Infection Dynamics Research Articles

Modeling the Impact of Ensitrelvir on SARS-CoV-2 Dynamics and Its Application for Assessment of Transmission Mitigation of Patients with COVID-19.

Mathematical modeling can provide quantitative understanding of the viral dynamics and viral reduction effects of drugs and enable simulations of the dynamics in various scenarios. In this study, a drug effect model of ensitrelvir was developed to describe the viral reduction effect. Using the model, we also estimated the impact of treatment with ensitrelvir on the reduction in the number of infected patients at the population level in Japan. The drug effect model of ensitrelvir was developed based on a viral dynamic model for the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and a population pharmacokinetic model of ensitrelvir using 10,477 samples of viral load from 1447 patients with coronavirus disease 2019 (COVID-19) in a phase 2/3 study. It was assumed that the drug effect on SARS-CoV-2 promoted the viral clearance depending on the free plasma concentrations. We estimated the impact of ensitrelvir treatment on the reduction in the number of infected patients at the population level in Japan using the susceptible-infectious-recovered-susceptible (SIRS) model including transmission mitigation. The viral reduction effect of ensitrelvir was characterized as a promotion of viral clearance depending on the plasma ensitrelvir concentrations using the Emax model. The maximum reduction effect was considered to depend on the time from symptom onset to treatment. The maximum transmission mitigation effect was observed when treatment was initiated within 12-24h of symptom onset, and secondary infections could be reduced by administering ensitrelvir as soon as possible after symptom onset. The viral reduction by ensitrelvir could be characterized based on the viral dynamics, and the dynamics could be estimated using the drug effect model. Furthermore, the drug effect on population level transmission based on the dynamics could be estimated. Thus, the simulation could be conducted for various conditions.

Epidemiology and clinical characteristics of acute viral gastroenteritis in 350 paediatric patients hospitalised between 2019 and 2022 in the Military Institute of Medicine – National Research Institute in Warsaw, Poland: a single-centre retrospective analysis

Introduction and objective: Acute gastroenteritis, a common childhood illness worldwide, manifests with symptoms including fever, abdominal pain, vomiting, and diarrhoea. It is highly contagious, with transmission occurring through contaminated water, food, or poor hygiene. Globally, 1.7 billion cases of diarrhoeal diseases are diagnosed annually, causing approximately 525,000 deaths among children under the age of five. Dehydration caused by diarrhoea is a primary cause of hospitalisation, particularly in developing countries. Aim: Analysis of the aetiology, frequency, and seasonal distribution of viral pathogens responsible for acute gastroenteritis in children hospitalised between 2019 and 2022 at the Military Institute of Medicine – National Research Institute in Warsaw, Poland. Materials and methods: Medical records of patients aged 0 to 18 diagnosed with rotavirus, norovirus, and adenovirus-induced acute gastroenteritis were analysed. The pathogens had been identified with reliable immunochromatographic tests. Exclusion criteria encompassed bacterial infections, dietary errors, and inflammatory conditions. Data examined included age, gender, aetiology of gastroenteritis, seasonality, duration of hospitalisation, and clinical symptoms (diarrhoea, vomiting, fever, abdominal pain, and dehydration). Laboratory results such as white blood cell count, C-reactive protein, electrolyte levels, and organ function markers were also evaluated. Results: Data from 350 children hospitalised between 2019 and 2022 were analysed. The highest number of hospitalisations occurred in 2019 and 2022, with fewer cases in 2020–2021, likely reflecting the impact of the COVID-19 pandemic. Most patients were between 6 and 12 months old. Rotavirus infection commonly presented with fever, vomiting, diarrhoea, and dehydration. Adenovirus and mixed infections were associated with slightly higher C-reactive protein levels, while rotavirus infections showed mildly elevated aspartate aminotransferase levels. Haematocrit, blood urea nitrogen, creatinine, and electrolyte levels were similar across all cases. Conclusions: Our analysis showed that rotavirus was the most frequent cause of acute viral diarrhoea in children, followed by norovirus and adenovirus, with mixed infections being the least common. The peak incidence occurred in autumn and winter, except in 2021, reflecting changes in the dynamics of infections related to the coronavirus pandemic. Biochemical findings were not sufficiently characteristic to infer the aetiology of the disease.

8074 Investigating Viral Infections, Systemic Inflammation, and their Complex Interplay in Type 1 Diabetes Pathogenesis

Abstract Disclosure: K. Girdhar: None. E. Elko: None. A. Pina: None. M. Atkinson: None. J. Ludvigsson: None. J. Ladner: None. E. Altindis: None. Type 1 diabetes (T1D) is an autoimmune disorder characterized by the immune-mediated destruction of insulin-producing beta cells. The etiological factors triggering the immunogenicity of endogenous β-cell antigens remain a focal point of investigation. Viral infections, particularly enteroviruses, coxsackieviruses, and rotaviruses, have been proposed as potential contributors to T1D onset and progression. However, the causal relationship between viral infections and T1D pathogenesis remains elusive. To address this, we conducted an in-depth analysis of viral infection histories of T1D patients and pediatric T1D progressors compared to the controls. To this end, we employed highly multiplexed serology using PepSeq sequencing, an in vitro platform designed for conducting proteomic assays against customizable targets via DNA-barcoded peptides. Utilizing samples from the All Babies in Sweden (ABIS) study (n=58) and the TrialNet study (n=116 new-onset T1D, n=25 established T1D, n=128 controls), we assessed seropositivity against 79 different human viruses. Contrary to previous reports, no significant differences were identified in viral infection histories between T1D individuals and controls. Subsequent analyses of enriched Peptide Count and Viral Homology Search (VHS) species provided further confirmation of the absence of distinctions in T1D progression. Next, we determined whether there are any differences in the markers of systematic inflammation in spite of their similar viral history profile. Therefore, we employed Luminex assays to investigate cytokine profiles in established T1D individuals (n=25). While we could not identify any differences when we compared T1D patients to controls, we identified notable sex-specific differences. Anti-inflammatory cytokines (IL-4, IL-13, IL-22) were lower in female T1D patients compared to female controls. Likewise, female T1D patients had higher pro-inflammatory cytokines (IL-17E, TNF-β). On the other hand, male T1D patients exhibited increased levels of epidermal growth factor and platelet-derived growth factor compared to male controls, with IL-22 uniquely elevated. Our findings challenge the direct association between viral infections and T1D, highlighting the importance of investigating alternative factors. Moreover, the observed sex-specific cytokine alterations underscore the complex interplay between immune responses and T1D, emphasizing the need for sex-specific therapeutic strategies. This research contributes valuable insights into the intricate dynamics of viral infections, systemic inflammation, and their potential roles in T1D pathogenesis. Presentation: 6/2/2024

8627 Viral Hormones: The Role Of Viral Insulin/IGF-1 Like Peptides In Host-Pathogen Interactions

Abstract Disclosure: A. Chuard: None. Viruses, encode proteins that are similar to host proteins to manipulate the host. Until our recent discovery of viral hormones, examples of viral mimicry were limited to immunomodulatory proteins and growth factors. We recently showed that six viruses in Iridoviridae family encode genes mimicking human insulin and IGF-1. We previously chemically synthesizing these viral insulin/IGF-1 like peptides (VILPs) and showed that VILPs can bind to human insulin and IGF-1 receptor and further stimulate post-receptor signaling. VILPs also stimulate glucose uptake and proliferation in mammalian cells and lower the blood glucose in mice. Originally, VILP-carrying viruses were isolated from fish; however, the role of VILPs in viral pathogenesis is not studied in depth. Here, we examined the effects of Grouper Iridovirus (GIV, one of the VILP carrying viruses) infection and GIV-VILP on host insulin/IGF signaling system using fish cells.To this end, we chemically synthesized GIV-VILP in its single chain (sc, IGF-1 like) and double chain (dc, insulin-like) forms. Stimulation of grouper kidney (GK) and AB9 zebrafish cells with these peptides, insulin and IGF-1, showed that IGF-1 was the most potent ligand in these cells indicating an abundance of IGF-1 receptor (IGF1R). Further, both forms of the VILPs were as potent as insulin in its activity on receptor phosphorylation and post-receptor signaling. Examining the viral kinetics, we also showed that GIV32 gene (encoding GIV VILP) is an early viral gene in both cell types. Virion analysis through mass spectrometry indicated that VILPs are not viral structural proteins. On the other hand, analysis of the supernatant from infected cells indicated that VILPs are secreted peptides during the viral cycle. Our results with supernatant transfer experiments from infected cells to uninfected cells support the same conclusion that VILPs are secreted. The secreted VILPs initiate autophosphorylation of IGF1R and/or insulin receptor (IR), consequently triggering downstream cellular signaling and metabolism. Through the use of specific inhibitors for each receptor, we illustrated that IR signaling is crucial for GIV viral replication. Surprisingly, inhibition of IGF1R increased viral replication and we are currently examining potential inhibitory effects of GIV VILP. Furthermore, VILPs appear to exert a biased effect on the AKT pathway compared to the Erk pathway. Furthermore, the inhibition of the AKT pathway significantly reduces GIV viral replication. In summary, our findings reveal that GIV-VILP is actively produced during infection and contributes to the viral replication. To our knowledge, VILPs are the first hormone-mimics characterized in viral replication. Elucidating the role of VILPs in host-pathogen interactions, we propose a novel viral pathogenesis mechanism where viruses mimic host hormones to manipulate the host's endocrine system. Presentation: 6/3/2024

Viral genetics and transmission dynamics in the second wave of mpox outbreak in Portugal and forecasting public health scenarios

ABSTRACT In 2023, a second wave of the global mpox epidemic, which is mainly affecting men who have sex with men (MSM), was observed in some countries. Herein, we benefited from a large viral sequence sampling (76/121; 63%) and vast epidemiological data to characterise the re-emergence and circulation of the Monkeypox virus (MPXV) in Portugal during 2023. We also modelled transmission and forecasted public health scenarios through a compartmental susceptible-exposed-infectious-recovered (SEIR) model. Our results suggest that the 2023 mpox wave in Portugal resulted from limited introduction(s) of MPXV belonging to C.1.1 sublineage, hypothetically from Asia, followed by sustained viral transmission and potential exportation to other countries. We estimated that the contribution of the MSM high sexual activity group to mpox transmission was 120 (95% CrI: 30–3553) times higher than that of the low sexual activity group. However, among the high sexual activity group, vaccinated individuals likely contributed approximately eight times less [0.123 (95% CrI: 0.068–0.208)] than the unvaccinated ones. Vaccination was also linked to potential reduced disease severity, with a Mpox Severity Score of 6.0 in the vaccinated group compared to 7.0 in unvaccinated individuals. Scenario analysis indicated that transmission is highly sensitive to sexual behaviour, projecting that a slight increase in the MSM sub-population with high sexual activity can trigger new mpox waves. This study strongly supports that continued vaccination, targeted awareness among risk groups and routine genomic epidemiology is needed to anticipate and respond to novel MPXV threats (e.g. global dissemination of clade I viruses).

Exploring pathogen population density as a metric for understanding post-COVID infectious disease surges.

After the easing of COVID-19 restrictions, peaks of common infectious diseases surpassed pre-pandemic levels, raising questions about causes and ways to monitor these changes. A proposed measure, the Pathogen Population Density (PPD) score, could help track these shifts. PPD refers to the concentration of infectious agents within a population at a given time and location, serving as a potential indicator of infection levels in susceptible individuals at the population level. It is likely that PPD remains relatively stable within a specific community, as an equilibrium forms between infections and susceptibility. During the pandemic, nonpharmaceutical interventions (NPIs) led to a reduction in infectious diseases, possibly lowering population immunity and decreasing the PPD score. Once NPIs were lifted, the PPD score likely increased sharply due to a larger pool of susceptible individuals, causing more primary infections and stronger recurrent infections, faster transmission, and more severe pathogenic outcomes at the individual level. Monitoring the PPD score over time could help predict when infection peaks will occur. PPD is influenced by factors such as public health strategies, vaccination programs, and the behavior of high-risk individuals. As a quantitative measure, PPD has the potential to serve as a valuable predictive and monitoring tool, helping public health officials anticipate and track changes in infectious disease dynamics. It could be an effective tool for managing future outbreaks or pandemics and serve as a communication tool between scientists and the public to understand the emergence of new disease peaks.

Infected wound repair correlates with collagen I induction and NOX2 activation by cold atmospheric plasma

Cold atmospheric plasma (CAP) is a promising complement to tissue repair and regenerative medicine approaches. CAP has therapeutic potential in infected cutaneous wounds by mechanisms which remain enigmatic. Here, CAP is shown to activate phagocyte NADPH oxidase complex NOX2. CAP induced increased intracellular reactive oxygen species, alleviated by NOX2 inhibitors. Genetic and pharmacological inhibitions of NOX2 in macrophages and bioengineered skin infected with Staphylococcus aureus and treated with CAP reduced intracellular oxidants and increased bacterial survival. CAP triggered Rac activation and phosphorylation of p40phox and p47phox required for NOX2 assembly and activity. Furthermore, CAP induced collagen I expression by fibroblasts. Infection and healing kinetics showed that murine skin wounds infected with S. aureus and treated with CAP are characterized by decreased bacterial burden, increased length of neoepidermis and extracellular matrix formation. Collectively, our findings identify mechanisms triggered by CAP that subdue infection and result in enhanced repair following skin injury.

Deep learning-derived optimal aviation strategies to control pandemics

The COVID-19 pandemic affected countries across the globe, demanding drastic public health policies to mitigate the spread of infection, which led to economic crises as a collateral damage. In this work, we investigate the impact of human mobility, described via international commercial flights, on COVID-19 infection dynamics on a global scale. We developed a graph neural network (GNN)-based framework called Dynamic Weighted GraphSAGE (DWSAGE), which operates over spatiotemporal graphs and is well-suited for dynamically changing flight information updated daily. This architecture is designed to be structurally sensitive, capable of learning the relationships between edge features and node features. To gain insights into the influence of air traffic on infection spread, we conducted local sensitivity analysis on our model through perturbation experiments. Our analyses identified Western Europe, the Middle East, and North America as leading regions in fueling the pandemic due to the high volume of air traffic originating or transiting through these areas. We used these observations to propose air traffic reduction strategies that can significantly impact controlling the pandemic with minimal disruption to human mobility. Our work provides a robust deep learning-based tool to study global pandemics and is of key relevance to policymakers for making informed decisions regarding air traffic restrictions during future outbreaks.

Modelling COVID-19 mutant dynamics: understanding the interplay between viral evolution and disease transmission dynamics

Understanding virus mutations is critical for shaping public health interventions. These mutations lead to complex multi-strain dynamics often under-represented in models. Aiming to understand the factors influencing variants’ fitness and evolution, we explore several scenarios of virus spreading to gain qualitative insight into the factors dictating which variants ultimately predominate at the population level. To this end, we propose a two-strain stochastic model that accounts for asymptomatic transmission, mutations and the possibility of disease import. We find that variants with milder symptoms are likely to spread faster than those with severe symptoms. This is because severe variants can prompt affected individuals to seek medical help earlier, potentially leading to quicker identification and isolation of cases. However, milder or asymptomatic cases may spread more widely, making it harder to control the spread. Therefore, increased transmissibility of milder variants can still result in higher hospitalizations and fatalities due to widespread infection. The proposed model highlights the interplay between viral evolution and transmission dynamics. Offering a nuanced view of factors influencing variant spread, the model provides a foundation for further investigation into mitigating strategies and public health interventions.

Modelling multiplex testing for outbreak control

During the SARS-CoV-2 pandemic, polymerase chain reaction (PCR) and lateral flow device (LFD) tests were frequently deployed to detect the presence of SARS-CoV-2. Many of these tests were singleplex, and only tested for the presence of a single pathogen. Multiplex tests can test for the presence of several pathogens using only a single swab, which can allow for: surveillance of more pathogens, targeting of antiviral interventions, a reduced burden of testing, and lower costs. Test sensitivity however, particularly in LFD tests, is highly conditional on the viral concentration dynamics of individuals. To inform the use of multiplex testing in outbreak detection it is therefore necessary to investigate the interactions between outbreak detection strategies and the differing viral concentration trajectories of key pathogens. Viral concentration trajectories are estimated for SARS-CoV-2, and Influenza A/B. Testing strategies for the first five symptomatic cases in an outbreak are then simulated and used to evaluate key performance indicators. Strategies that use a combination of multiplex LFD and PCR tests achieve; high levels of detection, detect outbreaks rapidly, and have the lowest burden of testing across multiple pathogens. Influenza B was estimated to have lower rates of detection due to its modelled viral concentration dynamics. Data availability statementThe SARS-CoV-2 viral load trajectory data may be accessed by contacting Killingley et. al. The influenza A/B viral load trajectory may be accessed at https://github.com/bcowling/pediatric-vaccine-trial/tree/master/data.

Protocol for detection of pathogenic enteric RNA viruses by regular monitoring of environmental samples from wastewater treatment plants using droplet digital PCR

BackgroundThe present comprehensive protocol is focused on the detection of pathogenic enteric RNA viruses, explicitly focusing on norovirus genogroup Ⅱ (GⅡ), astrovirus, rotavirus, Aichi virus, sapovirus, hepatitis A and E viruses in wastewater treatment plants through droplet digital PCR (ddPCR). Enteric viruses are of significant public health concern, as they are the leading cause of diseases like gastroenteritis. Regular monitoring of environmental samples, particularly from wastewater treatment plants, is crucial for early detection and control of these viruses. This research aims to improve the understanding of the prevalence and dynamics of enteric viruses in urban India and will serve as a model for similar studies in other regions. Our protocol objective is to establish a novel ddPCR-based methodology for the detection and molecular characterization of enteric viruses present in wastewater samples sourced from Bhopal, India. Our assay is capable of accurately quantifying virus concentrations without standard curves, minimizing extensive optimization, and enhancing sensitivity and precision, especially for low-abundance targets. MethodsThe study involves fortnightly collecting and analyzing samples from nine wastewater treatment plants over two years, ensuring comprehensive coverage and consistent data. Our study innovatively applies ddPCR to simultaneously detect and quantify enteric viruses in wastewater, a more advanced technique. Additionally, we will employ next-generation sequencing for detailed viral genome identification in samples tested positive for pathogenic viruses. ConclusionThis study will aid in understanding these viruses’ genetic diversity and mutation rates, which is crucial for developing tailored intervention strategies. The findings will be instrumental in shaping public health responses and improving epidemiological surveillance, especially in localities heaving sewage networks.

Genetic and antigenic characterization of influenza A(H3N2) virus after 13 consecutive years of influenza surveillance in Senegal, 2010-2022.

Despite decades of influenza surveillance in many African countries, little is known about the evolutionary dynamics of seasonal influenza viruses. This study aimed to characterize the epidemiological, genetic and antigenic profiles of A/H3N2 viruses in Senegal from 2010 to 2022. A/H3N2 infection was confirmed using reverse transcription-polymerase chain reaction. Subsequently, a representative of A/H3N2 isolates was selected for genome sequencing. Predicted vaccine efficacy was measured using the Pepitope model. During the study period, 22638 samples were tested and influenza was detected in 31.8%, among which type A was confirmed in 78.1%. Of the Influenza A cases, the H3N2 subtype was detected in 29.8%, peaking at expected times during the rainy season. Genome sequencing of 123A/H3N2 isolates yielded 24 complete and 99 partial genomic sequences. Phylogenetic analysis revealed the circulation of multiple clades of A/H3N2 in Senegal, including 2a.3, 3C.2 and 3C.3a. A/H3N2 isolates were mainly susceptible to the influenza antiviral drugs oseltamivir and zanamivir, but the primary adamantine-resistance marker, S31N was encountered in all isolates. At least nine potential N-linked glycosylation sites were predicted among A/H3N2 strains, six of which (at positions 24, 38, 79, 181, 262 and 301) remains conserved among all isolates. Antigenic distances between circulating strains and vaccine viruses indicated varying vaccine efficacies, from suboptimal to moderate protection. The findings emphasize the need to enhance local genomic and antigenic surveillance and further research on influenza epidemiology and genetic evolution in sub-Saharan Africa.

Viral load dynamics in asymptomatic and symptomatic patients during Omicron BA.2 outbreak in Shanghai, China, 2022: a longitudinal cohort study

The SARS-CoV-2 virus, particularly the Omicron BA.2 variant, led to a significant surge in Shanghai, 2022. However, the viral load dynamic in Omicron infections with varying clinical severities remain unclear. This prospective cohort included 48,830 hospitalized coronavirus disease 2019 (COVID-19) patients across three hospitals in Shanghai, China, between 23 March and 15 May 2022. Systematic nucleic acid testing was performed using RT-PCR Cycle threshold (Ct) value as a proxy of viral load. We analyzed the kinetic characteristics of viral shedding by clinical severity and identified associated risk factors. The study comprised 31.06% asymptomatic cases, 67.66% mild-moderate cases, 1.00% severe cases, 0.29% critical and fatal cases. Upon admission, 57% of patients tested positive, with peak viral load observed at 4 days (median Ct value 27.5), followed by a decrease and an average viral shedding time (VST) of 6.1 days (Interquartile range, 4.0–8.8 days). Although viral load exhibited variation by age and clinical severity, peak Ct values occurred at similar times. Unvaccinated status, age exceeding 60, and comorbidities including hypertension, renal issues kidney dialysis and kidney transplantation, neurological disorders, rheumatism, and psychotic conditions were found to correlate with elevated peak viral load and extended VST. Asymptomatic cases demonstrated a 40% likelihood of contagiousness within 6 days of detection, while mild-moderate and severe cases exhibited post-symptom resolution infectious probabilities of 27% and over 50%, respectively. These findings revealed that the initial Ct values serve as a predictive indicator of severe outcomes. Unvaccinated elderly individuals with particular comorbidities are at high-risk for elevated viral load and prolonged VST.

Beyond the biting - limited impact of explicit mosquito dynamics in dengue models

Mathematical models play a crucial role in assisting public health authorities in timely disease control decision-making. For vector-borne diseases, integrating host and vector dynamics into models can be highly complex, particularly due to limited data availability, making system validation challenging. In this study, two compartmental models akin to the SIR type were developed to characterize vector-borne infectious disease dynamics. Motivated by dengue fever epidemiology, the models varied in their treatment of vector dynamics, one with implicit vector dynamics and the other explicitly modeling mosquito-host contact. Both considered temporary immunity after primary infection and disease enhancement in secondary infection, analogous to the temporary cross-immunity and the Antibody-dependent enhancement biological processes observed in dengue epidemiology. Qualitative analysis using bifurcation theory and numerical experiments revealed that the immunity period and disease enhancement outweighed the impact of explicit vector dynamics. Both models demonstrated similar bifurcation structures, indicating that explicit vector dynamics are only justified when assessing the effects of vector control methods. Otherwise, the extra equations are irrelevant, as both systems display similar dynamics scenarios. The study underscores the importance of using simple models for mathematical analysis, initiating crucial discussions among the modeling community in vector-borne diseases.

Estimating the impact of sarcoptic mange epidemic on the population size of wild raccoon dogs (Nyctereutes procyonoides) from wildlife rescue data

The impact of infectious diseases on host populations is often not quantified because it is difficult to observe the host population and infectious disease dynamics. To address this problem, we developed a state-space model to simultaneously estimate host population and disease dynamics using wildlife rescue data. Using this model, we aimed to quantify the impact of sarcoptic mange on a Japanese raccoon dog population by estimating the change in their relative population size. We classified the status of rescued raccoon dogs into four categories: i) rescued due to infection with mange, ii) rescued due to traffic accidents without mange, iii) rescued due to traffic accidents with mange, and iv) rescued due to causes other than traffic accidents or mange. We modelled the observation process for each category and fitted the model to the reported number of raccoon dogs rescued between 1990 and 2010 at three wildlife rescue facilities in Kanagawa Prefecture, Japan. The mortality rate induced by mange was estimated to be 1.09 (95% credible interval (CI): 0.47–1.72) per year. The estimated prevalence of sarcoptic mange ranged between 4–80% in the study period. When a substantial prevalence of mange was observed (1995 to 2002), the host population size decreased by 91.2% (95% credible intervals: 86.3–94.7). We show that the impact of infectious disease outbreak on the wildlife population can be estimated from the time-series data of wildlife rescue events due to multiple causes. Our estimates suggest that sarcoptic mange triggered a substantial decrease in the Japanese wild raccoon dog populations.

Integrated Simulation Model of the Spatial Distribution of Dynamic Systems Using Intelligent Cellular Automaton

This research emphasizes the versatility of cellular automata in studying the spatial and temporal aspects of disease spread. By simulating various intervention strategies, such as vaccination campaigns and social distancing measures, the model provides a platform for assessing their efficacy and optimizing resource allocation. The findings contribute to the development of informed public health policies aimed at curbing the impact of infectious diseases. Mathematical modeling using cellular automata emerges as a valuable approach in unraveling the complexities of infectious disease dynamics. The versatility of this framework allows for a nuanced exploration of diverse scenarios, fostering a deeper understanding of the interplay between individual-level interactions and broader population dynamics. The insights gained from such models hold the potential to guide evidence-based decision-making in the ongoing battle against infectious diseases.

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.

Modelling the mechanisms of antibody mixtures in viral infections: the cases of sequential homologous and heterologous dengue infections.

Antibodies play an essential role in the immune response to viral infections, vaccination or antibody therapy. Nevertheless, they can be either protective or harmful during the immune response. Moreover, competition or cooperation between mixed antibodies can enhance or reduce this protective or harmful effect. Using the laws of chemical reactions, we propose a new approach to modelling the antigen-antibody complex activity. The resulting expression covers not only purely competitive or purely independent binding but also synergistic binding which, depending on the antibodies, can promote either neutralization or enhancement of viral activity. We then integrate this expression of viral activity in a within-host model and investigate the existence of steady-states and their asymptotic stability. We complete our study with numerical simulations to illustrate different scenarios: firstly, where both antibodies are neutralizing and secondly, where one antibody is neutralizing and the other enhancing. The results indicate that efficient viral neutralization is associated with purely independent antibody binding, whereas strong viral activity enhancement is expected in the case of purely competitive antibody binding. Finally, data collected during a secondary dengue infection were used to validate the model. The dataset includes sequential measurements of virus and antibody titres during viremia in patients. Data fitting shows that the two antibodies are in strong competition, as the synergistic binding is low. This contributes to the high levels of virus titres and may explain the antibody-dependent enhancement phenomenon. Besides, the mortality of infected cells is almost twice as high as that of susceptible cells, and the heterogeneity of viral kinetics in patients is associated with variability in antibody responses between individuals. Other applications of the model may be considered, such as the efficacy of vaccines and antibody-based therapies.

Circadian regulation of dengue virus transmission and replication: insights into vector activity and viral dynamics.

Dengue fever, caused by dengue virus, poses a significant global health challenge, particularly in tropical regions where Aedes aegypti serves as the primary vector. The circadian clock in Aedes aegypti governs key behavioral and physiological processes, including activity patterns, feeding behaviors, and susceptibility to dengue virus infection. This article explores the influence of circadian rhythms on the mosquito's ability to transmit dengue virus, emphasizing how the circadian regulation of gene expression, immune responses, and lipid metabolism in the mosquito vector creates temporal windows that affect viral replication efficiency.

Stability analysis of the SEIRS epidemic model with infectious dynamics during latent and infectious periods

Coronary pneumonia caused by the SARS-CoV-2 virus was one of the most significant public health threats in recent years. In this paper, we develop and investigate an SEIRS epidemic model that incorporates infectivity during both the latent and infectious stages to characterize the transmission dynamics of COVID-19. By calculating the basic reproduction number (R0) and applying monotonic dynamical system theory along with geometric methods, we validate the threshold theorem. Our analysis demonstrates that the disease-free equilibrium is globally stable when R0 < 1, while the endemic equilibrium becomes globally stable when R0 > 1.