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

Background: As of December 2020, COVID-19 has spread all over the world with more than 81 million cases and more than 1.8 million deaths. The rapidly increasing number of patients mandates the consideration of potential treatments for patients under severe and critical conditions. Convalescent plasma (CP) treatment refers to the approach of infusing patients with plasma from recently recovered patients. CP appears to be a possible therapeutic option to manage patients suffering from severe or even lethal infectious disorders, in which ‘traditional therapies’ have failed to obtain any result. Methods: In the present study, we develop a mathematical model on the treatment-donation-stockpile dynamics for an optimal implementation of CP therapy to examine potential benefits and complications in the logistic realization of this therapy in a large-scale population. We parametrize the model with COVID-19 epidemics in Italy, and conduct scenario analyses to estimate outcomes of population-wide CP therapy and to examine the maximum number of CP donation processions per day. Results: Under the assumption that the efficacy of CP is 90%, we show that by the end of year 2020, initiating the population-wide CP therapy from April 2020 can save as many as 19 215 lives (ranging from 5000 to 28 000 depending on donor availability), while the demand for apheresis use is manageable in all scenarios: the maximum daily demand is 156 (ranging from 27 to 519 depending on donor availability) for the first outbreak wave and 1434 (ranging from 224 to 4817 depending on donor availability) for the second wave. Given that Italy has 61 centres with apheresis this maximum demand level corresponds to a daily average of 2.5 and 23.5 processions of CP donation being performed by each centre with respect to each outbreak wave. Conclusions: Our analyses show that population-wide CP therapy can contribute to curbing COVID-19-related deaths, and the logistic implementation is feasible for developed countries. The reduction of deaths can be very significant if the CP therapy is started earlier in the outbreak, and remains significant even if it is implemented during the outbreak peak time.

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Decision letter (RSOS-202248.R1)
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COMMENTS ON RSOS-202248
The authors developed a mathematical model that couples the disease transmission dynamics with the treatment-donation-stockpile dynamics. They presented an interesting exploration for an optimal implementation of CP therapy to examine potential benefits and complications in the logistic realization of this therapy in a large-scale population. The model was parameterized with COVID-19 epidemics in Italy and their results indicate that population-wide CP treatment can contribute to curbing COVID-19 related deaths.
The manuscript overall is well presented. The idea behind the work holds relevance and could be of value to public health. Some issues can be addressed to increase the clarity of the manuscript.
• The transmission rate β(t) is assumed to decrease throughout the outbreak because of the gradually enhanced public health interventions. However, the transmission rate (contact rate*susceptibility*infectivity), for COVID-19, is much more complicated than other infectious diseases because of human behaviors and public intervention policies. Many studies indicated that it would be more reasonable to think β(t) first decrease, then rebound and relax to some level. Please clarify.
• It would be nice to see at least one important fitting outcome in the manuscript for readers.
• Many studies have shown that the asymptomatic/pre-symptomatic cases are the bulk of the transmission of COVID-19, I am wondering whether it is worth to be considered in this study and whether it will largely affect the number of donors and data fitting?
• lines 38: The transmission from susceptible to infectious population.

Review of RSOS-202248
The authors propose a mathematical model that couples SEIR transmission dynamics of Covid-19 with the Treatment-Donation-Stockpile system for convalescent plasma (CP) used as antiviral therapy in severe cases. The goal is assess the effect of CP-therapy, its efficacy and implementation strategies, on cumulative disease fatality. To calibrate transmission model parameters they used cumulative data (confirmed cases, deaths, and ICU numbers) from Italy.
The topic is timely and important, and the authors made good effort to address this complex issue. Their modeling approach however, raises several questions Major comments: 1. The SEIR transmission part of the model (Fig.1) drops all possible disease pathways (e.g. asymptomatic resolution L -> R, or undetected cases I->R), except hospitalization pathway (I->P) at rate Where does 1/6 come from? Given much uncertainty about undetected Covid transmission, the least authors should do is allow some range of κ s, and explore its implications.
2. The key assumption on transmission coefficient ( ) t β -exponential decay in time, looks very strange to me. In anything it should follow a different pattern -initial steep drop (due to lockdown), and the follow-up period maintenance or gradual relaxation. The authors could consult the available mobility data. To fit two epidemic waves (Figs. 3-7) they arbitrarily switch ( ) t β in September. This part needs a revision. The projections for the next 100 days (Fig. 2 appendix) is not convincing 3. The Treatment-Donation-Stockpile part of the system (appendix, (2)) looks reasonable except another vexing issue related to consumption /depletion of the plasma bank, taken as a sigmoid (saturation) function of B(t). It needs an explanation or revision. Whatever CV 'delivery' function  Table 1 (appendix) need some ranges of uncertainty.
Minor comments: 1. p4 L21: while there are many uncertainties re mechanisms of plasma therapy, it's not likely to accelerate immune development, but probably would slow it down. I would drop this comment. 2. P6 L57: might be better to replace 'fatality ratio of CP' by CP-efficacy (in term of death prevention)