Title : AI-driven drug discovery and emergency therapeutic allocation framework for global epidemic preparedness in the next 100 years (2025–2125)
Abstract:
The increasing frequency of emerging infectious diseases such as COVID-19, Ebola Virus Disease, and Zika Virus Disease highlights the need for a predictive and mathematically optimized global framework for rapid drug discovery and development during emergency epidemic situations. This study proposes a comprehensive computational-mathematical model integrating epidemiological dynamics, drug discovery pipelines, resource allocation, regulatory acceleration, and emergency quota distribution mechanisms. The framework combines epidemic transmission models with AI-driven molecular discovery and global supply chain optimization to minimize mortality and drug development time.
The epidemic spread is modeled using a modified compartmental approach where the infection dynamics are represented as:
dI(t))/dt = βS(t)I(t)-γI(t)-δI(t)
where S(t),I(t), and R(t) denote susceptible, infected, and recovered populations respectively, β represents transmission rate, γ recovery rate, and δ disease-induced mortality. Drug discovery efficiency is modeled as an optimization problem that integrates molecular screening rate Ms, Al prediction accuracy Ai , clinical trial acceleration factor Ca, and global collaboration index Gc. The overall drug development efficiency function can be expressed as:
De = (Ms×Ai×Gc)/(Td+Ca-1
where Td represents baseline drug development time. To ensure equitable distribution during emergencies, a global emergency drug quota allocation model is introduced:
Qi = (Pi×Ii×Vi)/(∑_j=1n(Pj Ij Vj )
where Qi denotes the drug quota allocated to region i,Pi population size, Ii infection prevalence, and Vi vulnerability index. This allocation strategy ensures proportional and ethical distribution of limited therapeutic resources during global health crises.
The proposed framework incorporates multi-factor parameters including pathogen mutation rate, healthcare infrastructure capacity, computational drug discovery throughput, regulatory approval acceleration, and pharmaceutical manufacturing scalability. Despite its advantages, the system faces challenges such as uncertainty in epidemiological parameters, unequal global research infrastructure, ethical concerns in emergency drug trials, potential misuse of emergency approval mechanisms, and limitations in manufacturing capacity.
The model provides a strategic foundation for future global epidemic preparedness over the next century by integrating mathematical epidemiology, artificial intelligence, and global health policy optimization. Such an approach could significantly reduce response time and improve equitable drug access during large-scale outbreaks similar to COVID-19 while maintaining scientific and regulatory integrity.
