Hasil untuk "Infectious and parasitic diseases"

Zacharias Komodromos, Kleanthis Malialis, Artemis Kontou et al.

Accurate forecasting of infectious disease incidence is critical for public health planning and timely intervention. While most data-driven forecasting approaches rely primarily on historical data from a single country, such data are often limited in length and variability, restricting the performance of machine learning (ML) models. In this work, we investigate a cross-country learning approach for infectious disease forecasting, in which a single model is trained on time series data from multiple countries and evaluated on a country of interest. This setting enables the model to exploit shared epidemic dynamics across countries and to benefit from an enlarged training set. We examine this approach through a case study on COVID-19 case forecasting in Cyprus, using surveillance data from European countries. We evaluate multiple ML models and analyse the impact of the lookback window length and cross-country `data augmentation' on multi-step forecasting performance. Our results show that incorporating data from other countries can lead to consistent improvements over models trained solely on national data. Although the empirical focus is on Cyprus and COVID-19, the proposed framework and findings are applicable to infectious disease forecasting more broadly, particularly in settings with limited national historical data.

Mark P. Rast, Luke I. Rast

Effective public health decisions require early reliable inference of infectious disease properties. In this paper we assess the ability to infer infectious disease attributes from population-level stochastic epidemic trajectories. In particular, we construct stochastic Kermack-McKendrick model trajectories, sample them with and without observational error, and evaluate inversions for the population mean infectiousness as a function of time since infection, the infection duration distribution, and its complementary cumulative distribution, the infection survival distribution. Based on an integro-differential equation formulation we employ a natural regression approach to fit the corresponding integral kernels and show that these disease attributes are recoverable from both multi-trajectory inversions and regularized single trajectory inversions. Moreover, we demonstrate that the infection duration distribution (or alternatively the infection survival distribution) and population mean infectiousness kernel recovered can be used to solve for the individual infectiousness profile, the infectiousness of an individual over the duration of their infection, assuming that individual infectiousness profiles are self-similar across individuals over the infection duration period. The work suggests that, aggressive monitoring of the stochastic evolution of a novel infectious disease outbreak in a single local well-mixed population can allow determination of the underlying disease attributes that characterize its spread.

Thomas J. Harris, Prescott C. Alexander, Anh B. D. Pham et al.

Social contact patterns are a key input to many infectious disease models. Contact surveys, where participants are asked to provide information on their recent close and casual contacts with others, are one of the standard methods to measure contact patterns in a population. Surveys that require detailed sociodemographic descriptions of contacts allow for the specification of fine-grained contact rates between subpopulations in models. However, perception biases affecting a surveyed person's ability to estimate sociodemographic attributes (e.g., age, race, socioeconomic status) of others could affect contact rates derived from survey data. Here, we simulate contact surveys using a synthetic contact network of New Mexico to investigate the impact of these biases on survey accuracy and infectious disease model projections. We found that perception biases affecting the estimation of another individual's age and race substantially decreased the accuracy of the derived contact patterns. Using these biased patterns in a Susceptible-Infectious-Recovered compartmental model lead to an underestimation of cumulative incidence among older people (65+ years) and individuals identifying as races other than White. Our study shows that perception biases can impact contact patterns estimated from surveys in ways that systematically underestimate disease burden in minority populations when used in transmission models.

Bili Wang, Wenqing Xiang, Zhouyue Zhu et al.

Abstract Objective To explore the influence of respiratory infections on the onset of Henoch-Schönlein Purpura (HSP) in children, along with exploring potential underlying mechanisms. Method The present study conducted a statistical analysis on renal involvement indicators in 296 children with HSP who came to the Children’s Hospital of Zhejiang University, as well as the IgA levels in 400 children with respiratory infections and 400 children with HSP. Results Compared with the control group, children with HSP exhibited a significant increase in urine red blood cell count, urine microalbuminuria, and urine protein/creatinine ratio (P < 0.001). The monthly outpatient visits for children with respiratory infections exhibited a similar pattern to those with HSP, demonstrating a heightened prevalence during the autumn and winter. The level of IgA in children with respiratory infections and HSP were significantly higher than those in the control group (P < 0.001). Conclusion HSP can give rise to complications such as renal involvement. There exists a certain correlation between respiratory infections and the occurrence of HSP, which may be attributed to the elevation of IgA induced by respiratory infections. In conclusion, children with HSP should reinforce protective measures during the peak influenza season in order to prevent respiratory infections.

Jiaqi Li, Weitao Huang, Qunhua Ke et al.

Abstract Lumpy Skin Disease (LSD), caused by the Lumpy Skin Disease Virus (LSDV), is a legally reportable disease recognized by the World Organization for Animal Health (WOAH) and has resulted in significant economic losses for the global cattle industry. Although several commercial LSDV vaccines are currently available, safer and more effective gene-deleted versions remain lacking. Therefore, screening key functional genes and developing gene-deleted live-attenuated vaccine strains hold substantial research value. In this study, we focused on ORF131, a gene whose function remains unclear. We successfully constructed an rLSDV-ΔORF131-EGFP gene deletion strain utilizing a homologous recombination, followed by purification using limiting dilution and single-cell subcloning techniques. Polymerase Chain Reaction (PCR) and Sanger sequencing validation confirmed that the deletion strain was successfully purified and free from wild-type virus contamination. Biological characterization indicated that the strain was genetically stable, with the optimal viral harvesting time in Madin-Darby Bovine Kidney (MDBK) cells being 72 h. Furthermore, RNA sequencing analysis of virus-infected cells revealed that rLSDV-ΔORF131-EGFP enhanced the immune and inflammatory responses of host cells compared to wild-type LSDV. This study not only provides a potential candidate strain for the development of an LSDV attenuated vaccine but also offers a theoretical foundation for the prevention and control strategies of LSD.

Laura Di Domenico, Eugenio Valdano, Vittoria Colizza

Traditional disease transmission models assume that the infectious period is exponentially distributed with a recovery rate fixed in time and across individuals. This assumption provides analytical and computational advantages, however it is often unrealistic. Efforts in modeling non-exponentially distributed infectious periods are either limited to special cases or lead to unsolvable models. Also, the link between empirical data (infectious period distribution) and the modeling needs (corresponding recovery rates) lacks a clear understanding. Here we introduce a mapping of an arbitrary distribution of infectious periods into a distribution of recovery rates. We show that the same infectious period distribution at the population level can be reproduced by two modeling schemes -- host-based and population-based -- depending on the individual response to the infection, and aggregated empirical data cannot easily discriminate the correct scheme. Besides being conceptually different, the two schemes also lead to different epidemic trajectories. Although sharing the same behavior close to the disease-free equilibrium, the host-based scheme deviates from the expected epidemic when reaching the endemic equilibrium of an SIS transmission model, while the population-based scheme turns out to be equivalent to assuming a homogeneous recovery rate. We show this through analytical computations and stochastic epidemic simulations on a contact network, using both generative network models and empirical contact data. It is therefore possible to reproduce heterogeneous infectious periods in network-based transmission models, however the resulting prevalence is sensitive to the modeling choice for the interpretation of the empirically collected data on infection duration. In absence of higher resolution data, studies should acknowledge such deviations in the epidemic predictions.

Anbang Du, Michael Head, Markus Brede

Interdisciplinary research fuels innovation. In this paper, we examine the interdisciplinarity of research output driven by funding. Considering 36 major infectious diseases, we model interdisciplinarity through temporal correlation networks based on funded and unfunded research from 1995-2022. Using hierarchical clustering, we identify coherent periods of time or regimes characterised by important research topics like vaccinations or the Zika outbreak. We establish that funded research is less interdisciplinary than unfunded research, but the effect has decreased markedly over time. In terms of network growth, we find a tendency of funded research to focus on readily established connections leading to compartmentalisation and conservatism. In contrast, unfunded research tends to be exploratory and bridge distant knowledge leading to knowledge integration. Our results show that interdisciplinary research on prominent infectious diseases like HIV and tuberculosis tends to have strong bridging effects facilitating global knowledge integration in the network. At the periphery of the network, we observe the emergence of vaccination-related and Zika-related knowledge clusters, both with limited systemic impact. We further show that despite the surge in publications related to COVID-19, its systematic impact on the disease network remains relatively low. Overall, this research provides a generalisable framework to examine the impact of funding in interdisciplinary knowledge creation. It can assist in priority setting, for example with horizon scanning for new and emerging threats to health, such as pandemic planning. Policymakers, funding agencies, and research institutions should consider revamping evaluation systems to reward interdisciplinary work and implement mechanisms that promote and support intelligent risk-taking.

Eva Gunn, Nikhil Sengupta, Ben Swallow

Gaussian process are a widely-used statistical tool for conducting non-parametric inference in applied sciences, with many computational packages available to fit to data and predict future observations. We study the use of the Greta software for Bayesian inference to apply Gaussian process regression to spatio-temporal data of infectious disease outbreaks and predict future disease spread. Greta builds on Tensorflow, making it comparatively easy to take advantage of the significant gain in speed offered by GPUs. In these complex spatio-temporal models, we show a reduction of up to 70\% in computational time relative to fitting the same models on CPUs. We show how the choice of covariance kernel impacts the ability to infer spread and extrapolate to unobserved spatial and temporal units. The inference pipeline is applied to weekly incidence data on tuberculosis in the East and West Midlands regions of England over a period of two years.

Ibrahim Bouzalmat, Benoîte de Saporta, Solym M. Manou-Abi

This study aims to estimate the parameters of a stochastic exposed-infected epidemiological model for the transmission dynamics of notifiable infectious diseases, based on observations related to isolated cases counts only. We use the setting of hidden multi-chain Markov models and adapt the Baum-Welch algorithm to the special structure of the multi-chain. From the estimated transition matrix, we retrieve the parameters of interest (contamination rates, incubation rate, and isolation rate) from analytical expressions of the moments and Monte Carlo simulations. The performance of this approach is investigated on synthetic data, together with an analysis of the impact of using a model with one less compartment to fit the data in order to help for model selection.

Lídia Gual-Gonzalez, Stella C. W. Self, Kia Zellars et al.

Abstract Background Spotted fever group Rickettsia (SFGR) is the largest group of Rickettsia species of clinical and veterinary importance emerging worldwide. Historically, SFGR cases were linked to Rickettsia rickettsii, the causal agent of Rocky Mountain spotted fever; however, recently discovered species Rickettsia parkeri and Rickettsia amblyommatis have been shown to cause a wide range of clinical symptoms. The role of R. amblyommatis in SFGR eco-epidemiology and the possible public health implications remain unknown. Methods This study evaluated statewide tick surveillance and land-use classification data to define the eco-epidemiological relationships between R. amblyommatis and R. parkeri among questing and feeding ticks collected across South Carolina between 2021 and 2022. Questing ticks from state parks and feeding ticks from animal shelters were evaluated for R. parkeri and R. amblyommatis using reverse transcriptase quantitative polymerase chain reaction (RT-qPCR) on pooled samples. A Bayesian multivariable logistic regression model for pool testing data was used to assess associations between R. parkeri or R. amblyommatis infection and land-use classification variables among questing ticks. The Spearman correlation was used to evaluate the relationship between the two tested pathogens. Results The infection prevalence for R. amblyommatis was 24.8% (23.4–26.3%) among questing ticks, and 39.5% (37.4–42.0%) among feeding ticks; conversely, for R. parkeri it was 19.0% (17.6–20.5%) among questing ticks and 22.4% (20.3–24.5%) among feeding ticks. A negative, refractory correlation was found between the species, with ticks significantly more likely to contain one or the other pathogen, but not both simultaneously. The Bayesian analysis revealed that R. amblyommatis infection was positively associated with deciduous, evergreen, and mixed forests, and negatively associated with hay and pasture fields, and emergent herbaceous wetlands. Rickettsia parkeri infection was positively associated with deciduous, mixed, and evergreen forests, herbaceous vegetation, cultivated cropland, woody wetlands, and emergent herbaceous wetlands, and negatively associated with hay and pasture fields. Conclusions This is the first study to evaluate the eco-epidemiological factors driving tick pathogenicity in South Carolina. The negative interactions between SFGR species suggest the possible inhibition between the two pathogens tested, which could have important public health implications. Moreover, land-use classification factors revealed environments associated with tick pathogenicity, highlighting the need for tick vector control in these areas. Graphical Abstract

Ghizal fatima, Risala H. Allami, Maitham G. Yousif

Precision medicine, tailored to individual patients based on their genetics, environment, and lifestyle, shows promise in managing complex diseases like infections. Integrating artificial intelligence (AI) into precision medicine can revolutionize disease management. This paper introduces a novel approach using AI to advance precision medicine in infectious diseases and beyond. It integrates diverse fields, analyzing patients' profiles using genomics, proteomics, microbiomics, and clinical data. AI algorithms process vast data, providing insights for precise diagnosis, treatment, and prognosis. AI-driven predictive modeling empowers healthcare providers to make personalized and effective interventions. Collaboration among experts from different domains refines AI models and ensures ethical and robust applications. Beyond infections, this AI-driven approach can benefit other complex diseases. Precision medicine powered by AI has the potential to transform healthcare into a proactive, patient-centric model. Research is needed to address privacy, regulations, and AI integration into clinical workflows. Collaboration among researchers, healthcare institutions, and policymakers is crucial in harnessing AI-driven strategies for advancing precision medicine and improving patient outcomes.

Ziwei Cui, Ming Cai, Zheng Zhu et al.

The importance of indoor human mobility in the transmission dynamics of respiratory infectious diseases has been acknowledged. Previous studies have predominantly addressed a single type of mobility behavior such as queueing and a series of behaviors under specific scenarios. However, these studies ignore the abstraction of mobility behavior in various scenes and the critical examination of how these abstracted behaviors impact disease propagation. To address these problems, this study considers people's mobility behaviors in a general scenario, abstracting them into two main categories: crowding behavior, related to the spatial aspect, and stopping behavior, related to the temporal aspect. Accordingly, this study investigates their impacts on disease spreading and the impact of individual spatio-temporal distribution resulting from these mobility behaviors on epidemic transmission. First, a point of interest (POI) method is introduced to quantify the crowding-related spatial POI factors (i.e., the number of crowdings and the distance between crowdings) and stopping-related temporal POI factors (i.e., the number of stoppings and the duration of each stopping). Besides, a personal space determined with Voronoi diagrams is used to construct the individual spatio-temporal distribution factor. Second, two indicators (i.e., the daily number of new cases and the average exposure risk of people) are applied to quantify epidemic transmission. These indicators are derived from a fundamental model which accurately predicts disease transmission between moving individuals. Third, a set of 200 indoor scenarios is constructed and simulated to help determine variable values. Concurrently, the influences and underlying mechanisms of these behavioral factors on disease transmission are examined using structural equation modeling and causal inference modeling......

Vitorino Modesto dos Santos, Lister A. Modesto dos Santos, Laura Campos-Modesto et al.

Lymphadenopathy related to vaccination has been reported as an adverse effect of mRNA-based COVID-19 vaccines. Most cases are regional lymph nodes near of injection site, with mild-moderate 18 F-fluorodeoxyglucose uptake on positron emission tomography. We report a middle-aged Brazilian man with mesenteric lymphadenitis manifested five days after the third dose of the Pfizer-BioNTech mRNA-based vaccine against COVID-19. The patient had no known risk factors and evolved with rapid clinical improvement. The imaging findings, laboratory determinations, histopathological and microbiological evaluations raised doubts about the hypothesis of an eventual adverse effect of the vaccine. The aim is to call attention to possible rare reactions of SARS-CoV-2 vaccinations.

Aminur Rahman, Angela Peace, Ramesh Kesawan et al.

The surprisingly mercurial Covid-19 pandemic has highlighted the need to not only accelerate research on infectious disease, but to also study them using novel techniques and perspectives. A major contributor to the difficulty of containing the current pandemic is due to the highly asymptomatic nature of the disease. In this investigation, we develop a modeling framework to study the spatio-temporal evolution of diseases with high rates of asymptomatic transmission, and we apply this framework to a hypothetical country with mathematically tractable geography; namely, square counties uniformly organized into a rectangle. We first derive a model for the temporal dynamics of susceptible, infected, and recovered populations, which is applied at the county level. Next we use likelihood-based parameter estimation to derive temporally varying disease transmission parameters on the state-wide level. While these two methods give us some spatial structure and show the effects of behavioral and policy changes, they miss the evolution of hot zones that have caused significant difficulties in resource allocation during the current pandemic. It is evident that the distribution of cases will not be stagnantly based on the population density, as with many other diseases, but will continuously evolve. We model this as a diffusive process where the diffusivity is spatially varying based on the population distribution, and temporally varying based on the current number of simulated asymptomatic cases. With this final addition coupled to the SIR model with temporally varying transmission parameters, we capture the evolution of "hot zones" in our hypothetical setup.

S. Jagtap, S. Harikumar, V. Vinayagamoorthy et al.

Abstract Background Women of reproductive age group have greater predilection to urinary tract infections (UTI). Various risk factors increase the prevalence in women. Emergence of multidrug resistant uropathogens make clinical management of UTI challenging. Here we assess holding of urine as risk factor of UTI in women and reasons for delayed voiding. We also investigate the relationship between frequency of UTIs and overall behavioural features, menstrual hygiene and attitude of women towards their own health issues. Methods A questionnaire based cross-sectional study was performed with 816 hostel residents with written consent. Self-reported data was statistically analysed using SPSS software. Urinalysis and urine culture were done for 50 women by random sampling to obtain the information on leading causative agents of UTI in the study population and their antimicrobial resistance profile. Results The prevalence of UTI among the participants without risk factors was found to be 27.5 (95% CI: 24.4–30.7). Attitude of women towards their own personal health issues and use of public toilets showed a correlation with prevalence of infection. Delay in urination on habitual basis was found to be associated with UTI. Uropathogens isolated by random sampling were resistant to multiple drugs that are generally used to treat UTI. Conclusions Holding urine for long time had proven to be an important risk factor and amongst different reasons of holding urine, holding due to poor sanitary condition of public toilets was the most common. Higher frequency of self-reported UTIs is related to holding of urine, behavioural features and attitude of women.

Lauren J Beesley, Dave Osthus, Sara Y Del Valle

Infectious disease forecasting is of great interest to the public health community and policymakers, since forecasts can provide insight into disease dynamics in the near future and inform interventions. Due to delays in case reporting, however, forecasting models may often underestimate the current and future disease burden. In this paper, we propose a general framework for addressing reporting delay in disease forecasting efforts with the goal of improving forecasts. We propose strategies for leveraging either historical data on case reporting or external internet-based data to estimate the amount of reporting error. We then describe several approaches for adapting general forecasting pipelines to account for under- or over-reporting of cases. We apply these methods to address reporting delay in data on dengue fever cases in Puerto Rico from 1990 to 2009 and to reports of influenza-like illness (ILI) in the United States between 2010 and 2019. Through a simulation study, we compare method performance and evaluate robustness to assumption violations. Our results show that forecasting accuracy and prediction coverage almost always increase when correction methods are implemented to address reporting delay. Some of these methods required knowledge about the reporting error or high quality external data, which may not always be available. Provided alternatives include excluding recently-reported data and performing sensitivity analysis. This work provides intuition and guidance for handling delay in disease case reporting and may serve as a useful resource to inform practical infectious disease forecasting efforts.

Ye Ye, Andrew Gu

During an infectious disease pandemic, it is critical to share electronic medical records or models (learned from these records) across regions. Applying one region's data/model to another region often have distribution shift issues that violate the assumptions of traditional machine learning techniques. Transfer learning can be a solution. To explore the potential of deep transfer learning algorithms, we applied two data-based algorithms (domain adversarial neural networks and maximum classifier discrepancy) and model-based transfer learning algorithms to infectious disease detection tasks. We further studied well-defined synthetic scenarios where the data distribution differences between two regions are known. Our experiments show that, in the context of infectious disease classification, transfer learning may be useful when (1) the source and target are similar and the target training data is insufficient and (2) the target training data does not have labels. Model-based transfer learning works well in the first situation, in which case the performance closely matched that of the data-based transfer learning models. Still, further investigation of the domain shift in real world research data to account for the drop in performance is needed.

Lucy D'Agostino McGowan, Kyra H. Grantz, Eleanor Murray

This primer describes the statistical uncertainty in mechanistic models and provides R code to quantify it. We begin with an overview of mechanistic models for infectious disease, and then describe the sources of statistical uncertainty in the context of a case study on SARS-CoV-2. We describe the statistical uncertainty as belonging to three categories: data uncertainty, stochastic uncertainty, and structural uncertainty. We demonstrate how to account for each of these via statistical uncertainty measures and sensitivity analyses broadly, as well as in a specific case study on estimating the basic reproductive number, $R_0$, for SARS-CoV-2.

Wesley Cota, David Soriano-Paños, Alex Arenas et al.

Human mobility, contact patterns, and their interplay are key aspects of our social behavior that shape the spread of infectious diseases across different regions. In the light of new evidence and data sets about these two elements, epidemic models should be refined to incorporate both the heterogeneity of human contacts and the complexity of mobility patterns. Here, we propose a theoretical framework that allows accommodating these two aspects in the form of a set of Markovian equations. We validate these equations with extensive mechanistic simulations and derive analytically the epidemic threshold. The expression of this critical value allows us to evaluate its dependence on the specific demographic distribution, the structure of mobility flows, and the heterogeneity of contact patterns, thus shedding light on the microscopic mechanisms responsible for the epidemic detriment driven by recurrent mobility patterns reported in the literature.

Maiara Medeiros Brum, Ana Teresa A. Ramos Cerqueira, Lenice do Rosário de Souza