Treatment–donation-stockpile dynamics in ebola convalescent blood transfusion therapy

Abstract

The interim guidance issued by the World Health Organization during the West Africa 2014 Ebola outbreak provides guidelines on the use of convalescent blood from Ebola survivors for transfusion therapy. Here we develop a novel mathematical model, based on the interim guidance, to examine the nonlinear transmission-treatment-donation-stockpile dynamics during an Ebola outbreak and with a large scale use of the transfusion therapy in the population. We estimate the reduction of case fatality ratio by introducing convalescent blood transfusion as a therapy, and inform optimal treatment-donation-stockpile strategies to balance the treatment need for case fatality ratio reduction and the strategic need of maintaining a minimal blood bank stockpile for other control priorities.

Effectiveness and feasibility of convalescent blood transfusion to reduce COVID-19 fatality ratio

“… α rate of donation −ln(1 − p d ) Derived. ω inflow rate of donation to blood bank α [ 6 ] p d probability of making donation varied ( a 0.1) — ξ loss rate of donors −ln(1 − p c ) Derived. p c probability of quitting donation varied ( a 0.1) — τ donor recovery period between consecutive donations 14 days [ 3 ] λ CP expiration rate [ 3 ] initial values on 23 February 2020 P c (0) critically ill cases 26 ICU data.…”

“…For instance, the logistical feasibility and potential benefits of using CP to treat severe H1N1 cases in Hong Kong was assessed, where of infected cases were severe [ 5 ]. Possible reductions in the case fatality ratio via a hypothetical CP therapy in the population were estimated for the 2014–2015 Ebola outbreak in West Africa, where all cases were severe [ 6 ].…”

“…The modelling framework is suitable for any population; however, we consider Italy as a case study. As both of the previous studies aforementioned [ 5 , 6 ] emphasized that the demand–supply balance is driven by the actual disease dynamics, we first calibrate the treatment dynamics from the reported case data, so as to simulate the dynamics of convalescent and critically ill patients in both outbreak waves in Italy. We then address the following questions: (i) reduction in the overall deceased cases with various assumed efficacy of the CP therapy; (ii) peak demand of hospital beds, treatment facilities and plasma collection centres under various scales of the CP implementations; and (iii) impacts of timings for the start of the population-wide CP therapy.…”