Lipid-PLGA hybrid nanoparticle-based oxycodone vaccines.

The current opioid epidemic is one of the most profound public health crises facing the United States. Despite that it has been under the spotlight for years, available treatments for opioid use disorder (OUD) and overdose are limited to opioid receptor ligands such as the agonist methadone and the overdose reversing drugs such as naloxone. Vaccines are emerging as an alternative strategy to combat OUD and prevent relapse and overdose. Most vaccine candidates consist of a conjugate structure containing the target opioid attached to an immunogenic carrier protein. However, conjugate vaccines have demonstrated some intrinsic shortfalls, such as fast degradation and poor recognition by immune cells. To overcome these challenges, we proposed a lipid-PLGA hybrid nanoparticle (hNP)-based vaccine against oxycodone (OXY), which is one of the most frequently misused opioid analgesics. The hNP-based OXY vaccine exhibited superior immunogenicity and pharmacokinetic efficacy in comparison to its conjugate vaccine counterpart. Specifically, the hNP-based OXY vaccine formulated with subunit keyhole limpet hemocyanin (sKLH) as the carrier protein and aluminum hydroxide (Alum) as the adjuvant (OXY-sKLH-hNP(Alum)) elicited the most potent OXY-specific antibody response in mice. The induced antibodies efficiently bound with OXY molecules in blood and suppressed their entry into the brain. In a following dose-response study, OXY-sKLH-hNP(Alum) equivalent to 60 µg of sKLH was determined to be the most promising OXY vaccine candidate moving forward. This study provides evidence that hybrid nanoparticle-based vaccines may be superior vaccine candidates than conjugate vaccines and will be beneficial in treating those suffering from OUD.

A useful parametric specification to model epidemiological data: Revival of the Richards' curve.

A useful parametric specification for the expected value of an epidemiological process is revived, and its statistical and empirical efficacy are explored. The Richards' curve is flexible enough to adapt to several growth phenomena, including recent epidemics and outbreaks. Here, two different estimation methods are described. The first, based on likelihood maximisation, is particularly useful when the outbreak is still ongoing and the main goal is to obtain sufficiently accurate estimates in negligible computational run-time. The second is fully Bayesian and allows for more ambitious modelling attempts such as the inclusion of spatial and temporal dependence, but it requires more data and computational resources. Regardless of the estimation approach, the Richards' specification properly characterises the main features of any growth process (e.g. growth rate, peak phase etc.), leading to a reasonable fit and providing good short- to medium-term predictions. To demonstrate such flexibility, we show different applications using publicly available data on recent epidemics where the data collection processes and transmission patterns are extremely heterogeneous, as well as benchmark datasets widely used in the literature as illustrative.

Epidemiological survey of calf diarrhea related viruses in several areas of Guangdong Province.

Introduction: Bovine torovirus (BToV), Bovine enterovirus (BEV), Bovine norovirus (BNoV), Bovine coronavirus (BCoV), Bovine rotavirus (BRV), and Bovine viral diarrhea virus (BVDV) are significant pathogens causing diarrhea in calves, characterized by their high prevalence and challenging prevention and control measures. Methods: We analyzed 295 calf diarrhea samples, amplifying the M gene from BToV-positive samples, the 5'UTR gene from BEV-positive samples, the RdRp gene from BNoV-positive samples, the VP7 gene from BRV-positive samples, the S gene from BCoV-positive samples, and the 5'UTR gene from BVDV-positive samples. Subsequent homology analysis and phylogenetic tree construction were performed. Results: The overall viral positive rate in Guangdong Province was 21.36%. Specific detection rates were as follows: Foshan City at 50.00% (18/36), Guangzhou City at 43.90% (36/82), Huizhou City at 21.21% (7/33), Yangjiang City at 2.08% (1/48), Meizhou City at 1.39% (1/72), and Heyuan City at 0.00% (0/24). The detection rates for BToV, BEV, BNoV, BCoV, BRV, and BVDV were 0.34% (1/295), 6.10% (18/295), 0.68% (2/295), 1.36% (4/295), 10.85% (32/295), and 2.03% (6/295), respectively. Notably, the highest overall virus detection rate was observed in the Guangzhou-Foshan region, with BRV and BEV showing the highest detection rates among the six viruses. This study marks the first report of BToV and BNoV in Guangdong Province. Phylogenetic analysis revealed that the BToV strain belonged to type II, sharing genetic similarities with epidemic strains from various provinces in China. The BEV strains were categorized into E and F types, with the F type being the predominant strain in Guangdong Province and exhibiting the closest genetic relationship to strains from Heilongjiang and Guangxi. The BNoV strains, along with Hebei strains, were identified as GIII.2 subgenotype. BCoV strains showed the highest genetic similarity to strains from Sichuan. All BRV strains were classified under the G6 subtype and had the closest genetic relationship with human rotavirus strains. BVDV strains were identified as subtype 1b, closely related to the Beijing strain. In conclusion, this study investigated the prevalence and evolutionary characteristics of diarrhea-associated viruses in calves in specific areas of Guangdong Province, providing a valuable reference for establishing effective prevention and control measures in cattle farms.

Space-time scanning statistics in the prediction and evaluation of dengue epidemic clusters.

Objectives: To detect clusters of dengue hemorrhagic fever in an urbanized district of Hai Phong City, Vietnam using Poisson space-time retrospective and prospective analysis. Methods: A cross-sectional and retrospective study analyzed dengue surveillance data in the period from January 01, 2018, to December 31, 2022. Spatial-temporal scanning statistics were performed using the free software SatScan v10.1.2. Results: A total of 519 cases were recorded. The cumulative incidence per 100,000 inhabitants was 3.37, 127.36, 10.96, 0, and 296.04 in 2018, 2019, 2020, 2021, and 2022, respectively. By retrospective Poisson model-based analysis, seven clusters were detected. Six of these seven detected outbreaks occurred in November and December 2022. The largest cluster had a relative risk (RR) of 1539.5 (P <0.00001). The smallest cluster has a RR of 316.1 (P = 0.006). Prospective analysis using the Poisson model significantly detected four active case clusters at the time of the study. The largest cluster of cases with RR was 47.7 (P <0.00001) and the smallest cluster with RR was 18.2 (P <0.00001). Conclusions: This study provides a basis for improving the effectiveness of interventions and conducting further investigations into risk factors in the study area, as well as in other urban and suburban areas nationwide.

Development and characterization of segment-specific enteroids from the pig small intestine in Matrigel and transwell inserts: insights into susceptibility to porcine epidemic diarrhea Virus.

Introduction: The critical early stages of infection and innate immune responses to porcine epidemic diarrhea virus (PEDV) at the intestinal epithelium remain underexplored due to the limitations of traditional cell culture and animal models. This study aims to establish a porcine enteroid culture model to investigate potential differences in susceptibility to infection across segments of the porcine small intestine (duodenum, jejunum, and ileum). Methods: Intestinal crypt cells from nursery pigs were cultured in Matrigel to differentiate into porcine enteroid monolayer cultures (PEMCs). Following characterization, PEMCs were enzymatically dissociated and subcultured on transwell inserts (PETCs) for apical surface exposure and infection studies. Characterization of region-specific PEMCs and PETCs included assessment of morphology, proliferation, viability, and cellular phenotyping via immunohistochemistry/immunocytochemistry and gene expression analysis. Subsequently, PETCs were inoculated with 105 TCID50 (50% tissue culture infectious dose)/mL of a high pathogenic PEDV non-S INDEL strain and incubated for 24 h. Infection outcomes were assessed by cytopathic effect, PEDV N protein expression (immunofluorescence assay, IFA), and PEDV N-gene detection (quantitative reverse transcription polymerase chain reaction, RT-qPCR). Results: No significant morphological and phenotypical differences were observed among PEMCs and PETCs across intestinal regions, resembling the porcine intestinal epithelium. Although PETCs established from different segments of the small intestine were susceptible to PEDV infection, jejunum-derived PETCs exhibited higher PEDV replication, confirmed by IFA and RT-qPCR. Discussion: This segment-specific enteroid culture model provides a reliable platform for virological studies, offering a controlled environment that overcomes the limitations of in vivo and traditional cell culture methods. Standardizing culture conditions and characterizing the model are essential for advancing enteroid-based infection models.

Quantifying age-specific household contacts in Aotearoa New Zealand for infectious disease modelling.

Accounting for population age structure and age-specific contact patterns is crucial for accurate modelling of human infectious disease dynamics and impact. A common approach is to use contact matrices, which estimate the number of contacts between individuals of different ages. These contact matrices are frequently based on data collected from populations with very different demographic and socio-economic characteristics from the population of interest. Here we use a comprehensive household composition dataset based on Aotearoa New Zealand census and administrative data to construct a household contact matrix and a synthetic population that can be used for modelling. We investigate the behaviour of a compartment-based and an agent-based epidemic model parametrized using these data, compared with a commonly used contact matrix that was constructed by projecting international data onto New Zealand's population. We find that using the New Zealand household data, either in a compartment-based model or in an agent-based model, leads to lower attack rates in older age groups compared with using the projected contact matrix. This difference becomes larger when household transmission is more dominant relative to non-household transmission. We provide electronic versions of the synthetic population and household contact matrix for other researchers to use in infectious disease models.

Historical drivers of HCV Subtypes 1b and 3a in Thailand and 6f in Phetchabun, an HCV endemic area of the country.

The World Health Organization has set a target to eliminate viral hepatitis as a public threat by 2030. In pursuit of this goal, Thailand initiated a hepatitis C virus (HCV) microelimination project targeting Phetchabun province, a well-recognized high-burden HCV endemic area. However, the historical transmission dynamics of HCV in Phetchabun, and in Thailand in general, remain unclear. This study investigates the epidemic histories of HCV in Phetchabun, focusing on Subtypes 1b, 3a, and 6f, and their relationship with HCV in other regions of Thailand, using molecular phylogenetic analyses. Our results reveal nationwide the presence of Subtypes 1b and 3a, while Subtype 6f is mainly confined to Phetchabun. The initial spread of Subtype 1b was inferred to coincide with World War II and the period of suboptimal medical and hygienic standards in Thai blood transfusion services, suggesting a correlation between the two. The early expansion of Subtype 3a was, on the other hand, found to correlate with the epidemic of intravenous drug use in Thailand during the time of Vietnam War. The early expansion of Subtype 6f, in contrast, appears to coincide with the period of severe regional political conflict and social and economic instability. All these findings suggest the complex interplay between social determinants of health and HCV transmission. Post the mid-1990s/early 2000s, all subtypes showed significantly reduced population growth rates, aligning with improvements in blood transfusion safety standards, the nationwide "War on Drugs" policy, and enhanced accessibility to public healthcare and HCV treatments. These combined efforts likely have contributed to curbing the spread of HCV in Thailand. Nevertheless, our analyses reveal that the prevalence of HCV in Thailand remains high overall, emphasizing the need for further research and a nationwide approach to more effectively reduce the HCV burden in Thailand.

An immuno-epidemiological model with non-exponentially distributed disease stage on complex networks.

Most of epidemic models assume that duration of the disease phase is distributed exponentially for the simplification of model formulation and analysis. Actually, the exponentially distributed assumption on the description of disease stages is hard to accurately approximate the interplay of drug concentration and viral load within host. In this article, we formulate an immuno-epidemiological epidemic model on complex networks, which is composed of ordinary differential equations and integral equations. The linkage of within- and between-host is connected by setting that the death caused by the disease is an increasing function in viral load within host. Mathematical analysis of the model includes the existence of the solution to the epidemiological model on complex networks, the existence and stability of equilibrium, which are completely determined by the basic reproduction number of the between-host system. Numerical analysis are shown that the non-exponentially distributions and the topology of networks have significant roles in the prediction of epidemic patterns.

A vaccination-based COVID-19 model: Analysis and prediction using Hamiltonian Monte Carlo.

Compartmental models have emerged as robust computational frameworks and have yielded remarkable success in the fight against COVID-19. This study proposes a vaccination-based compartmental model for COVID-19 transmission dynamics. The model reflects the specific stages of COVID-19 infection and integrates a vaccination strategy, allowing for a comprehensive analysis of how vaccination rates influence the disease spread. We fit this model to daily confirmed COVID-19 cases in Tennessee, United States of America (USA), from June 4 to November 26, 2021, in a Bayesian inference approach using the Hamiltonian Monte Carlo (HMC) algorithm. First, excluding vaccination dynamics from the model, we estimated key epidemiological parameters like infection, recovery, and disease-induced death rates. This analysis yielded a basic reproduction number ( R 0 ) of 1.5. Second, we incorporated vaccination dynamics and estimated the vaccination rate for three vaccines: 0.0051 per day for both Pfizer and Moderna and 0.0059 per day for Janssen. The fitted curves show reductions in the epidemic peak for all three vaccines. Pfizer and Moderna vaccines bring the peak down from 8,029 infected cases to 5,616 infected cases, while the Janssen vaccine reduces it, to 6,493 infected cases. Simulations of the model by varying the vaccination rate and vaccine efficacy were performed. A highly effective vaccine (95% efficacy) with a daily vaccination rate of 0.006 halved COVID-19 infections, reducing cases from 8,029 to around 4,000. The results also show that the model's prediction accuracy for new observations improves with the number of observed data used to train the model.

Mpox in People With Human Immunodeficiency Virus: Predictors of Diagnosis, Outcomes, and Vaccine Effectiveness in a Multisite Cohort.

INTRODUCTION: Since its global reemergence in 2022, monkeypox (mpox) has demonstrated increased incidence and severity among people with human immunodeficiency virus (HIV [PWH]). Predictors of mpox diagnosis, vaccination, and outcomes among PWH are limited. METHODS: We included PWH with primary care visits after 1 January 2022 at 9 US sites participating in the Centers for AIDS Research Network of Integrated Clinic Systems Network. We identified mpox diagnosed between 1 June 2022 and 31 May 2023, through a combination of polymerase chain reaction result, diagnosis code, and/or tecovirimat receipt. We examined validated clinical diagnoses, laboratory results, vaccine data, and patient reported outcomes. We evaluated relative risks (RR) of mpox diagnosis, hospitalization, tecovirimat treatment, and vaccine receipt. FINDINGS: Among 19 777 PWH in care, 413 mpox cases (all male sex at birth) occurred (2.2 cases/100 person-years). Age <40 years, geographic region, Hispanic/Latine ethnicity, lack of antiretroviral therapy, detectable HIV viral load, and recent bacterial sexually transmitted infection predicted mpox diagnosis. PWH with CD4 200-349 cells/mm3 were most likely to be hospitalized (adjusted RR, 3.20; 95% confidence interval: 1.44-7.09) compared to CD4 ≥500, but half as likely as those with CD4 <200 to receive tecovirimat. Overall, smallpox/mpox vaccine effectiveness of ≥1 vaccine was 71% (adjusted RR, 0.29; 95% confidence interval: .14-.47) at preventing mpox, and 86% or better with CD4 ≥350 or HIV viral suppression. Non-Hispanic Black PWH were less likely to be vaccinated than other racial/ethnic identities. INTERPRETATION: PWH not on antiretroviral therapy or with unsuppressed HIV were more likely to be diagnosed with, and hospitalized for, mpox. Mpox/smallpox vaccine effectiveness was high, inclusive of those with low CD4 count and HIV viremia.

A mixed method evaluation of knowledge, attitude and practice on dengue fever among Lalitpur Metropolitan City residents: a cross-sectional investigation.

BACKGROUND: Dengue poses a significant global public health challenge, including in Nepal. Understanding community's knowledge, attitudes, and behaviors concerning dengue fever is imperative to developing effective prevention and control strategies. This study aimed to assess the knowledge, attitude, and practices related to dengue fever among residents of Lalitpur Metropolitan City. METHODS: A descriptive cross-sectional household study was conducted using a mixed-method approach, which included quantitatively studying 636 individuals and conducting 20 qualitative interviews. The data was collected between April 2023 and June 2023. The multistage cluster sampling method was applied for household selection during the quantitative study and a purposive judgmental sampling method was used to identify participants for the qualitative interviews. Face-to-face interviews were conducted using a structured questionnaire for the quantitative study and an interview guide for the qualitative study. Quantitative data were analysed using logistic regression in STATA version 13, and thematic analysis was applied to the qualitative data. The findings were validated through triangulation of results from both the qualitative and quantitative study. RESULTS: Regarding knowledge, 64.94% (n = 413/636) reported being informed about dengue fever. In terms of attitude, a substantial majority, 91.51% (n = 582/636), expressed a positive attitudes toward dengue fever, indicating a favorable perception and knowledge of its significance. Concerning practice, 49.84% (n = 317/636) of respondents reported actively engaging in dengue fever prevention measures. The variables gender, previous history of dengue fever and residency were the determinants of dengue fever knowledge. Additionally, gender, residency, and attitude were predictors of preventive practices concerning dengue fever. CONCLUSION: Our study revealed that while the community demonstrated good knowledge of dengue fever and positive attitudes toward prevention, their preventive practices were inconsistent, indicating a gap between knowledge and action. A positive attitude was linked to better adherence to preventive measures. To address this gap, it is crucial to promote a positive attitude toward dengue prevention through initiatives like education efforts and social mobilization programs. Implementing Social and Behavior Change Communication (SBCC) programs focused on dengue prevention and control measures can help bridge this knowledge-action gap.

The Relationship Between Epidemic Perception and Cyberbullying Behaviors of Chinese Adolescents During the COVID-19 Pandemic: Cross-Sectional Study.

Background: In response to the COVID-19 outbreak, the government initiated measures for social distancing, leading to a gradual transition of adolescents' social interactions toward web-based platforms. Consequently, web-based behaviors, particularly cyberbullying, have become a prominent concern. Considering that adolescents experience more intense feelings, the widely increased negative emotions and strains perceived from the COVID-19 pandemic may end up engaging in cyberbullying behaviors. In addition, during the COVID-19 pandemic, adolescents experiencing insomnia and negative affect are more prone to diminished self-control, which is associated with cyberbullying behaviors. Objective: This study aims to investigate the relationship between epidemic perception and cyberbullying behaviors, while also examining the serial mediating roles of insomnia and negative affect on the relationship between epidemic perception and cyberbullying behaviors. Methods: This study presents a large-scale web-based survey conducted during the period of concentrated COVID-19 outbreaks, encompassing 20,000 Chinese adolescents. A total of 274 submitted questionnaires were discarded because of high levels of missing data or their answers were clearly fictitious or inconsistent. The final count of valid participants amounted to 19,726 (10,371 boys, age range: 12-18 years; mean 14.80, SD 1.63 years). The Perceptions of COVID-19 Scale, Negative Affect Scale, Insomnia Scale, and Cyberbullying Behavior Scale were used to assess participants' responses on the Questionnaire Star platform. Results: The results show that epidemic perception is positively correlated with cyberbullying behaviors (r=0.13; P<.001), insomnia (r=0.19; P<.001), and negative affect (r=0.25; P<.001). Insomnia is positively correlated with negative affect (r=0.44; P<.001) and cyberbullying behaviors (r=0.30; P<.001). Negative affect is positively correlated with cyberbullying behaviors (r=0.25; P<.001). And insomnia and negative affect play independent mediating and serial mediating roles in epidemic perception and cyberbullying behaviors. Conclusions: This study provides additional empirical evidence on the relationship between the perception of COVID-19 pandemic and cyberbullying in adolescents. In addition, the study offers recommendations for implementing interventions targeted at mitigating cyberbullying in adolescents during the COVID-19 pandemic.

Development of a safe and broad-spectrum attenuated PEDV vaccine candidate by S2 subunit replacement.

Porcine epidemic diarrhea (PED) has caused serious economic losses to the swine livestock industry. Due to the rapid variation in the PEDV) genome, especially the spike (S) protein, the cross-protection ability of antibodies between different vaccine strains is weakened. Hence, the rapid development of safe, broad-spectrum and highly effective attenuated PEDV vaccine still needs further research. Here, we found that the replacement of the S2 subunit had little effect on S protein immunogenicity. Moreover, the chimeric virus (YN-S2DR13), the S protein of the YN strain was replaced by the DR13 S2 subunit, which lost its trypsin tropism and increased its propagation ability (approximately 1 titer) in Vero cells. Then, the pathogenesis of YN-S2DR13 was evaluated in neonatal piglets. Importantly, quantitative real-time PCR, histopathology, and immunohistochemistry confirmed that the virulence of YN-S2DR13 was significantly reduced compared with that of YN. Immunization with YN-S2DR13 induced neutralizing antibodies against both YN and DR13 in weaned piglets. In vitro passaging data also showed that YN-S2DR13 had good genetic stability. Collectively, these results suggest that YN-S2DR13 has significant advantages as a novel vaccine candidate, including a capacity for viral propagation to high titers with no trypsin requirement and the potential to provide protection against both PEDV G1 and G2 strains infections. Our results also suggests that S2 subunit replacement using reverse genetics can be a rapid strategy for the rational design of live attenuated vaccines for PEDV. IMPORTANCE: Emerging highly virulent porcine epidemic diarrhea virus (PEDV) G2 strains has caused substantial economic losses worldwide. Vaccination with a live attenuated vaccine is a promising method to prevent and control PED because it can induce a strong immune response (including T- and B-cell immunity). Previous studies have demonstrated that the S2 subunit of the PEDV spike (S) protein is the determinant of PEDV trypsin independence. Here, we evaluated the pathogenicity, tissue tropism, and immunogenicity of the chimeric virus (YN-S2DR13) via animal experiments. We demonstrated that YN-S2DR13 strain, as a trypsin independent strain, increased intracellular proliferation capacity, significantly reduced virulence, and induced broad-spectrum neutralization protection against PEDV G1 and G2 strains. In vitro passaging data also validated the stability of YN-S2DR13. Our results showed that generating a chimeric PEDV strain that is trypsin-independent by replacing the S2 subunit is a promising approach for designing a live attenuated vaccine for PEDV in the future.

Addressing gaps in the diagnosis of TB in children.

Since 2000, efforts to develop new treatments for TB have been promising, but diagnosing TB, especially in children, remains a challenge. This issue of the Journal includes the first in a series of articles related to TB in children, highlighting new diagnostic tests that do not rely on sputum, and have great potential for improving diagnosis and treatment initiation. Key to a reduction in TB prevalence, experts are engaging with communities to find undiagnosed cases and combat the stigma of TB. International collaborations are also central to integrating novel diagnostics and providing support for these vulnerable populations.

First imported Cases of MPXV Clade Ib in Goma, Democratic Republic of the Congo: Implications for Global Surveillance and Transmission Dynamics.

The ongoing national mpox outbreak in the Democratic Republic of the Congo has resulted in more >30,000 suspected cases in the country from January 2023 to August 2024. While these historic case totals have been driven by primarily by zoonosis, the emergence of Clade Ib monkeypox virus (MPXV), which is connected to more sustained human-to-human transmission, has been associated with increasing public health impacts in eastern DRC. First identified in South Kivu province, Clade Ib MPXV has been identified in multiple non-endemic East African countries for the first time. In DRC, there have been concerns over broader Clade Ib expansion in the country that could further complicate containment and mitigation responses. Here, we report the first introductions of Clade Ib into North Kivu province, including within internal displacement camps, with suspected close contact transmission that includes non-intimate contacts and children. These findings demonstrate that mpox case investigations and community messaging campaigns should include considerations for non-sexual contact-mediated transmission of Clade Ib that includes children <15 years.

Thyroid cancer epidemiology in Ireland from 1994 to 2019 - Rising diagnoses without mortality benefit.

BACKGROUND: The epidemiology and management of thyroid cancer has changed radically in the recent past, with rising international incidence of early-stage papillary thyroid cancer (PTC) in particular. In this paper, we review the epidemiology of thyroid cancer in Ireland. METHODS: A retrospective cohort study of National Cancer Registry of Ireland data, 1994-2019. RESULTS: Records from 4158 patients were analysed. 73 % (n = 3040) were female. The average age was 50.4 years. Patient sex did not change over time (p = 0.662), while age decreased significantly (p < 0.0001). The most common diagnoses were PTC (n = 2,905, 70 %) and follicular thyroid carcinoma (n = 549, 13 %). Incidence rose over threefold (1.8-6.2 cases/100000 person-years). The incidence of T1 PTC rose over twelvefold (0.169-2.1 cases/100000 person-years), while the incidence of stage III and IV disease did not change significantly. Five-year disease-specific survival (DSS) was 85 % and varied significantly by diagnosis - 97 % for PTC versus 5 % for anaplastic thyroid carcinoma. Survival did not change significantly over time. Male sex was a risk factor for more advanced disease (p < 0.0001) but did not independently predict overall survival except in PTC (HR 1.6, p = 0.03). The use of radioactive iodine declined markedly from 49 % to 12.5 %. RAI improved DSS for PTC patients aged over 55 years (p = 0.02) without a notable effect on survival for those under 55 years (p = 0.99). CONCLUSION: The epidemiology and management of thyroid cancer in Ireland has changed dramatically in a manner reflective of international trends.

Forecasting severe respiratory disease hospitalizations using machine learning algorithms.

BACKGROUND: Forecasting models predicting trends in hospitalization rates have the potential to inform hospital management during seasonal epidemics of respiratory diseases and the associated surges caused by acute hospital admissions. Hospital bed requirements for elective surgery could be better planned if it were possible to foresee upcoming peaks in severe respiratory illness admissions. Forecasting models can also guide the use of intervention strategies to decrease the spread of respiratory pathogens and thus prevent local health system overload. In this study, we explore the capability of forecasting models to predict the number of hospital admissions in Auckland, New Zealand, within a three-week time horizon. Furthermore, we evaluate probabilistic forecasts and the impact on model performance when integrating laboratory data describing the circulation of respiratory viruses. METHODS: The dataset used for this exploration results from active hospital surveillance, in which the World Health Organization Severe Acute Respiratory Infection (SARI) case definition was consistently used. This research nurse-led surveillance has been implemented in two public hospitals in Auckland and provides a systematic laboratory testing of SARI patients for nine respiratory viruses, including influenza, respiratory syncytial virus, and rhinovirus. The forecasting strategies used comprise automatic machine learning, one of the most recent generative pre-trained transformers, and established artificial neural network algorithms capable of univariate and multivariate forecasting. RESULTS: We found that machine learning models compute more accurate forecasts in comparison to naïve seasonal models. Furthermore, we analyzed the impact of reducing the temporal resolution of forecasts, which decreased the model error of point forecasts and made probabilistic forecasting more reliable. An additional analysis that used the laboratory data revealed strong season-to-season variations in the incidence of respiratory viruses and how this correlates with total hospitalization cases. These variations could explain why it was not possible to improve forecasts by integrating this data. CONCLUSIONS: Active SARI surveillance and consistent data collection over time enable these data to be used to predict hospital bed utilization. These findings show the potential of machine learning as support for informing systems for proactive hospital management.