Vaccination efforts in Brazil: scenarios andperspectives under a mathematical modeling

approach

Thomas Nogueira Vilches1∗

Felipe Alves Rubio1,Rafael Augusto Forti Perroni2,Gabriel Berg de Almeida2 ,

Carlos Magno Castelo Branco Fortaleza2,Cláudia Pio Ferreira3 ,

1 Institute of Mathematics, Statistics and Scientific Computing, UNICAMP, Brazil,2 Medical School, UNESP, Botucatu Campus, Brazil,

3 Institute of Biosciences, UNESP, Botucatu Campus, Brazil.

Abstract

An agent-based model is proposed to access the impact of vaccination strategiesto halt the COVID-19 spread. The model is parameterized using data from SãoPaulo State, Brazil. It was considered the two vaccines that are already approvedfor emergency use in Brazil, the CoronaVac vaccine developed by the Chinese bio-pharmaceutical company Sinovac and the Oxford-AstraZeneca vaccine (ChadOx1)developed by Oxford University and the British laboratory AstraZeneca. Both ofthem are two-dose schemes, but the efficacy and the interval between doses aredifferent. We found that even in the worst scenario, in which the vaccine does notprevent infection either severe symptoms, the number of deaths decreases from 122to 99 for CoronaVac application and to 80 for ChadOx1 administration. The samepatterns have been seen in hospitalizations. Nevertheless, we show that when a lowrisk perception occurs, the reduction values decrease between 2% to 4%. Moreover,the increase of disease prevalence also jeopardizes immunization, showing the impor-tance of the mitigation measures maintenance. On the other hand, doubling the vac-cination rate would be able to significantly decrease the disease outcomes, reducing

∗Corresponding author: e-mail: thomvilches@gmail.com. Address: Rua Sérgio Buarque de Holanda,651, Campinas, SP, Brazil, 13083-859

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NOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice.

deaths by up to 74.4%. In conclusion, vaccination, along with non-pharmaceuticalmeasures, is key to the control of COVID-19 in Brazil.

Keywords: agent-based model, behavioral change, immunization scheme.

1 IntroductionStarting between November and December of 2020, the Coronavirus Disease 2019

(COVID-19), caused by the virus SARS-CoV-2, pandemic claimed several lives worldwide.More than fourteen months later, many questions are still not answered, including itsorigin, routes of transmission, and effective treatment.

After struggling for months to halt or diminish transmission solely through non-pharmacological measures such as quarantine, self-isolation, lockdown, mobility restrictionon several spatial scales, a local and global immunization program through vaccinationappears as a real possibility. Several vaccines have been approved for emergency use inseveral countries. They differ by technological and conceptual platforms, grouped basedon the protective immune response they trigger [1]. The main platforms are RNA, DNA,Vector (non-replicating), Vector (replicating), Inactivated, Live-attenuated, Protein sub-unit, and Virus-like particle. Currently, there are about fifty official vaccine projects thatreached human experimentation. Ten vaccine platforms are already in use, of which twoof them are RNA, three are vector (non-replicating), four are inactivated, and one issubunit [2].

In Brazil, it was not different. On January 17, 2021, the National Health SurveillanceAgency (Anvisa) approved the emergency use of the Coronavac vaccine developed bythe Chinese biopharmaceutical company Sinovac and the ChadOx1 vaccine developedby Oxford University and the British laboratory AstraZeneca. Both of them performedphase 3 of their study in Brazil within a collaboration with two important BrazilianInstitutions: Butantan and the Oswaldo Cruz Foundation (Fiocruz). The efficacy againstsymptoms presented by Coronavac and Oxford-AstraZeneca vaccines, after two doses, was50.38% and 70.42%, respectively [3,4]. However, according to Anvisa, the data presentedregarding efficacy had no statistical significance for severe cases, although some protectionis expected.

Brazil is known as one of the countries that produce and export vaccines to the restof the world. The leading factories of Butantan and Manguinhos (Fiocruz) can producefrom 1 to 3 million doses per day [5]. Moreover, the country counts with the NationalImmunization Program (PNI) through the Unified Health System (SUS). The PNI isresponsible for successfully eradicating and controlling several endemic diseases in thecountry, increasing the average Brazilian life expectancy for almost half a century. Therobust and complex infrastructure of SUS guarantees access to medicines, vaccines, andhealthcare to the population. It provides high-cost medicines, expensive medical proce-dures, and vaccines to the entire population, including those with private health insur-ance and people in difficult-to-access areas, such as indigenous people and quilombolas1

1Communities that used to be a slave settlement. Nowadays, they are protected areas in which the

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communities [6, 7]. This well-established unified system is an important tool against theCOVID-19 epidemics and can easily vaccinate almost one million individuals per dayas shown in other immunization campaigned [8]. However, the lack of vaccine produc-tion inputs makes the vaccination against COVID-19 in Brazil take slow steps towardscommunity immunization.

There is a worldly concern that the beginning of vaccination against COVID-19 maycause a decrease in the perception of the risk of infection [9]. The belief that immuniza-tion or recovering from a natural infection can promote full long-life protection is widelyspread information among the population [10]. Besides, even after the strengthening ofrestrictions due to the explosive increase of cases and death toll after the holiday sea-son, it is common to witness people-packed bars, streets, and even parties [11, 12]. Moreand more frequently, the news shows the lack of social distance between people, the ab-sence of masks in public places, among other neglect of the population. Particularly inBrazil, the lack of a national plan to control the spread of SARS-CoV-2 and its variantshas been jeopardizing the recovery of the economy and enhancing the health system’sfragility. Moreover, since the beginning of it, President Jair Bolsonaro has denied the im-portance of the epidemic, spreading fake news about the vaccine and encouraging the useof medications proven to be inefficient against COVID-19 [13,14].

Brazil has started vaccination on priority groups, such as health professionals, olderadults (80+), and indigenous people. But only about 2.7% of the Brazilian population hasbeen achieved. Besides, Amazon state has been also prioritizing because of the emergenceof new variants of the virus [15]. However, by the time vaccine is available for a broadpopulation, acceptance and engagement are concerns. According to the Brazilian Immu-nization Society (SBIm) [16], there are three main reasons for the population to refusevaccination: confidence, complacency, and convenience. In this context, we aimed to in-vestigate, using an agent-based model (ABM), the vaccination’s impact under differentscenarios of disease immunity, disease prevalence, individual behavior, and vaccinationrate.

2 Methodology

Mathematical ModelAn extension of a previous agent-based model is proposed to assess the impact of vac-

cination against COVID-19 in Brazil [9]. The population is divided into eight epidemiolog-ical classes given the history of the disease and a campaigned vaccination in course, whichare: susceptible; vaccinated; exposed; asymptomatic (and infectious); pre-symptomatic(and infectious); symptomatic with either mild or severe/critical illness, recovered; anddead. The model considers several types of heterogeneity, such as the contact matrixamong age classes, the disease incubation and recovery period, the daily contact numberamong individuals, the percentage of individuals that show disease symptoms, the pre-

descendant of those slaves live.

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existing herd immunity of each age class, prevalence of comorbidities in each age group,among others.

Every time-step, the number of daily contacts for each individual was sampled from aPoisson distribution based on their age group’s average number of contacts per day (seeTable A.3). With probability β, a susceptible individual is infected after contacting an in-fected individual. β is dependent on the status of the infected individual (asymptomatic,pre-symptomatic, mild, or severe symptomatic). The incubation period was calculatedfrom a Gamma distribution, based on an average estimate of 5.2 days. The time of incu-bation period in a pre-symptomatic stage is also calculated from a Gamma distribution.For symptomatic and asymptomatic individuals, the infectious period was also sampledfrom the Gamma distribution, with a mean of 3.2 days and five days, respectively. De-pending on the age, symptomatic individuals can develop mild or severe conditions (seeTable 1).

Table 1: Table of parameters.

Description 0–4 5–19 20–49 50–64 65–79 80+ SourceTransmission probability per contactduring presymptomatic stage Depending on the level of herd immunity Calibrated to

R=1.04 [17]Incubation period(days) LogNormal(shape: 1.434, scale: 0.661) [18]

Asymptomatic period(days) Gamma(shape: 5, scale: 1) Derived from

[19,20]Presymptomatic period(days) Gamma(shape: 1.058, scale: 2.174) Derived from

[21,22]Infectious period fromonset of symptoms (days) Gamma(shape: 2.768, scale: 1.1563) Derived from

[20]Proportion of infectionsthat are asymptomatic 0.30 0.38 0.33 0.33 0.19 0.19 [23–25]

Proportion of symptomatic casesthat exhibit mild symptoms 0.95 0.90 0.85 0.60 0.20 0.20 [9]

Proportion of cases hospitalizedwith one or more comorbidities 37.6%

[26,27]Non-ICU 67%ICU 33%

Proportion of cases hospitalizedwithout any comorbidities 9%

[26,27]Non-ICU 75%ICU 25%

Length of non-ICU stay (days) Gamma(shape: 4.5, scale: 2.75) Derived from[28,29]

Length of ICU stay (days) Gamma(shape: 4.5, scale: 2.75) + 2 Derived from[28,29]

Mild symptomatic cases and severely ill cases isolate themselves within 24 hours oncethe symptoms started. We assumed (due to Brazil’s lack of data) that the number of

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contacts is reduced to a maximum of three, until recovery, on self-isolation. A proportion ofthe severe cases were hospitalized in a period of 3 to 9 days from the commencement of thesymptoms [30], depending on their comorbidity status (see Table 1). The hospitalizationperiod for ICU and ward was sampled from Gamma distribution, averaging 14.4 days and12.4 days, respectively. Once hospitalized, the individual did not contribute to the spreadof infection any longer. We assumed that long-life immunity is achieved after a successfulrecovery from an infection. The mortality probability is derived from [31], per age group,and applied to severe cases (see Table A.4).

We implemented the two-dose vaccination campaign already in the course in Brazil.The vaccines doses are distributed following the vaccination plan announced by the HealthMinister of Brazil [32] and prioritizes the following groups (decreasing order): (i) health-care workers and the age group 80+; (ii) individuals that are between 60 to 79 years old;(iii) comorbid individuals in the age group 18 to 59 years old; (iv) general population.We assumed that vaccination takes place until the total coverage reaches 40% of the pop-ulation, which is enough to keep vaccinating during the whole simulated epidemic peak(that takes around 200 days).

Model’s parametrizationWe used the effective reproduction number of 1.04, reported for the São Paulo State on

January 10th [17], to calibrate the model and find the transmission probability per contactbetween a pre-symptomatic individual and a susceptible individual. The transmissionprobability of asymptomatic, mild, and severe symptomatic cases was considered to be26%, 44%, and 89% in the pre-symptomatic stage. The population’s age distribution isbased on the one reported in São Paulo State, encompassing ten age groups [33]. Thecontact distribution matrix is derived from Prem et al. [34], dividing the population intofive age groups (see Table A.3). Besides, the presence of comorbidities is stratified by age(see Table A.1) [35].

Vaccine efficacy against symptoms is 50.38% for CoronaVac (from Sinovac) and 70.42%for ChAdOx1 nCoV-19 vaccine (from Oxford-AstraZeneca) [3, 4]. For CoronaVac, due tothe lack of information about the single-dose efficacy, we supposed that the first dose pro-vides half of the second dose efficacy. Besides, we also supposed that, following vaccination,the first and second doses’ efficacy is established after 14 and 7 days, respectively. ForChadOx1, the first dose efficacy is 64.1%, and each dose’s protection is established after21 and 14 days after the vaccination. Since there is no statistical significance of protectionagainst severe symptoms during phase 3 of both vaccines, we simulated two scenarios ofprotection against severe symptoms: no protection (0%) and complete protection (100%).Any other result must lay between them.

Regarding the protection against infection, we supposed three different scenarios: 0%,50%, and 100% of the vaccine efficacy against symptoms. We supposed a vaccination rateof 0.3% of the population per day, which can be conservative given the vaccine produc-tion capacity reported both for CoronaVac and ChadOx1 in Brazil. We also simulateda scenario with a double vaccination rate, which may be achieved when combining both

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vaccines’ production capacity. Lastly, a low-risk perception scenario, where vaccinatedindividuals do not isolate themselves when they have mild symptoms, is analyzed.

The initial condition is given by one individual, in the pre-symptomatic stage, ar-riving at a population. We suppose a preexisting immunity in the population of 10, 20,and 30% [36]. Regarding the age distribution of population immunity, this was sam-pled following the reported age distribution of São Paulo State cases [31] (see Appendix,Table A.2). After, we relaxed the initial condition to assume a high prevalence of thedisease. In summary, the baseline scenario encompasses isolation of symptomatic indi-viduals and a vaccination rate of 0.3% of the population per day; in the double vac-cination rate, the rate is increased to 0.6% per day; and in the low-risk perception,the rate is kept at 0.3%, and vaccinated individuals do not isolate themselves whenhaving mild symptoms. The results shown are an average of 1000 independent Monte-Carlo realizations. The model was implemented in Julia language and is available athttps://github.com/thomasvilches/abm−brasil.

3 ResultsLet us define the "relative protection against infection" as the fraction of the vac-

cine efficacy against disease symptoms that is also applied against disease transmission.It means that, for instance, for the CoronaVac, as vaccine efficacy against symptomsis 50.38%, the scenario with 100% of relative protection means that protection againstdisease transmission is 50.38%; if the relative protection against infection is 50%, thenprotection falls to 25.19% against disease transmission; and if it is 0%, then there is noprotection against disease transmission. The efficacy against severe symptoms is either0% (no protection) or 100% (total protection), which means that none of the vaccinatedindividuals develops severe symptoms.

Firstly, we accessed the total number of infections, ward and ICU hospitalizations,and deaths resulting from each scenario of vaccine efficacy. Figures 1 and 2 display theresults, supposing a previous herd immunity of 20% in the population. In the absence ofa vaccine, it is generated almost 23,000 infections per 100,000 population. In a scenarioof vaccination where vaccine provides the same level of protection against infection asagainst symptoms and complete protection against severe symptoms (scenario 100%-100%in Figures 1 and 2), the number of infections decreases to 22,140 for CoronaVac and 19,320for ChadOx1. Notice that when the protection against infection is zero, the number ofinfections overcomes the no vaccination scenario for both vaccines (scenarios 0%-0% and0%-100% in Figures 1 and 2). This happens because asymptomatic individuals do notisolate themselves, and vaccination protects against disease symptoms.

It is important to highlight that even with a higher number of infections, vaccinationmay prevent many deaths and hospitalizations. Even in the case in which the vaccinedoes not provide any level of immunity against infection, the number of deaths decreasesfrom 122 to 99 for CoronaVac and 80 for ChadOx1 when there is no protection againstsevere disease (scenario 0%-0% in Figures 1 and 2). The same patterns have been seen

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in hospitalizations (ward and ICU). This happens because although the vaccine doesnot prevent infections, it prevents symptoms and avoids possible severe outcomes of thedisease.

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Figure 1: Outcomes for the baseline scenario and the CoronaVac vaccine. The second doseis given after 21 days.

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No Vaccination100% − 0%100% − 100%50% − 0%50% − 100%0% − 0%0% − 100%

Figure 2: Outcomes for the baseline scenario and the AstraZeneca/Oxford vaccine. Thesecond dose is given after 90 days.

Another way to compare the different scenarios is by measuring the percentage re-duction in the average number of outcomes concerning the scenario in the absence ofvaccination (Figures 3 and 4). In the baseline scenario, CoronaVac does not provide asignificant reduction in the total number of infections in one epidemic peak, which takesaround 200 days. However, it does prevent several of the disease outcomes. In the worstscenario of efficacy, in which the vaccine does not prevent either infection or severe symp-toms (scenario 0%-0% in Figure 3), the vaccination may avoid 19.5% of deaths (95% CI:12.79% - 25.93%), 11.1% (95% CI: 2.92% - 17.77%) of ICU admissions and 9.44% (95% CI:2.18% - 15.91%) of non-ICU hospitalizations. In the best case, when the vaccine preventsinfection and symptoms equally, as well as it protects 100% against severe cases (scenario100%-100% in Figure 3), a reduction of 45.2% (95% CI: 40.58% - 49.66%), 28.19% (95%CI: 22.43% - 33.99%), and 31.32% (95% CI: 25.25% - 36.58%) can be achieved regardingthe number of deaths, non-ICU hospitalizations, and ICU admissions.

Still in the baseline scenario, the vaccine ChadOx1 can perform even better (Figure4), given its reported efficacy. In the worst efficacy assumption, in which the vaccine doesnot prevent against neither infections nor severe symptoms (scenario 0%-0% in Figure4), the reductions in deaths, non-ICU hospitalizations and ICU admissions correspond to34.98% (95% CI: 29.65% - 40%), 27.31% (95% CI: 20.98% - 33.75%) and 30.88% (95%CI: 25.15% - 36.07%), respectively. When considering the best situation (scenario 100%-100% in Figure 4), ChadOx1 would prevent 56.98% (95% CI: 53.13% - 60.41%) of deaths,52.03% (95% CI: 48.24% - 56.04%) of ICU admissions, and 47.59% (95% CI: 43.15% -51.57%) of non-ICU hospitalizations.

Doubling the vaccination rate with the CoronaVac, the worst scenario (scenario 0%-

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0% in Figure 3) presents a reduction of 30.25% (95% CI: 24.77% - 35.24%) in the numberof deaths. The prevention of hospitalizations and ICU admissions more than doubledcompared to the baseline scenario, reaching reduction values of 24.71% (95% CI: 18.66%- 30.15%) and 27.35% (95% CI: 20.85% - 33.09%), respectively. When it is consideredthat vaccine prevents against infection at the same level as it prevents against symptoms,and protects 100% against severe cases (scenario 100%-100% in Figure 3), the reductionof deaths would be 65.68% (95% CI: 62.64% - 68.56%). The ICU admissions would bereduced by 60.94% (95% CI: 57.39% - 64.08%), and non-ICU hospitalizations 57% (95%CI: 53.48% - 60.58%).

Considering a double vaccination rate for the ChadOx1 vaccine, the results are evenbetter. The worst scenario (scenario 0%-0% in Figure 4) the number of deaths is reducedby 46.8% (95% CI: 42.37% - 50.81%), the non-ICU hospitalizations 39.59% (95% CI:34.54% - 44.47%), and ICU admissions 41.87% (95% CI: 37.05% - 46.37%). In the bestscenario (scenario 100%-100% in Figure 4), the reduction in the number of deaths exceeds70%, whose value is 74.43% (95% CI: 72.1% - 76.55%). The prevention regarding the non-ICU hospitalizations is 65.54% (95% CI: 62.85% - 68.29%), while the prevention of ICUadmissions is 68.57% (95% CI: 66.03% - 70.97%).

Nevertheless, when occurs a low-risk perception, the reduction values decrease. Con-sidering the vaccine ChadOx1 in the best scenario (scenario 100%-100% in Figure 4), thereduction in the number of deaths, non-ICU hospitalizations, and ICU admissions decreaseto 32.38% (95% CI: 26.72% - 37.47%), 24.58% (95% CI: 18.23% - 30.82%), and 27.95%(95% CI: 21.63% - 33.77%), respectively. While for CoronaVac (scenario 100%-100% inFigure 3), we reached the values of 43.59% (95% CI: 37.89% 47.11%), 25.4% (95% CI:19.18% - 31.06%), and 28.35% (95% CI: 21.88% - 34.19%). In general, the decrease inthe reduction ranges between 2% - 4%, revealing that if vaccinated people do not isolatethemselves when mildly symptomatic, the vaccination result will be less efficient, whichmay correspond to thousands of lives. It shows the necessity of keeping other mitigationstrategies while the process of vaccination occurs.

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Baseline Double vaccination rate Low risk perception

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100% − 0%100% − 100%50% − 0%50% − 100%0% − 0%0% − 100%

Figure 3: Percentage reduction in outcomes for the different scenarios and the CoronaVacvaccine. The second dose is given after 21 days.

Baseline Double vaccination rate Low risk perception

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Figure 4: Percentage reduction in outcomes for the different scenarios and the As-traZeneca/Oxford vaccine. The second dose is given after 90 days.

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Finally, Figures 5 and 6 show the reduction in the outcomes when considering dif-ferent initial infection prevalence scenarios. Instead of initializing the system with onepre-symptomatic individual, we variated the initial number of infections and assessedhow it changed the vaccination’s impact (average reduction of disease outcomes).

Regarding the CoronaVac, considering the best-case scenario (scenario 100%-100% inFigure 5), concerning the number of infections, it was possible to observe an increase inthe average reduction in outcomes when the initial number of infected individuals movesfrom one to five, from 3.58% (95% CI: -4.39% - 10.99%) to 6.96% (95% CI: 5.71% - 8.12%),followed by a decrease for higher number of initial infections, from 6.96% (95% CI: 5.71%- 8.12%) to 5.30% (95% CI: 5.23% - 5.37%). In relation to non-ICU hospitalizations, thedecrease in reduction was more drastic, going from 28.24% (95% CI: 22.27% - 34.12%) to18.24% (95% CI: 17.88% - 18.58%). In turn, the ICU hospitalizations presented similardecrease in reduction comparing to the rearmost, going from 31.28% (95% CI: 25.78% -36.50%) to 19.85% (95% CI: 19.36% - 20.30%). Again, the changes on the reduction indeaths are expressive, going from 45.30% (95% CI: 40.31% - 49.57%) to 34.35% (95% CI:33.90% - 34.79%).

For the ChadOx1, increasing the initial prevalence of the disease, we obtain a signif-icant decrease in the reduction of infections, non-ICU hospitalizations, ICU admissions,and deaths. In the best-case scenario (scenario 100%-100% in Figure 6), the reduction inthe number of infections has a slight increase when changing the initial prevalence fromone to five individuals, from 15.92% (95% CI: 9.19% - 22.55%) to 18.84% (95% CI: 17.66%- 20.05%). For a initial prevalence of 45 infections, the reduction in infections jumps to11.79% (95% CI: 11.71% - 11.88%). The non-ICU hospitalization reduction decreases from47.62% (95% CI: 43.47% - 51.41%) to 30.68% (95% CI: 30.37% - 31%), while the ICUadmission reduction was 51.97% (95% CI: 47.73% - 55.96%). The decline in the numberof deaths is even more remarkable going from almost 60% (95% CI: 53.55% - 60.45%), forone initial infection, to 44.15% (95% CI: 43.76% - 44.54%), for 45 initial infections.

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Infections Hospitalizations ICU Deaths

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100% − 0%100% − 100%50% − 0%50% − 100%0% − 0%0% − 100%

Figure 5: Average percentage reduction in outcomes in function of the initial number ofinfected individuals for the different scenarios and the CoronaVac vaccine. The seconddose is given after 21 days.

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Infections Hospitalizations ICU Deaths

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100% − 0%100% − 100%50% − 0%50% − 100%0% − 0%0% − 100%

Figure 6: Average percentage reduction in outcomes in function of the initial number ofinfected individuals for the different scenarios and the AstraZeneca/Oxford vaccine. Thesecond dose is given after 90 days.

4 DiscussionWe presented a stochastic agent-based model to simulate the SARS-CoV-2 transmis-

sion in a population and to discuss the scheme of vaccination already in the course inBrazil. The model is an extension of the one presented at [9], but it was parameterizedwith population characteristics of São Paulo State, Brazil. Besides, it explores the pos-sibility of a behavior change caused by a decrease in risk perception. We analyzed theimpact of the vaccination campaign supposing different vaccine efficacy scenarios, accord-ing to the two available vaccines in Brazil, the CoronaVac, from Sinovac-Butantan, andthe ChadOx1, from AstraZeneca-Oxford-Fiocruz. Other key points that can jeopardizeimmunization, such as the population’s immunity, the disease prevalence, and the vacci-nation rate, were also accessed.

Important to note that Buss et al. [37] have scrutinized, through donated blood sam-ples, the attack rate in Manaus and São Paulo, two of the most dramatic examples of therapid spread of COVID-19 in Brazil [37]. The authors have found that in June (2020),one month after the epidemic peak in Manaus, 44% of the population had detectableimmunoglobulin G (IgG) antibodies, with a 66% attack rate in June 2020 and 76% inOctober. The attack rate in São Paulo was 29% in October 2020. Specially Manaus wasseen as an example of a city where herd immunity was almost achieved. Therefore newwaves of the epidemic were not supposed to occur. Surprisingly, Manaus lives its worst

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epidemic moment now, with new variants of the virus emerging and spreading to othercities besides the health system’s collapse. This is a clear message that the only wayto diminish the deaths, halt transmission, and recover the economy is through a globalimmunization program.

The impact of the vaccination campaign over disease control is not only related to thevaccine efficacy but also the velocity of vaccine production and distribution [38]. Compe-tition about vaccine supplies is already in course, and low- and middle-income countriesare more impacted by it. The baseline scenario was chosen to have a vaccination rate thatcorresponds to 0.3% of the population per day. Considering the Brazilian population,it would correspond to 640 thousand doses per day in the whole country. Moreover, wesupposed vaccination campaign to be placed during the whole week, including weekends,which is already implemented in other countries, like the USA.

Given the reported number of vaccinated people per day during other immunizationprograms in Brazil, such as against poliomyelitis (which vaccinated 80 million children inthree months) [8], it is fair to suppose that its production gives the limitation of vaccinedistribution. In fact, the expected production rate of vaccines, when Butantan and Fiocruzreach their full capability, is around 1 to 3 million doses per day for each vaccine [5].Therefore, our results are conservative in this aspect since it is also expected that Brazilcan provide vaccine access to its neighborhoods countries in South America. The scenariowith a double vaccination rate was able to decrease the disease outcomes highly. Even forCoronaVac, which has a lower reported efficacy compared to ChadOx1, in the situationthat the vaccine does not prevents infections neither severe symptoms (scenario 0%-0%Figure 4), the reduction in ICU admissions and deaths was above 25%. This could takethe health system off the borderline of collapsing. Today, most non-elective surgeries arecanceled, prejudicing a lot of Brazilian patients that have their quality of life diminishedby it. A fast vaccination would also stop the emergence and spread of new variants of thevirus, which is already on course in Brazil, England, and Africa. Once fewer people getinfected, the lower is the mutation probability of the virus.

When comparing the baseline scenario to the scenario in which the risk perception ofvaccinated individuals is reduced (third column of Figures 3 and 4), we can see that 2%to 4% of the vaccination efficiency is lost, which can correspond to thousand of lives whenlooking at the number of deaths per 100 thousand population. This shows the importanceof keeping mitigations strategies and an information campaign taking place together withvaccination. According to the Brazilian Immunization Society (SBIm) [16], the vaccinationcoverage by the PNI has steeply decreased since 2015, and in 2019 and 2020, none of theimmunization programs has reached their goals. This is due to the lack of investmentsand good governance of the SUS services. As an example, the poor vaccination coveragehas caused the once eradicated measles to reappear in Brazil. Another example is thevaccine for human papillomavirus (HPV). Santos et al. [39] show that despite 96.6% ofthe studied population took the vaccine, 32.3% did not take the second dose, which isconsidered a high dropout rate.

This study shows that the vaccines are keystones to preventing high attack ratesand high tolls of hospitalizations and death, even when they provide limited protection

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against infection. The findings presented in our study are best interpreted by addressingthe assumptions and limitations of this investigation. It was particularly challenging tofind the data on the number of hospitalized individuals stratified by non-ICU and ICUtypes, as well as the steps addressed by the current national plan of immunization (PNI).Assessing the collective or individual behavior after the vaccination was not possible atthe moment. We assumed that the PNI was able to vaccinate the vast majority of high-risk individuals, made up of 90% of healthcare workers and 70% of comorbid individualsbelow 65 years old, and 80% of older adults. It was also assumed that the population tookboth doses and that the daily number of contacts in the model was age-dependent butnot activity-dependent. This means that we did not consider if daily contact was at theschool, grocery stores, shopping malls, open areas, or any other places. Moreover, due tothe lack of information about isolation in Brazil, we assumed that isolated people havea maximum of three contacts per day. The model also did not explicitly simulate othermitigation measures, such as mask use and social distance. Nonetheless, the model wascalibrated using the current effective reproduction number. Therefore, it is expected toreflect the current mitigation measures in place.

This study presents the substantial gains of the vaccination program and its capacityfor protecting vulnerable individuals. It is clear that once the public health actors applyadequate resources to ensure the proper mobility and action within the PNI framework,vaccination shall achieve the proposed goal of controlling outbreaks and lowering thedisease burden. Fake news and behavioral change can diminish the vaccination rate andthe population’s adherence to immunization. Therefore, a national and unique informativecampaign addressed to the population is urgent.

5 ConclusionOur findings support the immunization strategy proposed by the PNI to mitigate the

disease burden and its societal impacts. It is also clear that all non-pharmaceutical effortsmust continue to prevent the numbers from rising or remain at the same level. On the otherhand, if the vaccination program triggers a behavior change in the population this can leadto the program’s lower efficiency as a whole. In this matter, we conclude that vaccines,along with non-pharmaceutical measures and a widely campaigned immunization, are keyto the control of COVID-19 in Brazil.

AcknowledgmentsWe thank Professor Seyed M. Moghadas for the profitable discussion and comments,

and for allowing us to use the computational resources from ABM-Lab, York University.

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Conflict of interestsThe authors declare no conflicts.

Financial SupportTNV thanks to São Paulo Research Foundation (grant #:2018/24811-1); FAR and

RFP thanks to Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES(grant # 001). The research of CPF is supported by grants #2019/22157-5 and 2020/10964-0, São Paulo Research Foundation (FAPESP) and by grant #302984/2020-8, NationalCouncil for Scientific and Technological Development (CNPq).

References[1] G. Forni and A. Mantovani, “Covid-19 vaccines: where we stand and challenges

ahead,” Cell Death & Differentiation, pp. 1–14, 2021.

[2] Vaccine Centre - London School of Hygiene & Tropical Medicine, “Covid-19 vaccinetracker,” https://vac-lshtm.shinyapps.io/ncov_vaccine_landscape/, 02 2021, (Ac-cessed on 02/17/2021).

[3] N. Scalzaretto, “Coronavac has overall efficacy of 50.4 percent, says butantan,”Jan 2021. [Online]. Available: https://brazilian.report/coronavirus-brazil-live-blog/2021/01/12/coronavac-has-overall-efficacy-of-50-4-percent-says-butantan/

[4] W. Máximo, “Técnicos da anvisa recomendam uso emergencial da vacina de oxford,”Jan 2021. [Online]. Available: https://agenciabrasil.ebc.com.br/saude/noticia/2021-01/tecnicos-da-anvisa-recomendam-uso-emergencial-da-vacina-de-oxford

[5] FIOCRUZ, “Vacinas contra a covid-19,” https://portal.fiocruz.br/vacinascovid19, 022021, (Accessed on 02/05/2021).

[6] J. Paim, C. Travassos, C. Almeida, L. Bahia, and J. Macinko, “The brazilian healthsystem: history, advances, and challenges,” The Lancet, vol. 377, no. 9779, pp. 1778–1797, 2011.

[7] M. C. Castro, A. Massuda, G. Almeida, N. A. Menezes-Filho, M. V. Andrade,K. V. M. de Souza Noronha, R. Rocha, J. Macinko, T. Hone, R. Tasca et al., “Brazil’sunified health system: the first 30 years and prospects for the future,” The Lancet,vol. 394, no. 10195, pp. 345–356, 2019.

[8] Centro de Estudos Estratégicos - FIOCRUZ, “Programa nacionalde imunizações (pni) e covid-19: desafios a uma história de quasemeio século de sucesso | cee fiocruz,” http://www.cee.fiocruz.br/?q=Programa-Nacional-de-Imunizacoes-PNI-e-Covid-19, (Accessed on 02/10/2021).

16

. CC-BY-NC-ND 4.0 International licenseIt is made available under a perpetuity.

is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint The copyright holder for thisthis version posted February 23, 2021. ; https://doi.org/10.1101/2021.02.22.21252208doi: medRxiv preprint

[9] S. M. Moghadas, T. N. Vilches, K. Zhang, C. R. Wells, A. Shoukat, B. H. Singer,L. A. Meyers, K. M. Neuzil, J. M. Langley, M. C. Fitzpatrick et al., “The impact ofvaccination on covid-19 outbreaks in the united states,” medRxiv, 2020.

[10] A. Martinho, “Jovens ignoram covid-19 e lotam bares em sp:’a gente já meteu o louco’ [12/12/2020],” Dec 2020. [On-line]. Available: https://noticias.uol.com.br/cotidiano/ultimas-noticias/2020/12/12/jovens-se-aglomeram-em-bares-de-sp-a-gente-ja-meteu-o-louco.htm?foto=1

[11] F. Januzzi, “Rio tem festas lotadas no fim de se-mana com aglomerações; veja imagens,” Nov 2020. [On-line]. Available: https://g1.globo.com/rj/rio-de-janeiro/noticia/2020/11/22/rio-tem-festas-lotadas-no-fim-de-semana-com-aglomeracoes-veja-imagens.ghtml

[12] E. P. Cruz, “São paulo passa para fase vermelha a partir de hoje,” Jan2021. [Online]. Available: https://agenciabrasil.ebc.com.br/saude/noticia/2021-01/sao-paulo-passa-para-fase-vermelha-partir-de-hoje

[13] A. Cortegiani, M. Ippolito, G. Ingoglia, P. Iozzo, A. Giarratano, andS. Einav, “Update i. a systematic review on the efficacy and safety of chloro-quine/hydroxychloroquine for covid-19,” Journal of critical care, 2020.

[14] M. G. S. Borba, F. F. A. Val, V. S. Sampaio, M. A. A. Alexandre, G. C. Melo,M. Brito, M. P. G. Mourão, J. D. Brito-Sousa, D. Baía-da Silva, M. V. F. Guerraet al., “Effect of high vs low doses of chloroquine diphosphate as adjunctive therapyfor patients hospitalized with severe acute respiratory syndrome coronavirus 2 (sars-cov-2) infection: a randomized clinical trial,” JAMA network open, vol. 3, no. 4, pp.e208 857–e208 857, 2020.

[15] “Mapa da vacinação contra covid-19 no brasil,” 2021. [Online]. Available:https://especiais.g1.globo.com/bemestar/vacina/2021/mapa-brasil-vacina-covid/

[16] “Informes e notas técnicas,” 2021. [Online]. Available: https://sbim.org.br/informes-e-notas-tecnicas/sbim

[17] Obervatório COVID-19 BR, “Estados · observatório covid-19 br,” https://covid19br.github.io/estados.html?aba=aba3&uf=SP&q=dia#, (Accessed on 01/15/2021).

[18] A. A. Sayampanathan, C. S. Heng, P. H. Pin, J. Pang, T. Y. Leong, and V. J. Lee,“Infectivity of asymptomatic versus symptomatic covid-19,” The Lancet, vol. 397, no.10269, pp. 93–94, 2021.

[19] M. Gatto, E. Bertuzzo, L. Mari, S. Miccoli, L. Carraro, R. Casagrandi, and A. Ri-naldo, “Spread and dynamics of the covid-19 epidemic in italy: Effects of emergencycontainment measures,” Proceedings of the National Academy of Sciences, vol. 117,no. 19, pp. 10 484–10 491, 2020.

17

. CC-BY-NC-ND 4.0 International licenseIt is made available under a perpetuity.

is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint The copyright holder for thisthis version posted February 23, 2021. ; https://doi.org/10.1101/2021.02.22.21252208doi: medRxiv preprint

[20] R. Li, S. Pei, B. Chen, Y. Song, T. Zhang, W. Yang, and J. Shaman, “Substantialundocumented infection facilitates the rapid dissemination of novel coronavirus (sars-cov-2),” Science, vol. 368, no. 6490, pp. 489–493, 2020.

[21] S. M. Moghadas, M. C. Fitzpatrick, P. Sah, A. Pandey, A. Shoukat, B. H. Singer,and A. P. Galvani, “The implications of silent transmission for the control of covid-19outbreaks,” Proceedings of the National Academy of Sciences, vol. 117, no. 30, pp.17 513–17 515, 2020.

[22] X. He, E. H. Lau, P. Wu, X. Deng, J. Wang, X. Hao, Y. C. Lau, J. Y. Wong, Y. Guan,X. Tan et al., “Temporal dynamics in viral shedding and transmissibility of covid-19,”Nature medicine, vol. 26, no. 5, pp. 672–675, 2020.

[23] K. Mizumoto, K. Kagaya, A. Zarebski, and G. Chowell, “Estimating the asymp-tomatic proportion of coronavirus disease 2019 (covid-19) cases on board the dia-mond princess cruise ship, yokohama, japan, 2020,” Eurosurveillance, vol. 25, no. 10,p. 2000180, 2020.

[24] A. Kimball, K. M. Hatfield, M. Arons, A. James, J. Taylor, K. Spicer, A. C. Bardossy,L. P. Oakley, S. Tanwar, Z. Chisty et al., “Asymptomatic and presymptomatic sars-cov-2 infections in residents of a long-term care skilled nursing facility—king county,washington, march 2020,” Morbidity and Mortality Weekly Report, vol. 69, no. 13,p. 377, 2020.

[25] H. Nishiura, T. Kobayashi, T. Miyama, A. Suzuki, S.-m. Jung, K. Hayashi, R. Ki-noshita, Y. Yang, B. Yuan, A. R. Akhmetzhanov et al., “Estimation of the asymp-tomatic ratio of novel coronavirus infections (covid-19),” International journal ofinfectious diseases, vol. 94, p. 154, 2020.

[26] S. Garg, L. Kim, M. Whitaker, A. O’Halloran, C. Cummings, R. Holstein, M. Prill,S. J. Chai, P. D. Kirley, N. B. Alden et al., “Hospitalization rates and characteristicsof patients hospitalized with laboratory-confirmed coronavirus disease 2019—covid-net, 14 states, march 1–30, 2020,” Morbidity and mortality weekly report, vol. 69,no. 15, p. 458, 2020.

[27] Centers for Disease Control and Prevention, “Preliminary estimates of the prevalenceof selected underlying health conditions among patients with coronavirus disease2019 — united states, february 12–march 28, 2020 | mmwr,” https://www.cdc.gov/mmwr/volumes/69/wr/mm6913e2.htm, (Accessed on 01/15/2021).

[28] S. Sanche, Y. T. Lin, C. Xu, E. Romero-Severson, N. W. Hengartner, and R. Ke, “Thenovel coronavirus, 2019-ncov, is highly contagious and more infectious than initiallyestimated,” arXiv preprint arXiv:2002.03268, 2020.

[29] X. Yang, Y. Yu, J. Xu, H. Shu, H. Liu, Y. Wu, L. Zhang, Z. Yu, M. Fang, T. Yu et al.,“Clinical course and outcomes of critically ill patients with sars-cov-2 pneumonia

18

. CC-BY-NC-ND 4.0 International licenseIt is made available under a perpetuity.

is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint The copyright holder for thisthis version posted February 23, 2021. ; https://doi.org/10.1101/2021.02.22.21252208doi: medRxiv preprint

in wuhan, china: a single-centered, retrospective, observational study,” The LancetRespiratory Medicine, vol. 8, no. 5, pp. 475–481, 2020.

[30] W. J. Wiersinga, A. Rhodes, A. C. Cheng, S. J. Peacock, and H. C. Prescott, “Patho-physiology, transmission, diagnosis, and treatment of coronavirus disease 2019 (covid-19): a review,” Jama, vol. 324, no. 8, pp. 782–793, 2020.

[31] SEADE, “Coronavírus - dados completos,” https://www.seade.gov.br/coronavirus/,(Accessed on 01/15/2021).

[32] Health Minister of Brazil, “Plano nacional de operacionalização da vacinaçãocontra a covid-19,” https://www.gov.br/saude/pt-br/media/pdf/2021/janeiro/29/planovacinacaocovid_v2_29jan21_nucom.pdf, 01 2021, (Accessed on 02/03/2021).

[33] IBGE, “https://cidades.ibge.gov.br,” https://cidades.ibge.gov.br/, (Accessed on01/15/2021).

[34] K. Prem, K. van Zandvoort, P. Klepac, R. M. Eggo, N. G. Davies, A. R. Cook,M. Jit et al., “Projecting contact matrices in 177 geographical regions: an updateand comparison with empirical data for the covid-19 era,” medRxiv, 2020.

[35] G. M. Borges and C. D. Crespo, “Aspectos demográficos e socioeconômicos dos adul-tos brasileiros e a covid-19: uma análise dos grupos de risco a partir da pesquisanacional de saúde, 2013,” Cadernos de Saúde Pública, vol. 36, p. e00141020, 2020.

[36] B. H. Tess, C. F. Granato, M. C. Alves, M. C. Pintao, E. Rizzatti, M. C. Nunes,and F. C. Reinach, “Monitora covid,” https://www.monitoramentocovid19.org/, (Ac-cessed on 01/15/2021).

[37] L. F. Buss, C. A. Prete, C. M. Abrahim, A. Mendrone, T. Salomon, C. de Almeida-Neto, R. F. França, M. C. Belotti, M. P. Carvalho, A. G. Costa et al., “Three-quarters attack rate of sars-cov-2 in the brazilian amazon during a largely unmitigatedepidemic,” Science, vol. 371, no. 6526, pp. 288–292, 2021.

[38] C. A. G. Gadelha, P. S. d. C. Braga, K. B. M. Montenegro, and B. B. Cesário,“Access to vaccines in brazil and the global dynamics of the health economic-industrialcomplex,” Cadernos de Saúde Pública, vol. 36, p. e00154519, 2020.

[39] A. C. da Silva Santos, N. N. T. Silva, C. M. Carneiro, W. Coura-Vital, and A. A.Lima, “Knowledge about cervical cancer and hpv immunization dropout rate amongbrazilian adolescent girls and their guardians,” BMC public health, vol. 20, no. 1, pp.1–11, 2020.

[40] M. L. Adams, D. L. Katz, and J. Grandpre, “Population-based estimates of chronicconditions affecting risk for complications from coronavirus disease, united states,”Emerging infectious diseases, vol. 26, no. 8, p. 1831, 2020.

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A Supplementary informationThe age distribution of the in-silico population is taking from the Brazilian Institute

of Geography and Statistics [33], and it comprises seventeen age groups. Figure A.1 showsthe age distribution for São Paulo State.

0.0

00

0.0

25

0.0

50

0.0

75

0−4

5−9

10−14

15−19

20−24

25−29

30−34

35−39

40−44

45−49

50−54

55−59

60−64

65−69

70−74

75−79

80−100

Age groups

Pro

po

rtio

n

Figure A.1: Population distribution per age group in São Paulo State [33].

The prevalence of comorbidities related to COVID-19 per age group is found at [35].Table A.1 presents the percentage of the population that presents one or more comorbidi-ties. We considered the sex ratio (female/male) as 0.5.

Table A.1: Prevalence of comorbidity in the population [35].

Sex Age groups0-4∗ 5-17∗ 18-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60+

Female 5.0% 10.0% 12.2% 14.5% 23.3% 27.7% 37.5% 46.6% 52.1% 62.2% 75.0%Male 5.0% 10.0% 15.7% 21.0% 22.7% 31.4% 37.2% 43.0% 54.6% 60.6% 73.0%∗ Value is taken from reference [40].

The preexisting herd immunity is distributed according to the reported distributionof cases per age class in São Paulo State, found at [31]. Table A.2 shows the probabilityof a pre-existing case to belong to a given age group.

Table A.2: Distribution of pre-existing herd immunity in the population per age class(%) [31].

Age groups0-9 10-19 20-29 30-39 40-49 50-59 60-69 70-79 802.6 4.5 9.1 14.7 20.3 23.6 17.5 5.2 2.5

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The distribution of contacts between age groups, as well as the average number ofdaily contacts, is derived from [34]. Table A.3 shows the used distribution and the averagenumber of daily contacts per age group.

Table A.3: Mixing patterns among individuals and the daily number of contacts derivedfrom [34]. Daily numbers of contacts were sampled from Poisson distributions.

Age groups Proportion of contacts between age groups Average number of

daily contacts0-4 5-19 20-49 50-64 65+

Indiv

idual

’sgr

oup

0-4 0.285 0.176 0.405 0.098 0.035 11.80

5-19 0.035 0.644 0.256 0.050 0.015 21.32

20-49 0.043 0.163 0.652 0.119 0.022 15.80

50-64 0.060 0.170 0.529 0.203 0.038 12.71

65+ 0.064 0.266 0.388 0.161 0.121 6.33

The probability of death in a symptomatic case was taken from [31], and it is shownin Table A.4 for each age group. Since we are considering that only severe cases can die,a formula is applied to calculate the death probability in severe cases (ρsev):

ρsev =(1− θ)

θρsymp + ρsymp,

in which the ρsymp is the death probability of a symptomatic case and θ is the probabilitythat a symptomatic case is severe.

Table A.4: Percentage of symptomatic fatal cases per age group

Age groups 0-9 10-19 20-29 30-39 40-49 50-59 60-69 70-79 80-89 90+Percentageof fatal cases 0.1% 0.1% 0.1% 0.4% 1.0% 2.7% 8.1% 17.7% 30.8% 37.9%

B Percentage reduction on disease outcomes varyingthe preexisting immunity

We present the obtained results for CoronaVac and AstraZeneca/Oxford vaccines forseveral scenarios: (I) baseline, doubling the vaccination rate, and decreasing the risk per-ception.

Figures B.1 and B.2 show the results considering a pre-existing immunity of 10%, forCoronaVac and AstraZeneca/Oxford vaccines, respectively. And Figures B.3 and B.4, apre-existing immunity of 30%.

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Baseline Double vaccination rate Low risk perception

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) Relative protectionagainst infection −Severe−disease protection

100% − 0%100% − 100%50% − 0%50% − 100%0% − 0%0% − 100%

Figure B.1: Percentage reduction in outcomes for the different scenarios and the Coron-aVac vaccine, with pre-existing immunity in the population of 10%. The second dose isgiven after 21 days.

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Baseline Double vaccination rate Low risk perception

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100% − 0%100% − 100%50% − 0%50% − 100%0% − 0%0% − 100%

Figure B.2: Percentage reduction in disease outcomes for the different scenarios and theAstraZeneca/Oxford vaccine, with pre-existing immunity in the population of 10%. Thesecond dose is given after 90 days.

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Baseline Double vaccination rate Low risk perception

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) Relative protectionagainst infection −Severe−disease protection

100% − 0%100% − 100%50% − 0%50% − 100%0% − 0%0% − 100%

Figure B.3: Percentage reduction in disease outcomes for the different scenarios and theCoronaVac vaccine, with pre-existing immunity in the population of 30%. The seconddose is given after 21 days.

24

. CC-BY-NC-ND 4.0 International licenseIt is made available under a perpetuity.

is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint The copyright holder for thisthis version posted February 23, 2021. ; https://doi.org/10.1101/2021.02.22.21252208doi: medRxiv preprint

Baseline Double vaccination rate Low risk perception

Infe

ctio

ns

Ho

sp

italiz

atio

ns

ICU

Death

s

−10

0

10

20

30

40

20

30

40

50

60

70

30

40

50

60

70

30

40

50

60

70

Re

du

cti

on

(%

) Relative protectionagainst infection −Severe−disease protection

100% − 0%100% − 100%50% − 0%50% − 100%0% − 0%0% − 100%

Figure B.4: Percentage reduction in disease outcomes for the different scenarios and theAstraZeneca/Oxford vaccine, with pre-existing immunity in the population of 30%. Thesecond dose is given after 90 days.

Tables B.1 to B.6 show the percentage of reduction in the number of infections, non-ICU hospitalizations, ICU admissions, and deaths, considering different levels of pre-existing immunity. The listed values summarize the results shown in Figures B.1 to B.4and also the results in the main text.

25

. CC-BY-NC-ND 4.0 International licenseIt is made available under a perpetuity.

is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint The copyright holder for thisthis version posted February 23, 2021. ; https://doi.org/10.1101/2021.02.22.21252208doi: medRxiv preprint

TableB.1:Percentageredu

ctionin

diseaseou

tcom

esun

derdiffe

rent

protection

scenariosof

theCoron

aVac

vaccinean

dpre-existing

immun

ityin

thepo

pulation

of10%.

Relativeprotection

againstinfection(%

)Protectionag

ainst

severe

disease(%

)Scenarios

Reduction

(%)

Infections

Hospitalizations

ICU

Deaths

100

0Baseline

14.31(7.28,21

.06)

32.05(26.49

,37.19

)34

.17(28.85

,39.45

)43

.43(38.34

,47.69

)Dou

blevaccinationrate

32.39(26.78

,37.79

)55

.61(51.89

,59.03

)58

.07(54.47

,61.39

)62

.39(59.28

,65.41

)Lo

wrisk

perception

10.97(3.61,17

.47)

28.68(22.61

,34.38

)30

.94(24.8,36

.31)

40.07(35.23

,44.73

)

100

Baseline

15.16(7.98,21

.45)

40.44(35.46

,44.79

)43

.63(38.86

,48.04

)53

.95(49.98

,57.7)

Dou

blevaccinationrate

33.7

(28.22

,39.1)

66.1

(63.11

,68.58

)69.19(66.51

,71.57)

73.19(70.89

,75.24

)Lo

wrisk

perception

11.3

(4.76,18

.29)

37.29(32.24

,42.28

)40

.55(35.66

,45.3)

51.14(46.9,55

.13)

50

0Baseline

8.89

(1.34,15

.99)

25.32(19.2,31

.16)

27.4

(21.22

,32.98

)34.92(29.3,40

.23)

Dou

blevaccinationrate

20.17(13.83

,26.2)

44.31(39.32

,48.75

)46.52(42.07

,50.71)

50.51(46.53

,54.56

)Lo

wrisk

perception

5.04

(-2.46

,12.36

)21

.48(14.82

,27.57

)23

.1(16.37

,29.13

)31

.39(25.4,37

.38)

100

Baseline

9.74

(2.24,16

.83)

37.43(32.14

,42.24

)40

.8(35.61

,45.46

)51

.28(47,55

.18)

Dou

blevaccinationrate

22.17(15.79

,28.31

)62

.16(58.83

,65.1)

65.5

(62.76

,68.24)

69.29(66.47

,71.73

)Lo

wrisk

perception

5.67

(-2.13

,13)

34.52(29.23

,39.39

)37

.7(32.45

,42.52

)48

.4(44.29

,52.19

)

0

0Baseline

4.4(-4.13

,11.84

)19

.49(12.83

,26.14

)21

.34(15,26

.95)

27.49(21.42

,33.14

)Dou

blevaccinationrate

9.2(1.95,15

.68)

34.35(29.28

,39.24

)36.31(30.57

,41.18)

38.26(32.81

,43.13

)Lo

wrisk

perception

0.12

(-7.37

,8.56)

14.99(7.98,22

.17)

16.21(9.13,22

.99)

22.9

(16.59

,28.99

)

100

Baseline

5.55

(-2.24

,12.48

)35

.08(29.56

,40.19

)38

.15(33,42

.63)

49.32(45.27

,53.13

)Dou

blevaccinationrate

11.44(3.82,18

.29)

58.49(55.02

,62.04

)62.1

(58.93

,65.19)

65.81(62.87

,68.42

)Lo

wrisk

perception

0.99

(-7.18

,8.5)

31.58(25.44

,36.9)

35.14(29.48

,40.24

)45

.52(40.75

,49.67

)

26

. CC-BY-NC-ND 4.0 International licenseIt is made available under a perpetuity.

is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint The copyright holder for thisthis version posted February 23, 2021. ; https://doi.org/10.1101/2021.02.22.21252208doi: medRxiv preprint

TableB.2:P

ercentageredu

ctionin

diseaseou

tcom

esun

derdiffe

rent

protection

scenariosof

theAstraZe

neca/O

xfordvaccine

andpre-existing

immun

ityin

thepo

pulation

of10%.

Relativeprotection

againstinfection(%

)Protectionag

ainst

severe

disease(%

)Scenarios

Reduction

(%)

Infections

Hospitalizations

ICU

Deaths

100

0Baseline

27.33(21.56

,32.92

)53

.73(49.86

,57.25

)57

.67(54.31

,61.06

)60.85(57.63

,63.83)

Dou

blevaccinationrate

49.19(45.14

,53.17

)70

.95(68.51

,73.15

)73

.15(70.68

,75.14

)77

.35(75.26

,79.08

)Lo

wrisk

perception

25.13(19.25

,30.98

)52

.45(48.84

,56.1)

55.48(51.9,59

.03)

59.59(56.36

,62.93

)

100

Baseline

27.55(21.31

,32.96

)56

.76(53.35

,60.13

)60

.71(57.33

,63.82

)64.27(61.42

,67.13)

Dou

blevaccinationrate

49.48(45.13

,53.16

)73

.45(71.46

,75.56

)76

.06(73.99

,78.01

)80

.27(78.64

,81.86

)Lo

wrisk

perception

25.14(18.64

,30.55

)55

.15(51.8,58

.7)

59.08(55.9,62

.49)

63.19(60.25

,66)

50

0Baseline

16.84(9.96,23

.65)

45.15(40.84

,49.56

)48

.47(44.17

,52.31

)51

.81(47.73

,55.62

)Dou

blevaccinationrate

31.08(25.21

,36.51

)59

.47(56.21

,62.63

)61

.64(58.57

,64.67

)66

.31(63.44

,68.89

)Lo

wrisk

perception

14.04(6.77,21

.14)

42.78(38.09

,47.31

)46

.31(42,50

.34)

49.88(45.69

,53.66

)

100

Baseline

17.52(10.76

,23.99

)51

.77(47.8,55

.8)

55.78(52.45

,59.35

)59.55(56.28

,62.72)

Dou

blevaccinationrate

32.01(26.29

,37.2)

66.59(64.03

,69.19

)69.19(66.45

,71.73)

73.77(71.47

,75.93

)Lo

wrisk

perception

14.39(7.02,20

.69)

49.31(44.93

,53.55

)53

.71(49.66

,57.56

)57.35(53.81

,60.76)

0

0Baseline

7.58

(-0.34

,15.15

)36

.97(31.65

,42.21

)40

.23(35.32

,45.04

)42

.86(38.22

,47.58

)Dou

blevaccinationrate

13.93(6.43,20

.69)

48.08(44.2,52

.04)

49.84(45.67

,53.73

)53

.83(49.86

,57.58

)Lo

wrisk

perception

4.39

(-3.14

,11.46

)33

.94(28.82

,39.04

)37

.07(31.78

,42.21

)39

.65(34.75

,44.67

)

100

Baseline

8.5(0.94,15

.24)

46.71(42.17

,50.73

)50

.98(46.8,54

.7)

54.63(50.69

,58.16

)Dou

blevaccinationrate

14.88(8.05,21

.04)

59.86(56.46

,62.98

)62.45(59.24

,65.43)

67.18(64.55

,69.83

)Lo

wrisk

perception

4.7(-2.9,11

.63)

43.98(39.26

,48.24

)47

.88(43.3,52

.18)

51.71(47.61

,55.48

)

27

. CC-BY-NC-ND 4.0 International licenseIt is made available under a perpetuity.

is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint The copyright holder for thisthis version posted February 23, 2021. ; https://doi.org/10.1101/2021.02.22.21252208doi: medRxiv preprint

TableB.3:Percentageredu

ctionin

diseaseou

tcom

esun

derdiffe

rent

protection

scenariosof

theCoron

aVac

vaccinean

dpre-existing

immun

ityin

thepo

pulation

of20%.

Relativeprotection

againstinfection(%

)Protectionag

ainst

severe

disease(%

)Scenarios

Reduction

(%)

Infections

Hospitalizations

ICU

Deaths

100

0Baseline

2.93

(-4.8,10

.33)

19.93(13.74

,26.02

)22

.18(15.88

,28.48

)33

.47(28.16

,38.44

)Dou

blevaccinationrate

18.69(11.92

,24.96

)45

.04(40.39

,49.31

)48

.33(43.78

,52.54

)53

.52(49.79

,57.23

)Lo

wrisk

perception

-0.29(-8.47

,7.4)

16.58(10.2,22

.67)

19.3

(12.51

,25.88

)30

.46(24.66,35.46

)

100

Baseline

3.78

(-3.94

,11.48

)28

.19(22.43

,33.99

)31

.32(25.25

,36.58

)45

.2(40.58

,49.66

)Dou

blevaccinationrate

19.75(13.38

,25.84

)57

(53.48

,60.58

)60

.94(57.39

,64.08

)65

.68(62.64

,68.56

)Lo

wrisk

perception

-0.02(-8.26

,7.48)

25.4

(19.18

,31.06

)28.35(21.88

,34.19)

42.59(37.89,47.11

)

50

0Baseline

-1.1

(-9.42

,6.77)

14.57(7.87,21

.35)

16.5

(9.29,22

.93)

26.14(20.2,31.77)

Dou

blevaccinationrate

8.36

(0.61,15

.67)

34.32(28.95

,39.58

)37

.6(32.13

,42.36

)41

.76(37.08,46.33

)Lo

wrisk

perception

-5.21(-13

.8,3.07)

10.39(3.35,17

.53)

12.97(4.92,19

.66)

22.35(16.21

,28.25

)

100

Baseline

-0.19(-8.15

,7.6)

26.44(20.11

,31.93

)29

.45(23.45

,34.84

)42

.95(38.31

,47.34

)Dou

blevaccinationrate

9.76

(2.26,16

.8)

53.21(49.75

,56.69

)57.45(53.61

,60.75)

62.47(59.31

,65.33

)Lo

wrisk

perception

-4.53(-12

.84,3.46

)22.96(17.05

,29.06

)25.25(18.96

,30.75)

39.76(34.52

,44.65

)

0

0Baseline

-4.79(-13

.57,3.5)

9.44

(2.18,15

.91)

11.1

(2.92,17

.77)

19.5

(12.79

,25.93)

Dou

blevaccinationrate

-1.24(-9.93

,6.92)

24.71(18.66

,30.15

)27

.35(20.85

,33.09

)30

.25(24.77

,35.24

)Lo

wrisk

perception

-9.12(-18

.37,-1.57)

4.38

(-3.43

,11.29

)6.15

(-1.7,13

.67)

14.11(6.42,20

.6)

100

Baseline

-3.75(-12

.51,4.23

)24

.4(18.2,30

.06)

27.84(21.8,33

.34)

41.4

(36.54

,46.12

)Dou

blevaccinationrate

0.82

(-7.43

,8.97)

50.39(46.58

,54.13

)55

.06(51.12

,58.78

)59

.16(55.8,62

.41)

Low

risk

perception

-8.76(-17

.9,-0

.27)

20.38(13.91

,26.28

)23

.63(17.11

,29.74

)37

.85(32.44

,42.83

)

28

. CC-BY-NC-ND 4.0 International licenseIt is made available under a perpetuity.

is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint The copyright holder for thisthis version posted February 23, 2021. ; https://doi.org/10.1101/2021.02.22.21252208doi: medRxiv preprint

TableB.4:P

ercentageredu

ctionin

diseaseou

tcom

esun

derdiffe

rent

protection

scenariosof

theAstraZe

neca/O

xfordvaccine

andpre-existing

immun

ityin

thepo

pulation

of20%.

Relativeprotection

againstinfection(%

)Protectionag

ainst

severe

disease(%

)Scenarios

Reduction

(%)

Infections

Hospitalizations

ICU

Deaths

100

0Baseline

15.45(8.46,22

.02)

44.15(39.32

,48.21

)47

.85(43.31

,51.98

)53

.48(49.91

,57.2)

Dou

blevaccinationrate

36.61(31.17

,41.35

)62

.36(59.31

,65.24

)64

.99(61.97

,67.7)

71.09(68.64

,73.26

)Lo

wrisk

perception

13.64(6.3,20.29

)42

.24(37.22

,46.82

)46

.4(41.84

,50.49

)51.87(47.92

,55.81)

100

Baseline

16.1

(9.34,22

.47)

47.59(43.15

,51.57

)52

.03(48.24

,56.04

)56.98(53.13

,60.41)

Dou

blevaccinationrate

36.92(31.46

,42.08

)65

.54(62.85

,68.29

)68

.57(66.03

,70.97

)74

.43(72.1,76

.55)

Low

risk

perception

13.87(6.46,20

.81)

45.82(41.18

,50.09

)50

.21(45.99

,54.22

)55.45(51.96

,58.78)

50

0Baseline

6.05

(-1.69

,13.27

)35

.04(29.65

,40.39

)39

.03(34.04

,43.65

)43

.53(38.76

,48.18

)Dou

blevaccinationrate

19.86(12.83

,25.99

)51

.08(47.14

,54.97

)53

.85(49.95

,57.44

)59

.41(56,62

.61)

Low

risk

perception

3.69

(-4.22

,11.08

)33

.19(27.85

,38.52

)37

.06(31.93

,41.81

)41

.72(36.66

,46.47

)

100

Baseline

6.99

(-1.21

,14.44

)42

.51(37.73

,47.19

)46

.7(42.19

,51.11

)51

.97(47.71

,55.77

)Dou

blevaccinationrate

20.49(13.99

,26.6)

58.32(54.95

,61.6)

61.56(58.21

,64.69

)67

.88(65.04

,70.48

)Lo

wrisk

perception

3.91

(-3.87

,11.49

)39

.78(34.85

,44.43

)44

.34(39.96

,48.78

)49

.64(45.65

,53.6)

0

0Baseline

-2.04(-10

.32,6.05

)27

.31(20.98

,33.75

)30

.88(25.15

,36.07

)34

.98(29.65

,40)

Dou

blevaccinationrate

3.44

(-4.66

,10.95

)39

.59(34.54

,44.47

)41

.87(37.05

,46.37

)46

.8(42.37

,50.81

)Lo

wrisk

perception

-5.24(-14

,2.86)

24.58(18.23

,30.82

)27

.95(21.63,33.77

)32

.38(26.72

,37.47

)

100

Baseline

-1(-9.52

,7.71)

37.45(31.91

,42.56

)41

.82(36.95,46.17

)46

.93(42.35

,51.17

)Dou

blevaccinationrate

4.69

(-2.89

,12.41

)51

.39(47.37

,55.18

)55

.12(51.42

,58.61

)60

.86(57.72

,64.05

)Lo

wrisk

perception

-5(-13.3,2.71)

34.41(28.87

,39.19

)38

.75(33.36

,43.59

)44

.17(39.65

,48.88

)

29

. CC-BY-NC-ND 4.0 International licenseIt is made available under a perpetuity.

is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint The copyright holder for thisthis version posted February 23, 2021. ; https://doi.org/10.1101/2021.02.22.21252208doi: medRxiv preprint

TableB.5:Percentageredu

ctionin

diseaseou

tcom

esun

derdiffe

rent

protection

scenariosof

theCoron

aVac

vaccinean

dpre-existing

immun

ityin

thepo

pulation

of30%.

Relativeprotection

againstinfection(%

)Protectionag

ainst

severe

disease(%

)Scenarios

Reduction

(%)

Infections

Hospitalizations

ICU

Deaths

100

0Baseline

9.79

(2.53,16

.34)

23.08(16.71

,28.79

)24

.5(18.22

,30.01

)35

.69(30.35

,40.69

)Dou

blevaccinationrate

21.37(14.98

,27.41

)44

.59(40.23

,48.93

)47

.36(42.9,51

.41)

52.94(49.04

,56.77

)Lo

wrisk

perception

8.43

(1.31,14

.94)

21.3

(14.65

,27.33

)22

.95(17.18

,28.78

)34.78(28.95

,39.78)

100

Baseline

10.08(2.42,16

.58)

28.82(23.06

,34.48

)31

.36(26.02

,36.6)

46.31(42.15

,50.2)

Dou

blevaccinationrate

22.02(15.72

,27.83

)55

.3(51.59

,58.63

)59.69(56.56

,62.84)

64.42(61.49

,67.08

)Lo

wrisk

perception

8.83

(1.27,15

.94)

27.48(21.95

,32.88

)30

.11(24.53

,35.43

)44

.72(40.24

,48.97

)

50

0Baseline

7.08

(-0.5,14

.34)

18.92(12.27

,24.91

)19

.94(13.58

,26.22

)29

.72(24.08

,35.17

)Dou

blevaccinationrate

14.73(8.25,21

.4)

37.19(32.24

,42.3)

39.78(35,44

.51)

43.96(39.4,48

.38)

Low

risk

perception

5.66

(-2.2,12

.48)

16.41(10.5,22

.48)

18.85(12.29

,25.2)

28.01(22.03

,33.22

)

100

Baseline

7.46

(-0.01

,14.61

)27

.56(21.95

,32.9)

29.91(24.36

,35.41

)45

.01(40.36

,49.37

)Dou

blevaccinationrate

15.78(8.75,22

.29)

52.9

(49.02

,56.33

)57

.36(53.82

,60.68

)62

.02(58.9,64

.92)

Low

risk

perception

5.89

(-1.11

,12.68

)25

.85(19.98

,31.65

)28

.25(22.7,34

.02)

43.19(38.85

,47.79

)

0

0Baseline

4.68

(-2.93

,11.96

)15

.36(8.74,21

.3)

17.07(10.66

,23.71

)24

.37(18.54

,30.18

)Dou

blevaccinationrate

8.64

(1.31,15

.95)

29.72(24.22

,35.27

)31

.97(26.69

,37.11

)35

.01(29.56

,40.35

)Lo

wrisk

perception

3.26

(-4.37

,10.98

)13

.07(5.92,19

.54)

14.07(6.88,20

.81)

21.99(15.52

,27.79

)

100

Baseline

5.34

(-2.75

,12.22

)26

.57(20.72

,31.9)

28.58(22.62

,34.14

)43

.08(38.22

,47.59

)Dou

blevaccinationrate

9.89

(2.66,16

.65)

51.25(47.13

,55.01

)54

.95(51.17

,58.75

)60

.16(56.57

,63.35

)Lo

wrisk

perception

3.47

(-4.39

,10.93

)24

.4(18.29

,30.13

)27

.16(21.13

,32.81

)41

.88(37.1,46

.22)

30

. CC-BY-NC-ND 4.0 International licenseIt is made available under a perpetuity.

is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint The copyright holder for thisthis version posted February 23, 2021. ; https://doi.org/10.1101/2021.02.22.21252208doi: medRxiv preprint

TableB.6:P

ercentageredu

ctionin

diseaseou

tcom

esun

derdiffe

rent

protection

scenariosof

theAstraZe

neca/O

xfordvaccine

andpre-existing

immun

ityin

thepo

pulation

of30%.

Relativeprotection

againstinfection(%

)Protectionag

ainst

severe

disease(%

)Scenarios

Reduction

(%)

Infections

Hospitalizations

ICU

Deaths

100

0Baseline

19.2

(12.66

,25.41

)43

.49(39.14

,48.25

)47

.42(43.05

,51.49

)53

.61(50.23

,56.91

)Dou

blevaccinationrate

35.3

(30.06

,39.85

)60

.51(57.27

,63.6)

63.35(60.37

,66.2)

69.43(66.92

,71.71

)Lo

wrisk

perception

17.47(10.72

,23.72

)41

.99(37.32

,46.64

)46

.23(41.6,50

.1)

51.88(48.13

,55.81

)

100

Baseline

19.39(12.85

,25.03

)46

.35(41.98

,50.45

)50

.33(46.15

,54.25

)56.9

(53.31

,60.35

)Dou

blevaccinationrate

35.63(30.58

,40.66

)63

.49(60.77

,66.4)

66.57(63.97

,69.18)

72.73(70.39

,74.98

)Lo

wrisk

perception

17.49(11.05

,23.45

)45

.2(40.7,49

.34)

48.8

(44.51

,52.73

)55.44(51.98

,58.86)

50

0Baseline

12.41(5,19.17

)37

.09(32.16

,42.27

)39

.97(35.27

,44.53

)45

.82(41.43

,49.86

)Dou

blevaccinationrate

22.4

(16.22

,28.41

)51

.29(47.79

,54.95

)53.7

(49.91

,57.29)

59.28(55.84

,62.51

)Lo

wrisk

perception

10.66(3.36,17

.22)

35(29.58

,39.86

)38.31(33.21

,43.02)

43.88(39.36

,48.23

)

100

Baseline

12.77(5.63,19

.34)

42.62(37.96

,47.29

)46

.07(41.56

,50.56

)52

.41(48.69

,56.1)

Dou

blevaccinationrate

22.91(16.71

,29.07

)57

.47(54.06

,60.57

)60

.43(57.4,63

.45)

66.74(64.15

,69.38

)Lo

wrisk

perception

10.8

(3.12,17

.4)

40.55(35.7,45

.2)

44.45(40.13

,48.49

)50.4

(46.66

,54.03

)

0

0Baseline

6.78

(-0.64

,13.09

)30

.95(25.36

,36.55

)34

.27(29.11

,39.14

)38

.28(33.35

,43.1)

Dou

blevaccinationrate

10.5

(3.78,17

.02)

42.22(37.89

,46.33

)45

.02(40.95

,49.1)

49.77(45.71,53.74

)Lo

wrisk

perception

4.57

(-2.99

,11.47

)28

.53(23.44

,33.69

)31

.35(25.92

,36.59

)35

.84(31.2,40

.77)

100

Baseline

7.13

(-0.33

,13.82

)38

.85(34.13

,43.28

)42

.48(37.85

,47.36

)48

.07(44.07

,52.02

)Dou

blevaccinationrate

11.1

(4.17,17

.43)

51.69(47.75

,55.33

)55

.17(51.66

,58.59

)60

.83(57.81

,63.77

)Lo

wrisk

perception

4.7(-2.81

,12.44

)36

.55(31.31

,41.37

)40

.24(35.13

,44.85

)46

.17(41.89

,50.62

)

31

. CC-BY-NC-ND 4.0 International licenseIt is made available under a perpetuity.

is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint The copyright holder for thisthis version posted February 23, 2021. ; https://doi.org/10.1101/2021.02.22.21252208doi: medRxiv preprint