Infectious disease outbreaks have precipitated a profusion of mathematical models. Epidemic curves predicted by these models are typically qualitatively similar, despite distinct model assumptions, but there is no theoretical explanation for this similarity in terms of any recognised common structure. In addition, fits of epidemic models to time series conflate pathogen transmissibility with pre-existing population immunity, so only a single composite parameter can be inferred. Here, we introduce a unifying concept of "epidemic momentum" -- prevalence weighted by potential to infect -- which is more informative than prevalence, yet analytically tractable. Epidemic momentum reveals a common underlying geometry in which outbreak trajectories always follow contours of a conserved quantity. This previously unrecognised conservation law constrains how epidemics can unfold, enabling us to disentangle transmissibility from prior immunity and to infer each separately from the same time series. We illustrate the significance of these insights with a novel reappraisal of the transmissibility of influenza during the 1918 pandemic. Beyond resolving an apparent identifiability problem, epidemic momentum also exposes the true final size of an outbreak and a universal phase-plane description that links generic renewal models to the classical SIR system. A broader concept of "population momentum" has the potential to illuminate seemingly intractable nonlinear dynamical processes in many other areas of science.
Edmund F. Agyemang, Hansapani Rodrigo, Vincent Agbenyeavu
Influenza A is responsible for 290,000 to 650,000 respiratory deaths a year, though this estimate is an improvement from years past due to improvements in sanitation, healthcare practices, and vaccination programs. In this study, we perform a comparative analysis of traditional and deep learning models to predict Influenza A outbreaks. Using historical data from January 2009 to December 2023, we compared the performance of traditional ARIMA and Exponential Smoothing(ETS) models with six distinct deep learning architectures: Simple RNN, LSTM, GRU, BiLSTM, BiGRU, and Transformer. The results reveal a clear superiority of all the deep learning models, especially the state-of-the-art Transformer with respective average testing MSE and MAE of 0.0433 \pm 0.0020 and 0.1126 \pm 0.0016 for capturing the temporal complexities associated with Influenza A data, outperforming well known traditional baseline ARIMA and ETS models. These findings of this study provide evidence that state-of-the-art deep learning architectures can enhance predictive modeling for infectious diseases and indicate a more general trend toward using deep learning methods to enhance public health forecasting and intervention planning strategies. Future work should focus on how these models can be incorporated into real-time forecasting and preparedness systems at an epidemic level, and integrated into existing surveillance systems.
Jongmin Lee, Renier Mendoza, Victoria May P. Mendoza
et al.
During an infectious disease outbreak, policymakers must balance medical costs with social and economic burdens and seek interventions that minimize both. To support this decision-making process, we developed a framework that integrates multi-objective optimization, cost-benefit analysis, and an interactive dashboard. This platform enables users to input cost parameters and immediately obtain a cost-optimal intervention strategy. We applied this framework to the early outbreak of COVID-19 in South Korea. The results showed that cost-optimal solutions for costs per infection ranging from 4,410 USD to 361,000 USD exhibited a similar pattern. This indicates that once the cost per infection is specified, our approach generates the corresponding cost-optimal solution without additional calculations. Our framework supports decision-making by accounting for trade-offs between policy and infection costs. It delivers rapid optimization and cost-benefit analysis results, enabling timely and informed decision-making during the critical phases of a pandemic.
Daniela V. Staton, Jonah Tang, Matthew Barbisan
et al.
Background Although touted as a safer alternative to cigarette smoking, electronic cigarette usage has been increasingly linked to a myriad of health issues and appears to impact the oral microbiome. Meanwhile, nitrate supplementation has shown promise as a prebiotic that induces positive effects on the oral microbiome.Methods In this pilot study, the impact of nitrate supplementation as a countermeasure to e-cigarette usage was explored using in vitro growth and 16S rRNA analysis of microcosms derived from e-cigarette users and nonusers and supplementation with nitrate-rich beetroot juice extract.Results The impacts of e-cigarette usage and beetroot supplementation were somewhat limited, with beetroot juice extract supplementation having a significant impact on diversity according to some, but not all, diversity metrics examined. The saliva of the e-cigarette users was depleted in nitrate-reducing Neisseria spp. In terms of differentially abundant individual taxa, the addition of beetroot juice extract to the saliva-derived microcosms had a larger impact on the communities derived from the e-cigarette users compared to that of the nonusers.Conclusions Overall, this limited pilot study suggests that beetroot juice extract supplementation may impact the microbiota of e-cigarette users and adds to contemporary research paving the way for more in-depth studies examining the role of nitrate-rich supplements as prebiotics to promote oral health.
This study investigates the spatial integration of agent-based models (ABMs) and compartmental models for infectious disease modeling, presenting a novel hybrid approach and examining its implications. ABMs offer detailed insights by simulating interactions and decisions among individuals but are computationally expensive for large populations. Compartmental models capture population-level dynamics more efficiently but lack granular detail. We developed a hybrid model that aims to balance the granularity of ABMs with the computational efficiency of compartmental models, offering a more nuanced understanding of disease spread in diverse scenarios, including large populations. This model spatially couples discrete and continuous populations by integrating an ordinary differential equation model with a spatially explicit ABM. Our key objectives were to systematically assess the consistency of disease dynamics and the computational efficiency across various configurations. For this, we evaluated two experimental scenarios and varied the influence of each sub-model via spatial distribution. In the first, the ABM component modeled a homogeneous population; in the second, it simulated a heterogeneous population with landscape-driven movement. Results show that the hybrid model can significantly reduce computational costs but is sensitive to between-model differences, highlighting the importance of model equivalence in hybrid approaches. The code is available at: git.zib.de/ibostanc/hybrid_abm_ode
Ovi Sofia, Muna Amalia, Herryanto Thomassawa
et al.
Introduction. The imbalance of the immune response is an important factor contributing to the incidence of ocular toxoplasmosis (OT). Regulatory T cells (Treg) play a key role in maintaining the balance between Th1 and Th17 immune responses, while interleukin-27 (IL-27) levels are related to the differentiation of Th17 cells. This study analyzes the differences in the number of Treg cells and the level of IL-27 between OT patients and seropositive individuals without ocular lesions and its correlation with retinal lesion size. Methods. This analytic observational study, conducted for 8 months, involved 11 OT patients and 10 seropositive individuals without ocular lesions. All subjects underwent a comprehensive ophthalmological examination. Retinal lesions were documented by fundus photographs and the size was measured using Digimizer 4.2.2.0 software. Isolation of peripheral blood mononuclear cells (PBMC) was performed to measure the number of Treg cells using flow cytometry and interleukin-27 levels were assessed using the Sandwich enzyme-linked immunosorbent assay (ELISA) technique. Data were analyzed with SPSS. Result. The number of Treg cells in the OT group (47.16 ± 15.66%) was lower than in the seropositive group without the ocular lesions (62.86 ± 17.08%) (p=0.029). The serum IL-27 levels in the OT group were not significantly different from the seropositive group without the ocular lesions (p=0.360). The number of Treg cells was significantly related to retinal lesion size (p=0.043), with a correlation coefficient of −0.648, indicating a strong and inverse correlation. There was no significant correlation between serum IL-27 levels and retinal lesion size (p=0.556). Conclusion. Ocular toxoplasmosis patients have a low number of Treg cells that are inversely related to the retinal lesion size. The size of the retinal lesion increases as the number of Treg cells decreases.
The problem of evolutionary complexification of life is considered one of the fundamental aspects in contemporary evolutionary theory. Parasitism is ubiquitous, inevitable, and arises as soon as the first replicators appear, even during the prebiotic stages of evolution. Both in theoretical approaches (computer modeling and analysis) and in real experiments (replication of biological macromolecules), parasitic processes emerge almost immediately. An effective way to avoid the elimination of the host-parasite system is through compartmentalization. In both theory and experiments, the pressure of parasitism leads to the complexification of the host-parasite system into a network of cooperative replicators and their parasites. Parasites have the ability to create niches for new replicators. The co-evolutionary arms race between defense systems and counter-defense mechanisms among parasites and hosts can progress for a considerable duration, involving multiple stages, if not indefinitely.
We study a planner's provision of information to manage workplace occupancy when strategic workers (agents) face risk of infectious disease transmission. The planner implements an information mechanism to signal information about the underlying risk of infection at the workplace. Agents update their belief over the risk parameter using this information and choose to work in-person or remotely. We address the design of the optimal signaling mechanism that best aligns the workplace occupancy with the planner's preference (i.e., maintaining safe capacity limits and operational efficiency at workplace). For various forms of planner preferences, we show numerical and analytical proof that interval-based information mechanisms are optimal. These mechanisms partition the continuous domain of the risk parameter into disjoint intervals and provision information based on interval-specific probability distributions over a finite set of signals. When the planner seeks to achieve an occupancy that lies in one of finitely many pre-specified ranges independent of the underlying risk, we provide an optimal mechanism that uses at most two intervals. On the other hand, when the preference on the occupancy is risk-dependent, we show that an approximately optimal interval-based mechanism can be computed efficiently. We bound the approximation loss for preferences that are expressed through a Lipschitz continuous function of both occupancy and risk parameter. We provide examples that demonstrate the improvement of proposed signaling mechanisms relative to the common benchmarks in information provision. Our findings suggest that information provision over the risk of disease transmission is an effective intervention for maintaining desirable occupancy levels at the workplace.
Mingchi Xu, Dirk Douwes-Schultz, Alexandra M. Schmidt
In epidemiological studies, zero-inflated and hurdle models are commonly used to handle excess zeros in reported infectious disease cases. However, they can not model the persistence (changing from presence to presence) and reemergence (changing from absence to presence) of a disease separately. Covariates can sometimes have different effects on the reemergence and persistence of a disease. Recently, a zero-inflated Markov switching negative binomial model was proposed to accommodate this issue. We introduce a Markov switching negative binomial hurdle model as a competitor of that approach, as hurdle models are often also used as alternatives to zero-inflated models for accommodating excess zeroes. We begin the comparison by inspecting the underlying assumptions made by both models. Hurdle models assume perfect detection of the disease cases while zero-inflated models implicitly assume the case counts can be under-reported, thus we investigate when a negative binomial distribution can approximate the true distribution of reported counts. A comparison of the fit of the two types of Markov switching models is undertaken on chikungunya cases across the neighborhoods of Rio de Janeiro. We find that, among the fitted models, the Markov switching negative binomial zero-inflated model produces the best predictions and both Markov switching models produce remarkably better predictions than more traditional negative binomial hurdle and zero-inflated models.
Michael M. Wagner, William Hogan, John Levander
et al.
Objective: To create a commons for infectious disease (ID) epidemiology in which epidemiologists, public health officers, data producers, and software developers can not only share data and software, but receive assistance in improving their interoperability. Materials and Methods: We represented 586 datasets, 54 software, and 24 data formats in OWL 2 and then used logical queries to infer potentially interoperable combinations of software and datasets, as well as statistics about the FAIRness of the collection. We represented the objects in DATS 2.2 and a software metadata schema of our own design. We used these representations as the basis for the Content, Search, FAIR-o-meter, and Workflow pages that constitute the MIDAS Digital Commons. Results: Interoperability was limited by lack of standardization of input and output formats of software. When formats existed, they were human-readable specifications (22/24; 92%); only 3 formats (13%) had machine-readable specifications. Nevertheless, logical search of a triple store based on named data formats was able to identify scores of potentially interoperable combinations of software and datasets. Discussion: We improved the findability and availability of a sample of software and datasets and developed metrics for assessing interoperability. The barriers to interoperability included poor documentation of software input/output formats and little attention to standardization of most types of data in this field. Conclusion: Centralizing and formalizing the representation of digital objects within a commons promotes FAIRness, enables its measurement over time and the identification of potentially interoperable combinations of data and software.
Objective: To determine the prevalence of internet addiction and its associated factors among undergraduate students attending medical universities in Myanmar.
Methods: Internet addiction was assessed using Young’s internet addiction test. Multiple logistic regression analysis was applied to determine the factors associated with internet addiction. Altogether 950 students from all medical universities were included in the study voluntarily.
Result: The prevalence of internet addiction in the study population was 72.2% (95%CI: 69.3%, 75.0%). According to the results of multiple logistic regression analysis, age, percentage of pocket money spent for internet, time spent per day using the internet, peer pressure, health-related behaviours (irregular meals, sleep disturbances, and missing social gatherings), and academic performance (postponement of the study and inability to concentrate on studying) were significant predictors of internet addiction.
Conclusions: There is a high prevalence of internet addiction among Myanmar undergraduate medical students. Appropriate interventions, including promotion and strengthening of active and healthy lifestyles among students, should be implemented to prevent internet addiction and its adverse outcomes.
Biological sex is an important risk factor for the occurrence and severity of infectious and parasitic diseases. Although various studies and reviews have described sex differences in infectious diseases of humans, wildlife and laboratory animals, there has been little focus on biological sex as a risk factor for infectious and parasitic diseases of domestic animals. We aim to identify and synthesise evidence in dogs for the hypothesis that biological sex and gonadectomy status are determinants of occurrence and severity of disease across taxa of pathogens. This systematic review follows the Preferred Reporting Items for Systematic reviews and Meta-analyses (PRISMA) guidelines. We will search Web of Science, Scopus and PubMed for peer-reviewed studies published in English from database inception through 2021. All study designs for infectious and parasitic diseases of dogs will be included. This review will include the outcomes prevalence or incidence of infection or disease; and severity of disease as measured by case-fatality, time to death or recovery, hospitalisation time, pathogen burden (e.g. viral load or parasitaemia) or relevant clinicopathological parameters. Two reviewers will jointly assess the first 500 records from all three databases. Subsequently, one reviewer will screen the remaining records, and then the second reviewer will verify all records excluded by the first reviewer. Full-texts of all included records will be retrieved and assessed for eligibility by the first review author, and then the second author will review those records excluded by the first author. The risk of bias in individual studies will be assessed using the Risk of Bias Assessment tool for Nonrandomized Studies. We will synthesise the information from the studies and present this as a narrative in the text. The findings will be presented by outcome type and also grouped by pathogen type. Evidence on sex-specific effects will expand our understanding of infectious disease pathogenesis and underlying mechanisms, and this may be of importance in implementation of disease control interventions.
The emergence of infectious disease COVID-19 has challenged and changed the world in an unprecedented manner. The integration of wireless networks with edge computing (namely wireless edge networks) brings opportunities to address this crisis. In this paper, we aim to investigate the prediction of the infectious probability and propose precautionary measures against COVID-19 with the assistance of wireless edge networks. Due to the availability of the recorded detention time and the density of individuals within a wireless edge network, we propose a stochastic geometry-based method to analyze the infectious probability of individuals. The proposed method can well keep the privacy of individuals in the system since it does not require to know the location or trajectory of each individual. Moreover, we also consider three types of mobility models and the static model of individuals. Numerical results show that analytical results well match with simulation results, thereby validating the accuracy of the proposed model. Moreover, numerical results also offer many insightful implications. Thereafter, we also offer a number of countermeasures against the spread of COVID-19 based on wireless edge networks. This study lays the foundation toward predicting the infectious risk in realistic environment and points out directions in mitigating the spread of infectious diseases with the aid of wireless edge networks.
Mathematical modelling of epidemiological and coevolutionary dynamics is widely being used to improve disease management strategies of infectious diseases. Many diseases present some form of intra-host quiescent stage, also known as covert infection, while others exhibit dormant stages in the environment. As quiescent/dormant stages can be resistant to drug, antibiotics, fungicide treatments, it is of practical relevance to study the influence of these two life-history traits on the coevolutionary dynamics. We develop first a deterministic coevolutionary model with two parasite types infecting one host type and study analytically the stability of the dynamical system. We specifically derive a stability condition for a five-by-five system of equations with quiescence. Second, we develop a stochastic version of the model to study the influence of quiescence on stochasticity of the system dynamics. We compute the steady state distribution of the parasite types which follows a multivariate normal distribution. Furthermore, we obtain numerical solutions for the covariance matrix of the system under symmetric and asymmetric quiescence rates between parasite types. When parasite strains are identical, quiescence increases the variance of the number of infected individuals at high transmission rate and vice versa when the transmission rate is low. However, when there is competition between parasite strains with different quiescent rates, quiescence generates a moving average behaviour which dampen off stochasticity and decreases the variance of the number of infected hosts. The strain with the highest rate of entering quiescence determines the strength of the moving average and the magnitude of reduction of stochasticity. Thus, it is worth investigating simple models of multi-strain parasite under quiescence/dormancy to improve disease management strategies.
M. J. Lydeamore, B. G. Mitchell, T. Bucknall
et al.
Abstract Background Healthcare associated infections are of significant burden in Australia and globally. Previous estimates in Australia have relied on single-site studies, or combinations thereof, which have suggested the burden of these infections is high in Australia. Here, we estimate the burden of five healthcare associated infections (HAIs) in Australian public hospitals using a standard international framework, and compare these estimates to those observed in Europe. Methods We used data from an Australian point prevalence survey to estimate the burden of HAIs amongst adults in Australian public hospitals using an incidence-based approach, introduced by the ECDC Burden of Communicable Diseases in Europe. Results We estimate that 170,574 HAIs occur in adults admitted to public hospitals in Australia annually, resulting in 7583 deaths. Hospital acquired pneumonia is the most frequent HAI, followed by surgical site infections, and urinary tract infections. We find that blood stream infections contribute a small percentage of HAIs, but contribute the highest number of deaths (3207), more than twice that of the second largest, while pneumonia has the higher impact on years lived with disability. Conclusion This study is the first time the national burden of HAIs has been estimated for Australia from point prevalence data collected using validated surveillance definitions. Per-capita, estimates are similar to that observed in Europe, but with significantly higher occurrences of bloodstream infections and healthcare-associated pneumonia, primarily amongst women. Overall, the estimated burden is high and highlights the need for continued investment in HAI prevention.
Àlex Giménez-Romero, Amalia Grau, Iris E. Hendriks
et al.
The state of the art of epidemic modelling in terrestrial ecosystems is the compartmental SIR model and its extensions from the now classical work of Kermack-Mackendrick. In contrast, epidemic modelling of marine ecosystems is a bit behind, and compartmental models have been introduced only recently. One of the reasons is that many epidemic processes in terrestrial ecosystems can be described through a contact process, while modelling marine epidemics is more subtle in many cases. Here we present a model describing disease outbreaks caused by parasites in bivalve populations. The SIRP model is a multicompartmental model with four compartments, three of which describe the different states of the host, susceptible (i.e. healthy), S, infected, I, and removed (dead), R, and one compartment for the parasite in the marine medium, P, written as a 4-dimensional dynamical system. Even if this is the simplest model one can write to describe this system, it is still too complicated for both direct analytical manipulation and direct comparison with experimental observations, as it depends on four parameters to be fitted. We show that it is possible to simplify the model, including a reduction to the standard SIR model if the parameters fulfil certain conditions. The model is validated with available data for the recent Mass Mortality Event of the noble pen shell Pinna nobilis, a disease caused by the parasite Haplosporidium pinnae, showing that the reduced SIR model is able to fit the data. So, we show that a model in which the species that suffers the epidemics (host) cannot move, and contagion occurs through parasites, can be reduced to the standard SIR model that represents epidemic transmission between mobile hosts. The fit indicates that the assumptions made to simplify the model are reasonable in practice, although it leads to an indeterminacy in three of the original parameters.