A Comparison of Infectious Disease Forecasting Methods across Locations, Diseases, and Time

Meeting Global Health Needs via Infectious Disease Forecasting: Development of a Reliable Data-Driven Framework

Background

Infectious diseases (IDs) have a significant detrimental impact on global health. Timely and accurate ID forecasting can result in more informed implementation of control measures and prevention policies.

Objective

To meet the operational decision-making needs of real-world circumstances, we aimed to build a standardized, reliable, and trustworthy ID forecasting pipeline and visualization dashboard that is generalizable across a wide range of modeling techniques, IDs, and global locations.

Methods

We forecasted 6 diverse, zoonotic diseases (brucellosis, campylobacteriosis, Middle East respiratory syndrome, Q fever, tick-borne encephalitis, and tularemia) across 4 continents and 8 countries. We included a wide range of statistical, machine learning, and deep learning models (n=9) and trained them on a multitude of features (average n=2326) within the One Health landscape, including demography, landscape, climate, and socioeconomic factors. The pipeline and dashboard were created in consideration of crucial operational metrics-prediction accuracy, computational efficiency, spatiotemporal generalizability, uncertainty quantification, and interpretability-which are essential to strategic data-driven decisions.

Results

While no single best model was suitable for all disease, region, and country combinations, our ensemble technique selects the best-performing model for each given scenario to achieve the closest prediction. For new or emerging diseases in a region, the ensemble model can predict how the disease may behave in the new region using a pretrained model from a similar region with a history of that disease. The data visualization dashboard provides a clean interface of important analytical metrics, such as ID temporal patterns, forecasts, prediction uncertainties, and model feature importance across all geographic locations and disease combinations.

Conclusions

As the need for real-time, operational ID forecasting capabilities increases, this standardized and automated platform for data collection, analysis, and reporting is a major step forward in enabling evidence-based public health decisions and policies for the prevention and mitigation of future ID outbreaks.

Prediction analysis of human brucellosis cases in Ili Kazakh Autonomous Prefecture Xinjiang China based on time series

Human brucellosis remains a significant public health issue in the Ili Kazak Autonomous Prefecture, Xinjiang, China. To assist local Centers for Disease Control and Prevention (CDC) in promptly formulate effective prevention and control measures, this study leveraged time-series data on brucellosis cases from February 2010 to September 2023 in Ili Kazak Autonomous Prefecture. Three distinct predictive modeling techniques-Seasonal Autoregressive Integrated Moving Average (SARIMA), eXtreme Gradient Boosting (XGBoost), and Long Short-Term Memory (LSTM) networks-were employed for long-term forecasting. Further, the optimal model will be used to explore the impact of COVID-19 on the transmission of Human brucellosis in the region. We constructed a SARIMA(4,1,1)(3,1,2)12 model, an XGBoost model with a time lag of 22, and an LSTM model featuring 3 LSTM layers and 100 neurons in the fully connected layer to predict monthly reported cases from January 2021 to September 2023. The results indicated that the occurrence of brucellosis exhibits pronounced seasonal patterns, with higher incidence during summer and autumn, peaking in June annually. Performance evaluations revealed low Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Symmetric Mean Absolute Percentage Error (SMAPE) for all three models. Specifically, the coefficient of determination (R2) was 0.6177 for the SARIMA model, 0.8033 for the XGBoost model, and 0.6523 for the LSTM model. The study found that the XGBoost model outperformed the other two in long-term forecasting of brucellosis, demonstrating higher predictive accuracy. This discovery can aid public health departments in advancing the deployment of prevention and control resources, particularly during peak seasons of brucellosis. It was also found that the impact of the COVID-19 pandemic on the transmission of human brucellosis in the region was minimal. This research not only provides a reliable predictive tool but also offers a scientific basis for formulating early prevention and control strategies, potentially reducing the spread of this disease.

Role of artificial intelligence in early diagnosis and treatment of infectious diseases

Infectious diseases remain a global health challenge, necessitating innovative approaches for their early diagnosis and effective treatment. Artificial Intelligence (AI) has emerged as a transformative force in healthcare, offering promising solutions to address this challenge. This review article provides a comprehensive overview of the pivotal role AI can play in the early diagnosis and treatment of infectious diseases. It explores how AI-driven diagnostic tools, including machine learning algorithms, deep learning, and image recognition systems, enhance the accuracy and efficiency of disease detection and surveillance. Furthermore, it delves into the potential of AI to predict disease outbreaks, optimise treatment strategies, and personalise interventions based on individual patient data and how AI can be used to gear up the drug discovery and development (D3) process.The ethical considerations, challenges, and limitations associated with the integration of AI in infectious disease management are also examined. By harnessing the capabilities of AI, healthcare systems can significantly improve their preparedness, responsiveness, and outcomes in the battle against infectious diseases.

Machine learning to improve the understanding of rabies epidemiology in low surveillance settings

In low and middle-income countries, a large proportion of animal rabies investigations end without a conclusive diagnosis leading to epidemiologic interpretations informed by clinical, rather than laboratory data. We compared Extreme Gradient Boosting (XGB) with Logistic Regression (LR) for their ability to estimate the probability of rabies in animals investigated as part of an Integrated Bite Case Management program (IBCM). To balance our training data, we used Random Oversampling (ROS) and Synthetic Minority Oversampling Technique. We developed a risk stratification framework based on predicted rabies probabilities. XGB performed better at predicting rabies cases than LR. Oversampling strategies enhanced the model sensitivity making them the preferred technique to predict rare events like rabies in a biting animal. XGB-ROS classified most of the confirmed rabies cases and only a small proportion of non-cases as either high (confirmed cases = 85.2%, non-cases = 0.01%) or moderate (confirmed cases = 8.4%, non-cases = 4.0%) risk. Model-based risk stratification led to a 3.2-fold increase in epidemiologically useful data compared to a routine surveillance strategy using IBCM case definitions. Our study demonstrates the application of machine learning to strengthen zoonotic disease surveillance under resource-limited settings.

Enhancing infectious disease prediction model selection with multi-objective optimization: an empirical study

As the pandemic continues to pose challenges to global public health, developing effective predictive models has become an urgent research topic. This study aims to explore the application of multi-objective optimization methods in selecting infectious disease prediction models and evaluate their impact on improving prediction accuracy, generalizability, and computational efficiency. In this study, the NSGA-II algorithm was used to compare models selected by multi-objective optimization with those selected by traditional single-objective optimization. The results indicate that decision tree (DT) and extreme gradient boosting regressor (XGBoost) models selected through multi-objective optimization methods outperform those selected by other methods in terms of accuracy, generalizability, and computational efficiency. Compared to the ridge regression model selected through single-objective optimization methods, the decision tree (DT) and XGBoost models demonstrate significantly lower root mean square error (RMSE) on real datasets. This finding highlights the potential advantages of multi-objective optimization in balancing multiple evaluation metrics. However, this study's limitations suggest future research directions, including algorithm improvements, expanded evaluation metrics, and the use of more diverse datasets. The conclusions of this study emphasize the theoretical and practical significance of multi-objective optimization methods in public health decision support systems, indicating their wide-ranging potential applications in selecting predictive models.

Predictors of disease outbreaks at continental-scale in the African region: Insights and predictions with geospatial artificial intelligence using earth observations and routine disease surveillance data

Objectives

Our research adopts computational techniques to analyze disease outbreaks weekly over a large geographic area while maintaining local-level analysis by incorporating relevant high-spatial resolution cultural and environmental datasets. The abundance of data about disease outbreaks gives scientists an excellent opportunity to uncover patterns in disease spread and make future predictions. However, data over a sizeable geographic area quickly outpace human cognition. Our study area covers a significant portion of the African continent (about 17,885,000 km2). The data size makes computational analysis vital to assist human decision-makers.

Methods

We first applied global and local spatial autocorrelation for malaria, cholera, meningitis, and yellow fever case counts. We then used machine learning to predict the weekly presence of these diseases in the second-level administrative district. Lastly, we used machine learning feature importance methods on the variables that affect spread.

Results

Our spatial autocorrelation results show that geographic nearness is critical but varies in effect and space. Moreover, we identified many interesting hot and cold spots and spatial outliers. The machine learning model infers a binary class of cases or none with the best F1 score of 0.96 for malaria. Machine learning feature importance uncovered critical cultural and environmental factors affecting outbreaks and variations between diseases.

Conclusions

Our study shows that data analytics and machine learning are vital to understanding and monitoring disease outbreaks locally across vast areas. The speed at which these methods produce insights can be critical during epidemics and emergencies.

Predicting Kyasanur forest disease in resource-limited settings using event-based surveillance and transfer learning

In recent years, the reports of Kyasanur forest disease (KFD) breaking endemic barriers by spreading to new regions and crossing state boundaries is alarming. Effective disease surveillance and reporting systems are lacking for this emerging zoonosis, hence hindering control and prevention efforts. We compared time-series models using weather data with and without Event-Based Surveillance (EBS) information, i.e., news media reports and internet search trends, to predict monthly KFD cases in humans. We fitted Extreme Gradient Boosting (XGB) and Long Short Term Memory models at the national and regional levels. We utilized the rich epidemiological data from endemic regions by applying Transfer Learning (TL) techniques to predict KFD cases in new outbreak regions where disease surveillance information was scarce. Overall, the inclusion of EBS data, in addition to the weather data, substantially increased the prediction performance across all models. The XGB method produced the best predictions at the national and regional levels. The TL techniques outperformed baseline models in predicting KFD in new outbreak regions. Novel sources of data and advanced machine-learning approaches, e.g., EBS and TL, show great potential towards increasing disease prediction capabilities in data-scarce scenarios and/or resource-limited settings, for better-informed decisions in the face of emerging zoonotic threats.

Assigning cause for emerging diseases of aquatic organisms

Resolving the cause of disease (= aetiology) in aquatic organisms is a challenging but essential goal, heightened by increasing disease prevalence in a changing climate and an interconnected world of anthropogenic pathogen spread. Emerging diseases play important roles in evolutionary ecology, wildlife conservation, the seafood industry, recreation, cultural practices, and human health. As we emerge from a global pandemic of zoonotic origin, we must focus on timely diagnosis to confirm aetiology and enable response to diseases in aquatic ecosystems. Those systems' resilience, and our own sustainable use of seafood, depend on it. Synchronising traditional and recent advances in microbiology that span ecological, veterinary, and medical fields will enable definitive assignment of risk factors and causal agents for better biosecurity management and healthier aquatic ecosystems.

Predicting infectious disease for biopreparedness and response: A systematic review of machine learning and deep learning approaches

The complex, unpredictable nature of pathogen occurrence has required substantial efforts to accurately predict infectious diseases (IDs). With rising popularity of Machine Learning (ML) and Deep Learning (DL) techniques combined with their unique ability to uncover connections between large amounts of diverse data, we conducted a PRISMA systematic review to investigate advances in ID prediction for human and animal diseases using ML and DL. This review included the type of IDs modeled, ML and DL techniques utilized, geographical distribution, prediction tasks performed, input features utilized, spatial and temporal scales, error metrics used, computational efficiency, uncertainty quantification, and missing data handling methods. Among 237 relevant articles published between January 2001 and May 2021, highly contagious diseases in humans were most often represented, including COVID-19 (37.1%), influenza/influenza-like illnesses (9.3%), dengue (8.9%), and malaria (5.1%). Out of 37 diseases identified, 51.4% were zoonotic, 37.8% were human-only, and 8.1% were animal-only, with only 1.6% economically significant, non-zoonotic livestock diseases. Despite the number of zoonoses, 86.5% of articles modeled humans whereas only a few articles (5.1%) contained more than one host species. Eastern Asia (32.5%), North America (17.7%), and Southern Asia (13.1%) were the most represented locations. Frequent approaches included tree-based ML (38.4%) and feed-forward neural networks (26.6%). Articles predicted temporal incidence (66.7%), disease risk (38.0%), and/or spatial movement (31.2%). Less than 10% of studies addressed uncertainty quantification, computational efficiency, and missing data, which are essential to operational use and deployment. This study highlights trends and gaps in ML and DL for ID prediction, providing guidelines for future works to better support biopreparedness and response. To fully utilize ML and DL for improved ID forecasting, models should include the full disease ecology in a One-Health context, important food and agricultural diseases, underrepresented hotspots, and important metrics required for operational deployment.

Prevalence and Antimicrobial Resistance of Campylobacter spp. Isolated from Broilers Throughout the Supply Chain in Valencia, Spain

Campylobacter is a major foodborne pathogen and its antimicrobial resistance (AMR) has been described worldwide. The main objective of this study was to determine the occurrence and AMR of Campylobacter spp. isolated from broilers throughout the supply chain in Valencia, Spain. A total of 483 samples were included in the analysis: 430 from the slaughterhouse (chicken carcass and neck skin) and 53 from the point of sale (retail broiler and packaging). Taking into account the origin of the sample, the prevalence of Campylobacter spp. was 19% in carcass, 28.2% in neck skin, 36.7% in retail broiler, and 80% in packaging isolates. The prevalence of different species in the analyzed samples was 21.1% and 4.8% for Campylobacter jejuni and Campylobacter coli, respectively. AMR profiling of 125 Campylobacter isolates revealed that 122 (97.6%) of the isolates were resistant to one or more antimicrobials. C. jejuni samples presented high resistance to nalidixic acid and ciprofloxacin, 96.1% and 90.2% respectively, whereas C. coli showed 87% of resistance to both antimicrobials. Both species were resistant to tetracycline (C. jejuni 84.3% and C. coli 60.9%) and 26.1% of C. coli was resistant to streptomycin. These results showed no significant difference in the frequency of AMR (p ≥ 0.05) among isolates originated from different points in the food-processing chain at slaughterhouses and retail establishments. In contrast, three main patterns were detected: quinolone-tetracycline (64%), quinolone-only (17.6%), and quinolone-tetracycline-aminoglycosides (8%). Additionally, 12.8% of the isolates presented multidrug resistance, with significantly higher levels detected among C. coli (30.4%) isolates compared with C. jejuni (8.8%) and all the three strains were resistant to all six antibiotics tested. Therefore, these results indicate that broilers could be a source of antimicrobial-resistant Campylobacter in humans and consequently pose a risk to public health.