The evidence supporting the impact of global warming on the epidemiology of infectious diseases, including fungal infections, is increasing. Fungi have a remarkable ability to adapt to heat and pollution, and to disseminate via air, water ecosystems, or wildfire smoke. Their genetic plasticity can lead to thermotolerance, the ability to find new ecological niches, and fitness gains. Natural reservoirs of the fungal biomass, which are heavily affected by global warming, may serve as the environments from where new fungal diseases originate, as illustrated by the recent emergence of Candidozyma auris and Rhodosporidiobolus fluvialis. Moreover, global warming also affects human skin/mucosal integrity and local or systemic immune responses, which could increase host susceptibility to fungal infections. This review examines the impact of global warming on the complex fungi-host interactions, which can lead to new challenges in mycology, and discusses possible mitigation strategies.

ABSTRACT Between September 2023 and June 2025, a total of 454 samples from poultry farms across Shandong Province with clinically suspected avian tumorigenic diseases were subjected to laboratory testing. Using an integrated diagnostic approach combining gross and microscopic examination with molecular detection (qPCR and conventional PCR), cases were confirmed positive only when both tumour lesions and the corresponding oncogenic pathogen were identified. Overall, 90 samples were confirmed positive, yielding a detection rate of 19.8%. Among all tested samples, Marek's disease virus (MDV) was detected in 33.9%, avian leukosis virus (ALV) in 19.9%, and reticuloendotheliosis virus (REV) in 3.9%. Notably, co-infections involving two or more viruses were frequently observed: 33 of the 90 positive cases (36.7%) exhibited mixed infections. This study represents the first comprehensive investigation in Shandong Province to systematically characterize the epidemiology of avian infectious tumorigenic diseases by integrating multi-source clinical and laboratory data, including viral genotyping. It reveals a previously underappreciated epidemiological feature – a high co-infection rate of 36.7% among confirmed cases. Compared with prior regional studies, this work is distinguished by its larger sample size (n = 454), broader pathogen screening panel, and rigorous diagnostic criteria. Additionally, it delineates the genetic profiles of circulating oncogenic virus strains and identifies key epidemiological risk drivers, such as flock age, biosecurity level, and vaccination history, thereby providing evidence-based foundations for developing integrated prevention and control strategies to mitigate economic losses in the poultry industry. RESEARCH HIGHLIGHTS The co-infection rate of infectious tumour disease in chickens is 37% in China. The incidence rate is the highest in Dezhou City.

Breaking down barriers: A funder's perspective on achieving equity and inclusivity in infectious disease modelling and epidemiology.

The immune system in Latin America and the Caribbean: Insights into diseases and diversity from local perspectives.

Infections are a major cause of morbidity and mortality, but many aspects of the epidemiology of infections are unknown or unclear because key data are lacking. We created The Danish Infection Cohort by linking Danish National Health Survey data (including self-reported behavioral, health, and other information) to data on diagnoses and medications in Danish national health registries. We included individuals ≥ 18 years of age in surveys from 2010, 2013, 2017, and 2021. We identified evidence of infection in the 365 days post-survey by using two definitions: hospital-diagnosed infections, via primary or secondary discharge diagnoses in the Danish National Patient Registry (containing all Danish hospital-based outpatient, emergency, and inpatient encounters); and community-treated infections, via systemic anti-infective prescription redemptions at community pharmacies in the Danish National Prescription Registry. Overall, among 609,224 individuals, 196,980 (32.3%) had one or more infections within 365 days post-survey: 11,850 hospital-diagnosed and 185,130 community-treated. Because more than one infection occurred in some individuals, the total number of infections was 315,689: 25,385 hospital-diagnosed and 290,304 community-treated. The most common diagnoses were respiratory infections (34.9%), and the most common anti-infectives were antibiotics (80.6%). This cohort, with nearly 200,000 individuals with infections, and more than 315,000 infections overall, includes data on smoking, weight, physical activity, diet, alcohol use, and other factors, enabling a wide range of investigations including infectious disease patterns and etiologies over time.

Background and Objectives: New Caledonia, an archipelago in the South Pacific, experienced an unprecedented conjunction of prolonged border closure during the COVID-19 pandemic (2020 to 2022) and marked influence of the El Niño/Southern Oscillation (ENSO). This context provided a unique opportunity to explore how environmental drivers, island isolation, and socio-demographic factors interact to shape infectious disease dynamics. This study aimed to assess the respective and combined effects of climatic variability, travel restrictions, and socio-demographic factors on the dynamics of four priority infectious diseases. Materials and Methods: We retrospectively analysed data from 2017 to 2023 on four infectious diseases: leptospirosis, dengue, influenza, and hepatitis A (HAV). Satellite precipitation data and the Multivariate El Niño/Southern Oscillation Index (MEI) were used. Socio-demographic and economic variables were gathered. Statistical analyses employed descriptive analysis and Generalized Additive Mixed Models to evaluate the associations between climatic events, travel restrictions, and disease circulation using the communal level as a random effect and time (daily) as a spline effect. Results: We analysed 878 cases of leptospirosis, 165 of HAV, 6607 of influenza, and 7377 dengue cases. Influenza was associated with rainfall before lockdown (Odds Ratio (OR) 0.7, Confidence interval 95%, (CI95%), (0.6–0.8)) and disappeared during lockdown but resurged post-reopening losing its meteorological association. Dengue epidemics declined, coinciding with the Wolbachia program and border closure, and were associated with lower MEI (OR 0.78, CI95% (0.6–1) during the 2017 to 2020 period. HAV cases were correlated with the MEI (OR: 1.8, CI95% (1–3.3)). Leptospirosis cases were associated with cumulative rainfall (OR 1.12 (1.1–1.2)) and lower education (OR 1.04, CI95% (1–1.1)) and decreased with water supply (OR 0.7, CI95% (0.5–0.8)). Conclusions: Our findings highlight how climatic conditions, mobility restrictions, and socio-environmental inequities differentially shape infectious disease risks in island ecosystems. These results reinforce the need for integrated One Health surveillance that jointly addresses environmental change, social vulnerability, and infectious disease prevention.

Geospatial insights and artificial intelligence in West Nile virus disease Epidemiology: The Italy case study.

Household transmission studies are important for understanding infectious disease transmission and evaluating interventions; however, they are frequently constrained by methodological challenges, including in study design and sample size determination, and in estimating parameters of interest after collecting the data. Existing tools often lack flexibility in modeling age-specific susceptibility, infectivity patterns, and the impact of interventions such as vaccination or prophylaxis. Here, we develop HHBayes, an open-source R package that provides a unified framework for simulating and analyzing household transmission data using Bayesian methods. The package enables researchers to: (1) simulate realistic household transmission dynamics with highly customizable variables; (2) incorporate viral load data (measured in viral copies/mL or cycle threshold values) to model time-varying infectiousness; (3) estimate age-dependent susceptibility and infectivity parameters using Hamiltonian Monte Carlo methods implemented in Stan; and (4) evaluate intervention effects through user-defined covariates that modify susceptibility or infectivity. We demonstrate the capabilities of the package through simulation studies showing accurate parameter recovery and applications to seasonal respiratory virus transmission, including the impact of vaccination and antiviral prophylaxis on household attack rates. HHBayes addresses a critical gap in infectious disease epidemiology by providing researchers with accessible tools for both prospective study design and retrospective data analysis. The flexibility of the package in handling complex household structures, time-varying infectiousness, and intervention effects makes it valuable for studying diverse pathogens.

Recommended Vaccines for Immunocompetent Older Adults: A Work Group Report of the AAAAI Asthma, Allergic & Immunologic Diseases in Older Adults Committee.

Infectious disease epidemiology is shaped by engrained research cultures that privilege biomedical and quantitative knowledge systems, systematically marginalizing qualitative, contextual, and locally informed approaches. These hierarchies reflect deeper inequities in who leads, who participates, and whose knowledge counts-disparities often patterned along geography, gender, language, and disciplinary background. This perspectives paper examines how funding priorities, academic training, and publishing norms sustain epistemic and structural exclusion, particularly for researchers based in the Global South. Drawing on Ghana's COVID-19 response, we show how reliance on externally developed epidemiological models mirrored broader marginalization in research authorship, agenda-setting, and decision-making. We argue that equity-focused reforms in funding, training, and publishing-grounded in epistemic and distributive justice-are necessary to transform infectious disease research culture. A more just and inclusive research culture is not only an ethical imperative but essential to the effectiveness and legitimacy of epidemic responses.

Nationwide hospital discharge databases are increasingly used in infectious disease research, yet their methodological strengths and limitations are rarely synthesised. In Spain, the Minimum Basic Data Set (Conjunto Mínimo Básico de Datos, CMBD) was implemented in 1987 and provides near-universal coverage of acute-care hospitalisations and has been widely applied in infectious disease epidemiology. However, its overall contribution and intrinsic constraints have not been comprehensively mapped. Given the breadth of infections, study designs, populations and outcome definitions in CMBD-based research, effect-size synthesis was not feasible; therefore, we conducted a scoping review with an evidence-mapping approach. We aimed to synthesise the scope, applications and methodological limitations of CMBD-based infectious disease research since its implementation. We conducted a scoping review following JBI guidance and reported according to PRISMA-ScR. PubMed, Embase, Web of Science and Scopus were searched from inception to 25 November 2024 for peer-reviewed journal articles in English or Spanish using CMBD data to investigate infectious diseases in Spain (no restrictions were applied by study design; grey literature was excluded). Screening, data charting and synthesis were completed during 2025. Four reviewers independently screened records and charted data. Studies were classified by infectious disease focus, syndromic category, study design and geographical scope. A total of 359 studies published between 1996 and 2024 were included, mostly retrospective observational analyses. Infectious diseases were the primary focus in 225 studies (62.7%), most commonly respiratory, gastrointestinal/liver and vaccine-preventable infections. Subnational analyses were concentrated in a limited number of regions. Over 80% of reported limitations reflected intrinsic CMBD features. Over three decades, the CMBD has become a cornerstone of hospital-based infectious disease research in Spain, enabling robust national analyses. However, limitations in clinical detail, microbiological confirmation and coding consistency constrain aetiological specificity and causal inference, highlighting the need for data validation and linkage with complementary sources.

Abstract Compartmental models are widely used for dynamical systems where states are discrete, such as in infectious disease epidemiology with the so‐called Susceptible‐Infectious‐Recovered (SIR) framework. For mathematical simplicity, rates of transition between compartments are generally assumed to be independent of the dwell time (or secondary timescale in survival analysis): they are either constant or dependent on the epidemiological time (or primary timescale in survival analysis) only, either directly (e.g. environmental or behavioural forcings in epidemiological models) or indirectly through dependence on other variables of the system (e.g. the force of infection in epidemiological models). In some domains of application, this memoryless assumption leads to distributions of dwelling times that are incompatible with those observed in data (e.g. infectious periods for childhood diseases), which can lead to serious problems since the model predictions are highly sensitive to the exact shape of these distributions. Here, we propose a deterministic, continuous‐variable, numerical modelling approach that allows full flexibility on the dwell‐time distributions. The accompanying denim package provides a user‐friendly interface to implement our proposed method through a dedicated language for model definition. The package is open source and available on CRAN. As more detailed data on the clinical process of infections become available, the denim package will be extremely useful for building more realistic epidemiological models that provide more accurate projections.

National and international travel drives the spread of antimicrobial resistance in high-priority pathogens, including Neisseria gonorrhoeae. Border closures and travel restrictions in response to the COVID-19 pandemic had wide-reaching impacts on infectious disease epidemiology, including the transmission and genomic diversity of N gonorrhoeae. However, less is known about N gonorrhoeae population structures in the years following the lifting of pandemic restrictions. This study analyzed N gonorrhoeae genomic data collected for routine public health surveillance in South Australia, Australia, and contextual sequences from Victoria, Australia, before and after the cessation of COVID-19 interstate and international travel restrictions. N gonorrhoeae was highly clonal during periods with restricted travel, and genomic diversity markedly increased after restrictions were removed, possibly driven by increased transmission and the introduction of new strains. Routine genomic surveillance is an important public health tool for the monitoring of N gonorrhoeae, especially the introduction and spread of antimicrobial resistant strains.

Objective: This study aimed to analyze the epidemiologic trends of common nationally notifiable infectious diseases (NID) among children at a single center in Beijing from 2016 to 2024. Methods: A cross-sectional study. Demographic and clinical data were retrospectively collected from 156 006 pediatric patients diagnosed with NID and reported to the National Health Information Disease Prevention and Control Information System at the outpatient or inpatient departments of Beijing Children's Hospital, Capital Medical University, between January 2016 and December 2024. The study period was divided into 3 groups based on the implementation status of non-pharmaceutical interventions (NPI) associated with the SARS-CoV-2 pandemic: pre-NPI implementation (2016-2019), during NPI implementation (2020-2022), and post-NPI lifting (2023-2024). Between-group comparisons were conducted using the χ² test and the Kruskal-Wallis rank-sum test. Results: Among the 156 006 pediatric cases included in the analysis, 89 202 were male and 66 804 were female. The age was 4.8 (2.7, 7.2) years. No category A infectious diseases were reported. Category B infectious diseases accounted for 12 734 cases (8.2%), while category C infectious diseases accounted for 135 187 cases (86.7%). Following the implementation of NPI, the average annual number of reported cases decreased by 67.6% (1 036/1 532) for category B infectious diseases and by 48.7% (5 613/11 520) for category C infectious diseases. At the transmission route level, diseases transmitted via the fecal-oral route decreased by 79.7% (5 225/6 556), while airborne transmission diseases decreased by 27.0% (2 096/7 753). Before the implementation of the NPI, 57 352 cases were reported, with hand-foot-mouth disease (HFMD) accounting for 22 213 cases (38.7%), influenza for 19 680 cases (34.3%), and varicella for 5 134 cases (9.0%). During the NPI implementation period, 21 036 cases were reported; the three most common diseases were influenza 13 292 cases (63.2%), HFMD 2 006 cases (9.5%), and varicella 1 815 cases (8.6%). After the lifting of NPI, 77 618 cases were reported, among which influenza 64 871 cases (83.6%), HFMD 5 004 cases (6.4%) and SARS-CoV-2 infection 2 191 cases (2.8%). After the lifting of NPI, the age at onset for HFMD, varicella, scarlet fever, other infectious diarrheal diseases, and pertussis was significantly higher than that observed before NPI implementation (all P<0.001). Conclusion: The shifting spectrum of pediatric NID and age of susceptible populations highlight the importance of the need for sustained surveillance to guide prevention strategies and adjust public health preparedness.

Infectious pancreatic necrosis virus (IPNV) can cause devastating disease in Atlantic salmon (Salmon salar). IPNV has a broad host range and may threaten other aquaculture species. Understanding interspecies transmission of IPNV is crucial for protecting the aquaculture industry. With the expansion of fish farming (in Norway), it is important to assess whether a pathogencan transmit from one fish species to another and cause disease. We investigated whether IPNV-infected Atlantic cod can shed IPNV, leading to infection in other fish important to Norwegian aquaculture: halibut, salmon and lumpfish, using the cohabitation experimental trial method. Virus shedding, transmission, fish mortality and pathology were assessed. We documented virus shedding in water and mortality in IPNV-injected Atlantic cod. No mortality was observed in the cohabitated fish species during the experimental period. We confirmed lesions consistent with IPN by histopathology and immunohistochemistry in IPNV-injected Atlantic cod and in IPNV-PCR positive cohabitant Atlantic halibut. Cohabitant Atlantic cod, Atlantic salmon, Atlantic halibut and lumpfish were also found positive for IPNV by PCR, suggesting that IPNV-infected Atlantic cod can transfer infection to other farmed fish species. These findings highlight the potential risk of pathogen spread among farmed fish species and demonstrate the importance of understanding infectious fish disease epidemiology.

Strong information delay as a driver of epidemic waves: Mathematical modeling for drug trends and epidemic bio-preparedness.

The COVID-19 pandemic has profoundly impacted global healthcare, leading to shifts in the epidemiology, diagnosis, and management of various infectious diseases. Infective endocarditis (IE), a severe and life-threatening infection of the endocardial surface of the heart, has seen a resurgence in recent years, with oral streptococci remaining a predominant causative agent. This review explores the evolving relationship between dental infections, oral health, and the risk of IE in the post-COVID-19 landscape. We examine the pathophysiological mechanisms linking dental bacteremia to endocardial colonization, emphasizing the role of endothelial damage induced by SARS-CoV-2 as a potential novel risk factor. The pandemic led to significant disruptions in routine dental care, resulting in an accumulation of untreated dental disease and a cohort of patients with advanced oral infections. Concurrently, altered immune responses and the pro-thrombotic, pro-inflammatory state associated with COVID-19 may create a more susceptible environment for bacterial adhesion and vegetation formation. This article synthesizes current evidence on these interactions, discusses updates in antibiotic prophylaxis guidelines, and highlights the critical importance of restoring access to dental care and reinforcing collaborative efforts between cardiologists, infectious disease specialists, and dentists in the post-pandemic period.

Pathogen nucleic acids data in wastewater solids from 147 treatment plants in the United States: 2024–2025

Artificial intelligence, specifically deep learning, numerical optimization, and scalable computing, can play a key and innovative role in many areas of knowledge, including epidemiology, especially in infectious diseases. This is due to its potential to enable the accurate prediction of transmission patterns and the most effective measures to control outbreaks, ultimately promoting collective well-being. The objective is to highlight the advantages of using artificial intelligence to predict the epidemiological behavior of infectious diseases during epidemic outbreaks. This is a narrative review of high-quality scientific articles. Based on the analysis of the data found, the relevant aspects were categorized into the following sections: artificial intelligence in epidemic infectious diseases; disease propagation; modeling of mechanisms; detection of emerging and re-emerging pathogens; data collection and structuring; and information management for the community during epidemics, with the aim of facilitating reading and comprehension. It is concluded that artificial intelligence is a valuable ally in the fight against infectious diseases.

BackgroundFloods and climate-related disaster events have been associated with increased risk of certain infectious diseases. Japanese encephalitis virus (JEV) appeared unexpectedly for the first time on the mainland of Australia in 2022 amid major floods and may be associated with such events. Australian-based research investigating the association between recent and major flood events and infectious disease epidemiology remains limited. Early warning surveillance systems (EWSS) may provide early indicators of infectious disease threats after extreme weather events.Disease signals and rainfall level during the dry periodData from May 1 2021 to Jul 31 2021 was used for the dry periodDisease signals and rainfall level during the pre-flood periodData from Nov 22 2021 to Feb 21 2021 was used for the pre-flood periodMethodsWe extracted retrospective disease data from the EPIWATCH open-source intelligence-based (OSINT) surveillance system before, during and after a flood period to generate disease signals that included local government area (LGA) data where possible. We analyzed the spatiotemporal distribution of disease signals and rainfall water level over the major 2022 flood period for all of NSW and QLD. We included three time periods for disease signal and rainfall data analysis in addition to the mid-flood period, including a ‘dry’ period as the control, a pre-flood and post-flood period, all covering three months.Disease signals and rainfall level during the mid-flood periodData from Feb 22 2022 to Apr 5 2022 was used for the mid-flood period, which corresponds with heavy rain and flooding eventsDisease signals and rainfall level during the post-flood periodData from Apr 6 2022 to Jul 5 2022 was used for the post-flood periodResultsA total of 347 signals were included in the analysis. Signals were lowest (30) in the pre-flood period (30), increasing during the mid (80) post-flood periods (91), with animal disease signals peaking in the mid-flood period (14). Acute gastroenteritis in humans, pythiosis in animals and JEV in both humans and animals appeared to be associated with high levels of rainfall. The spatiotemporal distribution of signals suggests that outbreaks predominantly appeared in areas with increased rainfall during the mid and post-flood periods.ConclusionOpen-source signals of disease in both animals and humans seemed to correlate with major flooding events. In countries with increased risk of major flooding events, EWSS can assist with preparedness and response to diarrheal diseases and emerging infectious diseases, such as JEV. This study is among a limited number of papers investigating EWSS data and weather events in an Australian context. The addition of EWSS as an adjunct to formal surveillance can strengthen preparedness and response for the health effects of extreme weather events.DisclosuresRaina MacIntyre, MBBS Hons 1, FRACP, FAFPHM, M App Epid, PhD, National Health and Medical Research Council (NHMRC), Australia: Grant/Research Support|Sanofi: Grant/Research Support