Multicriteria Risk Ranking of Zoonotic Diseases in a Developing Country: A Case Study of Zambia

The integration of a multicriteria decision analysis approach, including techniques such as the Analytic Hierarchy Process (AHP) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), has yielded valuable insights in the realm of zoonotic disease risk assessment. This analytical framework draws from the OIE-supported manual, utilizing impact assessments, transmission pathways, and categorizations as provided by the OIE itself. Moreover, the consideration of specific zoonotic disease scenarios tailored to individual countries enhances the contextual relevance of the analysis. Through this approach, the ranking of zoonotic diseases is systematically established, offering a comprehensive evaluation of their potential impacts and risks. This methodology encompasses pivotal criteria, including prevalence, economic impact, health impact, transmission pathways, and healthcare capacity, collectively offering a holistic perspective that mirrors the intricate nature of zoonotic diseases. The resultant rankings, derived from both ECDC and OIE data, illuminate diseases that harbor significant threats to both human and animal populations. This ranking fosters the identification of diseases with potential for rapid spread and substantial impact, guiding resource allocation towards prevention, control, and mitigation strategies. The alignment between ECDC and OIE rankings underscores the robustness of the applied methodology, with Plague and Zoonotic TB consistently emerging as high-ranking diseases, reinforcing their acknowledged significance. A consolidated ranking, amalgamating data from both sources, provides an insightful overview of potential risks linked to various zoonotic diseases. Plague, Zoonotic TB, Brucellosis, Trypanosomiasis, and Rabies consistently occupy top positions, presenting a valuable instrument for policymakers, public health officials, and stakeholders in prioritizing resource allocation and intervention strategies. The implementation of a multicriteria decision analysis approach, integrating AHP and TOPSIS methodologies, underpins the generation of informed rankings for Zambian zoonotic diseases. The intricate interplay of criteria like prevalence, economic impact, health impact, transmission pathways, and healthcare capacity forms a comprehensive framework for evaluating the potential risks of diverse diseases. The ensuing ranking, led by Plague and succeeded by Anthrax, Rabies, and others, mirrors their collective risk scores calculated via the adopted methodology. This approach empowers strategic decision-making by pinpointing diseases with heightened potential for adverse impacts on both human and animal populations. The rankings serve as invaluable aids in directing resources, devising strategic interventions, and formulating targeted measures for prevention and control. However, acknowledgment of the dynamic disease landscape and the imperative of adaptive strategies underscores the ongoing importance of monitoring and managing zoonotic diseases effectively in Zambia. By amalgamating data from authoritative sources and embracing a systematic, evidence-based approach, this study accentuates the necessity of addressing zoonotic diseases with a holistic lens, fostering proactive perspectives that augment public health and avert future outbreaks

Epidemiological Tools in Focus: A Comprehensive Assessment of Their Role in Addressing Infectious Disease Challenges in Zambia

In the relentless pursuit of mitigating infectious diseases, this investigative study critically examines the nuanced application and effectiveness of epidemiological tools within the context of Zambia. The study meticulously navigates the landscape of infectious diseases in Zambia, considering its unique ecological and socio-economic features. Employing a rigorous methodology that integrates primary data from epidemiological reports, field observations, and laboratory analyses with insights from diverse scientific literature, the study investigates the types and applications of epidemiological tools such as spatial analysis, case-control studies, molecular epidemiology, and serological assays. Unfolding the challenges posed by resource constraints, data reliability issues, and the dynamic nature of infectious diseases in Zambia, the study offers a comprehensive assessment that extends to the implications of these tools for informed public health decision-making. This scholarly inquiry concludes by affirming the significance of ongoing refinement and adaptation of epidemiological tools, emphasizing their pivotal role in addressing infectious disease challenges within Zambia and advocating for their continued enhancement on the global public health stage

Advances in Artificial Intelligence for Infectious Disease Surveillance in Livestock in Zambia

The global livestock industry grapples with formidable challenges stemming from the escalation and dissemination of infectious diseases. Zambia, an agricultural cornerstone where livestock is pivotal for economic sustenance and food security, confronts the imperative task of effectually surveilling and managing infectious diseases. This study investigates into the possibilities of the application of artificial intelligence (AI) for infectious disease surveillance in the Zambian livestock sector. The study meticulously scrutinizes the prevailing state of infectious disease surveillance, evaluates the latent capabilities of AI technologies, and critically discusses the intricate landscape of challenges and opportunities entailed in their implementation. In the intricate tapestry of Zambia\u27s economy, livestock farming assumes a central and irreplaceable role, contributing substantially to the well-being and livelihoods of a significant portion of the populace. However, the omnipresent specter of infectious diseases perpetually menaces livestock health, casting a shadow on productivity and economic equilibrium. Conventional methodologies in disease surveillance exhibit inherent shortcomings, characterized by delays in reporting and inherent inaccuracies. This study is an exploration of possibilities of the AI applications designed to fortify infectious disease surveillance within Zambia\u27s livestock domain. The infusion of AI technologies holds the transformative potential to reshape disease monitoring paradigms, enabling early detection and facilitating swift response strategies in the face of emerging threats. The ensuing critical analysis navigates the intricate terrain of the application of AI in the Zambian livestock context, shedding light on its promising prospects, while pragmatically addressing the hurdles that may accompany its incorporation