A New Model for Predicting Infectious Disease Outbreaks

Abstract

This project integrated tools and hybrid methodologies historically used for early warning, intelligence, counter space, public health, informatics, and medical surveillance applications. A multidiscipline team assembled and explored non-medical prediction and analytical techniques that successfully predict critical events for low probability but high-regret national and global scenarios. The team then created novel approaches needed to fill nuanced and unique gaps for the infectious disease prediction challenge. The team adopted and applied those proven procedures to determine which would be efficacious in foretelling infectious disease outbreaks around the world. One outcome of that effort was a successful two-year development and validation project designated ‘RAID’ (Risk Awareness Framework for Infectious Diseases), which focused on malaria prediction. The project’s objective was to maximize the warning (prediction) window of impending malaria epidemic outbreaks with sufficient time to allow meaningful preventive intervention before widespread human infection. It is generally recognized the more protracted the prediction window extends before an event, the more time available for health authorities to muster and deploy resources, which lessen morbidity, mortality, and harmful economic effects. Also, the value of early warning for an imminent epidemic must have mitigation options, or the warning window would have no beneficial impact on health outcomes. Finally, early notice is preferable over surprise epidemics, as unexpected waves of patients seeking acute care can easily overwhelm most local medical systems, as history repeatedly teaches. This cliché keeps repeating, with recurring Ebola epidemics and the recent COVID-19 pandemic as prominent exemplars. Predictive lead times need to be adequate for an intervention to be relevant. RAID’s focus on malaria prediction met these criteria from a relevant clinical and humanitarian perspective. Subsequent papers will address successful external generalization of these methods in predicting other similar infectious diseases. The model presented in this manuscript supports the conclusion that an additional two weeks advance notice could be available to public health authorities utilizing these techniques. This foreknowledge would allow the deployment of limited health resources into areas where they would do the most good and just in time. The geographical specificity was examined down to 5 km x 5 km grid squares overlaid anywhere in the world. Most of the model’s input data were derived from remote sensing satellite sources that could combine with historical WHO (World Health Organization) or nation-reported existential pathogen loads to improve model accuracy; however, such data harmonization is not required. If ground sensors were integrated into the modeling, the confidence of the risk of infection would logically improve. The model provides a successful global risk assessment via commercially available remote space sensors, even without ground sensing. RAID provides a necessary and useful preliminary means to predictive situational awareness. This improved predictive awareness is sufficiently granular to identify last chance windows for public health interventions globally. This need will become even more pronounced as infectious diseases evolve biologically and migrate geographically at ever-increasing rates.