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This paper explores the potential of artificial intelligence, machine learning, and big data analytics in revolutionizing infection control. It addresses the challenges and innovative approaches in combating infectious diseases and antimicrobial resistance, emphasizing the critical role of interdisciplinary collaboration, ethical data practices, and integration of advanced computational tools in modern healthcare.

Antibiotic resistance poses a significant threat to global public health due to complex interactions between bacterial genetic factors and external influences such as antibiotic misuse. Artificial intelligence (AI) offers innovative strategies to address this crisis. For example, AI can analyze genomic data to detect resistance markers early on, enabling early interventions. In addition, AI-powered decision support systems can optimize antibiotic use by recommending the most effective treatments based on patient data and local resistance patterns. AI can accelerate drug discovery by predicting the efficacy of new compounds and identifying potential antibacterial agents. Although progress has been made, challenges persist, including data quality, model interpretability, and real-world implementation. A multidisciplinary approach that integrates AI with other emerging technologies, such as synthetic biology and nanomedicine, could pave the way for effective prevention and mitigation of antimicrobial resistance, preserving the efficacy of antibiotics for future generations.

This paper introduces a comprehensive dataset on West Nile virus outbreaks that have occurred in Italy from September 2012 to November 2022. We have digitized bulletins published by the Italian National Institute of Health to demonstrate the potential utilization of this data for the research community. Our aim is to establish a centralized open access repository that facilitates analysis and monitoring of the disease. We have collected and curated data on the type of infected host, along with additional information whenever available, including the type of infection, age, and geographic details at different levels of spatial aggregation. By combining our data with other sources of information such as weather data, it becomes possible to assess potential relationships between West Nile virus outbreaks and environmental factors. We strongly believe in supporting public oversight of government epidemic management, and we emphasize that open data play a crucial role in generating reliable results by enabling greater transparency.

Background Computational modelling of disease spread is crucial for understanding the dynamics of infectious outbreaks and assessing the effectiveness of control measures. In particular, network-based models for disease spreading offer detailed, granular insights into heterogeneous interactions and enable dynamic simulation of intervention strategies. Therefore, they offer valuable insights into the factors influencing disease spread, enabling public health authorities to develop effective containment strategies. Vaccination is among the most impactful interventions in controlling disease spread and has proven essential in preventing the spread of infectious diseases such as measles. However, recent trends indicate a concerning decline in the fraction of vaccinated individuals in various populations, increasing the risk of outbreaks. Methods In this study, we utilize computational simulations on graph-based models to analyze how vaccination affects the spread of infectious diseases. By representing populations as networks in which individuals (nodes) are connected by potential spread pathways (edges), we simulate different vaccination coverage scenarios and assess their impact on disease spread. Our simulations incorporate high and low vaccination coverage to reflect real-world trends and explore various conditions under which disease spread can be effectively blocked. Results The results demonstrate that adequate vaccination coverage is critical for halting outbreaks, with a marked reduction in disease spread observed as the fraction of vaccinated individuals increases. Conversely, insufficient vaccination rates lead to widespread outbreaks, underscoring the importance of maintaining high vaccination levels to achieve herd immunity and prevent resurgence. These findings highlight the vital role of vaccination as a preventative tool and emphasize the potential risks posed by declining vaccination rates. Conclusion This study provides a deeper understanding of how vaccination strategies can mitigate the spread of infectious diseases and serves as a reminder of the importance of maintaining robust immunization programs to protect public health.

On 20 September 2022, the Ministry of Health in Uganda, together with the World Health Organization—Regional Office for Africa (WHO AFRO) confirmed an outbreak of EVD due to Sudan ebolavirus in Mubende District, after one fatal case was confirmed. Real-time information are needed to provide crucial information to understand transmissibility, risk of geographical spread, routes of transmission, risk factors of infection, and provide the basis for epidemiological modelling that can inform response and containment planning to reduce the burden of disease. We made an effort to build a centralized repository of the Ebola virus cases from verified sources, providing information on dates of symptom onset, locations (aggregated to the district level), and when available, the gender and status of hospitals, reporting bed capacity and isolation unit occupancy rate according to the severity status of the patient. The proposed data repository provides researchers and policymakers timely, complete, and easy-accessible data to monitor the most recent trends of the Ebola outbreak in Ugandan districts with informative graphical outputs. This favors a rapid global response to the disease, enabling governments to prioritize and adjust their decisions quickly and effectively in response to the rapidly evolving emergency, with a solid data basis.

Neglected tropical diseases (NTDs) represent a group of chronic and debilitating infections that affect more than one billion people, predominantly in low-income communities with limited health infrastructure. This paper analyzes the factors that perpetuate the burden of NTDs, highlighting how poor health infrastructure, unfavorable socioeconomic conditions and lack of therapeutic resources exacerbate their impact. The effectiveness of current interventions, such as mass drug administration (MDA) programs and improved sanitation, in reducing disease prevalence is examined. In addition, the role of climate change, which alters transmission dynamics and expands affected territories, is discussed as an emerging challenge. The analysis suggests that integrated, multisectoral approaches, including health education and infrastructure interventions, are essential to breaking the cycle of poverty and disease. Although international programs have marked significant progress, achieving elimination targets by 2030 requires sustained commitment, innovation, and increased research capacity in endemic countries.

Background. Sudan virus (SUDV) has caused multiple outbreaks in Uganda over the past two decades, leading to significant morbidity and mortality. The recent outbreaks in 2022 and 2025 highlight the ongoing threat posed by SUDV and the challenges in its containment. This study aims to characterize the epidemiological patterns and phylogenomic evolution of SUDV outbreaks in Uganda, identifying key factors influencing transmission and disease severity. Methods. We conducted a retrospective observational study analyzing epidemiological and genomic data from SUDV outbreaks in Uganda between 2000 and 2025. Epidemiological data were collected from official sources, including the Ugandan Ministry of Health and the World Health Organization, supplemented with reports from public health organizations. Genomic sequences of SUDV were analyzed to investigate viral evolution and identify genetic variations associated with pathogenicity and transmissibility. Results. The 2022 outbreak involved 164 confirmed cases and a case fatality rate (CFR) of 33.5%, with significant geographic variation in case distribution. The 2025 outbreak, still ongoing, was first detected in Kampala, with evidence of both nosocomial and community transmission. Phylogenomic analysis revealed the presence of two main genetic groups, representing Sudan and Uganda, respectively. The genetic variability of the Ugandan cluster is higher than that observed in Sudan, suggesting a greater expansion potential, which aligns with the current outbreak. Epidemiological findings indicate that human mobility, weaknesses in the health system, and delays in detection contribute to the amplification of the outbreak. Conclusions. Our findings underscore the importance of integrated genomic and epidemiological surveillance in understanding SUDV transmission dynamics. The recurrent emergence of SUDV highlights the need for improved outbreak preparedness, rapid response mechanisms, and international collaboration. Strengthening real-time surveillance and enhancing healthcare system resilience are critical to mitigating the impact of future outbreaks.

The emergence of new viral threats continues to pose significant challenges to global health security [...].The emergence of new viral threats continues to pose significant challenges to global health security [...].