Introduction: Epidemiology studies the distribution and determinants of health events in populations to control and prevent diseases. The integration of artificial intelligence (AI) has recently transformed epidemiology by enabling analysis of complex, large-scale data to improve disease surveillance, prediction, and decision-making. Aim: To summarize recent advances in AI applications within epidemiology. Materials and Methods: A structured search of major databases identified English-language studies from 2018 to 2025. Relevant articles on AI techniques for modeling, prediction, outbreak detection, and integration with traditional methods were included. \ Results and Discussion: AI’s role in epidemiology has evolved from early machine learning to advanced deep learning and natural language processing, enhancing outbreak tracking, disease modeling, geospatial visualization, diagnosis, and public sentiment analysis. Integration of AI with mechanistic models has improved forecasting and intervention assessments by capturing complex transmission dynamics and adapting to real-time data. AI-driven tools outperform traditional methods in predictive accuracy, enabling earlier detection of diseases. AI also processes large, heterogeneous datasets, uncovering non-linear relationships and supporting causal inference. Challenges remain, including data bias, privacy concerns, and the opacity of “black box” models. Addressing these requires ethical frameworks, transparency, and interdisciplinary collaboration. The expanding AI epidemiology market, driven by globalization, climate change, and big data, offers opportunities for improved public health responsiveness. Future research should focus on standardizing validation, integrating biological and social factors, and ensuring transparency. Conclusion: AI has transformative potential for epidemiology, but responsible use depends on overcoming ethical, technical, and structural challenges through collaborative governance to promote health equity and public trust.
Infectious diseases remain one of the leading causes of morbidity in pregnant women and newborns. Maternal vaccination has become an important public health strategy to prevent maternal, fetal, and neonatal infections. This approach takes advantage of a natural biological phenomenon where antibodies are transferred from mother to fetus transplacentally, primarily in the second and third trimesters, and additionally through breast milk after birth, providing protection during the first few months of life before neonates can be vaccinated themselves. Currently, several safe and effective maternal vaccines are recommended universally: influenza, Covid-19, tetanus, diphtheria, acellular pertussis (Tdap), and respiratory syncytial virus (RSV), with some additional in development. Other vaccines, like the ones against hepatitis A and B, pneumococcal diseases, meningococcal diseases, Japanese encephalitis (JE), rabies, typhus, cholera, and polio (IPV), have the potential to be used in risk populations or when there is a risk of fatal outcomes, while some remain contraindicated due to theoretical risks. Despite the scientific advantages and growing evidence supporting vaccination during pregnancy, significant gaps exist in our understanding of their efficacy and safety. Additionally, public acceptance of maternal vaccination has historically been low, presenting another challenge to implementation. There are still gaps in the understanding of maternal vaccination, leading to hesitancy, and effort should be made to fix them in order to achieve better health outcomes for mothers and their children.
Dengue is a vector borne infectious disease. The disease is transmitted byAedes mosquitoes. In the present work, SEIR - SEI compartmental epidemiological model is used to describe dengue disease transmission dynamics. The human population is classified into four epidemiological states: Susceptible, Exposed, Infectious and Recovered humans. The mosquito population is divided into three epidemiological states: Susceptible, Exposed and Infectious mosquitoes. Associated basic reproduction number which determines whether the disease will persist or die out, is calculated by Next Generation method. Stability of the equilibrium points of the model is analyzed in terms of basic reproduction number. Sensitivity analysis is carried out to study importance of modelparameters.
Ana Paula Martins, María C. Vega-Hernández, Francisca Ribeiro Soares
et al.
The present study examines the factor structure of a Portuguese version of the Perceived Vulnerability to Disease Scale (PVD), designed to assess individual differences in chronic concerns about transmission of infectious diseases. Method: Data from a Portuguese convenience sample (n=1203), collected during the first Covid-19 pandemic lockdown. Results: the scale revealed, through an exploratory factor analysis (EFA) and a confirmatory factor analysis (CFA), a slight superiority of a three-factor model over the existing two-factor models of the 15-item original PVD and of the 10-item PVD established with another Portuguese sample (Ferreira et al., 2022). Conclusions: This higher level of differentiation in terms of a perceived resistance to infectious diseases could be explained by the pandemic context which may have differentiated the responses regarding the perception of Resistance. On the other hand, this new factor increases the comprehensive and evaluative dimension and implications of the construct assessed by PVD.
The Tallis-Leyton model is a simple model of parasite acquisition where there is no interaction between the host and the acquired parasites. We examine the effect of model parameters on the distribution of the host's parasite burden in the sense of the Lorenz order. This fits with an alternate view of parasite aggregation that has become widely used in empirical studies but is rarely used in the analysis of mathematical models of parasite acquisition.
Abstract Background Numerous studies have shown that Schistosoma japonicum infection correlates with an increased risk of liver hepatocellular carcinoma (LIHC). However, data regarding the role of this infection in LIHC oncogenesis are scarce. This study aimed to investigate the potential mechanisms of hepatocarcinogenesis associated with Schistosoma japonicum infection. Methods By examining chronic liver disease as a mediator, we identified the genes contributing to Schistosoma japonicum infection and LIHC. We selected 15 key differentially expressed genes (DEGs) using weighted gene co-expression network analysis (WGCNA) and random survival forest models. Consensus clustering revealed two subgroups with distinct prognoses. Least Absolute Shrinkage and Selection Operator (LASSO) and Cox regression identified six prognostic DEGs, forming an Schistosoma japonicum infection-associated signature for strong prognosis prediction. This signature, which is an independent LIHC risk factor, was significantly correlated with clinical variables. Four DEGs, including BMI1, were selected based on their protein expression levels in cancerous and normal tissues. We confirmed BMI1's role in LIHC using Schistosoma japonicum-infected mouse models and molecular experiments. Results We identified a series of DEGs that mediate schistosomiasis, the parasitic disease caused by Schistosoma japonicum infection, and hepatocarcinogenesis, and constructed a suitable prognostic model. We analyzed the mechanisms by which these DEGs regulate disease and present the differences in prognosis between the different genotypes. Finally, we verified our findings using molecular biology experiments. Conclusion Bioinformatics and molecular biology analyses confirmed a relationship between schistosomiasis and liver hepatocellular cancer. Furthermore, we validated the role of a potential oncoprotein factor that may be associated with infection and carcinogenesis. These findings enhance our understanding of Schistosoma japonicum infection's role in LIHC carcinogenesis.
Neoplasms. Tumors. Oncology. Including cancer and carcinogens, Infectious and parasitic diseases
Aqeela Moosa, Ebrahim Variava, Alistair D. Calver
et al.
Background: Liver disease is the leading cause of non-AIDS-related mortality in people living with HIV (PLWH). Steatotic liver disease (SLD) is increasingly recognised as an important aetiological factor in liver dysfunction in PLWH.
Objectives: This study aimed to determine the post-mortem prevalence and severity of SLD and determine HIV- and non-HIV-related risk factors associated with it.
Method: We conducted a retrospective cross-sectional study in which liver histology from 59 deceased people who were infected with HIV was assessed for steatosis, and findings correlated with clinical, epidemiological, and biochemical data.
Results: Decedents were predominantly men (33/59); 63% (37/59) were virologically supressed. Median CD4+ T-cell count was 139 cells/µL (interquartile range [IQR]: 47–344). Steatosis was present in 39% (23/59) of decedents: 74% mild, 9% moderate, and 17% severe steatosis. There were no cases of steatohepatitis, and one case with mild fibrosis. Factors associated with SLD were: CD4 T-lymphocyte count 200 cells/µL (odds ratio [OR]: 3.69; 95% confidence interval [CI]: 1.19–11.44), female sex (OR: 8.5; 95% CI: 2.57–28.17), hypertension (OR: 6.5; 95% CI: 2.05–21.00), and being normal or overweight (OR: 6.75; 95% CI: 1.12–40.56). Virological suppression and duration of antiretroviral drug use were not associated with steatosis.
Conclusion: We found a high proportion of SLD with heterogeneous causes in deceased people who were infected with HIV, exceeding previously reported prevalences from elsewhere in Africa. A preserved CD4 count and being female conferred the highest risk for steatosis, underscoring the need for screening in this subgroup and further research to delineate risks in a Southern African population.
Public aspects of medicine, Infectious and parasitic diseases
Abstract Background Brucellosis is a severe zoonotic disease that is often overlooked, particularly in impoverished countries. Timely identification of focal complications in brucellosis is crucial for improving treatment outcomes. However, there is currently a lack of established indicators or biomarkers for diagnosing these complications. Therefore, this study aimed to investigate potential warning signs of focal complications in human brucellosis, with the goal of providing practical parameters for clinicians to aid in the diagnosis and management of patients. Methods A multi-center cross-sectional study was conducted in China from December 2019 to August 2021. The study aimed to investigate the clinical characteristics and complications of patients with brucellosis using a questionnaire survey and medical record system. The presence of warning signs for complications was assessed using univariate and multivariate logistic regression models. Receiver operating characteristic (ROC) curves and the area under the curve (AUC) were used for variable screening and model evaluation. Results A total of 880 participants diagnosed with human brucellosis were enrolled. The median age of the patients was 50 years [interquartile range (IQR): 41.5–58.0], and 54.8% had complications. The most common organ system affected by complications was the osteoarticular system (43.1%), with peripheral arthritis (30.0%), spondylitis (16.6%), paravertebral abscess (5.0%), and sacroiliitis (2.7%) being the most prevalent. Complications in other organ systems included the genitourinary system (4.7%), respiratory system (4.7%), and hematologic system (4.6%). Several factors were found to be associated with focal brucellosis. These factors included a long delay in diagnosis [odds ratio (OR) = 3.963, 95% confidence interval (CI) 1.906–8.238 for > 90 days], the presence of underlying disease (OR = 1.675, 95% CI 1.176–2.384), arthralgia (OR = 3.197, 95% CI 1.986–5.148), eye bulging pain (OR = 3.482, 95% CI 1.349–8.988), C-reactive protein (CRP) > 10 mg/L (OR = 1.910, 95% CI 1.310–2.784) and erythrocyte sedimentation rate (ESR) elevation (OR = 1.663, 95% CI 1.145–2.415). The optimal cutoff value in ROC analysis was > 5.4 mg/L for CRP (sensitivity 73.4% and specificity 51.9%) and > 25 mm/h for ESR (sensitivity 47.9% and specificity 71.1%). Conclusions More than 50% of patients with brucellosis experienced complications. Factors such as diagnostic delay, underlying disease, arthralgia, eye pain, and elevated levels of CRP and ESR were identified as significant markers for the development of complications. Therefore, patients presenting with these conditions should be closely monitored for potential complications, regardless of their culture results and standard tube agglutination test titers.
Infectious and parasitic diseases, Public aspects of medicine
Zimbabwe, located in Southern Africa, faces a significant public health challenge due to schistosomiasis. We investigated this issue with emphasis on risk prediction of schistosomiasis for the entire population. To this end, we reviewed available data on schistosomiasis in Zimbabwe from a literature search covering the 1980-2022 period considering the potential impact of 26 environmental and socioeconomic variables obtained from public sources. We studied the population requiring praziquantel with regard to whether or not mass drug administration (MDA) had been regularly applied. Three machine-learning algorithms were tested for their ability to predict the prevalence of schistosomiasis in Zimbabwe based on the mean absolute error (MAE), the root mean squared error (RMSE) and the coefficient of determination (R2). The findings revealed different roles of the 26 factors with respect to transmission and there were particular variations between Schistosoma haematobium and S. mansoni infections. We found that the top-five correlation factors, such as the past (rather than current) time, unsettled MDA implementation, constrained economy, high rainfall during the warmest season, and high annual precipitation were closely associated with higher S. haematobium prevalence, while lower elevation, high rainfall during the warmest season, steeper slope, past (rather than current) time, and higher minimum temperature in the coldest month were rather related to higher S. mansoni prevalence. The random forest (RF) algorithm was considered as the formal best model construction method, with MAE = 0.108; RMSE = 0.143; and R2 = 0.517 for S. haematobium, and with the corresponding figures for S. mansoni being 0.053; 0.082; and 0.458. Based on this optimal model, the current total schistosomiasis prevalence in Zimbabwe under MDA implementation was 19.8%, with that of S. haematobium at 13.8% and that of S. mansoni at 7.1%, requiring annual MDA based on a population of 3,003,928. Without MDA, the current total schistosomiasis prevalence would be 23.2%, that of S. haematobium 17.1% and that of S. mansoni prevalence at 7.4%, requiring annual MDA based on a population of 3,521,466. The study reveals that MDA alone is insufficient for schistosomiasis elimination, especially that due to S. mansoni. This study predicts a moderate prevalence of schistosomiasis in Zimbabwe, with its elimination requiring comprehensive control measures beyond the currently used strategies, including health education, snail control, population surveillance and environmental management.
Various lipids and lipid metabolites are bound to and modify the proteins in eukaryotic cells, which are known as ‘protein lipidation’. There are four major types of the protein lipidation, i.e. myristoylation, palmitoylation, prenylation, and glycosylphosphatidylinositol anchor. N-myristoylation refers to the attachment of 14-carbon fatty acid myristates to the N-terminal glycine of proteins by N-myristoyltransferases (NMT) and affects their physiology such as plasma targeting, subcellular tracking and localization, thereby influencing the function of proteins. With more novel pathogenic N-myristoylated proteins are identified, the N-myristoylation will attract great attentions in various human diseases including infectious diseases, parasitic diseases, and cancers. In this review, we summarize the current understanding of N-myristoylation in physiological processes and discuss the hitherto implication of crosstalk between N-myristoylation and other protein modification. Furthermore, we mention several well-studied NMT inhibitors mainly in infectious diseases and cancers and generalize the relation of NMT and cancer progression by browsing the clinic database. This review also aims to highlight the further investigation into the dynamic crosstalk of N-myristoylation in physiological processes as well as the potential application of protein N-myristoylation in translational medicine.
Fabian Jan Schwarzendahl, Jens Grauer, Benno Liebchen
et al.
After more than 6 million deaths worldwide, the ongoing vaccination to conquer the COVID-19 disease is now competing with the emergence of increasingly contagious mutations, repeatedly supplanting earlier strains. Following the near-absence of historical examples of the long-time evolution of infectious diseases under similar circumstances, models are crucial to exemplify possible scenarios. Accordingly, in the present work we systematically generalize the popular susceptible-infected-recovered model to account for mutations leading to repeatedly occurring new strains, which we coarse grain based on tools from statistical mechanics to derive a model predicting the most likely outcomes. The model predicts that mutations can induce a super-exponential growth of infection numbers at early times, which self-amplify to giant infection waves which are caused by a positive feedback loop between infection numbers and mutations and lead to a simultaneous infection of the majority of the population. At later stage -- if vaccination progresses too slowly -- mutations can interrupt an ongoing decrease of infection numbers and can cause infection revivals which occur as single waves or even as whole wave trains featuring alternative periods of decreasing and increasing infection numbers. This panorama of possible mutation-induced scenarios should be tested in more detailed models to explore their concrete significance for specific infectious diseases. Further, our results might be useful for discussions regarding the importance of a release of vaccine-patents to reduce the risk of mutation-induced infection revivals but also to coordinate the release of measures following a downwards trend of infection numbers.
Giovanna Meza, Fátima Galián, Claudia Jaimes-Bernal
et al.
Individuals lacking interferon lambda 4 (IFNL4) protein due to a common null mutation (rs368234815) in the IFNL4 gene display higher resistance against several infections. The influence of IFNL4 on HIV-1 infection is still under discussion and conflicting results have been reported. This study intended to corroborate or refute the association of the null allele of IFNL4 and HIV-1 predisposition in a cohort of men who have sex with men (MSM). IFNL4 null genotype was assessed on 619 HIV-1-seronegative MSM who were followed for 36 months during a trial of a prophylactic vaccine against HIV-1. Of those, 257 individuals seroconverted during this period. A logistic regression model was constructed including demographic and IFNL4 genotype. In addition, a meta-analysis using data from the current study and other European populations was conducted. The null IFNL4 genotypes were correlated with lower HIV-1 seroconversion (Adjusted OR = 0.4 [95%CI: 0.2–0.8], P = 0.008) and longer time to seroconversion (889 vs. 938 days, P= 0.01). These results were validated by a meta-analysis incorporating data from other European populations and the result yielded a significant association of the IFNL4 null genotype under a dominant model with a lower probability of HIV-1 infection (OR=0.4 [95% CI: 0.3-0.6]; P= 1.3 x 10E-5).
Purpose: Ecological and laboratory studies suggest face masks are an effective non-pharmaceutical intervention for reducing spread of SARS-CoV-2. These studies cannot measure individual risk reduction or account for individual behavioral and demographic confounders. Here we present a novel longitudinal assessment of the protective role of masks in a national cohort of individuals enrolled in a syndromic surveillance tool prior to the first case of COVID-19 in the United States. Methods & Materials: The study population consisted of a subset of participants (N=4,723 adults) enrolled in Flu Near You (FNY), a web-based longitudinal syndromic surveillance platform. Weekly self-reports of respiratory syndromes were used to assess the onset of COVID-like illness (CLI) symptoms from January to June 2020. An annual retrospective questionnaire submitted by this subset of FNY participants assessed precautionary behaviors (masking, distancing, etc.) and demographic information. We used a previously validated exposure variable (self-reported likelihood to wear masks while visiting family and friends and while grocery shopping) to measure mask wearing. A Cox proportional hazards model was used to assess the effect of mask wearing on CLI while controlling for age, gender, precautionary behavior (social distancing contacts, adoption date), county population density and time-varying county COVID-19 burden. Results: There were 1,293 reports of respiratory symptoms over the study period. Individuals characterized as most likely to wear masks were 45% [24%-61%] less likely to report symptoms of COVID-like illness compared to individuals characterized as least likely to wear masks. Mask-wearing also demonstrated a protective effect for those characterized as somewhat likely to wear masks (HR: 0.60, 95% CI: 0.42-0.84, p=0.003) and those who were likely to wear masks in only one of the two circumstances (HR. 0.59, 95% CI: 0.42-0.83, p=0.002), compared to respondents least likely to wear masks. Sensitivity analyses with alternative broad and narrow CLI definitions produced a similar magnitude and protective effect. Conclusion: Face masks were effective as a non-pharmaceutical intervention at preventing respiratory illness in the FNY population. The individual risk reduction was consistent with previous ecological measures of the protective effect of face masks, as well as robust to adjustment for behavioral, demographic, and environmental confounders.
Background. Globally, over 600 million school children are living with intestinal parasites. The prevalence of intestinal parasitic infections (IPIs) among school children in Ethiopia and the Amhara region is 52% and 51%, respectively. The present study aimed to determine the prevalence and associated risk factors of IPIs among primary school children in Dera district, Northwest Ethiopia. Methods. A school-based cross-sectional study was conducted from December 2019 to February 2020. The study used a structured pretested questionnaire and stool tests to obtain epidemiological and disease data. Data were analyzed using appropriate univariate and multivariable logistic regression methods by statistical package for social science (SPSS) version 25.0. Results. Of the 382 students who were examined for IPIs, 238 (62.3%) (61.8% males, 62.8% females) were positive for one or more IPIs. One hundred thirty-six (35.6%), 98 (25.7%), and 4 (1.05%) were single, double, and triple infections, respectively. Out of the nine species of IPIs detected, Entamoeba sp. was the predominant species (29.6%) followed by hookworm (21.7%), Schistosoma mansoni (11.3%), Taenia sp. (9.2%), Giardia lamblia (5.2%), and Ascaris lumbricoides, Hymenolepis nana, and Enterobius vermicularis (4.2%) each, and Trichuris trichiura (0.5%). Family size greater than five (AOR = 1.8; 95% CI: 1.004, 3.13), open field school waste disposal (AOR = 15.88; 95% CI: 1.91, 132.1), and lack of knowledge about intestinal parasitic infection (AOR = 1.8; 95% CI: 1.1, 3.2) were the independent risk factors associated with the overall prevalence of IPIs. Conclusions. The prevalence of intestinal parasitic infection was high in the Dera district. Health education, extending school-based deworming, and mass treatments are recommended.
With the rise of global migration, international trade, and global environmental challenges such as climate change, it is not surprising that the interactions between humans and other animals are shifting. Salient infectious diseases, such as malaria and HIV (which have high burdens of disease), attract sophisticated public health frameworks and funding from global/regional organisations, such as the WHO. This unfortunately detracts attention from the many emerging zoonoses that fall under the radar as neglected tropical diseases (NTDs). This review considers the available literature and the attribution of burden of disease to the most insidious NTDs and recommends which five are deserving of policy prioritisation. In line with WHO analyses of NTDs, intestinal nematode infections, leishmaniasis, schistosomiasis, and lymphatic filariasis should be prioritised, as well as the burden of disease of cryptosporidiosis, which is largely underestimated. Both monitoring and treatment/prevention control methods for cryptosporidiosis are suggested and explored.
In recent studies, it has been shown that a cooperative interaction in a co-infection spread can lead to a discontinuous transition at a decreased threshold. Here, we investigate effects of immunization with a rate proportional to the extent of the infection on phase transitions of a cooperative co-infection. We use the mean-field approximation to illustrate how measures that remove a portion of the susceptible compartment, like vaccination, with high enough rates can change discontinuous transitions in two coupled susceptible-infectious-recovered dynamics into continuous ones while increasing the threshold of transitions. First, we introduce vaccination with a fixed rate into a symmetric spread of two diseases and investigate the numerical results. Second, we set the rate of measures proportional to the size of the infectious compartment and scrutinize the dynamics. We solve the equations numerically and analytically and probe the transitions for a wide range of parameters. We also determine transition points from the analytical solutions. Third, we adopt a heterogeneous mean-field approach to include heterogeneity and asymmetry in the dynamics and see if the results corresponding to homogeneous symmetric case stand.
We introduce a model of parasite infection in a cell population, where cells can be infected, either at birth through maternal transmission, from a contact with the parasites reservoir, or because of the parasites released in the cell medium after the lyses of infected cells. Inside the cells and between infection events, the quantity of parasites evolves as a general non linear branching process. We study the long time behaviour of the infection.