A stochastic inventory model of COVID-19 and robust, real-time identification of carriers at large and infection rate via asymptotic laws.

A critical operations management problem in the ongoing COVID-19 pandemic is cognizance of (a) the number of all carriers at large (CaL) conveying the SARS-CoV-2, including asymptomatic ones and (b) the infection rate (IR). Both are random and unobservable, affecting the spread of the disease, patient arrivals to health care units (HCUs) and the number of deaths. A novel, inventory perspective of COVID-19 is proposed, with random inflow, random losses and retrials (recurrent cases) and delayed/distributed exit, with randomly varying fractions of the exit distribution.

Optimization of a paediatric fixed dose combination mini-tablet and dosing regimen for the first line treatment of tuberculosis.

We aim to optimize the paediatric dosing regimen of isoniazid, rifampicin and pyrazinamide for the first-line treatment of tuberculosis, based on a fixed dose combination (FDC) mini-tablet using simulations. An optimization problem was set up to determine the 3 strengths of the drugs of the mini-tablet and 4 cutoff points that define the weight bands of a dosing chart, simultaneously. Using Monte Carlo simulations, first, exposure targets were determined for the 3 drugs, from published population pharmacokinetic models for adults, assuming that the approved doses for adults are de facto efficacious.