Qun He

Background: Artificial Intelligence (AI) has the potential to enhance disease surveillance, particularly at international airports, by improving the early detection and response to infectious diseases. This narrative review assesses the feasibility of implementing AI-driven disease surveillance systems at international airports in SSA. Methods: A comprehensive search of academic databases was conducted to identify relevant studies and policies. The review synthesized findings and categorized them into three thematic areas: AI effectiveness, ethical and privacy concerns, and infrastructure and capacity gaps. Results: The review suggests that implementing AI-driven disease surveillance systems at SSA international airports may be feasible in principle, although the available evidence is largely conceptual and policy-oriented rather than derived from empirical deployment at airports. Realizing this potential would likely depend on first addressing critical barriers such as inadequate data quality, insufficient infrastructure, and a shortage of trained personnel. These challenges might be mitigated through targeted investments in digital infrastructure, workforce capacity-building, and the establishment of clear regulatory frameworks to support ethical AI deployment. Conclusion: This study suggests that AI-driven disease surveillance could meaningfully strengthen public health security at international airports in Sub-Saharan Africa, provided that critical challenges in infrastructure, privacy, and regulation are addressed. The review offers a preliminary, evidence-informed framework rather than confirmation of operational feasibility. Country-specific feasibility studies and pilot implementations will be essential to test these systems under real-world conditions and to inform a possible shift toward more resilient, data-driven health infrastructures.

This study evaluates the feasibility of implementing artificial intelligence (AI)-driven disease surveillance systems at Julius Nyerere International Airport (JNIA) in Tanzania, a key hub for regional and international travel. Through a mixed-methods approach combining qualitative interviews and quantitative surveys, the research assesses the infrastructure, human resource capacity, and regulatory frameworks necessary for AI integration. Findings indicate that while there is strong optimism among Port Health Officers regarding AI’s potential to enhance disease detection, the airport faces significant barriers, including outdated infrastructure, insufficient technical resources, and a lack of trained personnel. Ethical and privacy concerns, particularly surrounding data security, also emerged as key challenges, compounded by limited public awareness and socio-cultural acceptability of AI systems. Furthermore, the study identifies gaps in national policies and inter-agency coordination that hinder the effective implementation of AI technologies. The research concludes that while current conditions render AI adoption infeasible, strategic investments in infrastructure, workforce training, and policy development could pave the way for future integration, enhancing public health surveillance at JNIA and potentially other airports in low- and middle-income countries. This study contributes critical insights into the barriers and opportunities for AI-driven disease surveillance in low-resource settings, specifically focusing on a high-priority transit point, international airports. It emphasizes the importance of region-specific solutions to enhance health security in East Africa and supports the broader global health agenda by advocating for international collaboration and the development of scalable disease surveillance systems. Future research should explore pilot AI implementations at other airports to evaluate real-world challenges and refine AI systems for broader applicability, including cost-effectiveness analyses and integration of public perspectives on AI.

BACKGROUND The increasing volume of international air travel has significantly contributed to the spread of infectious diseases, making airports critical points for disease surveillance. However, many airports in Sub-Saharan Africa (SSA), such as Julius Nyerere International Airport (JNIA) in Tanzania, still rely on basic methods like temperature screening due to limited resources. This systematic review evaluates the feasibility of implementing AI-driven disease surveillance systems at SSA airports, particularly JNIA, and identifies barriers to adoption. OBJECTIVE To evaluate the feasibility and potential impact of implementing AI-driven disease surveillance systems at airports in SSA, with a specific focus on Julius Nyerere International Airport (JNIA) in Tanzania, and provide strategies to overcome the challenges hindering the implementation of AI-driven systems at airports in resource-limited settings. METHODS A systematic review following PRISMA guidelines was conducted. Databases such as PubMed, ScienceDirect, and Web of Science were searched for studies published from 2019 to 2025 on AI applications in disease surveillance at airports. Risk of bias was assessed using ROBINS-I, AMSTAR 2, and CASP tools. Studies were analyzed narratively to identify key themes and challenges. RESULTS A total of 45 studies and seven policy frameworks were included. AI systems demonstrated potential for improving disease detection accuracy and response time. Barriers identified include inadequate infrastructure, limited workforce training, and concerns about data privacy and algorithmic bias. Key recommendations include investing in infrastructure, workforce development, and tailored ethical guidelines. CONCLUSIONS AI implementation at SSA airports is feasible but requires overcoming barriers related to infrastructure, training, and regulatory frameworks. International collaboration and policy development are essential for successfully adopting AI in public health surveillance.

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Background: The present study aimed to evaluate the association between the cumulative dose of glucocorticoids (GCs) and case fatality in hospitalized patients who developed pneumonia while receiving glucocorticoid therapy.Methods: This retrospective cohort study included 625 patients receiving long-term GC treatment who were hospitalized with pneumonia (322 male and 303 female). Data were obtained from the Dryad Digital Repository and were used to perform secondary analysis. Multivariable Cox proportional hazard regression model and restricted cubic splines (RCS) were used to evaluate the association between the cumulative dose of GCs and case fatality. Sensitivity analyses and subgroup analyses were performed.Results: The 30-day and 90-day death rates were 22.9 and 26.2%, respectively. After adjusting for potential confounders, compared with those in the lowest quintile (≤ 1.5 g), the Cox proportional hazard regression model analysis showed that patients with different cumulative doses of GCs (1.5 to 2.95, 2.95 to 5, 5 to 11.5, and > 11.5 g) had lower risks for 30-day death, with respective hazard ratios of 0.86 (95% CI, 0.52 to 1.42), 0.81 (0.49 to 1.33), 0.29 (0.15 to 0.55), and 0.42 (0.22 to 0.79). The multivariable-adjusted RCS analysis suggested a statistically significant N-shaped association between the cumulative dose of GCs and 30-day death. A higher cumulative dose of GC tended to first lead to an increase in 30-day death within 1.8 g, then to a statistically significant decrease until around 8 g [HR for 1 g = 0.82 (0.69 to 0.97)], and again to an increase afterward. Similar results were found in the subgroup analyses and sensitivity analyses.Conclusion: N-shaped association between the cumulative dose of GCs and case fatality was observed in patients receiving long-term GC treatment who were hospitalized with pneumonia. Our findings may help physicians manage these patients.

Backgroud: China has introduced series of policies to improve the professional self-image of GPs, however, Chinese general practitioners (GPs) generally have a low professional identity. This study evaluated the effects of on-job-training for medical health professionals that focuses on the recognition of GPs’ roles. Methods At a representative training base in Guangdong Province, the most economically developed province in Southern China, 62 workers from community health service centers underwent training for 6 months according to a before-after self-controlled design. A specific module related to professional value recognition was integrated into the training course. Trainees were invited to participate in the open-ended self-questionnair survey. Thematic analysis was used to explore themes within the data. Kappa test was used to compare consistency of career prospects before and after the training. Results Before training, participants generally lacked a full and clear understanding of the professional responsibilities of GPs. After the training, participants showed increased awareness of six aspects of general medical practice including a broad understanding of the term, disease treatment, disease prevention, service mode, role of general services in promoting public health, and role of GPs in a service team. Moreover, 84% of participants had a positive opinion of GPs’ career prospects and 90.3% preferred general practice to specialization as a career choice after the training. Current GPs preferred to become a specialist mainly for the higher salary, but current specialists preferred to be a GP for self-growth and sense of community. Conclusions These results demonstrate that appropriate on-the-job training can improve GPs’ professional identity in China, and should therefore be included in policies and integrated into programs for medical health professionals.

To disclose the relationship between current smoking behaviors and household characteristics among adults in Guangdong, in order to provide scientific evidence for household tobacco control strategy. A questionnaire survey was conducted among the adults sampled by four-stage randomized cluster sampling in ten districts of five cities in Guangdong from September to November, 2010. The questionnaire included demographic characteristics, smoking behaviors and smoking-related household characteristics. Chi-square test and unconditional logistic regression analysis were employed to explore the relationship between current smoking behaviors and household characteristics. This study included 2 150 respondents, with data from 2 121 eligible questionnaires used for analysis. The current smoking rates for respondents with household characteristics including per capita household income below 1 000 Yuan, three or more smokers in the family, no ban of smoking at home and treating visitors with cigarettes were 28.7%, 56.5%, 34.2% and 43.5% , respectively, and were significantly higher than those for respondents with household characteristics at other levels (P < 0.01 for all). After controlling for factors such as gender, age, personal income, education, employment status and residential location, we found that the number of family smokers, smoking restrictions at home and treating visitors with cigarettes had significant association with current smoking behaviors (P < 0.01 for all), except for per capita household income (P = 0.567). Household is one of the effective places in carrying out health education and tobacco control intervention. In order to reduce tobacco use, much effort should be made to create smoke-free homes, encourage smoking cessation and treat visitors with no cigarettes.