Artificial intelligence at the frontlines: Emerging infectious and parasitic diseases in the digital era

Abstract

Emerging infectious diseases are one of the most significant threats to global health, driven by many factors such as zoonotic spillovers, climate change, globalization, and antibiotic resistance. While a great deal of attention is focused on viral and bacterial pathogens (e.g., SARS-CoV-2, influenza, multidrug-resistant TB), parasitic diseases contribute to global morbidity and mortality that remain largely unrecognized. The recent development of artificial intelligence has introduced powerful computational tools that can integrate large and complex datasets to assist with infectious disease surveillance, diagnosis, outbreak prediction, and drug discovery. Artificial intelligence encompasses machine learning, deep learning, and natural language processing techniques, which allow for automated pattern recognition and predictive modeling based on very complex biomedical data sets. This narrative review explores the recent advancements in AI applications in four key areas related to infectious disease: disease surveillance and early-warning systems; diagnostics and clinical decision support; outbreak prediction and modeling; and drug/vaccine discovery. Emphasis will be placed on applications of AI to parasites such as malaria, leishmaniasis, and soil-transmitted helminths. In addition, we discuss several challenges related to AI implementation in endemic regions including limited data availability, algorithmic bias, limited infrastructure in endemic areas, and ethical issues regarding data governance. Integrating AI into the One Health framework of linking human, animal, and environmental health will potentially enhance global preparedness to respond to emerging infectious and parasitic diseases.

Graphical abstract

1. Introduction

Emerging and re-emerging infectious diseases pose significant global public health issues, with outbreaks of severe acute respiratory syndrome (SARS), H1N1 influenza, Ebola virus disease, Zika virus infection, coronavirus disease 2019 (COVID-19), and Mpox together representing a multitude of different, worldwide experiences over the last twenty years [1]. Antimicrobial resistance (AMR) has also emerged in recent years as a major, growing public health threat, adversely impacting the efficacy of available treatments. The burden of infectious disease remains substantial across the globe mainly in low- and middle-income countries, where malaria, leishmaniasis, cryptosporidiosis, and soil-transmitted helminths continue to be important contributors to morbidity and mortality [2].

A variety of interdependent factors contribute to the emergence and re-emergence of infectious diseases. At least two-thirds of all emerging pathogens are due to zoonotic spillover events stemming from human interactions with animals and the disturbance of the environment [3]. The effects of climate change on the distribution of disease vectors have resulted in the expansion of previously non-endemic areas affected by vector-borne diseases such as malaria, dengue fever, and leishmaniasis. Additionally, rapid dissemination of pathogens is facilitated by globalization and the increased number of international travelers, while the prevalence of disease is further influenced by urbanization and ecology [3].

The existing systems for disease surveillance and diagnosis are traditionally fragmented and unable to detect new disease outbreaks in a timely manner. Therefore, there is increased interest in utilizing artificial intelligence (AI) to improve the management of infectious diseases. AI is a term that encompasses computational systems that are designed to perform functions or processes requiring the use of "human-like" intelligence, such as pattern recognition, classification, and predictive modeling. Machine learning (ML) refers to the set of algorithms used in AI that allow the AI to learn patterns and make predictions from data. Deep learning (DL) involves the use of multilayered, neural networks to extract more complex features from very large sets of data such as images or genomic sequences [4].

In infectious disease research, AI can be used to integrate multiple sources of data, including clinical data and records, genomic data, climatic data, population movement data, and digital epidemiology, to enhance surveillance, diagnosis, and prediction of the occurrence and spread of infectious diseases [4]. This article reviews emerging AI technologies to aid in the management of infectious diseases, particularly in addressing infectious diseases that are new and also in addressing neglected parasitic diseases; Moreover, many neglected tropical diseases (NTDs) that have been confined to tropical and subtropical areas are now facing increased risks of either re-emergence or geographic expansion into temperate areas due to several factors including climate change, environmental changes/modifications, urbanization, and increased travel/trade between countries; these factors all impact host/pathogen transmission dynamics and vector ecology [4].

The aim of this research is to provide a review of the current literature concerning uses of AI in the field of emerging zoonotic and parasitic disease. A review of the existing literature in PubMed/MEDLINE, Scopus, Web of Science, and Google Scholar was performed using combinations of key terms including artificial intelligence, machine learning, deep learning, infectious diseases, parasitic diseases, surveillance, diagnosis, outbreak prediction, drug discovery, vaccine development, and One Health. The primary focus of this review was to summarize the literature published from January 2015 to January 2026. All articles that discusses AI and its application to one or more categories (surveillance, genomics, diagnosis, modeling outbreak risk, therapeutics, vaccine production, and/or any application of AI within One Health) in the context of either infectious or parasitic diseases were included for synthesis; all other articles were excluded (including any non-scholarly source, duplicate publications, and literature that was considered methodologically and/or thematically insufficient). Following identification and inclusion of relevant literature, the articles were synthesized by narrative synthesis based on the significant themes identified in each body of literature.

2. Artificial intelligence in disease surveillance and early warning

The advancement of artificial intelligence has dramatically enhanced the function of infectious disease surveillance systems due to the growing ability of health departments and researchers to conduct real-time analyses of extensive datasets. Digital epidemiology uses natural language processing (NLP) algorithms to extract useful data from online news articles, social media activity, and public health records. Digital epidemiology can identify disease outbreaks via signal detection sooner than traditional types of surveillance [5] as shown in Fig. 1.

Fig. 1.

Showing the usage of AI in disease surveillance and early warning.

There have been several successful examples of AI-supported digital surveillance systems implemented across the globe. The World Health Organization's HealthMap and BlueDot are AI-driven platforms that combine machine learning, global news, air travel statistics, and social media signal analysis to demonstrate possible early warning signals of infectious disease outbreaks. For example, AI-enabled digital surveillance systems provided evidence of clustering pneumonia cases in Wuhan prior to the World Health Organization issuing its initial alert to the global community regarding COVID-19 [6].

Beyond viral infections, AI-supported geospatial models are being utilized more often to predict how vector-borne diseases and parasitic diseases will spread geographically as well. AI-driven models integrate such environmental factors as temperature, precipitation, and vegetation indices derived via satellite to create a geospatial predictive model that determines regions likely to be impacted by disease transmission. An example is the successful predictive model developed by combining climatic and environmental data to estimate the incidence and geographic distribution of malaria in endemic regions [7].

Additionally, AI can be used to strengthen disease monitoring via wastewater systems that can identify pathogen genetic material in environmental samples. Machine learning models that analyzed wastewater samples using AI and machine learning were able to detect multiple pathogens, including Cryptosporidium and many other infectious agents, with improved sensitivity at the community level [8].

We compared the AI approach, the input data, and the outcomes as summarized in Table 1.

Table 1.

Use of AI technology for targeting emerging infectious and parasitic diseases.

Pathogen/Disease	AI Approach	Data Sources	Key Application	Example Study
SARS-CoV-2	Natural language processing (NLP), machine learning	Digital news reports, airline travel data, social media	Early detection of pneumonia clusters and outbreak alerts	[9]
Influenza	Ensemble machine learning	Search engine queries, syndromic surveillance data	Seasonal influenza forecasting	[10]
Malaria	Deep learning, geospatial machine learning	Climate variables (temperature, rainfall), satellite imagery	Predicting malaria incidence and identifying high-risk areas	[11]
Leishmaniasis	Geospatial machine learning	Climate, land use, socioeconomic data	Identification of emerging transmission hotspots	[12]
Cryptosporidiosis	Machine learning	Wastewater surveillance data	Community-level early detection of infection	[13]

3. Artificial intelligence in genomic and evolutionary surveillance

Vector data gained through advancements in large volumes of genomic data generated via novel sequencing techniques require advanced algorithms for data analysis. Genomic sequence analysis utilizing AI is being used to analyse pathogens for mutation detection, prediction of antimicrobial resistance and tracking the evolution of pathogens [14].

Advanced algorithms will be used to analyse large genomic repositories such as Global Initiative on Sharing All Influenza Data (GISAID) quickly to identify emerging Highly Concerned Variants (HCV) and evaluate their effect on virulence (transmissibility) and immune escape. During COVID-19, genomic tracking with AI allowed rapid identification and tracking of HCV, including the Alpha, Delta, and Omicron variants [15].

While much focus has been on viral genomic surveillance, genomic surveillance of emerging parasitic infections is increasing. For example, genomic analyses of Plasmodium falciparum populations have identified mutations associated with antimalarial drug resistance. By combining genomic and epidemiological information in an AI context, scientists can reconstruct transmission networks and understand how the dynamics of Infectious Disease Biology [16].

4. Artificial intelligence in diagnostics and clinical decision support

The advancements of AI have shown great potential for enhancing the ability to produce accurate diagnoses while also creating faster access to a patient's diagnosis due to AI based diagnostic systems, which utilize machine learning techniques based on previously annotated data and are assessed or evaluated with various Performance Indicators including but not limited to: Sensitivity, Specificity, Accuracy [17].

Deep Learning models have been shown to have diagnostic accuracy superior to that of radiologists for certain areas of medicine (i.e. Medical Imaging). While CNN (Convolutional Neural Network) based models have been developed using 'chest' radiographs and tomographic imaging to correctly identify pulmonary infections (tuberculosis & COVID-19) with a diagnostic accuracy equal to or better than radiologists; Additionally, there have been studies evaluating the potential for using 'smartphone-based cough analysis' systems to develop a low-cost method for diagnosing respiratory infections [18].

In the identification of malarial parasites via AI-assisted microscopy analysis of digitized blood smear images, CNN models have attained a diagnostic sensitivity rate of greater than 95% in some instances exceeding human microscopists; This technology may hold significant promise in the identification of malarial parasites in limited resource settings where expert laboratory diagnosis may not be available [19].

AI has also been utilized in clinical decision support systems to analyse patient data to predict possible outcomes from a health standpoint or to predict the likelihood of severity of disease. Studies have developed predictive algorithms that can identify patients with severe malaria who would benefit from earlier clinical intervention resulting in improved patient outcomes [20] as shown in Fig. 2.

Fig. 2.

Showing the usage of AI in diagnostic and clinical decision support

We compared the AI application, the diagnostic modalities, and the performance as summarized in Table 2.

Table 2.

AI-assisted diagnostics for emerging infections.

Disease	AI Model	Diagnostic Modality	Performance Metrics	Reference
COVID-19	Deep learning (CNN)	Chest CT and X-ray imaging	Accuracy ≈95%, high sensitivity and specificity	[21]
Tuberculosis	Deep learning	Chest radiography	Comparable performance to expert radiologists	[22]
Malaria	Convolutional neural networks	Microscopy images of blood smears	Sensitivity >95% for parasite detection	[23]
Mpox	Self-supervised learning + deep learning	Skin lesion image classification	Precision ≈99%	[24]
Respiratory infections	Machine learning acoustic models	Smartphone cough analysis	Rapid screening and triage tool	[25]

5. Artificial intelligence in outbreak prediction and modeling

Integrating multiple datasets will be required to successfully predict infectious disease outbreaks; these include climate, population mobility patterns, vectors, and demography. Because of the complexity of these datasets, artificial intelligence (AI) models are well-positioned to address this problem as they can also identify nonlinear relationships among distinct data sources [14].

Recent efforts have included the use of machine learning algorithms (e.g., support vector machines, random forests, neural networks) to predict seasonal outbreaks for influenza, dengue, and COVID-19 by combining epidemiological data with environmental and mobility datasets to develop several models for forecasting trends of these diseases [4].

Using AI predictions with regards to climate change, machine learning models have been used to estimate how climate impacts the distribution of vectors associated with parasitic diseases, such as malaria. One such example includes models predicting that climate-induced temperature increases could push malaria transmission up into previously unsuitable alpine and sub-alpine areas or already existing high peaks of the Himalayas. Additionally, GIS-based models that utilized AI were developed to predict the bases of the distributions of both Anopheles (the vector responsible for malaria) and Phlebotomus (the vector responsible for leishmaniasis) [26].

Lastly, machine learning patterns also allowed for the integration of sanitation, demographic, and environmental factors into models to identify areas at risk for re-infection with helminths. Such predictive models will help identify potential areas for coordinated responses via public health initiatives and strategies for distributing resources more effectively [27].

We compared the AI method, the predictive variable, and the application as summarized in Table 3.

Table 3.

AI applications in predicting outbreaks of both parasitic vs viral/bacterial diseases.

Disease	AI Method	Predictive Variables	Public Health Application	Reference
Dengue	Random Forest, Support Vector Machine	Climate variables, vector density, mobility data	Short-term outbreak forecasting	[23]
Influenza	Ensemble machine learning	Syndromic data, internet search queries	Seasonal influenza prediction	[28]
Malaria	Geospatial machine learning	Temperature, rainfall, humidity	Predicting geographic expansion into new regions	[29]
Leishmaniasis	Machine learning	Vector distribution, climate change indicators	Predicting spread of sandfly vectors	[30]
Helminth infections	Machine learning risk models	Sanitation coverage, demographic data, environmental conditions	Identifying reinfection hotspots	[31]

6. Artificial intelligence in Drug Discovery and Vaccine Development

Traditional drug development and vaccine production have always required a significant time and resource commitment. As a result, AI is being incorporated into both drug development and vaccine creation processes to expedite these systems [32] as shown in Fig. 3.

Fig. 3.

Showing the usage of AI in Drug Discovery and Vaccine Development.

Using machine learning models, large chemical libraries can be analyzed to identify prospective antimicrobial compounds. A successful example of this was demonstrated with the discovery of a novel antibiotic molecule using deep-learning methodologies to identify architectural patterns indicative of appropriate antimicrobial activity [33].

The use of artificial intelligence to accelerate the identification of new therapies for drug-resistant parasites has also been done through screening chemical compounds for potential antimalarial activity via predictive models as well as modeling potential interactions between parasite proteins and potential drugs [34].

Vaccine creation includes the use of artificial intelligence as well, by employing computational epitope prediction algorithms, researchers can perform an analysis of the protein sequence of the pathogen to find antigenic sites that could cause a response by the immune system. Furthermore, by utilizing AI tools for the prediction of the structure of proteins such as AlphaFold, scientists have a greater understanding of pathogen proteins and can therefore develop rational strategies for vaccine design [32].

We summarized the AI usage in drug discovery and Vaccine development as shown in Table 4.

Table 4.

Showing Artificial intelligence applications in drug discovery and vaccine development.

Application Area	AI Technique	Biological Target	Outcome	Reference
Antibiotic discovery	Deep learning molecular screening	Drug-resistant bacteria	Identification of novel antimicrobial molecules	[33]
Antimalarial drug discovery	Machine learning compound screening	Plasmodium falciparum proteins	Identification of potential antimalarial compounds	[35]
Vaccine design	Epitope prediction algorithms	Viral and parasitic antigens	Identification of immunogenic vaccine candidates	[36]
Protein structure prediction	Deep neural networks	Pathogen proteins	Improved understanding of antigen structure	[37]
Drug repurposing	AI-based network analysis	SARS-CoV-2 targets	Rapid identification of candidate therapeutics	[38]

7. Future directions and challenges

The use of AI to combat infectious disease offers many exciting possibilities; however, there are many major hurdles that remain. For example, several AI models have been built using datasets featuring a relatively small number of high-income countries, which may limit the practical use of these tools in many low-resource parts of the world where these types of diseases are more commonly found. Furthermore, many of the challenges associated with applying AI solutions in endemic settings are compounded by a lack of digital infrastructure, fragmented disease surveillance systems, and inconsistent data sharing across affected populations [39].

Emerging techniques such as federated learning may help provide some solutions to these difficulties by allowing researchers and developers to train collaborative models across multiple organizations, while still protecting patient privacy or the security of local health records. In addition, continuing to develop and implement explainability methods for AI will help to create more transparent and interpretable machine learning models, allowing clinicians and public health officials to better understand how to interpret AI model predictions [40].

Key stakeholders, including epidemiologists, clinicians, data scientists, and policymakers need to continue working collaboratively together to ensure the responsible development and equitable use of AI tools for global health.

AI technology offers substantial possibilities to enhance worldwide preparedness against infectious illnesses and develop a "One Health" framework which incorporates the connection between human, animal and environmental health. By integrating disparate data from veterinary disease surveillance networks, agricultural livestock production systems and ecological/biological field studies for monitoring animal populations, significant improvements can be made to the identification of possible zoonotic threats to public health and the emergence of new pathogens [12]. Geographical modeling of data related to the environment, animal health and livestock production has also been developed to identify potential zoonotic disease "hot spots" and estimate risks of these diseases presenting as zoonosis among the human population throughout multiple areas across the globe. Similarly, ecological and climate-based modeling of transmission patterns for vectors associated with wildlife populations, affected by changing climatic conditions, can help to determine distributions of vectors and their respective livestock and wildlife reservoirs [26]. Therefore, the combination of agricultural livestock production data, animal population data and data on environmental conditions based on the use of analytical AI platforms can help to improve overall forecasting of future outbreaks and assist in coordinated responses to those outbreaks at both a national scale and between the human and animal health sectors [39,41].

8. Conclusion

Recent developments in artificial intelligence have highlighted its transformative potential in infectious disease research and public health. Therefore, AI has already proven valuable within supporting numerous ways for these two fields; initially via disease detection, monitoring, managing outbreaks, and creating new therapeutic agents. However, the application of AI technology so far focused on predominantly both viral and bacterial conditions. Thus, from an AI perspective, many potential advances exist for the development of novel treatments for parasites [42].

To fully realize the potential of AI for infectious disease and public health, we must address several challenges related to 1) availability of sufficient data; 2) ensuring the absence of algorithmic bias; 3) limitations of infrastructure; and 4) ensuring an appropriate ethical governance framework. Therefore, in addition to dealing with above-mentioned areas, using a One Health methodology in incorporating AI into the Human/Animal/Environment connection will also allow for a significantly increased degree of preparedness at the global level to provide for an adequate response to any newly emerging infectious or parasitic diseases [41].

CRediT authorship contribution statement

Dina S. Nasr: Writing – review & editing, Conceptualization. Nour Bader Alraee: Writing – original draft. Sham Wathek Arabi Katbi: Writing – original draft. Najwa Mahmoud Kouli: Writing – original draft. Naziha Ismail Asaad: Writing – original draft. Mariam M. Ismail: Software, Visualization, Writing – review & editing. Shifan Khanday: Writing – review & editing.

Declaration of generative AI and AI-assisted technologies in the manuscript preparation process

During the preparation of this work the author(s) used Grammarly for language editing, and FigureLabs to generate the figures. After using this tool/service, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the published article.

Funding sources

This research did not receive any specific grant from funding agencies in the public, commercial, or non-profit sectors.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.