Equitable access to COVID-19 vaccines makes a life-saving difference to all countries

Despite broad agreement on the negative consequences of vaccine inequity, the distribution of COVID-19 vaccines is imbalanced. Access to vaccines in high-income countries (HICs) is far greater than in low- and middle-income countries (LMICs). As a result, there continue to be high rates of COVID-19 infections and deaths in LMICs. In addition, recent mutant COVID-19 outbreaks may counteract advances in epidemic control and economic recovery in HICs. To explore the consequences of vaccine (in)equity in the face of evolving COVID-19 strains, we examine vaccine allocation strategies using a multistrain metapopulation model. Our results show that vaccine inequity provides only limited and short-term benefits to HICs. Sharper disparities in vaccine allocation between HICs and LMICs lead to earlier and larger outbreaks of new waves. Equitable vaccine allocation strategies, in contrast, substantially curb the spread of new strains. For HICs, making immediate and generous vaccine donations to LMICs is a practical pathway to protect everyone.

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<strong>Note:</strong> This URL links to your confidential home page and associated information about manuscripts you may have submitted, or that you are reviewing for us. If you wish to forward this email to co-authors, please delete the link to your homepage. In their analysis Professor Zhang and colleagues use a multi-strain metapopulation model to explore SARS-CoV-2 epidemic trajectories in HICs and LMICs under different global allocation strategies for vaccines. Their claim is that inequitable distribution of vaccines provides short-term benefits to HICs and triggers important epidemics in LMICs. The latter may represent a threat also for HICs, first because of the likelihood of importing infections from LMICs, second because these epidemics may fuel the emergence of new (more transmissible and more severe) variants.
I appreciate the attempt of the authors to deal with such an important topic. Even if their claims are fully reasonable, my feeling is that they are poorly supported by modeling results. The title of the manuscript is catchy. The authors talk about "life-saving difference to all countries", a deceased compartment is included in the model, however, they do not quantify the burden of deaths under the different vaccination strategies. I have several serious concerns on both the Methods and the presentation of results which I summarize below.

Major comments
The authors initialize the model on June 15, 2021. 1. The number of infectious individuals at time 0 is assumed to be equal to the number of active cases on the day considered. It is well known that there is a certain degree of underreporting of SARS-CoV-2 infections. I expect that in LMICs this number could be much higher due to several reasons. Indeed, LMICs are generally characterized by a younger population compared to HICs, resulting in a higher proportion of cases in younger age groups, less likely to develop symptoms. The higher fraction of cases among young people coupled with less effective testing and monitoring strategies may result in a very low detection rate. Indeed, as shown in Figure 1 a-f, the fraction of infectious individuals at time 0 seems much lower in LICs than HICs. I believe that such a different time scale in the fraction of infectious at time 0 is not realistic at all. 2. The same comment applies also to the initial fraction of recovered. Initializing recovered by considering the cumulative number of reported cases up to June 15, 2021, especially for LMICs, characterized by low detection rates, could result in a significant underestimation of the fraction of immune in the population.
3. If I correctly understand, at model initialization all infectious cases are assumed to be generated by strain 1. The transmissibility of strain 1 (T_1) is computed assuming a basic reproduction number R_0=2.79. This value could have been reasonable for the original SARS-CoV-2 strain, circulating world-wide in 2020, but model initialization occurs on June 15, 2021. At that time, the original strain had been largely replaced by the alpha variant (characterized by ~50% higher transmissibility) and the delta variant (~50-60% more transmissible than the alpha) had started its path to become prevalent. As stated by the authors: "the most dangerous strain (Strain 5) has a 46.41% higher transmissibility than the original strain". This means that the authors are assuming that the most transmissible variant possibly appearing in the next 5 years will be characterized by a transmissibility lower than the one estimated for the alpha variant (already prevalent at the time at which the model is initialized). This assumption is far too optimistic. Please adjust the transmissibility of strain 1 at least to the value observed for the alpha variant and explore a higher range of transmissibility for variants (possibly up to R0=10-11). 4. I am not convinced by the assumption made on NPIs. I understand that the authors estimate the contact rate c_i in country i as a function of the effective reproduction number estimated for country i at June 15 and they keep this level of NPIs over the 5 years considered. First, given the poor detection of cases in LMICs I am not sure the estimates of the effective reproduction number in those countries are reliable. Second, even when assuming that they are reliable, it is known that NPIs are usually adapted by governments in the presence of re-emergence of cases and then released when the number of cases comes again under control. Assuming that the NPIs level will remain constant for the next 5 years to a value based on a picture of the effective reproduction number at a specific time point is a very strong assumption. I believe that this assumption could make the epidemic trajectories in the different countries hardly comparable. I don't know if there is a solution to this issue. One possibility could be to explore the future epidemic trajectories in the absence of NPIs. 5. Are prioritization criteria based on incidence/prevalence decided according to the value assumed at initialization or are they updated dynamically depending on the epidemic evolution within each country? 6. I was wondering which is the percentage of people immunized with single dose vaccines worldwide (e.g. Johnson&Johnson). If this percentage is low, please consider doubling the number of doses required to build full vaccinal immunity (it is not necessary to implement dynamically the administration of the doses separately). 7. Also, the assumption of an unlimited vaccination rate is quite strong. If possible, adding an upper bound to the vaccination rate based on (eventually rough) estimates of the maximum rate achieved by LMICs and HICs could certainly benefit the interpretation of results in light of the real-world context. 8. Do you eventually re-vaccinate individuals who have lost vaccine immunity? If vaccinated individuals who lose immunity become susceptible and eligible again for vaccination, I was wondering if this assumption coupled with an unlimited vaccination rate is basically equivalent (at least in countries with a big stock of vaccines) to not considering waning immunity at all. In fact, I find it surprising that in a model considering variants progressively more transmissible, with a reduced vaccine protection and short-living vaccine immunity, the end of the epidemic is achieved. I would expect zero-COVID not to occur. 9. I find that the Figures are not sufficiently clear to transmit the information needed: -What are panels k-o showing? Is this the fraction of new daily infections caused by strain m divided by the world population? I find it could be more interesting to show the share (%) of new daily cases due to the different strains (possibly on the same plot). I expect the curves to sum to 100% at each time step. Figure 3 -Panel b,c,e,f. How is this reduction/increase computed? Do the numbers on in the right legend represent net increases or percentages?
Minor comments: a. A reference for the average case fatality ratio worldwide (0.02) should be added. b. I do not see significant differences in results obtained using prioritization based on incidence and prevalence. This is somehow expected. I would simplify figures in the main text and place results on one of the two in the supplementary materials. c. It is known that COVID-19 severity strongly increases with ages. This could be one of the reasons why HICs countries, characterized by older populations, have been strongly hit by the pandemic, even in the presence of advanced and efficient health care systems, while in some other LMICs COVID-19 burden appears relatively low (see e.g. Trentini et al, BMC Medicine, 2021). Please acknowledge that one of the main limitations of your approach is that your model is not stratified by age. d. Pag.9: reference to Figure 3b in the text. Do you mean 3d? e. Pag.9. "Either a larger δ or a larger I_thre results in a larger reduction in cumulative cases in LMICs ( Fig. 3e and f), which means the larger proportion of vaccines they share, the fewer people in LMICs will be infected.". Looking at the figure, I_thre apparently play no role (or a very limited role). I would modify to: "Larger δ results in a larger reduction in cumulative cases in LMICs ( Fig. 3e and f), which means the larger proportion of vaccines they share, the fewer people in LMICs will be infected.". f. Pag 15: "We have proposed a mathematical model to investigate both the short-term and long-term impacts of vaccine equity taking account of immune escape and global transportation.". Apparently, the authors are not including immune escape (hosts recovered from either strain are immune to all other strains). Please specify. g. Results (pag.7): "In these new waves, infections in HICs are largely due to imported cases from LMICs.". Could you clarify if this assumption is based on your model outcome? Can the model separately keep track of secondary cases generated by imported infections?
Reviewer #2: Remarks to the Author: This paper aims to understand, through a multi-strain metapopulaiton mathematical model, how vaccine equity for COVID-19 can impact its global epidemiology. Briefly, it shows that vaccine unequity can only provide short term benefits to the HICs and that vaccine donations is the best strategy to decrease COVID-19 burden.
I think the paper is interesting, timely, and deserves to be published when my comments would have been included.

Major comments
My main concern is about the connectivity between LMICs and HIcs which is not explictly mentionned. However, this connectivity network is far from random and change impact dramatically their conclusion. I can understand that is not addressed explicitly, but it needs to be carefully discussed.
The second concern is about the lack of references to other works on that topic. Indeed, there are several papers discussing this topic (on other pathogens) and they have to clearly cited and discussed.
Finally, my last major concern is about the initialisation of the simulation, especially regarding the number of strains. From what I've understood, it starts with 5 strains but different initial conditions can produce very different outcomes.
Minor comments: The study is a very important and well-written paper aiming to measure inequity in COVID-19 vaccination distribution. The authors developed a mathematical model that explicitly considers 1) the inequity in vaccine distribution and 2) the viral evolutionary dynamics and their effects on vaccine efficacy. Their key finding suggests that vaccine inequity only provides limited and short-term benefits to HICs, leading to a moderate increase in infections and deaths in LMICs.
The work is timely and is of particular interest nowadays, with the initiation of the third booster dose in several countries. I find their mathematical model clear, transparent, and elegant. I do have two concerns: 1) it is clear that there is a wanning of both natural and vaccine immunity. It is not being considered in their model and might affect their results. 2) COVID-19 is mild in younger age groups and but may cause severe infection in the elderly. Population from HIC, in that sense, are more susceptible to a severe outcome. Given that the model is not age-structured, it is essential to have a different mortality rate in HIC and LIC. I might have missed it, but I did not see that the authors accounted for the difference in death rates in HIC and LIC. I have several more points that may help improve the paper ( please see below). I, therefore, recommend accepting the paper following a major revision. Introduction • Citation 6. It might be more beneficial to add policy papers (i.e, advisory committees, FDA regulations etc.) that explicitly call for a vaccination with booster doses (e.g., Israel, US, UK).
• The authors stated: "Thus, making COVID-19 3 vaccines distributed equitably is not only a moral obligation for high-income countries but also in their rational self-interest." If it has been previously found, add ref. If not, it sounds like a statement or an opinion and should not appear in the intro. I think it is part of their finding, so it should not be here. In the introduction, it might be useful to say that it has been previously shown for flu https://bmcpublichealth.biomedcentral.com/articles/10.1186/s12889-021-11601-2 , which provides a motivation for their study.
• The authors stated: "With these solutions, global vaccine distribution could no longer be a 'zero-sum game' but a 'cooperative game' " This is has been previously considered in the context of game-theoretic model. • The authors considered strategies as follows: o "• Population size. Priority to countries with larger population sizes. • Prevalence. Priority to countries with a higher number of active cases (currently infectious cases) per capita. • Incidence rate. Priority to countries with a higher incidence rate, which is defined as the number of new cases during two weeks as a share of the total population." o Typically, strategies are considered in the scientific literature to work of such kind -'morbidity based' and 'mortality based' are considered. I strongly suggest adding a mortality based strategy ( i.e., prioritizing in regions of higher mortality) Results • Figures 2 and 3 present infections. Given that COVID-19 is typically mild or asymptomatic in young age groups, it is more important to present mortalities or severe outcomes. Discussion Please add two limitations and try to explain if they should affect your main outcomes: 1) waning immunity following infection ( i.e., moving from recovered to or at least susceptible) 2) age-structured model I would like to wish the authors the best of luck in addressing the review. Thank you for submitting your revised manuscript "Promoting equitable access to COVID-19 vaccines makes a life-saving difference to all countries" (NATHUMBEHAV-210816308A). It has now been seen by the original referees and their comments are below. As you can see, the reviewers find that the paper has improved in revision. We will therefore be happy in principle to publish it in Nature Human Behaviour, pending minor revisions to satisfy the referees' final requests and to comply with our editorial and formatting guidelines. Please note that in addition to the reviews included below, Reviewer #3 has submitted confidential remarks to the editors, recommending publication of your work with no further requests.

Author Rebuttal to Initial comments
We are now performing detailed checks on your paper and will send you a checklist detailing our editorial and formatting requirements by tomorrow, so that you could work on the final revisions within the next couple of weeks, aiming to resubmit in the first weeks of January. Given the timeliness of your findings, we are hoping to be able to publish your work by the end of January. **Please do not upload the final materials and make any revisions until you receive this additional information from us.** Please do not hesitate to contact me if you have any questions. Please specify in the caption that dashed lines refers to the strategy defined by the same colour (or add an additional legend on the right).
2) In Figure 4f on the total number of doses. Are the y-axis labels correct? I find the presence of "%" on the y-axis labels and "x10^7" on the top of the plot confusing.
Reviewer #4 (Remarks to the Author): Dear prof. Arunas Radzvilavicius, I went over the revised paper and their reply to my comments. I think the authors made a wonderful job. They fully addressed all of my comments.
I also went over their code ( please note, the file you shared with me had some error, but I searched and found this linkhttps://github.com/jianan0099/VACEquity_initial ). It is very well documented and highly transparent.
I think their key messages are of high interest and are timely. I, therefore, think the journal will greatly benefit from a fast publication (particularly now, with the Omicron...). Thus, I highly recommend accepting the paper.
I hereby declare no conflict of interest and would like to wish the authors the best of luck.
Author Rebuttal, first revision:

Final Decision Letter:
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